Jill Ashey 2025-02-09
This script will use topGO to analyze functional enrichment of miRNA targets for Apul
Code used below was created by Jill Ashey, modified for
use with A.pulchra datasets by Kathleen Durkin
# Read in Apul annotations
annot_locations <- read.delim("../output/02-Apul-reference-annotation/Apulcra-genome-mRNA-IDmapping-2024_12_12.tab")
# Remove unneeded columns
annot_locations <- annot_locations %>% dplyr::select(-X, -V13)
# Ensure there are no duplicate rows
annot_locations <- annot_locations %>% distinct()
head(annot_locations)## V1 V3 Protein.names Organism
## 1 ntLink_4:1155-1537 P35061 Histone H2A Acropora formosa (Staghorn coral)
## 2 ntLink_4:2660-3441 P84239 Histone H3 Urechis caupo (Innkeeper worm) (Spoonworm)
## 3 ntLink_4:4515-6830 P35061 Histone H2A Acropora formosa (Staghorn coral)
## 4 ntLink_4:7096-7859 P84239 Histone H3 Urechis caupo (Innkeeper worm) (Spoonworm)
## 5 ntLink_4:8474-9669 P35061 Histone H2A Acropora formosa (Staghorn coral)
## 6 ntLink_4:11162-11925 P84239 Histone H3 Urechis caupo (Innkeeper worm) (Spoonworm)
## Gene.Ontology..biological.process.
## 1
## 2
## 3
## 4
## 5
## 6
## Gene.Ontology.IDs
## 1 GO:0000786; GO:0003677; GO:0005634; GO:0030527; GO:0046982
## 2 GO:0000786; GO:0003677; GO:0005634; GO:0030527; GO:0046982
## 3 GO:0000786; GO:0003677; GO:0005634; GO:0030527; GO:0046982
## 4 GO:0000786; GO:0003677; GO:0005634; GO:0030527; GO:0046982
## 5 GO:0000786; GO:0003677; GO:0005634; GO:0030527; GO:0046982
## 6 GO:0000786; GO:0003677; GO:0005634; GO:0030527; GO:0046982# Looks good!This file shows each gene as it’s genomic location. We want to use gene IDs to associate genes, so add gene IDs to this annotation table
Read in file that associates each mRNA genomic location with corresponding gene ID
mRNA_FUNids <- read.table("../output/15-Apul-annotate-UTRs/Apul-mRNA-FUNids.txt", header=FALSE, col.names=c("location", "type", "mRNA_ID", "gene_ID", "product"), sep="\t")
# Remove unwanted text from parent column
mRNA_FUNids$gene_ID <- gsub("Parent=", "", mRNA_FUNids$gene_ID)
# Only need to keep mRNA location and gene ID
mRNA_FUNids <- mRNA_FUNids %>% dplyr::select(location, gene_ID)join with annotation file
# join
annot <- left_join(annot_locations, mRNA_FUNids, by = c("V1" = "location"))
# ensure there are no duplicate rows
annot <- annot %>% distinct()Want to isolate a list of GO terms per gene
gene2go <- annot %>% filter(!is.na(Gene.Ontology.IDs)) %>% dplyr::select(gene_ID, Gene.Ontology.IDs)
gene2go <- gene2go %>% dplyr::rename(GO.ID = Gene.Ontology.IDs)
gene2go_list <- setNames(
strsplit(as.character(gene2go$GO.ID), ";"),
gene2go$gene_ID
)Note: I think this means genes that had a Uniprot ID but no GO terms are excluded from this analysis
Define reference set of genes. This should be all genes found in our samples, NOT all genes in the A.pulchra genome. Some genes (e.g., reproduction pathways) may not be found/expected in our samples for valid biological reasons.
# Read in counts matrix
Apul_counts <- read.csv("../output/07-Apul-Hisat/Apul-gene_count_matrix.csv")
# Exclude genes with all 0 counts
Apul_counts <- Apul_counts[rowSums(Apul_counts[, 2:6]) != 0, ]
# Select gene IDs of the genes present in our samples
all_genes <- Apul_counts$gene_id
length(all_genes)## [1] 33624So we have a reference set of 33624 genes present in our samples.
This is a table of all putative miRNA-mRNA binding predicted by miRanda, plus Pearsons correlation coefficients for coexpression of each putative binding pair.
data <- read.csv("../output/09-Apul-mRNA-miRNA-interactions/miranda_PCC_miRNA_mRNA.csv")
head(data)## X.1 X miRNA mRNA PCC.cor p_value adjusted_p_value score energy
## 1 1 562 Cluster_5981 FUN_028147 0.6825537 0.2041707 0.9986496 146 -22.19
## 2 2 767 Cluster_15340 FUN_013332 0.6371070 0.2476393 0.9986496 158 -23.15
## 3 3 796 Cluster_5981 FUN_041253 -0.2250869 0.7158492 0.9986496 153 -20.50
## 4 4 1100 Cluster_3366 FUN_010827 0.3671005 0.5433145 0.9986496 163 -22.14
## 5 5 1101 Cluster_3367 FUN_010827 0.5369304 0.3507987 0.9986496 163 -22.14
## 6 6 2093 Cluster_15340 FUN_003342 0.1096213 0.8607058 0.9986496 154 -20.65
## query_start_end subject_start_end total_bp_shared query_similar subject_similar
## 1 2 21 185 209 21 66.67% 71.43%
## 2 2 20 198 220 19 68.42% 84.21%
## 3 2 21 699 719 19 73.68% 73.68%
## 4 2 18 346 368 16 81.25% 93.75%
## 5 2 18 346 368 16 81.25% 93.75%
## 6 2 20 562 585 20 65.00% 80.00%Set function to select genes of interest (ie those that have pvalue < 0.05)
topDiffGenes <- function(allScore) {
return(allScore < 0.05)}Functional annotation of all putative miRNA targets
cor_bind_FA <- left_join(data, annot, by = c("mRNA" = "gene_ID")) %>% distinct()
nrow(cor_bind_FA)## [1] 4657nrow(cor_bind_FA[!is.na(cor_bind_FA$Gene.Ontology.IDs),])## [1] 923Of the 4657 putative miRNA targets predicted by miRanda, 923 have available annotations
sig_cor_bind_FA <- cor_bind_FA[cor_bind_FA$p_value < 0.05,]
nrow(sig_cor_bind_FA)## [1] 266nrow(sig_cor_bind_FA[!is.na(sig_cor_bind_FA$Gene.Ontology.IDs),])## [1] 52Of the 266 putative miRNA targets predicted by miRanda that are also have significantly correlated expression, only 52 have available annotations. This is an average of 1-2 annotated targets per miRNA. This unfortunately means functional enrichment analysis will likely be uninformative for most groups of significantly coexpressed targets.
Save
write.csv(cor_bind_FA, "../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/miRNA_targets_FA.csv")
write.csv(sig_cor_bind_FA, "../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/miRNA_sig_cor_targets_FA.csv")Create topGO function for use with miRNA names
miRNA_topGO_FE <- function(miRNA.name, input_interactions) {
#Isolate genes in our input module of interest
interacting_genes <- input_interactions %>%
filter(miRNA == miRNA.name) %>%
pull(mRNA)
if (length(interacting_genes) > 0) {
# Create factor for all reference genes, where 1 represents module membership and 0 means the gene is not in module of interest
gene_list <- factor(as.integer(all_genes %in% interacting_genes))
names(gene_list) <- all_genes
str(gene_list)
## Biological Process ##
# Create topGO object
GO_BP <- new("topGOdata",
ontology = "BP", # Biological Process
allGenes = gene_list,
annot = annFUN.gene2GO,
gene2GO = gene2go_list,
geneSel=topDiffGenes)
# Run GO enrichment test
GO_BP_FE <- runTest(GO_BP, algorithm = "weight01", statistic = "fisher")
# View the results
GO_BP_results <- GenTable(GO_BP, Fisher = GO_BP_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)
# Filter by significant results
GO_BP_results$Fisher<-as.numeric(GO_BP_results$Fisher)
GO_BP_results_sig<-GO_BP_results[GO_BP_results$Fisher<0.05,]
## Molecular Function ##
# Create topGO object
GO_MF <- new("topGOdata",
ontology = "MF", # Molecular Function
allGenes = gene_list,
annot = annFUN.gene2GO,
gene2GO = gene2go_list,
geneSel=topDiffGenes)
# Run GO enrichment test
GO_MF_FE <- runTest(GO_MF, algorithm = "weight01", statistic = "fisher")
# View the results
GO_MF_results <- GenTable(GO_MF, Fisher = GO_MF_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)
# Filter by significant results
GO_MF_results$Fisher<-as.numeric(GO_MF_results$Fisher)
GO_MF_results_sig<-GO_MF_results[GO_MF_results$Fisher<0.05,]
# Return
# Add type column only if results exist
if (nrow(GO_BP_results_sig) > 0) {
GO_BP_results_sig$type <- "Biological.Process"
}
if (nrow(GO_MF_results_sig) > 0) {
GO_MF_results_sig$type <- "Molecular.Function"
}
GO_results <- rbind(GO_BP_results_sig, GO_MF_results_sig)
print(GO_results)
}
}
miRNA_topGO_FE("Cluster_10093", cor_bind_FA)## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 52 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 2 nodes to be scored (63 eliminated genes)
##
## Level 9: 4 nodes to be scored (63 eliminated genes)
##
## Level 8: 5 nodes to be scored (65 eliminated genes)
##
## Level 7: 4 nodes to be scored (69 eliminated genes)
##
## Level 6: 5 nodes to be scored (118 eliminated genes)
##
## Level 5: 8 nodes to be scored (270 eliminated genes)
##
## Level 4: 10 nodes to be scored (279 eliminated genes)
##
## Level 3: 8 nodes to be scored (456 eliminated genes)
##
## Level 2: 3 nodes to be scored (736 eliminated genes)
##
## Level 1: 1 nodes to be scored (894 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 34 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 4 nodes to be scored (269 eliminated genes)
##
## Level 6: 4 nodes to be scored (273 eliminated genes)
##
## Level 5: 6 nodes to be scored (347 eliminated genes)
##
## Level 4: 7 nodes to be scored (373 eliminated genes)
##
## Level 3: 7 nodes to be scored (723 eliminated genes)
##
## Level 2: 3 nodes to be scored (770 eliminated genes)
##
## Level 1: 1 nodes to be scored (1465 eliminated genes)
## GO.ID Term Annotated Significant
## 1 GO:0000722 telomere maintenance via recombination 1 1
## 2 GO:0001755 neural crest cell migration 2 1
## 3 GO:0000822 inositol hexakisphosphate binding 1 1
## 4 GO:0001409 guanine nucleotide transmembrane transporter activi... 35 2
## Expected Fisher type
## 1 0.00 0.0021 Biological.Process
## 2 0.00 0.0042 Biological.Process
## 3 0.00 0.0021 Molecular.Function
## 4 0.07 0.0021 Molecular.FunctionLoop through all miRNA and run functional enrichment on the miRNA’s targets (all predicted targets)
interacting_miRNAs <- unique(cor_bind_FA$miRNA) %>% na.omit
results_all_targets <- NULL # initialize empty df
for(miRNA in interacting_miRNAs) {
# Run topGO enrichment function
miRNA_results <- miRNA_topGO_FE(miRNA, cor_bind_FA)
# Only keep results if not empty
if (nrow(miRNA_results) > 0) {
# Add the miRNA source column
miRNA_results$miRNA <- miRNA
# Bind to the accumulating results data frame
results_all_targets <- rbind(results_all_targets, miRNA_results)
}
}## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 169 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 3 nodes to be scored (0 eliminated genes)
##
## Level 12: 3 nodes to be scored (11 eliminated genes)
##
## Level 11: 4 nodes to be scored (155 eliminated genes)
##
## Level 10: 8 nodes to be scored (203 eliminated genes)
##
## Level 9: 11 nodes to be scored (205 eliminated genes)
##
## Level 8: 11 nodes to be scored (214 eliminated genes)
##
## Level 7: 17 nodes to be scored (243 eliminated genes)
##
## Level 6: 25 nodes to be scored (326 eliminated genes)
##
## Level 5: 31 nodes to be scored (463 eliminated genes)
##
## Level 4: 27 nodes to be scored (619 eliminated genes)
##
## Level 3: 18 nodes to be scored (891 eliminated genes)
##
## Level 2: 9 nodes to be scored (1024 eliminated genes)
##
## Level 1: 1 nodes to be scored (1188 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 90 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (0 eliminated genes)
##
## Level 7: 8 nodes to be scored (269 eliminated genes)
##
## Level 6: 19 nodes to be scored (341 eliminated genes)
##
## Level 5: 22 nodes to be scored (521 eliminated genes)
##
## Level 4: 18 nodes to be scored (798 eliminated genes)
##
## Level 3: 13 nodes to be scored (1629 eliminated genes)
##
## Level 2: 5 nodes to be scored (1994 eliminated genes)
##
## Level 1: 1 nodes to be scored (2289 eliminated genes)
## GO.ID Term Annotated Significant
## 1 GO:0001745 compound eye morphogenesis 3 1
## 2 GO:0001933 negative regulation of protein phosphorylation 5 1
## 3 GO:0009734 auxin-activated signaling pathway 5 1
## 4 GO:0004459 L-lactate dehydrogenase activity 1 1
## 5 GO:0004760 serine-pyruvate transaminase activity 1 1
## 6 GO:0004742 dihydrolipoyllysine-residue acetyltransferase activ... 1 1
## 7 GO:0003779 actin binding 18 2
## Expected Fisher type
## 1 0.03 0.027 Biological.Process
## 2 0.05 0.045 Biological.Process
## 3 0.05 0.045 Biological.Process
## 4 0.01 0.012 Molecular.Function
## 5 0.01 0.012 Molecular.Function
## 6 0.01 0.012 Molecular.Function
## 7 0.22 0.019 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 180 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 4 nodes to be scored (0 eliminated genes)
##
## Level 11: 5 nodes to be scored (140 eliminated genes)
##
## Level 10: 7 nodes to be scored (210 eliminated genes)
##
## Level 9: 9 nodes to be scored (217 eliminated genes)
##
## Level 8: 11 nodes to be scored (288 eliminated genes)
##
## Level 7: 19 nodes to be scored (322 eliminated genes)
##
## Level 6: 31 nodes to be scored (372 eliminated genes)
##
## Level 5: 38 nodes to be scored (602 eliminated genes)
##
## Level 4: 26 nodes to be scored (729 eliminated genes)
##
## Level 3: 19 nodes to be scored (1000 eliminated genes)
##
## Level 2: 9 nodes to be scored (1095 eliminated genes)
##
## Level 1: 1 nodes to be scored (1292 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 100 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 5 nodes to be scored (0 eliminated genes)
##
## Level 8: 5 nodes to be scored (101 eliminated genes)
##
## Level 7: 10 nodes to be scored (448 eliminated genes)
##
## Level 6: 15 nodes to be scored (532 eliminated genes)
##
## Level 5: 17 nodes to be scored (731 eliminated genes)
##
## Level 4: 22 nodes to be scored (898 eliminated genes)
##
## Level 3: 19 nodes to be scored (1411 eliminated genes)
##
## Level 2: 5 nodes to be scored (2009 eliminated genes)
##
## Level 1: 1 nodes to be scored (2470 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0016567 protein ubiquitination 37 3 0.47 0.0028 Biological.Process
## 2 GO:0001701 in utero embryonic development 15 2 0.19 0.0143 Biological.Process
## 3 GO:0001756 somitogenesis 2 1 0.03 0.0251 Biological.Process
## 4 GO:0007605 sensory perception of sound 2 1 0.03 0.0251 Biological.Process
## 5 GO:0004407 histone deacetylase activity 1 1 0.01 0.0110 Molecular.Function
## 6 GO:0004637 phosphoribosylamine-glycine ligase activity 1 1 0.01 0.0110 Molecular.Function
## 7 GO:0004615 phosphomannomutase activity 1 1 0.01 0.0110 Molecular.Function
## 8 GO:0003714 transcription corepressor activity 4 1 0.05 0.0440 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 59 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (67 eliminated genes)
##
## Level 7: 3 nodes to be scored (98 eliminated genes)
##
## Level 6: 8 nodes to be scored (103 eliminated genes)
##
## Level 5: 13 nodes to be scored (111 eliminated genes)
##
## Level 4: 11 nodes to be scored (187 eliminated genes)
##
## Level 3: 9 nodes to be scored (636 eliminated genes)
##
## Level 2: 6 nodes to be scored (869 eliminated genes)
##
## Level 1: 1 nodes to be scored (991 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 48 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 2 nodes to be scored (0 eliminated genes)
##
## Level 6: 6 nodes to be scored (2 eliminated genes)
##
## Level 5: 10 nodes to be scored (2 eliminated genes)
##
## Level 4: 14 nodes to be scored (145 eliminated genes)
##
## Level 3: 10 nodes to be scored (458 eliminated genes)
##
## Level 2: 4 nodes to be scored (638 eliminated genes)
##
## Level 1: 1 nodes to be scored (1722 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0016311 dephosphorylation 1 1 0.00 0.0035 Biological.Process
## 2 GO:0001558 regulation of cell growth 3 1 0.01 0.0105 Biological.Process
## 3 GO:0004334 fumarylacetoacetase activity 1 1 0.00 0.0046 Molecular.Function
## 4 GO:0005347 ATP transmembrane transporter activity 2 1 0.01 0.0091 Molecular.Function
## 5 GO:0004126 cytidine deaminase activity 5 1 0.02 0.0227 Molecular.Function
## 6 GO:0005539 glycosaminoglycan binding 5 1 0.02 0.0227 Molecular.Function
## 7 GO:0004601 peroxidase activity 7 1 0.03 0.0316 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 56 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (30 eliminated genes)
##
## Level 7: 3 nodes to be scored (62 eliminated genes)
##
## Level 6: 7 nodes to be scored (65 eliminated genes)
##
## Level 5: 11 nodes to be scored (75 eliminated genes)
##
## Level 4: 12 nodes to be scored (175 eliminated genes)
##
## Level 3: 9 nodes to be scored (627 eliminated genes)
##
## Level 2: 6 nodes to be scored (872 eliminated genes)
##
## Level 1: 1 nodes to be scored (991 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 41 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 2 nodes to be scored (0 eliminated genes)
##
## Level 6: 5 nodes to be scored (2 eliminated genes)
##
## Level 5: 8 nodes to be scored (2 eliminated genes)
##
## Level 4: 12 nodes to be scored (133 eliminated genes)
##
## Level 3: 8 nodes to be scored (398 eliminated genes)
##
## Level 2: 4 nodes to be scored (569 eliminated genes)
##
## Level 1: 1 nodes to be scored (1434 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0016311 dephosphorylation 1 1 0.00 0.0035 Biological.Process
## 2 GO:0001558 regulation of cell growth 3 1 0.01 0.0105 Biological.Process
## 3 GO:0003179 heart valve morphogenesis 4 1 0.01 0.0140 Biological.Process
## 4 GO:0004334 fumarylacetoacetase activity 1 1 0.00 0.0039 Molecular.Function
## 5 GO:0005347 ATP transmembrane transporter activity 2 1 0.01 0.0077 Molecular.Function
## 6 GO:0004126 cytidine deaminase activity 5 1 0.02 0.0192 Molecular.Function
## 7 GO:0005539 glycosaminoglycan binding 5 1 0.02 0.0192 Molecular.Function
## 8 GO:0004601 peroxidase activity 7 1 0.03 0.0268 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 62 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 3 nodes to be scored (140 eliminated genes)
##
## Level 9: 3 nodes to be scored (142 eliminated genes)
##
## Level 8: 2 nodes to be scored (142 eliminated genes)
##
## Level 7: 4 nodes to be scored (143 eliminated genes)
##
## Level 6: 10 nodes to be scored (157 eliminated genes)
##
## Level 5: 14 nodes to be scored (243 eliminated genes)
##
## Level 4: 10 nodes to be scored (298 eliminated genes)
##
## Level 3: 6 nodes to be scored (380 eliminated genes)
##
## Level 2: 3 nodes to be scored (539 eliminated genes)
##
## Level 1: 1 nodes to be scored (666 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 22 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (4 eliminated genes)
##
## Level 6: 2 nodes to be scored (30 eliminated genes)
##
## Level 5: 4 nodes to be scored (90 eliminated genes)
##
## Level 4: 4 nodes to be scored (102 eliminated genes)
##
## Level 3: 2 nodes to be scored (137 eliminated genes)
##
## Level 2: 3 nodes to be scored (153 eliminated genes)
##
## Level 1: 1 nodes to be scored (211 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0004736 pyruvate carboxylase activity 1 1 0 0.0007 Molecular.Function
## 2 GO:0005388 P-type calcium transporter activity 4 1 0 0.0028 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 10 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 2 nodes to be scored (0 eliminated genes)
##
## Level 5: 2 nodes to be scored (105 eliminated genes)
##
## Level 4: 2 nodes to be scored (113 eliminated genes)
##
## Level 3: 1 nodes to be scored (155 eliminated genes)
##
## Level 2: 1 nodes to be scored (173 eliminated genes)
##
## Level 1: 1 nodes to be scored (175 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 57 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (101 eliminated genes)
##
## Level 7: 5 nodes to be scored (390 eliminated genes)
##
## Level 6: 7 nodes to be scored (404 eliminated genes)
##
## Level 5: 10 nodes to be scored (568 eliminated genes)
##
## Level 4: 12 nodes to be scored (609 eliminated genes)
##
## Level 3: 13 nodes to be scored (1013 eliminated genes)
##
## Level 2: 3 nodes to be scored (1485 eliminated genes)
##
## Level 1: 1 nodes to be scored (1923 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0000082 G1/S transition of mitotic cell cycle 105 2 0.15 0.0054 Biological.Process
## 2 GO:0008170 N-methyltransferase activity 1 1 0.00 0.0035 Molecular.Function
## 3 GO:0003913 DNA photolyase activity 1 1 0.00 0.0035 Molecular.Function
## 4 GO:0004016 adenylate cyclase activity 2 1 0.01 0.0070 Molecular.Function
## 5 GO:0005496 steroid binding 8 1 0.03 0.0278 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 82 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 4 nodes to be scored (140 eliminated genes)
##
## Level 10: 4 nodes to be scored (203 eliminated genes)
##
## Level 9: 5 nodes to be scored (205 eliminated genes)
##
## Level 8: 4 nodes to be scored (206 eliminated genes)
##
## Level 7: 6 nodes to be scored (209 eliminated genes)
##
## Level 6: 13 nodes to be scored (240 eliminated genes)
##
## Level 5: 17 nodes to be scored (350 eliminated genes)
##
## Level 4: 14 nodes to be scored (413 eliminated genes)
##
## Level 3: 7 nodes to be scored (557 eliminated genes)
##
## Level 2: 3 nodes to be scored (734 eliminated genes)
##
## Level 1: 1 nodes to be scored (829 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 25 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 4 nodes to be scored (0 eliminated genes)
##
## Level 5: 5 nodes to be scored (135 eliminated genes)
##
## Level 4: 6 nodes to be scored (163 eliminated genes)
##
## Level 3: 6 nodes to be scored (599 eliminated genes)
##
## Level 2: 2 nodes to be scored (849 eliminated genes)
##
## Level 1: 1 nodes to be scored (1732 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0006665 sphingolipid metabolic process 2 1 0.01 0.0056 Biological.Process
## 2 GO:0004566 beta-glucuronidase activity 2 1 0.00 0.0035 Molecular.Function
## 3 GO:0003697 single-stranded DNA binding 9 1 0.02 0.0157 Molecular.Function
## 4 GO:0004508 steroid 17-alpha-monooxygenase activity 17 1 0.03 0.0296 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 180 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 4 nodes to be scored (140 eliminated genes)
##
## Level 10: 5 nodes to be scored (203 eliminated genes)
##
## Level 9: 10 nodes to be scored (205 eliminated genes)
##
## Level 8: 13 nodes to be scored (221 eliminated genes)
##
## Level 7: 17 nodes to be scored (265 eliminated genes)
##
## Level 6: 29 nodes to be scored (308 eliminated genes)
##
## Level 5: 39 nodes to be scored (545 eliminated genes)
##
## Level 4: 27 nodes to be scored (707 eliminated genes)
##
## Level 3: 22 nodes to be scored (971 eliminated genes)
##
## Level 2: 9 nodes to be scored (1081 eliminated genes)
##
## Level 1: 1 nodes to be scored (1297 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 85 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 4 nodes to be scored (0 eliminated genes)
##
## Level 8: 5 nodes to be scored (101 eliminated genes)
##
## Level 7: 10 nodes to be scored (135 eliminated genes)
##
## Level 6: 11 nodes to be scored (149 eliminated genes)
##
## Level 5: 17 nodes to be scored (342 eliminated genes)
##
## Level 4: 18 nodes to be scored (449 eliminated genes)
##
## Level 3: 12 nodes to be scored (1318 eliminated genes)
##
## Level 2: 6 nodes to be scored (1666 eliminated genes)
##
## Level 1: 1 nodes to be scored (2233 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0002224 toll-like receptor signaling pathway 1 1 0.01 0.011 Biological.Process
## 2 GO:0006598 polyamine catabolic process 2 1 0.02 0.022 Biological.Process
## 3 GO:0001578 microtubule bundle formation 2 1 0.02 0.022 Biological.Process
## 4 GO:0006699 bile acid biosynthetic process 4 1 0.04 0.044 Biological.Process
## 5 GO:0005471 ATP:ADP antiporter activity 1 1 0.01 0.006 Molecular.Function
## 6 GO:0003960 NADPH:quinone reductase activity 1 1 0.01 0.006 Molecular.Function
## 7 GO:0004675 transmembrane receptor protein serine/threonine kin... 3 1 0.02 0.018 Molecular.Function
## 8 GO:0004860 protein kinase inhibitor activity 3 1 0.02 0.018 Molecular.Function
## 9 GO:0004857 enzyme inhibitor activity 11 2 0.07 0.044 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 122 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 2 nodes to be scored (0 eliminated genes)
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 5 nodes to be scored (155 eliminated genes)
##
## Level 10: 6 nodes to be scored (203 eliminated genes)
##
## Level 9: 10 nodes to be scored (214 eliminated genes)
##
## Level 8: 8 nodes to be scored (215 eliminated genes)
##
## Level 7: 9 nodes to be scored (227 eliminated genes)
##
## Level 6: 18 nodes to be scored (331 eliminated genes)
##
## Level 5: 23 nodes to be scored (480 eliminated genes)
##
## Level 4: 19 nodes to be scored (573 eliminated genes)
##
## Level 3: 11 nodes to be scored (830 eliminated genes)
##
## Level 2: 7 nodes to be scored (969 eliminated genes)
##
## Level 1: 1 nodes to be scored (1079 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 63 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 5 nodes to be scored (2 eliminated genes)
##
## Level 7: 8 nodes to be scored (319 eliminated genes)
##
## Level 6: 10 nodes to be scored (413 eliminated genes)
##
## Level 5: 9 nodes to be scored (493 eliminated genes)
##
## Level 4: 12 nodes to be scored (604 eliminated genes)
##
## Level 3: 9 nodes to be scored (957 eliminated genes)
##
## Level 2: 5 nodes to be scored (1625 eliminated genes)
##
## Level 1: 1 nodes to be scored (1988 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0004995 tachykinin receptor activity 16 2 0.10 0.0038
## 2 GO:0005231 excitatory extracellular ligand-gated ion channel a... 2 1 0.01 0.0119
## 3 GO:0008061 chitin binding 6 1 0.04 0.0354
## type
## 1 Molecular.Function
## 2 Molecular.Function
## 3 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 65 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 1 nodes to be scored (15 eliminated genes)
##
## Level 10: 1 nodes to be scored (63 eliminated genes)
##
## Level 9: 3 nodes to be scored (63 eliminated genes)
##
## Level 8: 4 nodes to be scored (64 eliminated genes)
##
## Level 7: 5 nodes to be scored (75 eliminated genes)
##
## Level 6: 7 nodes to be scored (116 eliminated genes)
##
## Level 5: 9 nodes to be scored (277 eliminated genes)
##
## Level 4: 13 nodes to be scored (397 eliminated genes)
##
## Level 3: 12 nodes to be scored (616 eliminated genes)
##
## Level 2: 7 nodes to be scored (674 eliminated genes)
##
## Level 1: 1 nodes to be scored (1055 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 84 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 3 nodes to be scored (0 eliminated genes)
##
## Level 9: 5 nodes to be scored (0 eliminated genes)
##
## Level 8: 6 nodes to be scored (50 eliminated genes)
##
## Level 7: 9 nodes to be scored (342 eliminated genes)
##
## Level 6: 14 nodes to be scored (360 eliminated genes)
##
## Level 5: 14 nodes to be scored (458 eliminated genes)
##
## Level 4: 15 nodes to be scored (558 eliminated genes)
##
## Level 3: 12 nodes to be scored (1406 eliminated genes)
##
## Level 2: 5 nodes to be scored (1642 eliminated genes)
##
## Level 1: 1 nodes to be scored (2352 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000266 mitochondrial fission 4 1 0.02 0.0170
## 2 GO:0003360 brainstem development 5 1 0.02 0.0210
## 3 GO:0000413 protein peptidyl-prolyl isomerization 9 1 0.04 0.0370
## 4 GO:0002291 T cell activation via T cell receptor contact with ... 10 1 0.04 0.0410
## 5 GO:0002221 pattern recognition receptor signaling pathway 11 1 0.05 0.0450
## 6 GO:0004013 adenosylhomocysteinase activity 1 1 0.01 0.0053
## 7 GO:0004402 histone acetyltransferase activity 1 1 0.01 0.0053
## 8 GO:0005200 structural constituent of cytoskeleton 4 1 0.02 0.0209
## 9 GO:0005109 frizzled binding 5 1 0.03 0.0261
## 10 GO:0004758 serine C-palmitoyltransferase activity 6 1 0.03 0.0313
## 11 GO:0005245 voltage-gated calcium channel activity 7 1 0.04 0.0364
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Biological.Process
## 4 Biological.Process
## 5 Biological.Process
## 6 Molecular.Function
## 7 Molecular.Function
## 8 Molecular.Function
## 9 Molecular.Function
## 10 Molecular.Function
## 11 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 46 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 4 nodes to be scored (4 eliminated genes)
##
## Level 6: 7 nodes to be scored (4 eliminated genes)
##
## Level 5: 9 nodes to be scored (63 eliminated genes)
##
## Level 4: 9 nodes to be scored (259 eliminated genes)
##
## Level 3: 7 nodes to be scored (403 eliminated genes)
##
## Level 2: 6 nodes to be scored (564 eliminated genes)
##
## Level 1: 1 nodes to be scored (693 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 39 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (101 eliminated genes)
##
## Level 7: 3 nodes to be scored (121 eliminated genes)
##
## Level 6: 4 nodes to be scored (134 eliminated genes)
##
## Level 5: 8 nodes to be scored (189 eliminated genes)
##
## Level 4: 7 nodes to be scored (224 eliminated genes)
##
## Level 3: 8 nodes to be scored (828 eliminated genes)
##
## Level 2: 3 nodes to be scored (1338 eliminated genes)
##
## Level 1: 1 nodes to be scored (1841 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0002021 response to dietary excess 1 1 0.00 0.0035
## 2 GO:0002674 negative regulation of acute inflammatory response 4 1 0.01 0.0140
## 3 GO:0001508 action potential 4 1 0.01 0.0140
## 4 GO:0003958 NADPH-hemoprotein reductase activity 1 1 0.00 0.0028
## 5 GO:0004693 cyclin-dependent protein serine/threonine kinase ac... 3 1 0.01 0.0084
## 6 GO:0001965 G-protein alpha-subunit binding 16 1 0.05 0.0442
## 7 GO:0003779 actin binding 18 1 0.05 0.0496
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Biological.Process
## 4 Molecular.Function
## 5 Molecular.Function
## 6 Molecular.Function
## 7 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 115 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 4 nodes to be scored (140 eliminated genes)
##
## Level 9: 6 nodes to be scored (142 eliminated genes)
##
## Level 8: 6 nodes to be scored (147 eliminated genes)
##
## Level 7: 11 nodes to be scored (171 eliminated genes)
##
## Level 6: 17 nodes to be scored (185 eliminated genes)
##
## Level 5: 24 nodes to be scored (287 eliminated genes)
##
## Level 4: 20 nodes to be scored (411 eliminated genes)
##
## Level 3: 13 nodes to be scored (511 eliminated genes)
##
## Level 2: 7 nodes to be scored (730 eliminated genes)
##
## Level 1: 1 nodes to be scored (888 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 76 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 1 nodes to be scored (2 eliminated genes)
##
## Level 10: 2 nodes to be scored (2 eliminated genes)
##
## Level 9: 4 nodes to be scored (4 eliminated genes)
##
## Level 8: 5 nodes to be scored (105 eliminated genes)
##
## Level 7: 9 nodes to be scored (396 eliminated genes)
##
## Level 6: 13 nodes to be scored (415 eliminated genes)
##
## Level 5: 14 nodes to be scored (728 eliminated genes)
##
## Level 4: 11 nodes to be scored (793 eliminated genes)
##
## Level 3: 11 nodes to be scored (973 eliminated genes)
##
## Level 2: 3 nodes to be scored (1248 eliminated genes)
##
## Level 1: 1 nodes to be scored (1841 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0003351 epithelial cilium movement involved in extracellula... 1 1 0.00 0.0028
## 2 GO:0001933 negative regulation of protein phosphorylation 5 1 0.01 0.0140
## 3 GO:0001867 complement activation, lectin pathway 16 1 0.04 0.0441
## 4 GO:0004615 phosphomannomutase activity 1 1 0.00 0.0032
## 5 GO:0008641 ubiquitin-like modifier activating enzyme activity 1 1 0.00 0.0032
## 6 GO:0005314 high-affinity L-glutamate transmembrane transporter... 2 1 0.01 0.0063
## 7 GO:0003684 damaged DNA binding 4 1 0.01 0.0126
## 8 GO:0003697 single-stranded DNA binding 9 1 0.03 0.0282
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Biological.Process
## 4 Molecular.Function
## 5 Molecular.Function
## 6 Molecular.Function
## 7 Molecular.Function
## 8 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 107 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 3 nodes to be scored (140 eliminated genes)
##
## Level 9: 3 nodes to be scored (142 eliminated genes)
##
## Level 8: 4 nodes to be scored (142 eliminated genes)
##
## Level 7: 10 nodes to be scored (143 eliminated genes)
##
## Level 6: 17 nodes to be scored (182 eliminated genes)
##
## Level 5: 23 nodes to be scored (435 eliminated genes)
##
## Level 4: 20 nodes to be scored (535 eliminated genes)
##
## Level 3: 14 nodes to be scored (821 eliminated genes)
##
## Level 2: 6 nodes to be scored (1077 eliminated genes)
##
## Level 1: 1 nodes to be scored (1231 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 103 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
##
## Level 9: 6 nodes to be scored (0 eliminated genes)
##
## Level 8: 9 nodes to be scored (143 eliminated genes)
##
## Level 7: 14 nodes to be scored (193 eliminated genes)
##
## Level 6: 17 nodes to be scored (300 eliminated genes)
##
## Level 5: 17 nodes to be scored (522 eliminated genes)
##
## Level 4: 20 nodes to be scored (618 eliminated genes)
##
## Level 3: 12 nodes to be scored (1248 eliminated genes)
##
## Level 2: 5 nodes to be scored (1620 eliminated genes)
##
## Level 1: 1 nodes to be scored (2431 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0003682 chromatin binding 14 2 0.09 0.0037
## 2 GO:0004995 tachykinin receptor activity 16 2 0.11 0.0048
## 3 GO:0004129 cytochrome-c oxidase activity 1 1 0.01 0.0067
## 4 GO:0001626 nociceptin receptor activity 1 1 0.01 0.0067
## 5 GO:0004499 N,N-dimethylaniline monooxygenase activity 2 1 0.01 0.0133
## 6 GO:0003987 acetate-CoA ligase activity 2 1 0.01 0.0133
## 7 GO:0005506 iron ion binding 31 2 0.21 0.0175
## 8 GO:0003925 G protein activity 4 1 0.03 0.0265
## 9 GO:0004560 alpha-L-fucosidase activity 6 1 0.04 0.0395
## type
## 1 Molecular.Function
## 2 Molecular.Function
## 3 Molecular.Function
## 4 Molecular.Function
## 5 Molecular.Function
## 6 Molecular.Function
## 7 Molecular.Function
## 8 Molecular.Function
## 9 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 52 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 2 nodes to be scored (63 eliminated genes)
##
## Level 9: 4 nodes to be scored (63 eliminated genes)
##
## Level 8: 5 nodes to be scored (65 eliminated genes)
##
## Level 7: 4 nodes to be scored (69 eliminated genes)
##
## Level 6: 5 nodes to be scored (118 eliminated genes)
##
## Level 5: 8 nodes to be scored (270 eliminated genes)
##
## Level 4: 10 nodes to be scored (279 eliminated genes)
##
## Level 3: 8 nodes to be scored (456 eliminated genes)
##
## Level 2: 3 nodes to be scored (736 eliminated genes)
##
## Level 1: 1 nodes to be scored (894 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 34 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 4 nodes to be scored (269 eliminated genes)
##
## Level 6: 4 nodes to be scored (273 eliminated genes)
##
## Level 5: 6 nodes to be scored (347 eliminated genes)
##
## Level 4: 7 nodes to be scored (373 eliminated genes)
##
## Level 3: 7 nodes to be scored (723 eliminated genes)
##
## Level 2: 3 nodes to be scored (770 eliminated genes)
##
## Level 1: 1 nodes to be scored (1465 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000722 telomere maintenance via recombination 1 1 0.00 0.0021
## 2 GO:0001755 neural crest cell migration 2 1 0.00 0.0042
## 3 GO:0000822 inositol hexakisphosphate binding 1 1 0.00 0.0021
## 4 GO:0001409 guanine nucleotide transmembrane transporter activi... 35 2 0.07 0.0021
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Molecular.Function
## 4 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 12 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 3 nodes to be scored (0 eliminated genes)
##
## Level 4: 3 nodes to be scored (11 eliminated genes)
##
## Level 3: 3 nodes to be scored (36 eliminated genes)
##
## Level 2: 1 nodes to be scored (447 eliminated genes)
##
## Level 1: 1 nodes to be scored (508 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 9 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 2 nodes to be scored (0 eliminated genes)
##
## Level 4: 2 nodes to be scored (57 eliminated genes)
##
## Level 3: 2 nodes to be scored (204 eliminated genes)
##
## Level 2: 1 nodes to be scored (205 eliminated genes)
##
## Level 1: 1 nodes to be scored (572 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0000272 polysaccharide catabolic process 11 1 0.01 0.0077 Biological.Process
## 2 GO:0003779 actin binding 18 1 0.01 0.0130 Molecular.Function
## 3 GO:0000287 magnesium ion binding 57 1 0.04 0.0400 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 20 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 2 nodes to be scored (9 eliminated genes)
##
## Level 6: 3 nodes to be scored (9 eliminated genes)
##
## Level 5: 3 nodes to be scored (114 eliminated genes)
##
## Level 4: 3 nodes to be scored (122 eliminated genes)
##
## Level 3: 4 nodes to be scored (187 eliminated genes)
##
## Level 2: 2 nodes to be scored (210 eliminated genes)
##
## Level 1: 1 nodes to be scored (704 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 46 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (101 eliminated genes)
##
## Level 7: 4 nodes to be scored (390 eliminated genes)
##
## Level 6: 6 nodes to be scored (404 eliminated genes)
##
## Level 5: 7 nodes to be scored (450 eliminated genes)
##
## Level 4: 9 nodes to be scored (458 eliminated genes)
##
## Level 3: 9 nodes to be scored (761 eliminated genes)
##
## Level 2: 4 nodes to be scored (1080 eliminated genes)
##
## Level 1: 1 nodes to be scored (1388 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000082 G1/S transition of mitotic cell cycle 105 5 0.44 1.1e-05
## 2 GO:0001523 retinoid metabolic process 9 1 0.04 3.7e-02
## 3 GO:0015101 organic cation transmembrane transporter activity 1 1 0.00 2.5e-03
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 85 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 3 nodes to be scored (140 eliminated genes)
##
## Level 9: 3 nodes to be scored (142 eliminated genes)
##
## Level 8: 2 nodes to be scored (142 eliminated genes)
##
## Level 7: 6 nodes to be scored (143 eliminated genes)
##
## Level 6: 12 nodes to be scored (157 eliminated genes)
##
## Level 5: 19 nodes to be scored (323 eliminated genes)
##
## Level 4: 14 nodes to be scored (387 eliminated genes)
##
## Level 3: 12 nodes to be scored (571 eliminated genes)
##
## Level 2: 7 nodes to be scored (762 eliminated genes)
##
## Level 1: 1 nodes to be scored (1058 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 53 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 5 nodes to be scored (269 eliminated genes)
##
## Level 6: 8 nodes to be scored (273 eliminated genes)
##
## Level 5: 10 nodes to be scored (451 eliminated genes)
##
## Level 4: 11 nodes to be scored (485 eliminated genes)
##
## Level 3: 13 nodes to be scored (1005 eliminated genes)
##
## Level 2: 3 nodes to be scored (1669 eliminated genes)
##
## Level 1: 1 nodes to be scored (2294 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000902 cell morphogenesis 8 1 0.03 0.0330
## 2 GO:0003863 3-methyl-2-oxobutanoate dehydrogenase (2-methylprop... 1 1 0.00 0.0046
## 3 GO:0046982 protein heterodimerization activity 1 1 0.00 0.0046
## 4 GO:0004485 methylcrotonoyl-CoA carboxylase activity 1 1 0.00 0.0046
## 5 GO:0000340 RNA 7-methylguanosine cap binding 4 1 0.02 0.0182
## 6 GO:0004177 aminopeptidase activity 7 1 0.03 0.0316
## type
## 1 Biological.Process
## 2 Molecular.Function
## 3 Molecular.Function
## 4 Molecular.Function
## 5 Molecular.Function
## 6 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 35 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 2 nodes to be scored (19 eliminated genes)
##
## Level 6: 6 nodes to be scored (20 eliminated genes)
##
## Level 5: 8 nodes to be scored (126 eliminated genes)
##
## Level 4: 7 nodes to be scored (223 eliminated genes)
##
## Level 3: 7 nodes to be scored (469 eliminated genes)
##
## Level 2: 2 nodes to be scored (628 eliminated genes)
##
## Level 1: 1 nodes to be scored (705 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 22 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 2 nodes to be scored (0 eliminated genes)
##
## Level 5: 5 nodes to be scored (0 eliminated genes)
##
## Level 4: 5 nodes to be scored (55 eliminated genes)
##
## Level 3: 6 nodes to be scored (449 eliminated genes)
##
## Level 2: 3 nodes to be scored (906 eliminated genes)
##
## Level 1: 1 nodes to be scored (1706 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000278 mitotic cell cycle 162 3 0.45 0.00038
## 2 GO:0004345 glucose-6-phosphate dehydrogenase activity 2 1 0.00 0.00280
## type
## 1 Biological.Process
## 2 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 9 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 2 nodes to be scored (6 eliminated genes)
##
## Level 4: 2 nodes to be scored (26 eliminated genes)
##
## Level 3: 1 nodes to be scored (36 eliminated genes)
##
## Level 2: 1 nodes to be scored (105 eliminated genes)
##
## Level 1: 1 nodes to be scored (248 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 32 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (269 eliminated genes)
##
## Level 6: 4 nodes to be scored (273 eliminated genes)
##
## Level 5: 5 nodes to be scored (416 eliminated genes)
##
## Level 4: 6 nodes to be scored (433 eliminated genes)
##
## Level 3: 9 nodes to be scored (559 eliminated genes)
##
## Level 2: 2 nodes to be scored (1236 eliminated genes)
##
## Level 1: 1 nodes to be scored (1833 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0001738 morphogenesis of a polarized epithelium 6 1 0.00 0.0042
## 2 GO:0004222 metalloendopeptidase activity 15 1 0.02 0.0210
## type
## 1 Biological.Process
## 2 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 135 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 5 nodes to be scored (140 eliminated genes)
##
## Level 9: 7 nodes to be scored (142 eliminated genes)
##
## Level 8: 8 nodes to be scored (185 eliminated genes)
##
## Level 7: 10 nodes to be scored (202 eliminated genes)
##
## Level 6: 16 nodes to be scored (294 eliminated genes)
##
## Level 5: 27 nodes to be scored (419 eliminated genes)
##
## Level 4: 26 nodes to be scored (518 eliminated genes)
##
## Level 3: 21 nodes to be scored (985 eliminated genes)
##
## Level 2: 8 nodes to be scored (1086 eliminated genes)
##
## Level 1: 1 nodes to be scored (1337 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 83 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (101 eliminated genes)
##
## Level 7: 9 nodes to be scored (145 eliminated genes)
##
## Level 6: 13 nodes to be scored (226 eliminated genes)
##
## Level 5: 16 nodes to be scored (447 eliminated genes)
##
## Level 4: 16 nodes to be scored (525 eliminated genes)
##
## Level 3: 15 nodes to be scored (1167 eliminated genes)
##
## Level 2: 6 nodes to be scored (1566 eliminated genes)
##
## Level 1: 1 nodes to be scored (2366 eliminated genes)
## GO.ID Term Annotated Significant Expected
## 1 GO:0009734 auxin-activated signaling pathway 5 2 0.04
## 2 GO:0003924 GTPase activity 67 4 0.52
## 3 GO:0001965 G-protein alpha-subunit binding 16 2 0.12
## 4 GO:0003923 GPI-anchor transamidase activity 1 1 0.01
## 5 GO:0003858 3-hydroxybutyrate dehydrogenase activity 1 1 0.01
## 6 GO:0003730 mRNA 3'-UTR binding 2 1 0.02
## 7 GO:0004611 phosphoenolpyruvate carboxykinase activity 2 1 0.02
## 8 GO:0004198 calcium-dependent cysteine-type endopeptidase activ... 3 1 0.02
## 9 GO:0000900 mRNA regulatory element binding translation repress... 3 1 0.02
## 10 GO:0001530 lipopolysaccharide binding 4 1 0.03
## 11 GO:0004993 G protein-coupled serotonin receptor activity 4 1 0.03
## Fisher type
## 1 0.00053 Biological.Process
## 2 0.00150 Molecular.Function
## 3 0.00640 Molecular.Function
## 4 0.00770 Molecular.Function
## 5 0.00770 Molecular.Function
## 6 0.01540 Molecular.Function
## 7 0.01540 Molecular.Function
## 8 0.02300 Molecular.Function
## 9 0.02300 Molecular.Function
## 10 0.03060 Molecular.Function
## 11 0.03060 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 111 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 2 nodes to be scored (1 eliminated genes)
##
## Level 10: 3 nodes to be scored (70 eliminated genes)
##
## Level 9: 7 nodes to be scored (75 eliminated genes)
##
## Level 8: 10 nodes to be scored (106 eliminated genes)
##
## Level 7: 12 nodes to be scored (142 eliminated genes)
##
## Level 6: 18 nodes to be scored (240 eliminated genes)
##
## Level 5: 22 nodes to be scored (428 eliminated genes)
##
## Level 4: 18 nodes to be scored (541 eliminated genes)
##
## Level 3: 10 nodes to be scored (657 eliminated genes)
##
## Level 2: 5 nodes to be scored (876 eliminated genes)
##
## Level 1: 1 nodes to be scored (991 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 57 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
##
## Level 9: 5 nodes to be scored (0 eliminated genes)
##
## Level 8: 4 nodes to be scored (143 eliminated genes)
##
## Level 7: 7 nodes to be scored (177 eliminated genes)
##
## Level 6: 10 nodes to be scored (192 eliminated genes)
##
## Level 5: 9 nodes to be scored (383 eliminated genes)
##
## Level 4: 9 nodes to be scored (427 eliminated genes)
##
## Level 3: 7 nodes to be scored (644 eliminated genes)
##
## Level 2: 3 nodes to be scored (1139 eliminated genes)
##
## Level 1: 1 nodes to be scored (1818 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000390 spliceosomal complex disassembly 1 1 0.00 0.0042
## 2 GO:0000338 protein deneddylation 3 1 0.01 0.0126
## 3 GO:0001933 negative regulation of protein phosphorylation 5 1 0.02 0.0209
## 4 GO:0002042 cell migration involved in sprouting angiogenesis 7 1 0.03 0.0291
## 5 GO:0001054 RNA polymerase I activity 1 1 0.00 0.0032
## 6 GO:0004181 metallocarboxypeptidase activity 10 1 0.03 0.0313
## 7 GO:0000978 RNA polymerase II cis-regulatory region sequence-sp... 101 2 0.32 0.0382
## 8 GO:0008376 acetylgalactosaminyltransferase activity 14 1 0.04 0.0435
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Biological.Process
## 4 Biological.Process
## 5 Molecular.Function
## 6 Molecular.Function
## 7 Molecular.Function
## 8 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 5 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 1 nodes to be scored (0 eliminated genes)
##
## Level 3: 1 nodes to be scored (21 eliminated genes)
##
## Level 2: 1 nodes to be scored (24 eliminated genes)
##
## Level 1: 1 nodes to be scored (59 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 12 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 2 nodes to be scored (131 eliminated genes)
##
## Level 5: 3 nodes to be scored (134 eliminated genes)
##
## Level 4: 1 nodes to be scored (137 eliminated genes)
##
## Level 3: 2 nodes to be scored (399 eliminated genes)
##
## Level 2: 1 nodes to be scored (510 eliminated genes)
##
## Level 1: 1 nodes to be scored (828 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0003015 heart process 21 1 0.01 0.015 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO
## terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 16 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (67 eliminated genes)
##
## Level 5: 3 nodes to be scored (67 eliminated genes)
##
## Level 4: 2 nodes to be scored (80 eliminated genes)
##
## Level 3: 4 nodes to be scored (479 eliminated genes)
##
## Level 2: 3 nodes to be scored (590 eliminated genes)
##
## Level 1: 1 nodes to be scored (1227 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0005044 scavenger receptor activity 13 1 0.02 0.018 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 58 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (15 eliminated genes)
##
## Level 7: 5 nodes to be scored (52 eliminated genes)
##
## Level 6: 8 nodes to be scored (117 eliminated genes)
##
## Level 5: 11 nodes to be scored (137 eliminated genes)
##
## Level 4: 10 nodes to be scored (193 eliminated genes)
##
## Level 3: 10 nodes to be scored (380 eliminated genes)
##
## Level 2: 7 nodes to be scored (766 eliminated genes)
##
## Level 1: 1 nodes to be scored (1017 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 40 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (269 eliminated genes)
##
## Level 6: 4 nodes to be scored (289 eliminated genes)
##
## Level 5: 6 nodes to be scored (313 eliminated genes)
##
## Level 4: 8 nodes to be scored (402 eliminated genes)
##
## Level 3: 11 nodes to be scored (640 eliminated genes)
##
## Level 2: 4 nodes to be scored (929 eliminated genes)
##
## Level 1: 1 nodes to be scored (2097 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0001964 startle response 6 1 0.02 0.017 Biological.Process
## 2 GO:0002218 activation of innate immune response 15 1 0.04 0.041 Biological.Process
## 3 GO:0005496 steroid binding 8 1 0.02 0.020 Molecular.Function
## 4 GO:0004995 tachykinin receptor activity 16 1 0.04 0.039 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 147 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 3 nodes to be scored (0 eliminated genes)
##
## Level 11: 5 nodes to be scored (140 eliminated genes)
##
## Level 10: 6 nodes to be scored (141 eliminated genes)
##
## Level 9: 9 nodes to be scored (153 eliminated genes)
##
## Level 8: 9 nodes to be scored (157 eliminated genes)
##
## Level 7: 13 nodes to be scored (196 eliminated genes)
##
## Level 6: 23 nodes to be scored (275 eliminated genes)
##
## Level 5: 34 nodes to be scored (486 eliminated genes)
##
## Level 4: 20 nodes to be scored (547 eliminated genes)
##
## Level 3: 15 nodes to be scored (791 eliminated genes)
##
## Level 2: 8 nodes to be scored (916 eliminated genes)
##
## Level 1: 1 nodes to be scored (1011 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 61 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
##
## Level 9: 4 nodes to be scored (0 eliminated genes)
##
## Level 8: 5 nodes to be scored (102 eliminated genes)
##
## Level 7: 7 nodes to be scored (131 eliminated genes)
##
## Level 6: 10 nodes to be scored (164 eliminated genes)
##
## Level 5: 11 nodes to be scored (276 eliminated genes)
##
## Level 4: 8 nodes to be scored (331 eliminated genes)
##
## Level 3: 8 nodes to be scored (775 eliminated genes)
##
## Level 2: 5 nodes to be scored (1332 eliminated genes)
##
## Level 1: 1 nodes to be scored (2050 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000712 resolution of meiotic recombination intermediates 1 1 0.01 0.0056
## 2 GO:0004402 histone acetyltransferase activity 1 1 0.00 0.0042
## 3 GO:0005200 structural constituent of cytoskeleton 4 1 0.02 0.0168
## 4 GO:0004843 cysteine-type deubiquitinase activity 6 1 0.03 0.0251
## 5 GO:0004940 beta1-adrenergic receptor activity 9 1 0.04 0.0374
## 6 GO:0000064 L-ornithine transmembrane transporter activity 11 1 0.05 0.0455
## 7 GO:0004966 galanin receptor activity 12 1 0.05 0.0496
## type
## 1 Biological.Process
## 2 Molecular.Function
## 3 Molecular.Function
## 4 Molecular.Function
## 5 Molecular.Function
## 6 Molecular.Function
## 7 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 81 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 2 nodes to be scored (0 eliminated genes)
##
## Level 10: 3 nodes to be scored (7 eliminated genes)
##
## Level 9: 6 nodes to be scored (16 eliminated genes)
##
## Level 8: 6 nodes to be scored (17 eliminated genes)
##
## Level 7: 7 nodes to be scored (26 eliminated genes)
##
## Level 6: 9 nodes to be scored (26 eliminated genes)
##
## Level 5: 16 nodes to be scored (70 eliminated genes)
##
## Level 4: 15 nodes to be scored (208 eliminated genes)
##
## Level 3: 10 nodes to be scored (813 eliminated genes)
##
## Level 2: 5 nodes to be scored (1001 eliminated genes)
##
## Level 1: 1 nodes to be scored (1102 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 65 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (101 eliminated genes)
##
## Level 7: 6 nodes to be scored (145 eliminated genes)
##
## Level 6: 12 nodes to be scored (159 eliminated genes)
##
## Level 5: 15 nodes to be scored (261 eliminated genes)
##
## Level 4: 12 nodes to be scored (438 eliminated genes)
##
## Level 3: 9 nodes to be scored (1172 eliminated genes)
##
## Level 2: 4 nodes to be scored (1643 eliminated genes)
##
## Level 1: 1 nodes to be scored (2249 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0006836 neurotransmitter transport 6 1 0.03 0.0250
## 2 GO:0001843 neural tube closure 7 1 0.03 0.0290
## 3 GO:0015969 guanosine tetraphosphate metabolic process 9 1 0.04 0.0370
## 4 GO:0000413 protein peptidyl-prolyl isomerization 9 1 0.04 0.0370
## 5 GO:0004064 arylesterase activity 1 1 0.01 0.0056
## 6 GO:0005262 calcium channel activity 24 2 0.14 0.0076
## 7 GO:0004312 fatty acid synthase activity 3 1 0.02 0.0168
## 8 GO:0005539 glycosaminoglycan binding 5 1 0.03 0.0278
## 9 GO:0004560 alpha-L-fucosidase activity 6 1 0.03 0.0333
## 10 GO:0000977 RNA polymerase II transcription regulatory region s... 109 3 0.61 0.0402
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Biological.Process
## 4 Biological.Process
## 5 Molecular.Function
## 6 Molecular.Function
## 7 Molecular.Function
## 8 Molecular.Function
## 9 Molecular.Function
## 10 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 95 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 3 nodes to be scored (0 eliminated genes)
##
## Level 9: 4 nodes to be scored (0 eliminated genes)
##
## Level 8: 6 nodes to be scored (49 eliminated genes)
##
## Level 7: 11 nodes to be scored (57 eliminated genes)
##
## Level 6: 16 nodes to be scored (78 eliminated genes)
##
## Level 5: 21 nodes to be scored (137 eliminated genes)
##
## Level 4: 15 nodes to be scored (398 eliminated genes)
##
## Level 3: 10 nodes to be scored (689 eliminated genes)
##
## Level 2: 8 nodes to be scored (799 eliminated genes)
##
## Level 1: 1 nodes to be scored (1044 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 60 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 5 nodes to be scored (269 eliminated genes)
##
## Level 6: 10 nodes to be scored (340 eliminated genes)
##
## Level 5: 12 nodes to be scored (492 eliminated genes)
##
## Level 4: 14 nodes to be scored (638 eliminated genes)
##
## Level 3: 12 nodes to be scored (989 eliminated genes)
##
## Level 2: 3 nodes to be scored (1649 eliminated genes)
##
## Level 1: 1 nodes to be scored (2058 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0001878 response to yeast 1 1 0.00 0.0042
## 2 GO:0001947 heart looping 4 1 0.02 0.0167
## 3 GO:0001758 retinal dehydrogenase activity 1 1 0.01 0.0053
## 4 GO:0004802 transketolase activity 1 1 0.01 0.0053
## 5 GO:0004735 pyrroline-5-carboxylate reductase activity 2 1 0.01 0.0105
## 6 GO:0000287 magnesium ion binding 57 2 0.30 0.0351
## 7 GO:0003729 mRNA binding 7 1 0.04 0.0364
## 8 GO:0004721 phosphoprotein phosphatase activity 9 1 0.05 0.0466
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Molecular.Function
## 4 Molecular.Function
## 5 Molecular.Function
## 6 Molecular.Function
## 7 Molecular.Function
## 8 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 16 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 2 nodes to be scored (0 eliminated genes)
##
## Level 5: 3 nodes to be scored (105 eliminated genes)
##
## Level 4: 4 nodes to be scored (113 eliminated genes)
##
## Level 3: 3 nodes to be scored (177 eliminated genes)
##
## Level 2: 2 nodes to be scored (207 eliminated genes)
##
## Level 1: 1 nodes to be scored (665 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 64 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
##
## Level 9: 5 nodes to be scored (0 eliminated genes)
##
## Level 8: 5 nodes to be scored (143 eliminated genes)
##
## Level 7: 8 nodes to be scored (445 eliminated genes)
##
## Level 6: 10 nodes to be scored (530 eliminated genes)
##
## Level 5: 10 nodes to be scored (619 eliminated genes)
##
## Level 4: 11 nodes to be scored (717 eliminated genes)
##
## Level 3: 9 nodes to be scored (1047 eliminated genes)
##
## Level 2: 3 nodes to be scored (1306 eliminated genes)
##
## Level 1: 1 nodes to be scored (2017 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0006091 generation of precursor metabolites and energy 12 1 0.03 0.025
## 2 GO:0003341 cilium movement 22 1 0.05 0.046
## type
## 1 Biological.Process
## 2 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 149 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 2 nodes to be scored (0 eliminated genes)
##
## Level 12: 5 nodes to be scored (0 eliminated genes)
##
## Level 11: 7 nodes to be scored (2 eliminated genes)
##
## Level 10: 9 nodes to be scored (65 eliminated genes)
##
## Level 9: 12 nodes to be scored (68 eliminated genes)
##
## Level 8: 13 nodes to be scored (77 eliminated genes)
##
## Level 7: 15 nodes to be scored (137 eliminated genes)
##
## Level 6: 19 nodes to be scored (155 eliminated genes)
##
## Level 5: 26 nodes to be scored (437 eliminated genes)
##
## Level 4: 21 nodes to be scored (576 eliminated genes)
##
## Level 3: 12 nodes to be scored (901 eliminated genes)
##
## Level 2: 7 nodes to be scored (1022 eliminated genes)
##
## Level 1: 1 nodes to be scored (1125 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 44 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (269 eliminated genes)
##
## Level 6: 7 nodes to be scored (273 eliminated genes)
##
## Level 5: 8 nodes to be scored (316 eliminated genes)
##
## Level 4: 10 nodes to be scored (500 eliminated genes)
##
## Level 3: 9 nodes to be scored (759 eliminated genes)
##
## Level 2: 4 nodes to be scored (1446 eliminated genes)
##
## Level 1: 1 nodes to be scored (1988 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0007175 negative regulation of epidermal growth factor-acti... 1 1 0.00 0.0049
## 2 GO:0002100 tRNA wobble adenosine to inosine editing 1 1 0.00 0.0049
## 3 GO:0003143 embryonic heart tube morphogenesis 5 1 0.02 0.0243
## 4 GO:0016980 creatinase activity 1 1 0.00 0.0028
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Biological.Process
## 4 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 105 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 2 nodes to be scored (7 eliminated genes)
##
## Level 9: 4 nodes to be scored (7 eliminated genes)
##
## Level 8: 8 nodes to be scored (21 eliminated genes)
##
## Level 7: 12 nodes to be scored (31 eliminated genes)
##
## Level 6: 15 nodes to be scored (43 eliminated genes)
##
## Level 5: 18 nodes to be scored (203 eliminated genes)
##
## Level 4: 18 nodes to be scored (528 eliminated genes)
##
## Level 3: 18 nodes to be scored (881 eliminated genes)
##
## Level 2: 7 nodes to be scored (1046 eliminated genes)
##
## Level 1: 1 nodes to be scored (1277 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 39 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (101 eliminated genes)
##
## Level 7: 5 nodes to be scored (121 eliminated genes)
##
## Level 6: 5 nodes to be scored (152 eliminated genes)
##
## Level 5: 7 nodes to be scored (201 eliminated genes)
##
## Level 4: 7 nodes to be scored (210 eliminated genes)
##
## Level 3: 5 nodes to be scored (637 eliminated genes)
##
## Level 2: 3 nodes to be scored (922 eliminated genes)
##
## Level 1: 1 nodes to be scored (1283 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0001878 response to yeast 1 1 0.01 0.0056
## 2 GO:0060271 cilium assembly 3 1 0.02 0.0167
## 3 GO:0001843 neural tube closure 7 1 0.04 0.0387
## 4 GO:0005290 L-histidine transmembrane transporter activity 9 1 0.01 0.0130
## 5 GO:0004966 galanin receptor activity 12 1 0.02 0.0170
## 6 GO:0001540 amyloid-beta binding 24 1 0.03 0.0330
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Biological.Process
## 4 Molecular.Function
## 5 Molecular.Function
## 6 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 26 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 2 nodes to be scored (9 eliminated genes)
##
## Level 6: 3 nodes to be scored (52 eliminated genes)
##
## Level 5: 3 nodes to be scored (52 eliminated genes)
##
## Level 4: 3 nodes to be scored (52 eliminated genes)
##
## Level 3: 5 nodes to be scored (77 eliminated genes)
##
## Level 2: 6 nodes to be scored (137 eliminated genes)
##
## Level 1: 1 nodes to be scored (706 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 31 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (270 eliminated genes)
##
## Level 6: 3 nodes to be scored (279 eliminated genes)
##
## Level 5: 3 nodes to be scored (371 eliminated genes)
##
## Level 4: 5 nodes to be scored (385 eliminated genes)
##
## Level 3: 8 nodes to be scored (432 eliminated genes)
##
## Level 2: 4 nodes to be scored (462 eliminated genes)
##
## Level 1: 1 nodes to be scored (1337 eliminated genes)
## GO.ID Term Annotated Significant Expected
## 1 GO:0002412 antigen transcytosis by M cells in mucosal-associat... 43 6 0.21
## 2 GO:0001523 retinoid metabolic process 9 1 0.04
## 3 GO:0005384 manganese ion transmembrane transporter activity 1 1 0.00
## 4 GO:0000149 SNARE binding 4 1 0.01
## 5 GO:0003777 microtubule motor activity 16 1 0.02
## Fisher type
## 1 3.60e-09 Biological.Process
## 2 4.30e-02 Biological.Process
## 3 1.40e-03 Molecular.Function
## 4 5.60e-03 Molecular.Function
## 5 2.23e-02 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 23 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (17 eliminated genes)
##
## Level 6: 4 nodes to be scored (17 eliminated genes)
##
## Level 5: 5 nodes to be scored (124 eliminated genes)
##
## Level 4: 3 nodes to be scored (132 eliminated genes)
##
## Level 3: 2 nodes to be scored (188 eliminated genes)
##
## Level 2: 2 nodes to be scored (273 eliminated genes)
##
## Level 1: 1 nodes to be scored (275 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 6 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 1 nodes to be scored (1 eliminated genes)
##
## Level 3: 1 nodes to be scored (3 eliminated genes)
##
## Level 2: 1 nodes to be scored (3 eliminated genes)
##
## Level 1: 1 nodes to be scored (15 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0001762 beta-alanine transport 17 1 0.02 0.02400 Biological.Process
## 2 GO:0004034 aldose 1-epimerase activity 1 1 0.00 0.00035 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO
## terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 26 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 2 nodes to be scored (269 eliminated genes)
##
## Level 6: 5 nodes to be scored (273 eliminated genes)
##
## Level 5: 4 nodes to be scored (281 eliminated genes)
##
## Level 4: 4 nodes to be scored (417 eliminated genes)
##
## Level 3: 6 nodes to be scored (496 eliminated genes)
##
## Level 2: 2 nodes to be scored (588 eliminated genes)
##
## Level 1: 1 nodes to be scored (1420 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0003943 N-acetylgalactosamine-4-sulfatase activity 8 1 0.01 0.011
## type
## 1 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 74 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 4 nodes to be scored (13 eliminated genes)
##
## Level 7: 5 nodes to be scored (15 eliminated genes)
##
## Level 6: 6 nodes to be scored (17 eliminated genes)
##
## Level 5: 14 nodes to be scored (86 eliminated genes)
##
## Level 4: 15 nodes to be scored (106 eliminated genes)
##
## Level 3: 17 nodes to be scored (418 eliminated genes)
##
## Level 2: 8 nodes to be scored (953 eliminated genes)
##
## Level 1: 1 nodes to be scored (1103 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 60 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 6 nodes to be scored (0 eliminated genes)
##
## Level 6: 10 nodes to be scored (67 eliminated genes)
##
## Level 5: 14 nodes to be scored (291 eliminated genes)
##
## Level 4: 12 nodes to be scored (509 eliminated genes)
##
## Level 3: 12 nodes to be scored (1174 eliminated genes)
##
## Level 2: 4 nodes to be scored (1503 eliminated genes)
##
## Level 1: 1 nodes to be scored (2298 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000226 microtubule cytoskeleton organization 44 3 0.28 0.012
## 2 GO:0006598 polyamine catabolic process 2 1 0.01 0.013
## 3 GO:0002790 peptide secretion 2 1 0.01 0.013
## 4 GO:0004720 protein-lysine 6-oxidase activity 2 1 0.01 0.012
## 5 GO:0004559 alpha-mannosidase activity 6 1 0.04 0.035
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Biological.Process
## 4 Molecular.Function
## 5 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 36 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 1 nodes to be scored (63 eliminated genes)
##
## Level 9: 2 nodes to be scored (63 eliminated genes)
##
## Level 8: 2 nodes to be scored (64 eliminated genes)
##
## Level 7: 2 nodes to be scored (66 eliminated genes)
##
## Level 6: 2 nodes to be scored (97 eliminated genes)
##
## Level 5: 4 nodes to be scored (264 eliminated genes)
##
## Level 4: 7 nodes to be scored (290 eliminated genes)
##
## Level 3: 8 nodes to be scored (380 eliminated genes)
##
## Level 2: 5 nodes to be scored (536 eliminated genes)
##
## Level 1: 1 nodes to be scored (653 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 15 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (101 eliminated genes)
##
## Level 7: 1 nodes to be scored (121 eliminated genes)
##
## Level 6: 2 nodes to be scored (131 eliminated genes)
##
## Level 5: 2 nodes to be scored (134 eliminated genes)
##
## Level 4: 1 nodes to be scored (137 eliminated genes)
##
## Level 3: 2 nodes to be scored (268 eliminated genes)
##
## Level 2: 2 nodes to be scored (510 eliminated genes)
##
## Level 1: 1 nodes to be scored (828 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0001649 osteoblast differentiation 27 2 0.08 0.002 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 8 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (44 eliminated genes)
##
## Level 4: 1 nodes to be scored (44 eliminated genes)
##
## Level 3: 2 nodes to be scored (87 eliminated genes)
##
## Level 2: 1 nodes to be scored (117 eliminated genes)
##
## Level 1: 1 nodes to be scored (154 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO
## terms!
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000226 microtubule cytoskeleton organization 44 1 0.03 0.031
## type
## 1 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 12 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (36 eliminated genes)
##
## Level 4: 1 nodes to be scored (45 eliminated genes)
##
## Level 3: 3 nodes to be scored (73 eliminated genes)
##
## Level 2: 4 nodes to be scored (249 eliminated genes)
##
## Level 1: 1 nodes to be scored (358 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 48 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (42 eliminated genes)
##
## Level 7: 4 nodes to be scored (55 eliminated genes)
##
## Level 6: 7 nodes to be scored (59 eliminated genes)
##
## Level 5: 10 nodes to be scored (137 eliminated genes)
##
## Level 4: 11 nodes to be scored (168 eliminated genes)
##
## Level 3: 6 nodes to be scored (778 eliminated genes)
##
## Level 2: 4 nodes to be scored (1222 eliminated genes)
##
## Level 1: 1 nodes to be scored (1763 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000165 MAPK cascade 36 2 0.05 0.00062
## 2 GO:0004729 oxygen-dependent protoporphyrinogen oxidase activit... 1 1 0.00 0.00250
## 3 GO:0000827 inositol-1,3,4,5,6-pentakisphosphate kinase activit... 2 1 0.00 0.00490
## 4 GO:0004508 steroid 17-alpha-monooxygenase activity 17 1 0.04 0.04120
## type
## 1 Biological.Process
## 2 Molecular.Function
## 3 Molecular.Function
## 4 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 14 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (30 eliminated genes)
##
## Level 7: 1 nodes to be scored (37 eliminated genes)
##
## Level 6: 1 nodes to be scored (37 eliminated genes)
##
## Level 5: 2 nodes to be scored (40 eliminated genes)
##
## Level 4: 2 nodes to be scored (85 eliminated genes)
##
## Level 3: 3 nodes to be scored (128 eliminated genes)
##
## Level 2: 1 nodes to be scored (447 eliminated genes)
##
## Level 1: 1 nodes to be scored (499 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 6 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 1 nodes to be scored (1 eliminated genes)
##
## Level 3: 1 nodes to be scored (2 eliminated genes)
##
## Level 2: 1 nodes to be scored (12 eliminated genes)
##
## Level 1: 1 nodes to be scored (115 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0000209 protein polyubiquitination 30 1 0.02 0.02100
## 2 GO:0004591 oxoglutarate dehydrogenase (succinyl-transferring) ... 1 1 0.00 0.00035
## type
## 1 Biological.Process
## 2 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO
## terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 13 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 3 nodes to be scored (10 eliminated genes)
##
## Level 3: 6 nodes to be scored (11 eliminated genes)
##
## Level 2: 1 nodes to be scored (292 eliminated genes)
##
## Level 1: 1 nodes to be scored (1047 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0005542 folic acid binding 10 1 0 0.0035 Molecular.Functionhead(results_all_targets)## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0001745 compound eye morphogenesis 3 1 0.03 0.027
## 2 GO:0001933 negative regulation of protein phosphorylation 5 1 0.05 0.045
## 3 GO:0009734 auxin-activated signaling pathway 5 1 0.05 0.045
## 4 GO:0004459 L-lactate dehydrogenase activity 1 1 0.01 0.012
## 5 GO:0004760 serine-pyruvate transaminase activity 1 1 0.01 0.012
## 6 GO:0004742 dihydrolipoyllysine-residue acetyltransferase activ... 1 1 0.01 0.012
## type miRNA
## 1 Biological.Process Cluster_5981
## 2 Biological.Process Cluster_5981
## 3 Biological.Process Cluster_5981
## 4 Molecular.Function Cluster_5981
## 5 Molecular.Function Cluster_5981
## 6 Molecular.Function Cluster_5981Save results
write.csv(results_all_targets, "../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/miRNA_all_targets_topGO_FE.csv")Loop through all miRNA and run functional enrichment on the miRNA’s significantly correlated targets
interacting_miRNAs_sig <- unique(sig_cor_bind_FA$miRNA) %>% na.omit
results_sig_cor_targets <- NULL # initialize empty df
for(miRNA in interacting_miRNAs_sig) {
# Run topGO enrichment function
miRNA_results <- miRNA_topGO_FE(miRNA, sig_cor_bind_FA)
# Only keep results if not empty
if (nrow(miRNA_results) > 0) {
# Add the miRNA source column
miRNA_results$miRNA <- miRNA
# Bind to the accumulating results data frame
results_sig_cor_targets <- rbind(results_sig_cor_targets, miRNA_results)
}
}## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 31 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (4 eliminated genes)
##
## Level 7: 4 nodes to be scored (5 eliminated genes)
##
## Level 6: 6 nodes to be scored (8 eliminated genes)
##
## Level 5: 7 nodes to be scored (26 eliminated genes)
##
## Level 4: 4 nodes to be scored (85 eliminated genes)
##
## Level 3: 1 nodes to be scored (230 eliminated genes)
##
## Level 2: 2 nodes to be scored (241 eliminated genes)
##
## Level 1: 1 nodes to be scored (248 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0001947 heart looping 4 1 0 0.0028 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 5 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 1 nodes to be scored (0 eliminated genes)
##
## Level 3: 1 nodes to be scored (22 eliminated genes)
##
## Level 2: 1 nodes to be scored (22 eliminated genes)
##
## Level 1: 1 nodes to be scored (66 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 22 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 2 nodes to be scored (269 eliminated genes)
##
## Level 6: 2 nodes to be scored (273 eliminated genes)
##
## Level 5: 3 nodes to be scored (281 eliminated genes)
##
## Level 4: 3 nodes to be scored (283 eliminated genes)
##
## Level 3: 7 nodes to be scored (334 eliminated genes)
##
## Level 2: 2 nodes to be scored (346 eliminated genes)
##
## Level 1: 1 nodes to be scored (1596 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0003341 cilium movement 22 1 0.02 0.015
## 2 GO:0004842 ubiquitin-protein transferase activity 35 1 0.02 0.024
## type
## 1 Biological.Process
## 2 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 17 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 2 nodes to be scored (11 eliminated genes)
##
## Level 4: 3 nodes to be scored (11 eliminated genes)
##
## Level 3: 4 nodes to be scored (105 eliminated genes)
##
## Level 2: 5 nodes to be scored (288 eliminated genes)
##
## Level 1: 1 nodes to be scored (420 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 13 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 3 nodes to be scored (0 eliminated genes)
##
## Level 4: 3 nodes to be scored (53 eliminated genes)
##
## Level 3: 3 nodes to be scored (305 eliminated genes)
##
## Level 2: 2 nodes to be scored (380 eliminated genes)
##
## Level 1: 1 nodes to be scored (766 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0002221 pattern recognition receptor signaling pathway 11 1 0.01 0.0077
## 2 GO:0004252 serine-type endopeptidase activity 53 1 0.04 0.0370
## type
## 1 Biological.Process
## 2 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 42 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (5 eliminated genes)
##
## Level 7: 5 nodes to be scored (12 eliminated genes)
##
## Level 6: 7 nodes to be scored (12 eliminated genes)
##
## Level 5: 9 nodes to be scored (16 eliminated genes)
##
## Level 4: 6 nodes to be scored (259 eliminated genes)
##
## Level 3: 5 nodes to be scored (340 eliminated genes)
##
## Level 2: 3 nodes to be scored (546 eliminated genes)
##
## Level 1: 1 nodes to be scored (665 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0001933 negative regulation of protein phosphorylation 5 1 0 0.0035
## type
## 1 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 7 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 2 nodes to be scored (17 eliminated genes)
##
## Level 3: 1 nodes to be scored (17 eliminated genes)
##
## Level 2: 1 nodes to be scored (34 eliminated genes)
##
## Level 1: 1 nodes to be scored (115 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0004508 steroid 17-alpha-monooxygenase activity 17 1 0.01 0.006
## type
## 1 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 29 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (269 eliminated genes)
##
## Level 6: 4 nodes to be scored (340 eliminated genes)
##
## Level 5: 5 nodes to be scored (348 eliminated genes)
##
## Level 4: 4 nodes to be scored (364 eliminated genes)
##
## Level 3: 7 nodes to be scored (393 eliminated genes)
##
## Level 2: 2 nodes to be scored (407 eliminated genes)
##
## Level 1: 1 nodes to be scored (1535 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0004459 L-lactate dehydrogenase activity 1 1 0 0.0011 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 36 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (4 eliminated genes)
##
## Level 6: 4 nodes to be scored (7 eliminated genes)
##
## Level 5: 8 nodes to be scored (128 eliminated genes)
##
## Level 4: 9 nodes to be scored (139 eliminated genes)
##
## Level 3: 6 nodes to be scored (330 eliminated genes)
##
## Level 2: 2 nodes to be scored (545 eliminated genes)
##
## Level 1: 1 nodes to be scored (705 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 9 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (135 eliminated genes)
##
## Level 4: 2 nodes to be scored (135 eliminated genes)
##
## Level 3: 2 nodes to be scored (147 eliminated genes)
##
## Level 2: 1 nodes to be scored (269 eliminated genes)
##
## Level 1: 1 nodes to be scored (425 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0006699 bile acid biosynthetic process 4 1 0.01 0.0084
## 2 GO:0000082 G1/S transition of mitotic cell cycle 105 2 0.22 0.0153
## 3 GO:0003964 RNA-directed DNA polymerase activity 135 1 0.05 0.0470
## type
## 1 Biological.Process
## 2 Biological.Process
## 3 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 62 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 3 nodes to be scored (140 eliminated genes)
##
## Level 9: 3 nodes to be scored (142 eliminated genes)
##
## Level 8: 2 nodes to be scored (142 eliminated genes)
##
## Level 7: 4 nodes to be scored (143 eliminated genes)
##
## Level 6: 10 nodes to be scored (157 eliminated genes)
##
## Level 5: 14 nodes to be scored (243 eliminated genes)
##
## Level 4: 10 nodes to be scored (298 eliminated genes)
##
## Level 3: 6 nodes to be scored (380 eliminated genes)
##
## Level 2: 3 nodes to be scored (539 eliminated genes)
##
## Level 1: 1 nodes to be scored (666 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 5 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 4: 1 nodes to be scored (0 eliminated genes)
##
## Level 3: 1 nodes to be scored (0 eliminated genes)
##
## Level 2: 2 nodes to be scored (5 eliminated genes)
##
## Level 1: 1 nodes to be scored (297 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0005539 glycosaminoglycan binding 5 1 0 0.0035 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 21 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 2 nodes to be scored (0 eliminated genes)
##
## Level 7: 3 nodes to be scored (24 eliminated genes)
##
## Level 6: 3 nodes to be scored (95 eliminated genes)
##
## Level 5: 3 nodes to be scored (168 eliminated genes)
##
## Level 4: 4 nodes to be scored (190 eliminated genes)
##
## Level 3: 2 nodes to be scored (222 eliminated genes)
##
## Level 2: 2 nodes to be scored (227 eliminated genes)
##
## Level 1: 1 nodes to be scored (577 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0005262 calcium channel activity 24 1 0.02 0.017 Molecular.Function
## 2 GO:0003924 GTPase activity 67 1 0.05 0.047 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 8 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 2 nodes to be scored (10 eliminated genes)
##
## Level 4: 1 nodes to be scored (10 eliminated genes)
##
## Level 3: 1 nodes to be scored (203 eliminated genes)
##
## Level 2: 1 nodes to be scored (234 eliminated genes)
##
## Level 1: 1 nodes to be scored (243 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0001640 adenylate cyclase inhibiting G protein-coupled glut... 10 1 0 0.0035
## type
## 1 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 3 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 3: 1 nodes to be scored (0 eliminated genes)
##
## Level 2: 1 nodes to be scored (0 eliminated genes)
##
## Level 1: 1 nodes to be scored (11 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0003712 transcription coregulator activity 11 1 0 0.0039 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 4: 1 nodes to be scored (0 eliminated genes)
##
## Level 3: 1 nodes to be scored (0 eliminated genes)
##
## Level 2: 1 nodes to be scored (162 eliminated genes)
##
## Level 1: 1 nodes to be scored (175 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 68 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 3 nodes to be scored (140 eliminated genes)
##
## Level 9: 3 nodes to be scored (142 eliminated genes)
##
## Level 8: 2 nodes to be scored (142 eliminated genes)
##
## Level 7: 4 nodes to be scored (143 eliminated genes)
##
## Level 6: 10 nodes to be scored (157 eliminated genes)
##
## Level 5: 15 nodes to be scored (243 eliminated genes)
##
## Level 4: 11 nodes to be scored (298 eliminated genes)
##
## Level 3: 8 nodes to be scored (407 eliminated genes)
##
## Level 2: 5 nodes to be scored (601 eliminated genes)
##
## Level 1: 1 nodes to be scored (744 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 14 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (101 eliminated genes)
##
## Level 7: 1 nodes to be scored (121 eliminated genes)
##
## Level 6: 2 nodes to be scored (131 eliminated genes)
##
## Level 5: 2 nodes to be scored (134 eliminated genes)
##
## Level 4: 1 nodes to be scored (137 eliminated genes)
##
## Level 3: 2 nodes to be scored (268 eliminated genes)
##
## Level 2: 1 nodes to be scored (510 eliminated genes)
##
## Level 1: 1 nodes to be scored (828 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0001649 osteoblast differentiation 27 1 0.04 0.037
## 2 GO:0000978 RNA polymerase II cis-regulatory region sequence-sp... 101 1 0.04 0.036
## type
## 1 Biological.Process
## 2 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 23 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 2 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (101 eliminated genes)
##
## Level 7: 2 nodes to be scored (121 eliminated genes)
##
## Level 6: 4 nodes to be scored (131 eliminated genes)
##
## Level 5: 4 nodes to be scored (144 eliminated genes)
##
## Level 4: 2 nodes to be scored (165 eliminated genes)
##
## Level 3: 4 nodes to be scored (318 eliminated genes)
##
## Level 2: 2 nodes to be scored (656 eliminated genes)
##
## Level 1: 1 nodes to be scored (1356 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0004181 metallocarboxypeptidase activity 10 1 0.01 0.007 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 14 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (30 eliminated genes)
##
## Level 7: 1 nodes to be scored (37 eliminated genes)
##
## Level 6: 1 nodes to be scored (37 eliminated genes)
##
## Level 5: 2 nodes to be scored (40 eliminated genes)
##
## Level 4: 2 nodes to be scored (85 eliminated genes)
##
## Level 3: 3 nodes to be scored (128 eliminated genes)
##
## Level 2: 1 nodes to be scored (447 eliminated genes)
##
## Level 1: 1 nodes to be scored (499 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 7 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 2 nodes to be scored (5 eliminated genes)
##
## Level 3: 1 nodes to be scored (7 eliminated genes)
##
## Level 2: 1 nodes to be scored (22 eliminated genes)
##
## Level 1: 1 nodes to be scored (386 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0000209 protein polyubiquitination 30 1 0.02 0.0210 Biological.Process
## 2 GO:0004126 cytidine deaminase activity 5 1 0.00 0.0018 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 8 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (67 eliminated genes)
##
## Level 5: 1 nodes to be scored (67 eliminated genes)
##
## Level 4: 1 nodes to be scored (80 eliminated genes)
##
## Level 3: 1 nodes to be scored (80 eliminated genes)
##
## Level 2: 1 nodes to be scored (80 eliminated genes)
##
## Level 1: 1 nodes to be scored (386 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0003924 GTPase activity 67 1 0.02 0.024 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 12 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 3 nodes to be scored (0 eliminated genes)
##
## Level 4: 3 nodes to be scored (11 eliminated genes)
##
## Level 3: 3 nodes to be scored (36 eliminated genes)
##
## Level 2: 1 nodes to be scored (447 eliminated genes)
##
## Level 1: 1 nodes to be scored (508 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0000272 polysaccharide catabolic process 11 1 0.01 0.0077 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 4 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 4: 1 nodes to be scored (0 eliminated genes)
##
## Level 3: 1 nodes to be scored (0 eliminated genes)
##
## Level 2: 1 nodes to be scored (22 eliminated genes)
##
## Level 1: 1 nodes to be scored (26 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0000981 DNA-binding transcription factor activity, RNA poly... 22 1 0.01 0.0077 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 62 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 3 nodes to be scored (140 eliminated genes)
##
## Level 9: 3 nodes to be scored (142 eliminated genes)
##
## Level 8: 2 nodes to be scored (142 eliminated genes)
##
## Level 7: 4 nodes to be scored (143 eliminated genes)
##
## Level 6: 10 nodes to be scored (157 eliminated genes)
##
## Level 5: 14 nodes to be scored (243 eliminated genes)
##
## Level 4: 10 nodes to be scored (298 eliminated genes)
##
## Level 3: 6 nodes to be scored (380 eliminated genes)
##
## Level 2: 3 nodes to be scored (539 eliminated genes)
##
## Level 1: 1 nodes to be scored (666 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 5 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 1 nodes to be scored (0 eliminated genes)
##
## Level 3: 1 nodes to be scored (200 eliminated genes)
##
## Level 2: 1 nodes to be scored (234 eliminated genes)
##
## Level 1: 1 nodes to be scored (243 eliminated genes)
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 9 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (12 eliminated genes)
##
## Level 5: 1 nodes to be scored (32 eliminated genes)
##
## Level 4: 2 nodes to be scored (33 eliminated genes)
##
## Level 3: 1 nodes to be scored (200 eliminated genes)
##
## Level 2: 1 nodes to be scored (242 eliminated genes)
##
## Level 1: 1 nodes to be scored (243 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0004966 galanin receptor activity 12 1 0 0.0042 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 6 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (0 eliminated genes)
##
## Level 4: 1 nodes to be scored (1 eliminated genes)
##
## Level 3: 1 nodes to be scored (1 eliminated genes)
##
## Level 2: 1 nodes to be scored (20 eliminated genes)
##
## Level 1: 1 nodes to be scored (386 eliminated genes)
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0016980 creatinase activity 1 1 0 0.00035 Molecular.Function
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 12 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 1 nodes to be scored (0 eliminated genes)
##
## Level 5: 1 nodes to be scored (36 eliminated genes)
##
## Level 4: 1 nodes to be scored (45 eliminated genes)
##
## Level 3: 3 nodes to be scored (73 eliminated genes)
##
## Level 2: 4 nodes to be scored (249 eliminated genes)
##
## Level 1: 1 nodes to be scored (358 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0000165 MAPK cascade 36 1 0.03 0.025 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 12 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (0 eliminated genes)
##
## Level 7: 1 nodes to be scored (9 eliminated genes)
##
## Level 6: 1 nodes to be scored (9 eliminated genes)
##
## Level 5: 1 nodes to be scored (9 eliminated genes)
##
## Level 4: 1 nodes to be scored (9 eliminated genes)
##
## Level 3: 3 nodes to be scored (32 eliminated genes)
##
## Level 2: 2 nodes to be scored (37 eliminated genes)
##
## Level 1: 1 nodes to be scored (532 eliminated genes)
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## GO.ID Term Annotated Significant Expected Fisher type
## 1 GO:0001523 retinoid metabolic process 9 1 0.01 0.0063 Biological.Process
## Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "names")= chr [1:33624] "FUN_035039" "FUN_035038" "FUN_035031" "FUN_035030" ...
##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 0 nontrivial nodes
## parameters:
## test statistic: fisher
## Warning in getSigGroups(object, test.stat): No enrichment can pe performed - there are no feasible GO terms!
## [1] GO.ID Term Annotated Significant Expected Fisher
## <0 rows> (or 0-length row.names)head(results_sig_cor_targets)## GO.ID Term Annotated Significant Expected Fisher
## 1 GO:0001947 heart looping 4 1 0.00 0.0028
## 2 GO:0003341 cilium movement 22 1 0.02 0.0150
## 3 GO:0004842 ubiquitin-protein transferase activity 35 1 0.02 0.0240
## 4 GO:0002221 pattern recognition receptor signaling pathway 11 1 0.01 0.0077
## 5 GO:0004252 serine-type endopeptidase activity 53 1 0.04 0.0370
## 6 GO:0001933 negative regulation of protein phosphorylation 5 1 0.00 0.0035
## type miRNA
## 1 Biological.Process Cluster_19193
## 2 Biological.Process Cluster_10051
## 3 Molecular.Function Cluster_10051
## 4 Biological.Process Cluster_16409
## 5 Molecular.Function Cluster_16409
## 6 Biological.Process Cluster_3250Save results
write.csv(results_sig_cor_targets, "../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/miRNA_sig_cor_targets_topGO_FE.csv")Filter PCC miranda data
# Filter so that only negative correlations remain
neg_corr_data <- data %>%
filter(PCC.cor < 0)Make list of target genes for input to topGO
# Genes of interest - ie those targeted by miRNAs
target_genes <- as.character(unique(neg_corr_data$mRNA))# Apply 1 or 0 if gene is gene of interest
GeneList <- factor(as.integer(all_genes %in% target_genes))
names(GeneList) <- all_genesThe following code will perform GO enrichment using the weighted Fisher’s exact test to assess whether specific GO terms are overrepresented in the genes targeted by miRNAs, regardless of correlation significance.
Create topGOdata object, which is required for topGO
analysis
GO_BP <-new("topGOdata",
ontology="BP",
gene2GO=gene2go_list,
allGenes=GeneList,
annot = annFUN.gene2GO,
geneSel=topDiffGenes)##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )Run GO enrichment test
GO_BP_FE <- runTest(GO_BP, algorithm="weight01", statistic="fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 373 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 14: 1 nodes to be scored (0 eliminated genes)
##
## Level 13: 5 nodes to be scored (0 eliminated genes)
##
## Level 12: 5 nodes to be scored (11 eliminated genes)
##
## Level 11: 7 nodes to be scored (157 eliminated genes)
##
## Level 10: 16 nodes to be scored (211 eliminated genes)
##
## Level 9: 27 nodes to be scored (226 eliminated genes)
##
## Level 8: 34 nodes to be scored (337 eliminated genes)
##
## Level 7: 47 nodes to be scored (451 eliminated genes)
##
## Level 6: 65 nodes to be scored (627 eliminated genes)
##
## Level 5: 72 nodes to be scored (908 eliminated genes)
##
## Level 4: 50 nodes to be scored (1025 eliminated genes)
##
## Level 3: 32 nodes to be scored (1219 eliminated genes)
##
## Level 2: 11 nodes to be scored (1343 eliminated genes)
##
## Level 1: 1 nodes to be scored (1417 eliminated genes)Generate results table
GO_BP_En <- GenTable(GO_BP, Fisher = GO_BP_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)Only taking the top 100 GO terms
Filter by significant results
GO_BP_En$Fisher<-as.numeric(GO_BP_En$Fisher)
GO_BP_En_sig<-GO_BP_En[GO_BP_En$Fisher<0.05,]Merge GO_BP_En_sig with GO and gene info.
# Separate GO terms
neg_cor_gene2go <- gene2go %>%
separate_rows(GO.ID, sep = ";")
# Ensure GO terms in both datasets are formatted similarly (trim whitespaces)
neg_cor_gene2go$GO.ID <- trimws(neg_cor_gene2go$GO.ID)
GO_BP_En_sig$GO.ID <- trimws(GO_BP_En_sig$GO.ID)
# Join the datasets based on GO term
GO_BP_En_sig_gene <- neg_cor_gene2go %>%
left_join(GO_BP_En_sig, by = "GO.ID") %>%
na.omit()
# Add ontology column
GO_BP_En_sig_gene$ontology <- "Biological Processes"
# Keep only unique rows
GO_BP_En_sig_gene <- GO_BP_En_sig_gene %>% distinct()Create topGOdata object, which is required for topGO
analysis
GO_MF <-new("topGOdata",
ontology="MF",
gene2GO=gene2go_list,
allGenes=GeneList,
annot = annFUN.gene2GO,
geneSel=topDiffGenes)##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )Run GO enrichment test
GO_MF_FE <- runTest(GO_MF, algorithm="weight01", statistic="fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 243 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 4 nodes to be scored (0 eliminated genes)
##
## Level 9: 12 nodes to be scored (0 eliminated genes)
##
## Level 8: 19 nodes to be scored (146 eliminated genes)
##
## Level 7: 30 nodes to be scored (481 eliminated genes)
##
## Level 6: 47 nodes to be scored (621 eliminated genes)
##
## Level 5: 47 nodes to be scored (930 eliminated genes)
##
## Level 4: 50 nodes to be scored (1352 eliminated genes)
##
## Level 3: 24 nodes to be scored (2004 eliminated genes)
##
## Level 2: 9 nodes to be scored (2371 eliminated genes)
##
## Level 1: 1 nodes to be scored (2681 eliminated genes)Generate results table
GO_MF_En <- GenTable(GO_MF, Fisher = GO_MF_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)Only taking the top 100 GO terms
Filter by significant results
GO_MF_En$Fisher<-as.numeric(GO_MF_En$Fisher)
GO_MF_En_sig<-GO_MF_En[GO_MF_En$Fisher<0.05,]Merge GO_MF_En_sig with GO and gene info.
# Separate GO terms
neg_cor_gene2go <- gene2go %>%
separate_rows(GO.ID, sep = ";")
# Ensure GO terms in both datasets are formatted similarly (trim whitespaces)
neg_cor_gene2go$GO.ID <- trimws(neg_cor_gene2go$GO.ID)
GO_MF_En_sig$GO.ID <- trimws(GO_MF_En_sig$GO.ID)
# Join the datasets based on GO term
GO_MF_En_sig_gene <- neg_cor_gene2go %>%
left_join(GO_MF_En_sig, by = "GO.ID") %>%
na.omit()
# Add ontology column
GO_MF_En_sig_gene$ontology <- "Molecular Functions"
# Keep only unique rows
GO_MF_En_sig_gene <- unique(GO_MF_En_sig_gene)Bind so there is a df that has significantly enriched GO terms for all ontologies
GO_neg_corr_df <- rbind(GO_BP_En_sig_gene, GO_MF_En_sig_gene)Merge with GO_neg_corr_df
test <- GO_neg_corr_df %>%
inner_join(neg_corr_data, by = c("gene_ID" = "mRNA")) #%>%
#mutate(direction = ifelse(PCC.cor > 0, "Positive", "Negative")) #%>%
#filter(ontology != "Cellular Components") #%>%
#filter(p_value < 0.1)
# Save as csv
write.csv(test, "../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_neg_corr_target_enrichment.csv")Plot!
plot<-ggplot(test, aes(x = Term, y = Fisher, fill = p_value)) +
#ylim(0, 1) +
#geom_hline(yintercept = 0.05, linetype = "solid", color = "black", linewidth = 1)+
#geom_hline(yintercept = 0.05, color = "black", linetype = "solid", linewidth = 0.5) + # Add line at 0.05
geom_point(shape = 21, size = 5) +
#scale_size(range = c(2, 20)) +
xlab('') +
ylab("Fisher p-value") +
theme_bw(base_size = 24) +
facet_grid(vars(ontology), scales = "free", space = "free_y") +
coord_flip(); plot
# Save plot
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_neg_corr_target_enrichment.pdf", plot, width = 20, height = 35, dpi = 300)
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_neg_corr_target_enrichment.png", plot, width = 20, height = 35, dpi = 300)Examine the top 10 most significant GO terms for BP and MF
# Function to get top 5 unique terms
get_top_10_unique <- function(data, ontology_type) {
data %>%
filter(ontology == ontology_type) %>%
arrange(Fisher) %>%
distinct(Term, .keep_all = TRUE) %>%
slice_head(n = 10)
}
# Get top 5 unique Biological Processes
top_10_BP <- get_top_10_unique(test, "Biological Processes")
# Get top 5 unique Molecular Functions
top_10_MF <- get_top_10_unique(test, "Molecular Functions")
# Combine results
top_10_combined <- bind_rows(top_10_BP, top_10_MF)
unique(top_10_combined$Term)## [1] "auxin-activated signaling pathway" "protein ubiquitination"
## [3] "MAPK cascade" "alternative mRNA splicing, via spliceosome"
## [5] "chromatin binding"# Plot
top_10_combined <- top_10_combined %>%
arrange(desc(Fisher)) %>%
mutate(Term = factor(Term, levels = unique(Term)))
plot<-ggplot(top_10_combined, aes(x = Term, y = Fisher, fill = p_value)) +
#ylim(0, 1) +
#geom_hline(yintercept = 0.05, linetype = "solid", color = "black", linewidth = 1)+
#geom_hline(yintercept = 0.05, color = "black", linetype = "solid", linewidth = 0.5) + # Add line at 0.05
geom_point(size = 10, color = "black") +
scale_size(range = c(2, 20)) +
xlab('') +
ylab("Fisher p-value") +
theme_bw(base_size = 35) +
facet_grid(vars(ontology), scales = "free", space = "free_y") +
coord_flip(); plot
# Save plot
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/top10GO_neg_corr_target_enrichment.pdf", plot, width = 20, height = 25, dpi = 300)
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/top10GO_neg_corr_target_enrichment.png", plot, width = 20, height = 25, dpi = 300)Filter PCC miranda data
# Filter so that only positive correlations remain
pos_corr_data <- data %>%
filter(PCC.cor > 0)
# Make list of target genes for input to topGO
# Genes of interest - ie those targeted by miRNAs
target_genes <- as.character(unique(pos_corr_data$mRNA))
# Apply 1 or 0 if gene is gene of interest
GeneList <- factor(as.integer(all_genes %in% target_genes))
names(GeneList) <- all_genesThe following code will perform GO enrichment using the weighted Fisher’s exact test to assess whether specific GO terms are overrepresented in the genes targeted by miRNAs with positively correlated expression, regardless of correlation significance.
Create topGOdata object, which is required for topGO
analysis
GO_BP <-new("topGOdata",
ontology="BP",
gene2GO=gene2go_list,
allGenes=GeneList,
annot = annFUN.gene2GO,
geneSel=topDiffGenes)##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )Run GO enrichment test
GO_BP_FE <- runTest(GO_BP, algorithm="weight01", statistic="fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 456 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 2 nodes to be scored (0 eliminated genes)
##
## Level 12: 9 nodes to be scored (0 eliminated genes)
##
## Level 11: 13 nodes to be scored (141 eliminated genes)
##
## Level 10: 21 nodes to be scored (219 eliminated genes)
##
## Level 9: 38 nodes to be scored (235 eliminated genes)
##
## Level 8: 50 nodes to be scored (331 eliminated genes)
##
## Level 7: 58 nodes to be scored (466 eliminated genes)
##
## Level 6: 78 nodes to be scored (664 eliminated genes)
##
## Level 5: 85 nodes to be scored (939 eliminated genes)
##
## Level 4: 53 nodes to be scored (1067 eliminated genes)
##
## Level 3: 37 nodes to be scored (1276 eliminated genes)
##
## Level 2: 11 nodes to be scored (1342 eliminated genes)
##
## Level 1: 1 nodes to be scored (1402 eliminated genes)Generate results table
GO_BP_En <- GenTable(GO_BP, Fisher = GO_BP_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)Only taking the top 100 GO terms
Filter by significant results
GO_BP_En$Fisher<-as.numeric(GO_BP_En$Fisher)
GO_BP_En_sig<-GO_BP_En[GO_BP_En$Fisher<0.05,]Merge GO_BP_En_sig with GO and gene info.
# Separate GO terms
pos_cor_gene2go <- gene2go %>%
separate_rows(GO.ID, sep = ";")
# Ensure GO terms in both datasets are formatted similarly (trim whitespaces)
pos_cor_gene2go$GO.ID <- trimws(pos_cor_gene2go$GO.ID)
GO_BP_En_sig$GO.ID <- trimws(GO_BP_En_sig$GO.ID)
# Join the datasets based on GO term
GO_BP_En_sig_gene <- pos_cor_gene2go %>%
left_join(GO_BP_En_sig, by = "GO.ID") %>%
na.omit()
# Add ontology column
GO_BP_En_sig_gene$ontology <- "Biological Processes"
# Keep only unique rows
GO_BP_En_sig_gene <- unique(GO_BP_En_sig_gene)Create topGOdata object, which is required for topGO
analysis
GO_MF <-new("topGOdata",
ontology="MF",
gene2GO=gene2go_list,
allGenes=GeneList,
annot = annFUN.gene2GO,
geneSel=topDiffGenes)##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )Run GO enrichment test
GO_MF_FE <- runTest(GO_MF, algorithm="weight01", statistic="fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 320 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 1 nodes to be scored (2 eliminated genes)
##
## Level 10: 5 nodes to be scored (2 eliminated genes)
##
## Level 9: 12 nodes to be scored (4 eliminated genes)
##
## Level 8: 20 nodes to be scored (155 eliminated genes)
##
## Level 7: 38 nodes to be scored (499 eliminated genes)
##
## Level 6: 71 nodes to be scored (635 eliminated genes)
##
## Level 5: 69 nodes to be scored (1005 eliminated genes)
##
## Level 4: 63 nodes to be scored (1501 eliminated genes)
##
## Level 3: 27 nodes to be scored (2192 eliminated genes)
##
## Level 2: 11 nodes to be scored (2507 eliminated genes)
##
## Level 1: 1 nodes to be scored (2692 eliminated genes)Generate results table
GO_MF_En <- GenTable(GO_MF, Fisher = GO_MF_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)Only taking the top 100 GO terms
Filter by significant results
GO_MF_En$Fisher<-as.numeric(GO_MF_En$Fisher)
GO_MF_En_sig<-GO_MF_En[GO_MF_En$Fisher<0.05,]Merge GO_MF_En_sig with GO and gene info.
# Separate GO terms
pos_cor_gene2go <- gene2go %>%
separate_rows(GO.ID, sep = ";")
# Ensure GO terms in both datasets are formatted similarly (trim whitespaces)
pos_cor_gene2go$GO.ID <- trimws(pos_cor_gene2go$GO.ID)
GO_MF_En_sig$GO.ID <- trimws(GO_MF_En_sig$GO.ID)
# Join the datasets based on GO term
GO_MF_En_sig_gene <- pos_cor_gene2go %>%
left_join(GO_MF_En_sig, by = "GO.ID") %>%
na.omit()
# Add ontology column
GO_MF_En_sig_gene$ontology <- "Molecular Functions"
# Keep only unique rows
GO_MF_En_sig_gene <- unique(GO_MF_En_sig_gene)Bind so there is a df that has significantly enriched GO terms for all ontologies
GO_pos_corr_df <- rbind(GO_BP_En_sig_gene, GO_MF_En_sig_gene)Merge with GO_pos_corr_df
test <- GO_pos_corr_df %>%
inner_join(pos_corr_data, by = c("gene_ID" = "mRNA")) #%>%
#mutate(direction = ifelse(PCC.cor > 0, "Positive", "Negative")) #%>%
#filter(ontology != "Cellular Components") #%>%
#filter(p_value < 0.1)
# Save as csv
write.csv(test, "../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_pos_corr_target_enrichment.csv")Plot!
plot<-ggplot(test, aes(x = Term, y = Fisher, fill = p_value)) +
#ylim(0, 1) +
#geom_hline(yintercept = 0.05, linetype = "solid", color = "black", linewidth = 1)+
#geom_hline(yintercept = 0.05, color = "black", linetype = "solid", linewidth = 0.5) + # Add line at 0.05
geom_point(shape = 21, size = 5) +
#scale_size(range = c(2, 20)) +
xlab('') +
ylab("Fisher p-value") +
theme_bw(base_size = 24) +
facet_grid(vars(ontology), scales = "free", space = "free_y") +
coord_flip(); plot
# Save plot
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_pos_corr_target_enrichment.pdf", plot, width = 20, height = 35, dpi = 300)
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_pos_corr_target_enrichment.png", plot, width = 20, height = 35, dpi = 300)Examine the top 10 most significant GO terms for BP and MF
# Function to get top 5 unique terms
get_top_10_unique <- function(data, ontology_type) {
data %>%
filter(ontology == ontology_type) %>%
arrange(Fisher) %>%
distinct(Term, .keep_all = TRUE) %>%
slice_head(n = 10)
}
# Get top 5 unique Biological Processes
top_10_BP <- get_top_10_unique(test, "Biological Processes")
# Get top 5 unique Molecular Functions
top_10_MF <- get_top_10_unique(test, "Molecular Functions")
# Combine results
top_10_combined <- bind_rows(top_10_BP, top_10_MF)
unique(top_10_combined$Term)## [1] "polyamine catabolic process"
## [2] "negative regulation of protein phosphorylation"
## [3] "RNA polymerase II cis-regulatory region sequence-sp..."
## [4] "cytidine deaminase activity"
## [5] "glycosaminoglycan binding"# Plot
top_10_combined <- top_10_combined %>%
arrange(desc(Fisher)) %>%
mutate(Term = factor(Term, levels = unique(Term)))
plot<-ggplot(top_10_combined, aes(x = Term, y = Fisher, fill = p_value)) +
#ylim(0, 1) +
#geom_hline(yintercept = 0.05, linetype = "solid", color = "black", linewidth = 1)+
#geom_hline(yintercept = 0.05, color = "black", linetype = "solid", linewidth = 0.5) + # Add line at 0.05
geom_point(size = 10, color = "black") +
scale_size(range = c(2, 20)) +
xlab('') +
ylab("Fisher p-value") +
theme_bw(base_size = 35) +
facet_grid(vars(ontology), scales = "free", space = "free_y") +
coord_flip(); plot
# Save plot
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/top10GO_pos_corr_target_enrichment.pdf", plot, width = 20, height = 25, dpi = 300)
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/top10GO_pos_corr_target_enrichment.png", plot, width = 20, height = 25, dpi = 300)Filter PCC miranda data
# Filter so that only significant negative correlations remain
sig_neg_corr_data <- data %>%
filter(PCC.cor < 0) %>%
filter(p_value < 0.05)
# Make list of target genes for input to topGO
# Genes of interest - ie those targeted by miRNAs
target_genes <- as.character(unique(sig_neg_corr_data$mRNA))
# Apply 1 or 0 if gene is gene of interest
GeneList <- factor(as.integer(all_genes %in% target_genes))
names(GeneList) <- all_genesThe following code will perform GO enrichment using the weighted Fisher’s exact test to assess whether specific GO terms are overrepresented in the genes targeted by miRNAs with significantly negatively correlated expression.
Create topGOdata object, which is required for topGO
analysis
GO_BP <-new("topGOdata",
ontology="BP",
gene2GO=gene2go_list,
allGenes=GeneList,
annot = annFUN.gene2GO,
geneSel=topDiffGenes)##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )Run GO enrichment test
GO_BP_FE <- runTest(GO_BP, algorithm="weight01", statistic="fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 40 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 1 nodes to be scored (0 eliminated genes)
##
## Level 8: 1 nodes to be scored (30 eliminated genes)
##
## Level 7: 3 nodes to be scored (37 eliminated genes)
##
## Level 6: 5 nodes to be scored (37 eliminated genes)
##
## Level 5: 8 nodes to be scored (181 eliminated genes)
##
## Level 4: 7 nodes to be scored (253 eliminated genes)
##
## Level 3: 8 nodes to be scored (369 eliminated genes)
##
## Level 2: 5 nodes to be scored (708 eliminated genes)
##
## Level 1: 1 nodes to be scored (806 eliminated genes)Generate results table
GO_BP_En <- GenTable(GO_BP, Fisher = GO_BP_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)Only taking the top 100 GO terms
Filter by significant results
GO_BP_En$Fisher<-as.numeric(GO_BP_En$Fisher)
GO_BP_En_sig<-GO_BP_En[GO_BP_En$Fisher<0.05,]Merge GO_BP_En_sig with GO and gene info.
# Separate GO terms
sig_neg_cor_gene2go <- gene2go %>%
separate_rows(GO.ID, sep = ";")
# Ensure GO terms in both datasets are formatted similarly (trim whitespaces)
sig_neg_cor_gene2go$GO.ID <- trimws(sig_neg_cor_gene2go$GO.ID)
GO_BP_En_sig$GO.ID <- trimws(GO_BP_En_sig$GO.ID)
# Join the datasets based on GO term
GO_BP_En_sig_gene <- sig_neg_cor_gene2go %>%
left_join(GO_BP_En_sig, by = "GO.ID") %>%
na.omit()
# Add ontology column
GO_BP_En_sig_gene$ontology <- "Biological Processes"
# Keep only unique rows
GO_BP_En_sig_gene <- unique(GO_BP_En_sig_gene)Create topGOdata object, which is required for topGO
analysis
GO_MF <-new("topGOdata",
ontology="MF",
gene2GO=gene2go_list,
allGenes=GeneList,
annot = annFUN.gene2GO,
geneSel=topDiffGenes)##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )Run GO enrichment test
GO_MF_FE <- runTest(GO_MF, algorithm="weight01", statistic="fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 60 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 4 nodes to be scored (101 eliminated genes)
##
## Level 7: 6 nodes to be scored (145 eliminated genes)
##
## Level 6: 10 nodes to be scored (238 eliminated genes)
##
## Level 5: 10 nodes to be scored (469 eliminated genes)
##
## Level 4: 13 nodes to be scored (518 eliminated genes)
##
## Level 3: 8 nodes to be scored (862 eliminated genes)
##
## Level 2: 4 nodes to be scored (1296 eliminated genes)
##
## Level 1: 1 nodes to be scored (2156 eliminated genes)Generate results table
GO_MF_En <- GenTable(GO_MF, Fisher = GO_MF_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)Only taking the top 100 GO terms
Filter by significant results
GO_MF_En$Fisher<-as.numeric(GO_MF_En$Fisher)
GO_MF_En_sig<-GO_MF_En[GO_MF_En$Fisher<0.05,]Merge GO_MF_En_sig with GO and gene info.
# Separate GO terms
sig_neg_cor_gene2go <- gene2go %>%
separate_rows(GO.ID, sep = ";")
# Ensure GO terms in both datasets are formatted similarly (trim whitespaces)
sig_neg_cor_gene2go$GO.ID <- trimws(sig_neg_cor_gene2go$GO.ID)
GO_MF_En_sig$GO.ID <- trimws(GO_MF_En_sig$GO.ID)
# Join the datasets based on GO term
GO_MF_En_sig_gene <- sig_neg_cor_gene2go %>%
left_join(GO_MF_En_sig, by = "GO.ID") %>%
na.omit()
# Add ontology column
GO_MF_En_sig_gene$ontology <- "Molecular Functions"
# Keep only unique rows
GO_MF_En_sig_gene <- unique(GO_MF_En_sig_gene)Bind so there is a df that has significantly enriched GO terms for all ontologies
GO_sig_neg_corr_df <- rbind(GO_BP_En_sig_gene, GO_MF_En_sig_gene)Merge with GO_sig_neg_corr_df
test <- GO_sig_neg_corr_df %>%
inner_join(sig_neg_corr_data, by = c("gene_ID" = "mRNA")) #%>%
#mutate(direction = ifelse(PCC.cor > 0, "Positive", "Negative")) #%>%
#filter(ontology != "Cellular Components") #%>%
#filter(p_value < 0.1)
# Save as csv
write.csv(test, "../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_sig_neg_corr_target_enrichment.csv")Plot!
plot<-ggplot(test, aes(x = Term, y = Fisher, fill = p_value)) +
#ylim(0, 1) +
#geom_hline(yintercept = 0.05, linetype = "solid", color = "black", linewidth = 1)+
#geom_hline(yintercept = 0.05, color = "black", linetype = "solid", linewidth = 0.5) + # Add line at 0.05
geom_point(shape = 21, size = 5) +
#scale_size(range = c(2, 20)) +
xlab('') +
ylab("Fisher p-value") +
theme_bw(base_size = 24) +
facet_grid(vars(ontology), scales = "free", space = "free_y") +
coord_flip(); plot
# Save plot
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_sig_neg_corr_target_enrichment.pdf", plot, width = 20, height = 35, dpi = 300)
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_sig_neg_corr_target_enrichment.png", plot, width = 20, height = 35, dpi = 300)Examine the top 10 most significant GO terms for BP and MF
# Function to get top 5 unique terms
get_top_10_unique <- function(data, ontology_type) {
data %>%
filter(ontology == ontology_type) %>%
arrange(Fisher) %>%
distinct(Term, .keep_all = TRUE) %>%
slice_head(n = 10)
}
# Get top 5 unique Biological Processes
top_10_BP <- get_top_10_unique(test, "Biological Processes")
# Get top 5 unique Molecular Functions
top_10_MF <- get_top_10_unique(test, "Molecular Functions")
# Combine results
top_10_combined <- bind_rows(top_10_BP, top_10_MF)
unique(top_10_combined$Term)## [1] "polysaccharide catabolic process" "G1/S transition of mitotic cell cycle"
## [3] "creatinase activity" "cytidine deaminase activity"
## [5] "GTPase activity" "galanin receptor activity"# Plot
top_10_combined <- top_10_combined %>%
arrange(desc(Fisher)) %>%
mutate(Term = factor(Term, levels = unique(Term)))
plot<-ggplot(top_10_combined, aes(x = Term, y = Fisher, fill = p_value)) +
#ylim(0, 1) +
#geom_hline(yintercept = 0.05, linetype = "solid", color = "black", linewidth = 1)+
#geom_hline(yintercept = 0.05, color = "black", linetype = "solid", linewidth = 0.5) + # Add line at 0.05
geom_point(size = 10, color = "black") +
scale_size(range = c(2, 20)) +
xlab('') +
ylab("Fisher p-value") +
theme_bw(base_size = 35) +
facet_grid(vars(ontology), scales = "free", space = "free_y") +
coord_flip(); plot
# Save plot
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/top10GO_sig_neg_corr_target_enrichment.pdf", plot, width = 20, height = 25, dpi = 300)
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/top10GO_sig_neg_corr_target_enrichment.png", plot, width = 20, height = 25, dpi = 300)Filter PCC miranda data
# Filter so that only significant negative correlations remain
sig_pos_corr_data <- data %>%
filter(PCC.cor > 0) %>%
filter(p_value < 0.05)
# Make list of target genes for input to topGO
# Genes of interest - ie those targeted by miRNAs
target_genes <- as.character(unique(sig_pos_corr_data$mRNA))
# Apply 1 or 0 if gene is gene of interest
GeneList <- factor(as.integer(all_genes %in% target_genes))
names(GeneList) <- all_genesThe following code will perform GO enrichment using the weighted Fisher’s exact test to assess whether specific GO terms are overrepresented in the genes targeted by miRNAs with significantly positively correlated expression.
Create topGOdata object, which is required for topGO
analysis
GO_BP <-new("topGOdata",
ontology="BP",
gene2GO=gene2go_list,
allGenes=GeneList,
annot = annFUN.gene2GO,
geneSel=topDiffGenes)##
## Building most specific GOs .....
## ( 273 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1231 GO terms and 2377 relations. )
##
## Annotating nodes ...............
## ( 1427 genes annotated to the GO terms. )Run GO enrichment test
GO_BP_FE <- runTest(GO_BP, algorithm="weight01", statistic="fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 155 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 13: 1 nodes to be scored (0 eliminated genes)
##
## Level 12: 2 nodes to be scored (0 eliminated genes)
##
## Level 11: 3 nodes to be scored (140 eliminated genes)
##
## Level 10: 5 nodes to be scored (140 eliminated genes)
##
## Level 9: 9 nodes to be scored (142 eliminated genes)
##
## Level 8: 11 nodes to be scored (151 eliminated genes)
##
## Level 7: 17 nodes to be scored (173 eliminated genes)
##
## Level 6: 24 nodes to be scored (193 eliminated genes)
##
## Level 5: 34 nodes to be scored (325 eliminated genes)
##
## Level 4: 25 nodes to be scored (509 eliminated genes)
##
## Level 3: 15 nodes to be scored (848 eliminated genes)
##
## Level 2: 8 nodes to be scored (1075 eliminated genes)
##
## Level 1: 1 nodes to be scored (1236 eliminated genes)Generate results table
GO_BP_En <- GenTable(GO_BP, Fisher = GO_BP_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)Only taking the top 100 GO terms
Filter by significant results
GO_BP_En$Fisher<-as.numeric(GO_BP_En$Fisher)
GO_BP_En_sig<-GO_BP_En[GO_BP_En$Fisher<0.05,]Merge GO_BP_En_sig with GO and gene info.
# Separate GO terms
sig_pos_cor_gene2go <- gene2go %>%
separate_rows(GO.ID, sep = ";")
# Ensure GO terms in both datasets are formatted similarly (trim whitespaces)
sig_pos_cor_gene2go$GO.ID <- trimws(sig_pos_cor_gene2go$GO.ID)
GO_BP_En_sig$GO.ID <- trimws(GO_BP_En_sig$GO.ID)
# Join the datasets based on GO term
GO_BP_En_sig_gene <- sig_pos_cor_gene2go %>%
left_join(GO_BP_En_sig, by = "GO.ID") %>%
na.omit()
# Add ontology column
GO_BP_En_sig_gene$ontology <- "Biological Processes"
# Keep only unique rows
GO_BP_En_sig_gene <- unique(GO_BP_En_sig_gene)Create topGOdata object, which is required for topGO
analysis
GO_MF <-new("topGOdata",
ontology="MF",
gene2GO=gene2go_list,
allGenes=GeneList,
annot = annFUN.gene2GO,
geneSel=topDiffGenes)##
## Building most specific GOs .....
## ( 461 GO terms found. )
##
## Build GO DAG topology ..........
## ( 910 GO terms and 1191 relations. )
##
## Annotating nodes ...............
## ( 2843 genes annotated to the GO terms. )Run GO enrichment test
GO_MF_FE <- runTest(GO_MF, algorithm="weight01", statistic="fisher")##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 65 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 1 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 3 nodes to be scored (101 eliminated genes)
##
## Level 7: 6 nodes to be scored (390 eliminated genes)
##
## Level 6: 10 nodes to be scored (471 eliminated genes)
##
## Level 5: 14 nodes to be scored (502 eliminated genes)
##
## Level 4: 11 nodes to be scored (592 eliminated genes)
##
## Level 3: 12 nodes to be scored (1037 eliminated genes)
##
## Level 2: 4 nodes to be scored (1359 eliminated genes)
##
## Level 1: 1 nodes to be scored (2212 eliminated genes)Generate results table
GO_MF_En <- GenTable(GO_MF, Fisher = GO_MF_FE, orderBy = "Fisher", topNodes = 100, numChar = 51)Only taking the top 100 GO terms
Filter by significant results
GO_MF_En$Fisher<-as.numeric(GO_MF_En$Fisher)
GO_MF_En_sig<-GO_MF_En[GO_MF_En$Fisher<0.05,]Merge GO_MF_En_sig with GO and gene info.
# Separate GO terms
sig_pos_cor_gene2go <- gene2go %>%
separate_rows(GO.ID, sep = ";")
# Ensure GO terms in both datasets are formatted similarly (trim whitespaces)
sig_pos_cor_gene2go$GO.ID <- trimws(sig_pos_cor_gene2go$GO.ID)
GO_MF_En_sig$GO.ID <- trimws(GO_MF_En_sig$GO.ID)
# Join the datasets based on GO term
GO_MF_En_sig_gene <- sig_pos_cor_gene2go %>%
left_join(GO_MF_En_sig, by = "GO.ID") %>%
na.omit()
# Add ontology column
GO_MF_En_sig_gene$ontology <- "Molecular Functions"
# Keep only unique rows
GO_MF_En_sig_gene <- unique(GO_MF_En_sig_gene)Bind so there is a df that has significantly enriched GO terms for all ontologies
GO_sig_pos_corr_df <- rbind(GO_BP_En_sig_gene, GO_MF_En_sig_gene)Merge with GO_pos_corr_df
test <- GO_sig_pos_corr_df %>%
inner_join(sig_pos_corr_data, by = c("gene_ID" = "mRNA")) #%>%
#mutate(direction = ifelse(PCC.cor > 0, "Positive", "Negative")) #%>%
#filter(ontology != "Cellular Components") #%>%
#filter(p_value < 0.1)
# Save as csv
write.csv(test, "../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_sig_pos_corr_target_enrichment.csv")Plot!
plot<-ggplot(test, aes(x = Term, y = Fisher, fill = p_value)) +
#ylim(0, 1) +
#geom_hline(yintercept = 0.05, linetype = "solid", color = "black", linewidth = 1)+
#geom_hline(yintercept = 0.05, color = "black", linetype = "solid", linewidth = 0.5) + # Add line at 0.05
geom_point(shape = 21, size = 5) +
#scale_size(range = c(2, 20)) +
xlab('') +
ylab("Fisher p-value") +
theme_bw(base_size = 24) +
facet_grid(vars(ontology), scales = "free", space = "free_y") +
coord_flip(); plot
# Save plot
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_sig_pos_corr_target_enrichment.pdf", plot, width = 20, height = 35, dpi = 300)
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/topGO_sig_pos_corr_target_enrichment.png", plot, width = 20, height = 35, dpi = 300)Examine the top 10 most significant GO terms for BP and MF
# Function to get top 5 unique terms
get_top_10_unique <- function(data, ontology_type) {
data %>%
filter(ontology == ontology_type) %>%
arrange(Fisher) %>%
distinct(Term, .keep_all = TRUE) %>%
slice_head(n = 10)
}
# Get top 5 unique Biological Processes
top_10_BP <- get_top_10_unique(test, "Biological Processes")
# Get top 5 unique Molecular Functions
top_10_MF <- get_top_10_unique(test, "Molecular Functions")
# Combine results
top_10_combined <- bind_rows(top_10_BP, top_10_MF)
unique(top_10_combined$Term)## [1] "bile acid biosynthetic process"
## [2] "heart looping"
## [3] "negative regulation of protein phosphorylation"
## [4] "L-lactate dehydrogenase activity"
## [5] "glycosaminoglycan binding"
## [6] "metallocarboxypeptidase activity"
## [7] "adenylate cyclase inhibiting G protein-coupled glut..."# Plot
top_10_combined <- top_10_combined %>%
arrange(desc(Fisher)) %>%
mutate(Term = factor(Term, levels = unique(Term)))
plot<-ggplot(top_10_combined, aes(x = Term, y = Fisher, fill = p_value)) +
#ylim(0, 1) +
#geom_hline(yintercept = 0.05, linetype = "solid", color = "black", linewidth = 1)+
#geom_hline(yintercept = 0.05, color = "black", linetype = "solid", linewidth = 0.5) + # Add line at 0.05
geom_point(size = 10, color = "black") +
scale_size(range = c(2, 20)) +
xlab('') +
ylab("Fisher p-value") +
theme_bw(base_size = 35) +
facet_grid(vars(ontology), scales = "free", space = "free_y") +
coord_flip(); plot
# Save plot
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/top10GO_sig_pos_corr_target_enrichment.pdf", last_plot(), width = 20, height = 25, dpi = 300)
ggsave("../output/09.1-Apul-mRNA-miRNA-interactions-functional-enrichment/top10GO_sig_pos_corr_target_enrichment.png", last_plot(), width = 20, height = 25, dpi = 300)