> library(goseq)
> library(GO.db)
> library(qvalue)
> # capture list of genes for functional enrichment testing
> factor_labeling = read.table("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.infected-UP.subset", row.names=1)
> factor_labeling[,1] = rep('custom_list', dim(factor_labeling)[1])
> factor_labeling = factor_labeling[,1,drop=F]
> colnames(factor_labeling) = c('type')
> factor_list = unique(factor_labeling[,1])
> DE_genes = rownames(factor_labeling)
> 
> 
> # get gene lengths
> gene_lengths = read.table("/gscratch/scrubbed/samwhite/outputs/20200207_cbai_DEG/infected-vs-uninfected/Trinity.gene_lengths.txt", header=T, row.names=1, com='')
> gene_lengths = as.matrix(gene_lengths[,1,drop=F])
> 
> 
> # get background gene list
> background = read.table("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.count_matrix", header=T, row.names=1)
> background.gene_ids = rownames(background)
> background.gene_ids = unique(c(background.gene_ids, DE_genes))
> sample_set_gene_ids = background.gene_ids
> 
> 
> # parse GO assignments
> GO_info = read.table("/gscratch/srlab/sam/data/C_bairdi/transcriptomes/20200126.cbai.trinotate.go_annotations.txt", header=F, row.names=1,stringsAsFactors=F)
> GO_info_listed = apply(GO_info, 1, function(x) unlist(strsplit(x,',')))
> names(GO_info_listed) = rownames(GO_info)
> get_GO_term_descr =  function(x) {
+     d = 'none';
+     go_info = GOTERM[[x]];
+     if (length(go_info) >0) { d = paste(Ontology(go_info), Term(go_info), sep=' ');}
+     return(d);
+ }
> 
> 
> #organize go_id -> list of genes
> GO_to_gene_list = list()
> for (gene_id in intersect(names(GO_info_listed), sample_set_gene_ids)) {
+     go_list = GO_info_listed[[gene_id]]
+     for (go_id in go_list) {
+         GO_to_gene_list[[go_id]] = c(GO_to_gene_list[[go_id]], gene_id)
+     }
+ }
> 
> 
> # GO-Seq protocol: build pwf based on ALL DE features
> missing_gene_lengths = sample_set_gene_ids[! sample_set_gene_ids %in% rownames(gene_lengths)]
> if (length(missing_gene_lengths) > 0) {
+      stop("Error, missing gene lengths for features: ", paste(missing_gene_lengths, collapse=', '))
+ }
> sample_set_gene_lengths = gene_lengths[sample_set_gene_ids,]
> GO_info_listed = GO_info_listed[ names(GO_info_listed) %in% sample_set_gene_ids ]
> cat_genes_vec = as.integer(sample_set_gene_ids %in% rownames(factor_labeling))
> pwf=nullp(cat_genes_vec, bias.data=sample_set_gene_lengths)
> rownames(pwf) = sample_set_gene_ids
> 
> 
> # perform functional enrichment testing for each category.
> for (feature_cat in factor_list) {
+     message('Processing category: ', feature_cat)
+     gene_ids_in_feature_cat = rownames(factor_labeling)[factor_labeling$type == feature_cat]
+     cat_genes_vec = as.integer(sample_set_gene_ids %in% gene_ids_in_feature_cat)
+     pwf$DEgenes = cat_genes_vec
+     res = goseq(pwf,gene2cat=GO_info_listed, use_genes_without_cat=TRUE)
+     ## over-represented categories:
+      pvals = res$over_represented_pvalue
+      pvals[pvals > 1 - 1e-10] = 1 - 1e-10
+      q = qvalue(pvals)
+      res$over_represented_FDR = q$qvalues
+ go_enrich_filename = paste("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.infected-UP.subset", '.GOseq.enriched', sep='')
+     result_table = res[res$over_represented_pvalue<=0.05,]
+     descr = unlist(lapply(result_table$category, get_GO_term_descr))
+     result_table$go_term = descr;
+     result_table$gene_ids = do.call(rbind, lapply(result_table$category, function(x) { 
+             gene_list = GO_to_gene_list[[x]]
+             gene_list = gene_list[gene_list %in% gene_ids_in_feature_cat]
+             paste(gene_list, collapse=', ');
+      }) )
+     write.table(result_table[order(result_table$over_represented_pvalue),], file=go_enrich_filename, sep='	', quote=F, row.names=F)
+     ## under-represented categories:
+      pvals = res$under_represented_pvalue
+      pvals[pvals>1-1e-10] = 1 - 1e-10
+      q = qvalue(pvals)
+      res$under_represented_FDR = q$qvalues
+     go_depleted_filename = paste("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.infected-UP.subset", '.GOseq.depleted', sep='')
+     result_table = res[res$under_represented_pvalue<=0.05,]
+     descr = unlist(lapply(result_table$category, get_GO_term_descr))
+     result_table$go_term = descr;
+     write.table(result_table[order(result_table$under_represented_pvalue),], file=go_depleted_filename, sep='	', quote=F, row.names=F)
+ }
> 
> library(goseq)
> library(GO.db)
> library(qvalue)
> # capture list of genes for functional enrichment testing
> factor_labeling = read.table("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.uninfected-UP.subset", row.names=1)
> factor_labeling[,1] = rep('custom_list', dim(factor_labeling)[1])
> factor_labeling = factor_labeling[,1,drop=F]
> colnames(factor_labeling) = c('type')
> factor_list = unique(factor_labeling[,1])
> DE_genes = rownames(factor_labeling)
> 
> 
> # get gene lengths
> gene_lengths = read.table("/gscratch/scrubbed/samwhite/outputs/20200207_cbai_DEG/infected-vs-uninfected/Trinity.gene_lengths.txt", header=T, row.names=1, com='')
> gene_lengths = as.matrix(gene_lengths[,1,drop=F])
> 
> 
> # get background gene list
> background = read.table("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.count_matrix", header=T, row.names=1)
> background.gene_ids = rownames(background)
> background.gene_ids = unique(c(background.gene_ids, DE_genes))
> sample_set_gene_ids = background.gene_ids
> 
> 
> # parse GO assignments
> GO_info = read.table("/gscratch/srlab/sam/data/C_bairdi/transcriptomes/20200126.cbai.trinotate.go_annotations.txt", header=F, row.names=1,stringsAsFactors=F)
> GO_info_listed = apply(GO_info, 1, function(x) unlist(strsplit(x,',')))
> names(GO_info_listed) = rownames(GO_info)
> get_GO_term_descr =  function(x) {
+     d = 'none';
+     go_info = GOTERM[[x]];
+     if (length(go_info) >0) { d = paste(Ontology(go_info), Term(go_info), sep=' ');}
+     return(d);
+ }
> 
> 
> #organize go_id -> list of genes
> GO_to_gene_list = list()
> for (gene_id in intersect(names(GO_info_listed), sample_set_gene_ids)) {
+     go_list = GO_info_listed[[gene_id]]
+     for (go_id in go_list) {
+         GO_to_gene_list[[go_id]] = c(GO_to_gene_list[[go_id]], gene_id)
+     }
+ }
> 
> 
> # GO-Seq protocol: build pwf based on ALL DE features
> missing_gene_lengths = sample_set_gene_ids[! sample_set_gene_ids %in% rownames(gene_lengths)]
> if (length(missing_gene_lengths) > 0) {
+      stop("Error, missing gene lengths for features: ", paste(missing_gene_lengths, collapse=', '))
+ }
> sample_set_gene_lengths = gene_lengths[sample_set_gene_ids,]
> GO_info_listed = GO_info_listed[ names(GO_info_listed) %in% sample_set_gene_ids ]
> cat_genes_vec = as.integer(sample_set_gene_ids %in% rownames(factor_labeling))
> pwf=nullp(cat_genes_vec, bias.data=sample_set_gene_lengths)
> rownames(pwf) = sample_set_gene_ids
> 
> 
> # perform functional enrichment testing for each category.
> for (feature_cat in factor_list) {
+     message('Processing category: ', feature_cat)
+     gene_ids_in_feature_cat = rownames(factor_labeling)[factor_labeling$type == feature_cat]
+     cat_genes_vec = as.integer(sample_set_gene_ids %in% gene_ids_in_feature_cat)
+     pwf$DEgenes = cat_genes_vec
+     res = goseq(pwf,gene2cat=GO_info_listed, use_genes_without_cat=TRUE)
+     ## over-represented categories:
+      pvals = res$over_represented_pvalue
+      pvals[pvals > 1 - 1e-10] = 1 - 1e-10
+      q = qvalue(pvals)
+      res$over_represented_FDR = q$qvalues
+ go_enrich_filename = paste("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.uninfected-UP.subset", '.GOseq.enriched', sep='')
+     result_table = res[res$over_represented_pvalue<=0.05,]
+     descr = unlist(lapply(result_table$category, get_GO_term_descr))
+     result_table$go_term = descr;
+     result_table$gene_ids = do.call(rbind, lapply(result_table$category, function(x) { 
+             gene_list = GO_to_gene_list[[x]]
+             gene_list = gene_list[gene_list %in% gene_ids_in_feature_cat]
+             paste(gene_list, collapse=', ');
+      }) )
+     write.table(result_table[order(result_table$over_represented_pvalue),], file=go_enrich_filename, sep='	', quote=F, row.names=F)
+     ## under-represented categories:
+      pvals = res$under_represented_pvalue
+      pvals[pvals>1-1e-10] = 1 - 1e-10
+      q = qvalue(pvals)
+      res$under_represented_FDR = q$qvalues
+     go_depleted_filename = paste("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.uninfected-UP.subset", '.GOseq.depleted', sep='')
+     result_table = res[res$under_represented_pvalue<=0.05,]
+     descr = unlist(lapply(result_table$category, get_GO_term_descr))
+     result_table$go_term = descr;
+     write.table(result_table[order(result_table$under_represented_pvalue),], file=go_depleted_filename, sep='	', quote=F, row.names=F)
+ }
> 
> library(goseq)
> library(GO.db)
> library(qvalue)
> # capture list of genes for functional enrichment testing
> factor_labeling = read.table("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.DE.subset", row.names=1)
> factor_labeling[,1] = rep('custom_list', dim(factor_labeling)[1])
> factor_labeling = factor_labeling[,1,drop=F]
> colnames(factor_labeling) = c('type')
> factor_list = unique(factor_labeling[,1])
> DE_genes = rownames(factor_labeling)
> 
> 
> # get gene lengths
> gene_lengths = read.table("/gscratch/scrubbed/samwhite/outputs/20200207_cbai_DEG/infected-vs-uninfected/Trinity.gene_lengths.txt", header=T, row.names=1, com='')
> gene_lengths = as.matrix(gene_lengths[,1,drop=F])
> 
> 
> # get background gene list
> background = read.table("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.count_matrix", header=T, row.names=1)
> background.gene_ids = rownames(background)
> background.gene_ids = unique(c(background.gene_ids, DE_genes))
> sample_set_gene_ids = background.gene_ids
> 
> 
> # parse GO assignments
> GO_info = read.table("/gscratch/srlab/sam/data/C_bairdi/transcriptomes/20200126.cbai.trinotate.go_annotations.txt", header=F, row.names=1,stringsAsFactors=F)
> GO_info_listed = apply(GO_info, 1, function(x) unlist(strsplit(x,',')))
> names(GO_info_listed) = rownames(GO_info)
> get_GO_term_descr =  function(x) {
+     d = 'none';
+     go_info = GOTERM[[x]];
+     if (length(go_info) >0) { d = paste(Ontology(go_info), Term(go_info), sep=' ');}
+     return(d);
+ }
> 
> 
> #organize go_id -> list of genes
> GO_to_gene_list = list()
> for (gene_id in intersect(names(GO_info_listed), sample_set_gene_ids)) {
+     go_list = GO_info_listed[[gene_id]]
+     for (go_id in go_list) {
+         GO_to_gene_list[[go_id]] = c(GO_to_gene_list[[go_id]], gene_id)
+     }
+ }
> 
> 
> # GO-Seq protocol: build pwf based on ALL DE features
> missing_gene_lengths = sample_set_gene_ids[! sample_set_gene_ids %in% rownames(gene_lengths)]
> if (length(missing_gene_lengths) > 0) {
+      stop("Error, missing gene lengths for features: ", paste(missing_gene_lengths, collapse=', '))
+ }
> sample_set_gene_lengths = gene_lengths[sample_set_gene_ids,]
> GO_info_listed = GO_info_listed[ names(GO_info_listed) %in% sample_set_gene_ids ]
> cat_genes_vec = as.integer(sample_set_gene_ids %in% rownames(factor_labeling))
> pwf=nullp(cat_genes_vec, bias.data=sample_set_gene_lengths)
> rownames(pwf) = sample_set_gene_ids
> 
> 
> # perform functional enrichment testing for each category.
> for (feature_cat in factor_list) {
+     message('Processing category: ', feature_cat)
+     gene_ids_in_feature_cat = rownames(factor_labeling)[factor_labeling$type == feature_cat]
+     cat_genes_vec = as.integer(sample_set_gene_ids %in% gene_ids_in_feature_cat)
+     pwf$DEgenes = cat_genes_vec
+     res = goseq(pwf,gene2cat=GO_info_listed, use_genes_without_cat=TRUE)
+     ## over-represented categories:
+      pvals = res$over_represented_pvalue
+      pvals[pvals > 1 - 1e-10] = 1 - 1e-10
+      q = qvalue(pvals)
+      res$over_represented_FDR = q$qvalues
+ go_enrich_filename = paste("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.DE.subset", '.GOseq.enriched', sep='')
+     result_table = res[res$over_represented_pvalue<=0.05,]
+     descr = unlist(lapply(result_table$category, get_GO_term_descr))
+     result_table$go_term = descr;
+     result_table$gene_ids = do.call(rbind, lapply(result_table$category, function(x) { 
+             gene_list = GO_to_gene_list[[x]]
+             gene_list = gene_list[gene_list %in% gene_ids_in_feature_cat]
+             paste(gene_list, collapse=', ');
+      }) )
+     write.table(result_table[order(result_table$over_represented_pvalue),], file=go_enrich_filename, sep='	', quote=F, row.names=F)
+     ## under-represented categories:
+      pvals = res$under_represented_pvalue
+      pvals[pvals>1-1e-10] = 1 - 1e-10
+      q = qvalue(pvals)
+      res$under_represented_FDR = q$qvalues
+     go_depleted_filename = paste("salmon.gene.counts.matrix.infected_vs_uninfected.edgeR.DE_results.P0.05_C1.DE.subset", '.GOseq.depleted', sep='')
+     result_table = res[res$under_represented_pvalue<=0.05,]
+     descr = unlist(lapply(result_table$category, get_GO_term_descr))
+     result_table$go_term = descr;
+     write.table(result_table[order(result_table$under_represented_pvalue),], file=go_depleted_filename, sep='	', quote=F, row.names=F)
+ }
> 
> library(cluster)
> library(Biobase)
> library(qvalue)
> library(fastcluster)
> options(stringsAsFactors = FALSE)
> NO_REUSE = F
> 
> # try to reuse earlier-loaded data if possible
> if (file.exists("diffExpr.P0.05_C1.matrix.RData") && ! NO_REUSE) {
+     print('RESTORING DATA FROM EARLIER ANALYSIS')
+     load("diffExpr.P0.05_C1.matrix.RData")
+ } else {
+     print('Reading matrix file.')
+     primary_data = read.table("diffExpr.P0.05_C1.matrix", header=T, com='', row.names=1, check.names=F, sep='\t')
+     primary_data = as.matrix(primary_data)
+ }
[1] "Reading matrix file."
> source("/gscratch/srlab/programs/trinityrnaseq-v2.9.0/Analysis/DifferentialExpression/R/heatmap.3.R")
> source("/gscratch/srlab/programs/trinityrnaseq-v2.9.0/Analysis/DifferentialExpression/R/misc_rnaseq_funcs.R")
> source("/gscratch/srlab/programs/trinityrnaseq-v2.9.0/Analysis/DifferentialExpression/R/pairs3.R")
> source("/gscratch/srlab/programs/trinityrnaseq-v2.9.0/Analysis/DifferentialExpression/R/vioplot2.R")
> data = primary_data
> myheatcol = colorpanel(75, 'purple','black','yellow')
> samples_data = read.table("/gscratch/scrubbed/samwhite/outputs/20200207_cbai_DEG/infected-vs-uninfected/infected-vs-uninfected.samples.txt", header=F, check.names=F, fill=T)
> samples_data = samples_data[samples_data[,2] != '',]
> colnames(samples_data) = c('sample_name', 'replicate_name')
> sample_types = as.character(unique(samples_data[,1]))
> rep_names = as.character(samples_data[,2])
> data = data[, colnames(data) %in% rep_names, drop=F ]
> nsamples = length(sample_types)
> sample_colors = rainbow(nsamples)
> names(sample_colors) = sample_types
> sample_type_list = list()
> for (i in 1:nsamples) {
+     samples_want = samples_data[samples_data[,1]==sample_types[i], 2]
+     sample_type_list[[sample_types[i]]] = as.vector(samples_want)
+ }
> sample_factoring = colnames(data)
> for (i in 1:nsamples) {
+     sample_type = sample_types[i]
+     replicates_want = sample_type_list[[sample_type]]
+     sample_factoring[ colnames(data) %in% replicates_want ] = sample_type
+ }
> initial_matrix = data # store before doing various data transformations
> data = log2(data+1)
> sample_factoring = colnames(data)
> for (i in 1:nsamples) {
+     sample_type = sample_types[i]
+     replicates_want = sample_type_list[[sample_type]]
+     sample_factoring[ colnames(data) %in% replicates_want ] = sample_type
+ }
> sampleAnnotations = matrix(ncol=ncol(data),nrow=nsamples)
> for (i in 1:nsamples) {
+   sampleAnnotations[i,] = colnames(data) %in% sample_type_list[[sample_types[i]]]
+ }
> sampleAnnotations = apply(sampleAnnotations, 1:2, function(x) as.logical(x))
> sampleAnnotations = sample_matrix_to_color_assignments(sampleAnnotations, col=sample_colors)
> rownames(sampleAnnotations) = as.vector(sample_types)
> colnames(sampleAnnotations) = colnames(data)
> data = as.matrix(data) # convert to matrix
> 
> # Centering rows
> data = t(scale(t(data), scale=F))
> 
> write.table(data, file="diffExpr.P0.05_C1.matrix.log2.centered.dat", quote=F, sep='	');
> if (nrow(data) < 2) { stop("
+ 
+ **** Sorry, at least two rows are required for this matrix.
+ 
+ ");}
> if (ncol(data) < 2) { stop("
+ 
+ **** Sorry, at least two columns are required for this matrix.
+ 
+ ");}
> sample_cor = cor(data, method='pearson', use='pairwise.complete.obs')
> write.table(sample_cor, file="diffExpr.P0.05_C1.matrix.log2.centered.sample_cor.dat", quote=F, sep='	')
> sample_dist = dist(t(data), method='euclidean')
> hc_samples = hclust(sample_dist, method='complete')
> pdf("diffExpr.P0.05_C1.matrix.log2.centered.sample_cor_matrix.pdf")
> sample_cor_for_plot = sample_cor
> heatmap.3(sample_cor_for_plot, dendrogram='both', Rowv=as.dendrogram(hc_samples), Colv=as.dendrogram(hc_samples), col = myheatcol, scale='none', symm=TRUE, key=TRUE,density.info='none', trace='none', symkey=FALSE, symbreaks=F, margins=c(10,10), cexCol=1, cexRow=1, cex.main=0.75, main=paste("sample correlation matrix
+ ", "diffExpr.P0.05_C1.matrix.log2.centered") , ColSideColors=sampleAnnotations, RowSideColors=t(sampleAnnotations))
> dev.off()
null device 
          1 
> gene_cor = NULL
> gene_dist = dist(data, method='euclidean')
> if (nrow(data) <= 1) { message('Too few genes to generate heatmap'); quit(status=0); }
> hc_genes = hclust(gene_dist, method='complete')
> heatmap_data = data
> pdf("diffExpr.P0.05_C1.matrix.log2.centered.genes_vs_samples_heatmap.pdf")
> heatmap.3(heatmap_data, dendrogram='both', Rowv=as.dendrogram(hc_genes), Colv=as.dendrogram(hc_samples), col=myheatcol, scale="none", density.info="none", trace="none", key=TRUE, keysize=1.2, cexCol=1, margins=c(10,10), cex.main=0.75, main=paste("samples vs. features
+ ", "diffExpr.P0.05_C1.matrix.log2.centered" ) , ColSideColors=sampleAnnotations)
> dev.off()
null device 
          1 
> save(list=ls(all=TRUE), file="diffExpr.P0.05_C1.matrix.RData")
>