Apul molecular energetic state gene expression analysis ================ 2025-02-19 Analysis of gene expression of A pulchra energetic state using the following gene set GO categories: - Glycolysis - Gluconeogenesis - Lipolysis/lipid catabolism - Fatty acid beta oxidation - Starvation - Lipid biosynthesis - Protein catabolic process # Set up Load libraries ``` r library(ggplot2) library(vegan) ``` ## Loading required package: permute ## Loading required package: lattice ## This is vegan 2.6-6.1 ``` r library(mixOmics) ``` ## Loading required package: MASS ## ## Loaded mixOmics 6.28.0 ## Thank you for using mixOmics! ## Tutorials: http://mixomics.org ## Bookdown vignette: https://mixomicsteam.github.io/Bookdown ## Questions, issues: Follow the prompts at http://mixomics.org/contact-us ## Cite us: citation('mixOmics') ``` r library(readxl) library(factoextra) ``` ## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa ``` r library(ggfortify) library(ComplexHeatmap) ``` ## Loading required package: grid ## ======================================== ## ComplexHeatmap version 2.20.0 ## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/ ## Github page: https://github.com/jokergoo/ComplexHeatmap ## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference ## ## If you use it in published research, please cite either one: ## - Gu, Z. Complex Heatmap Visualization. iMeta 2022. ## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional ## genomic data. Bioinformatics 2016. ## ## ## The new InteractiveComplexHeatmap package can directly export static ## complex heatmaps into an interactive Shiny app with zero effort. Have a try! ## ## This message can be suppressed by: ## suppressPackageStartupMessages(library(ComplexHeatmap)) ## ======================================== ``` r library(viridis) ``` ## Loading required package: viridisLite ``` r library(lme4) ``` ## Loading required package: Matrix ``` r library(lmerTest) ``` ## ## Attaching package: 'lmerTest' ## The following object is masked from 'package:lme4': ## ## lmer ## The following object is masked from 'package:stats': ## ## step ``` r library(emmeans) ``` ## Welcome to emmeans. ## Caution: You lose important information if you filter this package's results. ## See '? untidy' ``` r library(broom.mixed) library(broom) library(tidyverse) ``` ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.5 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ lubridate 1.9.3 ✔ tibble 3.2.1 ## ✔ purrr 1.0.2 ✔ tidyr 1.3.1 ## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ## ✖ tidyr::expand() masks Matrix::expand() ## ✖ dplyr::filter() masks stats::filter() ## ✖ dplyr::lag() masks stats::lag() ## ✖ purrr::map() masks mixOmics::map() ## ✖ tidyr::pack() masks Matrix::pack() ## ✖ dplyr::select() masks MASS::select() ## ✖ tidyr::unpack() masks Matrix::unpack() ## ℹ Use the conflicted package () to force all conflicts to become errors ``` r library(RVAideMemoire) ``` ## *** Package RVAideMemoire v 0.9-83-7 *** ## ## Attaching package: 'RVAideMemoire' ## ## The following object is masked from 'package:broom': ## ## bootstrap ## ## The following object is masked from 'package:lme4': ## ## dummy ``` r library(Hmisc) ``` ## ## Attaching package: 'Hmisc' ## ## The following objects are masked from 'package:dplyr': ## ## src, summarize ## ## The following objects are masked from 'package:base': ## ## format.pval, units ``` r library(corrplot) ``` ## corrplot 0.92 loaded # Read in gene count matrix Gene count matrix for all genes ``` r # raw gene counts data (will filter and variance stabilize) Apul_genes <- read_csv("D-Apul/output/02.20-D-Apul-RNAseq-alignment-HiSat2/apul-gene_count_matrix.csv") ``` ## Rows: 44371 Columns: 41 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## chr (1): gene_id ## dbl (40): 1A1, 1A10, 1A12, 1A2, 1A8, 1A9, 1B1, 1B10, 1B2, 1B5, 1B9, 1C10, 1C... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` r Apul_genes <- as.data.frame(Apul_genes) # format gene IDs as rownames (instead of a column) rownames(Apul_genes) <- Apul_genes$gene_id Apul_genes <- Apul_genes%>%dplyr::select(!gene_id) # load and format metadata metadata <- read_csv("M-multi-species/data/rna_metadata.csv")%>%dplyr::select(AzentaSampleName, ColonyID, Timepoint) %>% filter(grepl("ACR", ColonyID)) ``` ## New names: ## Rows: 117 Columns: 19 ## ── Column specification ## ──────────────────────────────────────────────────────── Delimiter: "," chr ## (13): SampleName, WellNumber, AzentaSampleName, ColonyID, Timepoint, Sam... dbl ## (5): SampleNumber, Plate, TotalAmount-ng, Volume-uL, Conc-ng.uL lgl (1): ## MethodUsedForSpectrophotometry ## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ ## Specify the column types or set `show_col_types = FALSE` to quiet this message. ## • `` -> `...19` ``` r metadata$Sample <- paste(metadata$ColonyID, metadata$Timepoint, sep = "_") colonies <- unique(metadata$ColonyID) # Load physiological data phys<-read_csv("https://github.com/urol-e5/timeseries/raw/refs/heads/master/time_series_analysis/Output/master_timeseries.csv")%>%filter(colony_id_corr %in% colonies)%>% dplyr::select(colony_id_corr, species, timepoint, site, Host_AFDW.mg.cm2, Sym_AFDW.mg.cm2, Am, AQY, Rd, Ik, Ic, calc.umol.cm2.hr, cells.mgAFDW, prot_mg.mgafdw, Ratio_AFDW.mg.cm2, Total_Chl, Total_Chl_cell, cre.umol.mgafdw) ``` ## Rows: 448 Columns: 46 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## chr (10): colony_id, colony_id_corr, species, timepoint, month, site, nutrie... ## dbl (36): cre.umol.mgprot, Host_AFDW.mg.cm2, Sym_AFDW.mg.cm2, Host_DW.mg.cm2... ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` r # format timepoint phys$timepoint <- gsub("timepoint", "TP", phys$timepoint) #add column with full sample info phys <- merge(phys, metadata, by.x = c("colony_id_corr", "timepoint"), by.y = c("ColonyID", "Timepoint")) %>% dplyr::select(-AzentaSampleName) #add site information into metadata metadata$Site<-phys$site[match(metadata$ColonyID, phys$colony_id_corr)] # Rename gene column names to include full sample info (as in miRNA table) colnames(Apul_genes) <- metadata$Sample[match(colnames(Apul_genes), metadata$AzentaSampleName)] ``` # Gene set 1: Glycolysis Load in gene set generated by Apul-energy-go script ``` r glycolysis_go<-read_table(file="D-Apul/output/23-Apul-energy-GO/Apul_blastp-GO:0006096_out.tab")%>%pull(var=1) ``` ## Warning: Duplicated column names deduplicated: '6' => '6_1' [9] ## ## ── Column specification ──────────────────────────────────────────────────────── ## cols( ## `FUN_000589-T1` = col_character(), ## `sp|Q9NQ88|TIGAR_HUMAN` = col_character(), ## `36.260` = col_double(), ## `262` = col_double(), ## `147` = col_double(), ## `6` = col_double(), ## `43` = col_double(), ## `288` = col_double(), ## `6_1` = col_double(), ## `263` = col_double(), ## `4.01e-43` = col_double(), ## `146` = col_double() ## ) ``` r glycolysis_go <- str_remove(glycolysis_go, "-T1$") glycolysis_go <- str_remove(glycolysis_go, "-T2$") ``` Subset gene count matrix for this gene set. ``` r glycolysis_genes<-Apul_genes%>% filter(rownames(.) %in% glycolysis_go) ``` Calculate the sum of the total gene set for each sample. ``` r glycolysis_genes<-as.data.frame(t(glycolysis_genes)) glycolysis_genes$Sample<-rownames(glycolysis_genes) glycolysis_genes<-glycolysis_genes %>% rowwise() %>% mutate(glycolysis_count = sum(c_across(where(is.numeric)))) %>% ungroup()%>% as.data.frame() ``` Merge into master data frame with metadata and physiology as a new column called “glycolysis”. ``` r data<-left_join(phys, glycolysis_genes) ``` ## Joining with `by = join_by(Sample)` Plot over timepoints. ``` r plot<-data%>% ggplot(aes(x=timepoint, y=glycolysis_count, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-7-1.png) Plot as a PCA. ``` r pca_data <- data %>% dplyr::select(c(starts_with("FUN"), colony_id_corr, timepoint)) # Identify numeric columns numeric_cols <- sapply(pca_data, is.numeric) # Among numeric columns, find those with non-zero sum non_zero_cols <- colSums(pca_data[, numeric_cols]) != 0 # Combine non-numeric columns with numeric columns that have non-zero sum pca_data_cleaned <- cbind( pca_data[, !numeric_cols], # All non-numeric columns pca_data[, numeric_cols][, non_zero_cols] # Numeric columns with non-zero sum ) ``` ``` r scaled.pca<-prcomp(pca_data_cleaned%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_cleaned%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_cleaned, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_cleaned, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 914 0.15721 2.1763 0.004 ** ## Residual 35 4900 0.84279 ## Total 38 5814 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in glycolysis gene expression profile between time points. View by species ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_cleaned, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-11-1.png) Which genes are driving this? Run PLSDA and VIP. ``` r #assigning datasets X <- pca_data_cleaned levels(as.factor(X$timepoint)) ``` ## [1] "TP1" "TP2" "TP3" "TP4" ``` r Y <- as.factor(X$timepoint) #select treatment names Y ``` ## [1] TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 ## [20] TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 ## [39] TP3 ## Levels: TP1 TP2 TP3 TP4 ``` r X<-X%>%dplyr::select(where(is.numeric)) #pull only data columns # run PLSDA MyResult.plsda <- plsda(X,Y) # 1 Run the method plotIndiv(MyResult.plsda, ind.names = FALSE, legend=TRUE, legend.title = "Glycolysis", ellipse = FALSE, title="", style = "graphics", centroid=FALSE, point.lwd = 2, cex=2) ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-12-1.png) Extract VIPs. ``` r #extract treatment_VIP <- PLSDA.VIP(MyResult.plsda) treatment_VIP_df <- as.data.frame(treatment_VIP[["tab"]]) treatment_VIP_df ``` ## VIP ## FUN_010519 2.1288653 ## FUN_004577 1.9754806 ## FUN_037979 1.9440275 ## FUN_018531 1.9409098 ## FUN_012871 1.9163118 ## FUN_038373 1.6968997 ## FUN_032220 1.5950702 ## FUN_023442 1.5806763 ## FUN_028145 1.5761666 ## FUN_026023 1.5606720 ## FUN_025367 1.5410538 ## FUN_012969 1.5215856 ## FUN_001193 1.4895900 ## FUN_001396 1.4881482 ## FUN_038166 1.4717319 ## FUN_015292 1.4651100 ## FUN_001356 1.4598205 ## FUN_042050 1.4592599 ## FUN_041521 1.4188102 ## FUN_039790 1.4110821 ## FUN_006649 1.4006538 ## FUN_014904 1.3251108 ## FUN_039893 1.3121526 ## FUN_029410 1.3107630 ## FUN_036246 1.3049162 ## FUN_032389 1.3002010 ## FUN_010945 1.2783056 ## FUN_009488 1.2705454 ## FUN_008485 1.2612065 ## FUN_025750 1.2572145 ## FUN_001204 1.2530099 ## FUN_012684 1.2503302 ## FUN_004777 1.2382277 ## FUN_026016 1.2346559 ## FUN_025325 1.2286447 ## FUN_022677 1.2114909 ## FUN_001665 1.2084743 ## FUN_022596 1.2064956 ## FUN_009301 1.1819083 ## FUN_022598 1.1811234 ## FUN_017952 1.1757480 ## FUN_037768 1.1715280 ## FUN_014497 1.1530174 ## FUN_001040 1.1522475 ## FUN_026011 1.1517161 ## FUN_006864 1.1460675 ## FUN_023367 1.1280969 ## FUN_006260 1.1164242 ## FUN_008390 1.1122289 ## FUN_012284 1.1052573 ## FUN_025232 1.1022338 ## FUN_015434 1.0911157 ## FUN_007060 1.0758043 ## FUN_025257 1.0730376 ## FUN_039293 1.0561923 ## FUN_035615 1.0550289 ## FUN_041658 1.0342009 ## FUN_032243 1.0087694 ## FUN_004114 0.9842377 ## FUN_016506 0.9761898 ## FUN_008052 0.9760703 ## FUN_023023 0.9724545 ## FUN_026015 0.9722445 ## FUN_026008 0.9638713 ## FUN_022957 0.9565013 ## FUN_023003 0.9553941 ## FUN_018032 0.9355156 ## FUN_023521 0.9333803 ## FUN_029093 0.9293140 ## FUN_035758 0.9278127 ## FUN_031686 0.9229733 ## FUN_037862 0.9227477 ## FUN_006927 0.9142849 ## FUN_035334 0.8864964 ## FUN_015352 0.8746305 ## FUN_026024 0.8657006 ## FUN_016624 0.8613922 ## FUN_036155 0.8602654 ## FUN_041494 0.8585809 ## FUN_026013 0.8225270 ## FUN_008891 0.8179643 ## FUN_041704 0.8150857 ## FUN_025984 0.8109893 ## FUN_016573 0.8059268 ## FUN_018146 0.7993596 ## FUN_023408 0.7989779 ## FUN_040243 0.7893793 ## FUN_018152 0.7857153 ## FUN_001326 0.7854408 ## FUN_017964 0.7727781 ## FUN_000595 0.7671790 ## FUN_023462 0.7636918 ## FUN_005036 0.7625914 ## FUN_005030 0.7613389 ## FUN_008233 0.7581316 ## FUN_018150 0.7552860 ## FUN_001327 0.7500429 ## FUN_043173 0.7435288 ## FUN_040725 0.7421683 ## FUN_025315 0.7381481 ## FUN_043236 0.7376851 ## FUN_041519 0.7169502 ## FUN_016444 0.7038184 ## FUN_026210 0.6894447 ## FUN_023513 0.6873819 ## FUN_008661 0.6823127 ## FUN_016576 0.6784520 ## FUN_029709 0.6633587 ## FUN_037955 0.6569389 ## FUN_033720 0.6421136 ## FUN_031896 0.6335685 ## FUN_004752 0.6282256 ## FUN_025663 0.6250329 ## FUN_043349 0.6230188 ## FUN_004279 0.6225788 ## FUN_016741 0.6151887 ## FUN_015351 0.5992228 ## FUN_029324 0.5798686 ## FUN_043411 0.5758391 ## FUN_034122 0.5623450 ## FUN_032379 0.5451365 ## FUN_029325 0.5411937 ## FUN_001683 0.5335098 ## FUN_042828 0.5331379 ## FUN_006328 0.5330667 ## FUN_001201 0.5271327 ## FUN_005159 0.4980410 ## FUN_016623 0.4815631 ## FUN_023461 0.4800093 ## FUN_026010 0.4677959 ## FUN_012230 0.4622414 ## FUN_023465 0.4620422 ## FUN_009532 0.4450765 ## FUN_005132 0.4398121 ## FUN_043408 0.4285969 ## FUN_008246 0.4208288 ## FUN_035761 0.4074231 ## FUN_013147 0.4017838 ## FUN_032429 0.3938155 ## FUN_022857 0.3913155 ## FUN_015356 0.3912805 ## FUN_028292 0.3833509 ## FUN_022775 0.3786351 ## FUN_004517 0.3638627 ## FUN_015353 0.3398587 ## FUN_015354 0.3385141 ## FUN_001205 0.3274299 ## FUN_001202 0.3209141 ## FUN_043410 0.2927694 ## FUN_014746 0.2237363 ## FUN_025561 0.2124103 ## FUN_041844 0.1705924 ## FUN_038426 0.1442279 ``` r # Converting row names to column treatment_VIP_table <- rownames_to_column(treatment_VIP_df, var = "Gene") #filter for VIP > 1 treatment_VIP_1 <- treatment_VIP_table %>% filter(VIP >= 1.5) #plot VIP_list_plot<-treatment_VIP_1 %>% arrange(VIP) %>% ggplot( aes(x = VIP, y = reorder(Gene,VIP,sum))) + geom_point() + ylab("Gene") + xlab("VIP Score") + ggtitle("Glycolysis") + theme_bw() + theme(panel.border = element_rect(linetype = "solid", color = "black"), panel.grid.major = element_blank(), #Makes background theme white panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"));VIP_list_plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-13-1.png) Gene FUN_010519 is the most important - plot this. This is also the gene most important for lipolysis. ``` r plot<-data%>% ggplot(aes(x=timepoint, y=FUN_010519, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-14-1.png) Plot second most important. ``` r plot<-data%>% ggplot(aes(x=timepoint, y=FUN_004577, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-15-1.png) Plot third most important. ``` r plot<-data%>% ggplot(aes(x=timepoint, y=FUN_037979, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-16-1.png) Look at a PCA of the differentiating genes. ``` r #extract list of VIPs vip_genes<-treatment_VIP_1%>%pull(Gene) #turn to wide format with pca_data_vips<-pca_data_cleaned%>%dplyr::select(all_of(c("timepoint", "colony_id_corr", vip_genes))) ``` ``` r scaled.pca<-prcomp(pca_data_vips%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_vips%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_vips, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_vips, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 206.58 0.45302 9.6627 0.001 *** ## Residual 35 249.42 0.54698 ## Total 38 456.00 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in glycolysis gene expression profile between time points. View by species ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_vips, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ ggtitle("Glycolysis")+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-20-1.png) Pull out PC1 score for each sample for GO term. ``` r scores <- scaled.pca$x scores<-as.data.frame(scores) scores<-scores%>%dplyr::select(PC1) scores$sample<-pca_data_vips$colony_id_corr scores$timepoint<-pca_data_vips$timepoint scores<-scores%>% rename(glycolysis=PC1) head(scores) ``` ## glycolysis sample timepoint ## 1 -1.190941 ACR-139 TP1 ## 2 -3.751002 ACR-139 TP2 ## 3 -0.367541 ACR-139 TP3 ## 4 1.435906 ACR-139 TP4 ## 5 4.333006 ACR-145 TP1 ## 6 -1.959780 ACR-145 TP2 # Gene set 2: Gluconeogenesis Load in gene set generated by Apul-energy-go script ``` r gluconeo_go<-read_table(file="D-Apul/output/23-Apul-energy-GO/Apul_blastp-GO:0006094_out.tab")%>%pull(var=1) ``` ## ## ── Column specification ──────────────────────────────────────────────────────── ## cols( ## `FUN_000184-T1` = col_character(), ## `sp|Q15043|S39AE_HUMAN` = col_character(), ## `32.591` = col_double(), ## `494` = col_double(), ## `268` = col_double(), ## `13` = col_double(), ## `254` = col_double(), ## `717` = col_double(), ## `32` = col_double(), ## `490` = col_double(), ## `4.78e-71` = col_double(), ## `237` = col_double() ## ) ``` r gluconeo_go <- str_remove(gluconeo_go, "-T1$") gluconeo_go <- str_remove(gluconeo_go, "-T2$") gluconeo_go <- str_remove(gluconeo_go, "-T3$") ``` Subset gene count matrix for this gene set. ``` r gluconeo_genes<-Apul_genes%>% filter(rownames(.) %in% gluconeo_go) ``` Calculate the sum of the total gene set for each sample. ``` r gluconeo_genes<-as.data.frame(t(gluconeo_genes)) gluconeo_genes$Sample<-rownames(gluconeo_genes) gluconeo_genes<-gluconeo_genes %>% rowwise() %>% mutate(gluconeo_count = sum(c_across(where(is.numeric)))) %>% ungroup()%>% as.data.frame() ``` Merge into master data frame with metadata and physiology as a new column called “glycolysis”. ``` r data2<-left_join(phys, gluconeo_genes) ``` ## Joining with `by = join_by(Sample)` Plot over timepoints. ``` r plot<-data2%>% ggplot(aes(x=timepoint, y=gluconeo_count, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-26-1.png) Plot as a PCA. ``` r pca_data <- data2 %>% dplyr::select(c(starts_with("FUN"), colony_id_corr, timepoint)) # Identify numeric columns numeric_cols <- sapply(pca_data, is.numeric) # Among numeric columns, find those with non-zero sum non_zero_cols <- colSums(pca_data[, numeric_cols]) != 0 # Combine non-numeric columns with numeric columns that have non-zero sum pca_data_cleaned <- cbind( pca_data[, !numeric_cols], # All non-numeric columns pca_data[, numeric_cols][, non_zero_cols] # Numeric columns with non-zero sum ) ``` ``` r scaled.pca<-prcomp(pca_data_cleaned%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_cleaned%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_cleaned, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_cleaned, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 1213.5 0.20212 2.9554 0.001 *** ## Residual 35 4790.5 0.79788 ## Total 38 6004.0 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in gluconeo gene expression profile between time points. View by species ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_cleaned, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-30-1.png) Which genes are driving this? Run PLSDA and VIP. ``` r #assigning datasets X <- pca_data_cleaned levels(as.factor(X$timepoint)) ``` ## [1] "TP1" "TP2" "TP3" "TP4" ``` r Y <- as.factor(X$timepoint) #select treatment names Y ``` ## [1] TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 ## [20] TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 ## [39] TP3 ## Levels: TP1 TP2 TP3 TP4 ``` r X<-X%>%dplyr::select(where(is.numeric)) #pull only data columns # run PLSDA MyResult.plsda <- plsda(X,Y) # 1 Run the method plotIndiv(MyResult.plsda, ind.names = FALSE, legend=TRUE, legend.title = "Gluconeogenesis", ellipse = FALSE, title="", style = "graphics", centroid=FALSE, point.lwd = 2, cex=2) ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-31-1.png) Extract VIPs. ``` r #extract treatment_VIP <- PLSDA.VIP(MyResult.plsda) treatment_VIP_df <- as.data.frame(treatment_VIP[["tab"]]) treatment_VIP_df ``` ## VIP ## FUN_001654 1.8868411 ## FUN_007020 1.8019643 ## FUN_010519 1.7054464 ## FUN_012871 1.5364835 ## FUN_011845 1.5113808 ## FUN_018090 1.5015122 ## FUN_006166 1.4906735 ## FUN_012969 1.4875040 ## FUN_001898 1.4580573 ## FUN_022104 1.4322722 ## FUN_042843 1.4190286 ## FUN_043379 1.4105130 ## FUN_015008 1.3961879 ## FUN_032004 1.3710842 ## FUN_029276 1.3519184 ## FUN_038373 1.3453741 ## FUN_025367 1.3299488 ## FUN_008266 1.3190867 ## FUN_032389 1.2799538 ## FUN_010945 1.2772272 ## FUN_035467 1.2735459 ## FUN_035191 1.2695141 ## FUN_001771 1.2686687 ## FUN_014921 1.2579121 ## FUN_038476 1.2530444 ## FUN_034828 1.2467635 ## FUN_026025 1.2415372 ## FUN_041837 1.2375618 ## FUN_016433 1.2349420 ## FUN_039946 1.2328590 ## FUN_014916 1.2241477 ## FUN_033989 1.2185459 ## FUN_031637 1.2100539 ## FUN_000908 1.2006997 ## FUN_035773 1.1988465 ## FUN_023442 1.1965394 ## FUN_010642 1.1946226 ## FUN_039776 1.1704454 ## FUN_029122 1.1654705 ## FUN_007024 1.1580872 ## FUN_023365 1.1514663 ## FUN_012279 1.1464247 ## FUN_029410 1.1349484 ## FUN_027067 1.1237264 ## FUN_025305 1.1234767 ## FUN_022677 1.1198540 ## FUN_001396 1.1182081 ## FUN_043347 1.1159688 ## FUN_026299 1.1097992 ## FUN_037687 1.1022299 ## FUN_004157 1.0997233 ## FUN_012545 1.0971490 ## FUN_015434 1.0872145 ## FUN_001219 1.0793784 ## FUN_039790 1.0774001 ## FUN_001655 1.0753233 ## FUN_025654 1.0743658 ## FUN_023497 1.0712624 ## FUN_016052 1.0686487 ## FUN_022599 1.0632173 ## FUN_039293 1.0591232 ## FUN_037768 1.0575144 ## FUN_043003 1.0474362 ## FUN_023006 1.0428416 ## FUN_043250 1.0396385 ## FUN_036996 1.0392972 ## FUN_000437 1.0311445 ## FUN_025232 1.0285641 ## FUN_038916 1.0252655 ## FUN_025368 1.0245576 ## FUN_014964 1.0079987 ## FUN_023003 1.0063710 ## FUN_015005 1.0042243 ## FUN_040244 0.9983805 ## FUN_033958 0.9922898 ## FUN_000440 0.9908340 ## FUN_036246 0.9860259 ## FUN_008036 0.9842119 ## FUN_022812 0.9832399 ## FUN_007846 0.9730182 ## FUN_024011 0.9582687 ## FUN_012266 0.9579493 ## FUN_008237 0.9554695 ## FUN_043010 0.9501818 ## FUN_025750 0.9438805 ## FUN_004622 0.9280796 ## FUN_027805 0.9256450 ## FUN_027835 0.9252651 ## FUN_005878 0.9216516 ## FUN_004114 0.9199097 ## FUN_026015 0.9152004 ## FUN_007022 0.9103194 ## FUN_002667 0.9076518 ## FUN_032427 0.9029771 ## FUN_008520 0.8846165 ## FUN_034090 0.8700758 ## FUN_041710 0.8700433 ## FUN_006328 0.8691462 ## FUN_001217 0.8576999 ## FUN_043005 0.8558385 ## FUN_009828 0.8501895 ## FUN_008390 0.8413969 ## FUN_014169 0.8392470 ## FUN_043626 0.8390069 ## FUN_012677 0.8365829 ## FUN_006107 0.8344757 ## FUN_017004 0.8299554 ## FUN_022237 0.8082375 ## FUN_041658 0.8041591 ## FUN_034890 0.8036703 ## FUN_028002 0.8030186 ## FUN_043621 0.7944881 ## FUN_025257 0.7816218 ## FUN_022529 0.7770480 ## FUN_014858 0.7741684 ## FUN_028510 0.7741681 ## FUN_014919 0.7730201 ## FUN_012333 0.7685928 ## FUN_030682 0.7643141 ## FUN_022600 0.7449855 ## FUN_039885 0.7184951 ## FUN_025564 0.7177550 ## FUN_000595 0.7153109 ## FUN_035891 0.7086151 ## FUN_019341 0.7084297 ## FUN_043519 0.6850681 ## FUN_041938 0.6761026 ## FUN_016381 0.6682169 ## FUN_024763 0.6546763 ## FUN_012229 0.6477517 ## FUN_040103 0.6404148 ## FUN_010947 0.6401708 ## FUN_007960 0.6367937 ## FUN_031725 0.6345620 ## FUN_005913 0.6050601 ## FUN_021107 0.5864809 ## FUN_037323 0.5449513 ## FUN_008881 0.5245874 ## FUN_000270 0.5125753 ## FUN_041758 0.5061889 ## FUN_031896 0.4951702 ## FUN_028151 0.4303198 ## FUN_028246 0.4099866 ## FUN_038004 0.3981940 ## FUN_031685 0.3887729 ## FUN_035468 0.3761364 ## FUN_001808 0.3695814 ## FUN_004647 0.3527228 ## FUN_031966 0.3506633 ## FUN_005132 0.3251152 ## FUN_026286 0.2810643 ## FUN_001118 0.2736183 ## FUN_022775 0.2500787 ## FUN_010828 0.2108932 ## FUN_007124 0.1989417 ## FUN_032429 0.1844865 ## FUN_005249 0.1584136 ## FUN_039965 0.1261533 ``` r # Converting row names to column treatment_VIP_table <- rownames_to_column(treatment_VIP_df, var = "Gene") #filter for VIP > 1 treatment_VIP_1 <- treatment_VIP_table %>% filter(VIP >= 1) #plot VIP_list_plot<-treatment_VIP_1 %>% arrange(VIP) %>% ggplot( aes(x = VIP, y = reorder(Gene,VIP,sum))) + geom_point() + ylab("Gene") + xlab("VIP Score") + ggtitle("Gluconeogenesis") + theme_bw() + theme(panel.border = element_rect(linetype = "solid", color = "black"), panel.grid.major = element_blank(), #Makes background theme white panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"));VIP_list_plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-32-1.png) Gene FUN_001654 is the most important - plot this. This is also the gene most important for lipolysis. ``` r plot<-data2%>% ggplot(aes(x=timepoint, y=FUN_001654, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-33-1.png) Plot second most important. ``` r plot<-data2%>% ggplot(aes(x=timepoint, y=FUN_007020, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-34-1.png) Plot third most important. ``` r plot<-data2%>% ggplot(aes(x=timepoint, y=FUN_010519, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-35-1.png) Look at a PCA of the differentiating genes. ``` r #extract list of VIPs vip_genes<-treatment_VIP_1%>%pull(Gene) #turn to wide format with pca_data_vips<-pca_data_cleaned%>%dplyr::select(all_of(c("timepoint", "colony_id_corr", vip_genes))) ``` ``` r scaled.pca<-prcomp(pca_data_vips%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_vips%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_vips, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_vips, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 845.59 0.30483 5.1157 0.001 *** ## Residual 35 1928.41 0.69517 ## Total 38 2774.00 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in gluconeogenesis gene expression profile between time points. View by species ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_vips, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ ggtitle("Gluconeogenesis")+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-39-1.png) Pull out PC1 score for each sample for GO term. ``` r scores1 <- scaled.pca$x scores1<-as.data.frame(scores1) scores1<-scores1%>%dplyr::select(PC1) scores1$sample<-pca_data_vips$colony_id_corr scores1$timepoint<-pca_data_vips$timepoint scores1<-scores1%>% rename(gluconeogenesis=PC1) scores<-left_join(scores, scores1) ``` ## Joining with `by = join_by(sample, timepoint)` ``` r head(scores) ``` ## glycolysis sample timepoint gluconeogenesis ## 1 -1.190941 ACR-139 TP1 -1.7424776 ## 2 -3.751002 ACR-139 TP2 -7.5823907 ## 3 -0.367541 ACR-139 TP3 -3.6596187 ## 4 1.435906 ACR-139 TP4 0.5056709 ## 5 4.333006 ACR-145 TP1 4.3200803 ## 6 -1.959780 ACR-145 TP2 0.8176864 # Gene set 3: Lipolysis/lipid catabolism Load in gene set generated by Apul-energy-go script ``` r lipolysis_go<-read_table(file="D-Apul/output/23-Apul-energy-GO/Apul_blastp-GO:0016042_out.tab")%>%pull(var=1) ``` ## ## ── Column specification ──────────────────────────────────────────────────────── ## cols( ## `FUN_000228-T1` = col_character(), ## `sp|P36411|RAB7A_DICDI` = col_character(), ## `38.506` = col_double(), ## `174` = col_double(), ## `92` = col_double(), ## `3` = col_double(), ## `10` = col_double(), ## `179` = col_double(), ## `8` = col_double(), ## `170` = col_double(), ## `2.63e-35` = col_double(), ## `121` = col_double() ## ) ``` r lipolysis_go <- str_remove(lipolysis_go, "-T1$") lipolysis_go <- str_remove(lipolysis_go, "-T2$") lipolysis_go <- str_remove(lipolysis_go, "-T3$") lipolysis_go <- str_remove(lipolysis_go, "-T4$") ``` Subset gene count matrix for this gene set. ``` r lipolysis_genes<-Apul_genes%>% filter(rownames(.) %in% lipolysis_go) ``` Calculate the sum of the total gene set for each sample. ``` r lipolysis_genes<-as.data.frame(t(lipolysis_genes)) lipolysis_genes$Sample<-rownames(lipolysis_genes) lipolysis_genes<-lipolysis_genes %>% rowwise() %>% mutate(lipolysis_count = sum(c_across(where(is.numeric)))) %>% ungroup()%>% as.data.frame() ``` Merge into master data frame with metadata and physiology as a new column called “glycolysis”. ``` r data3<-left_join(phys, lipolysis_genes) ``` ## Joining with `by = join_by(Sample)` Plot over timepoints. ``` r plot<-data3%>% ggplot(aes(x=timepoint, y=lipolysis_count, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-45-1.png) Plot as a PCA. ``` r pca_data <- data3 %>% dplyr::select(c(starts_with("FUN"), colony_id_corr, timepoint)) # Identify numeric columns numeric_cols <- sapply(pca_data, is.numeric) # Among numeric columns, find those with non-zero sum non_zero_cols <- colSums(pca_data[, numeric_cols]) != 0 # Combine non-numeric columns with numeric columns that have non-zero sum pca_data_cleaned <- cbind( pca_data[, !numeric_cols], # All non-numeric columns pca_data[, numeric_cols][, non_zero_cols] # Numeric columns with non-zero sum ) ``` ``` r scaled.pca<-prcomp(pca_data_cleaned%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_cleaned%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_cleaned, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_cleaned, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 4717.2 0.15854 2.1981 0.005 ** ## Residual 35 25036.8 0.84146 ## Total 38 29754.0 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in gluconeo gene expression profile between time points. View by timepoint ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_cleaned, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=FALSE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-49-1.png) Which genes are driving this? Run PLSDA and VIP. ``` r #assigning datasets X <- pca_data_cleaned levels(as.factor(X$timepoint)) ``` ## [1] "TP1" "TP2" "TP3" "TP4" ``` r Y <- as.factor(X$timepoint) #select treatment names Y ``` ## [1] TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 ## [20] TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 ## [39] TP3 ## Levels: TP1 TP2 TP3 TP4 ``` r X<-X%>%dplyr::select(where(is.numeric)) #pull only data columns # run PLSDA MyResult.plsda <- plsda(X,Y) # 1 Run the method plotIndiv(MyResult.plsda, ind.names = FALSE, legend=TRUE, legend.title = "Lipolysis", ellipse = FALSE, title="", style = "graphics", centroid=FALSE, point.lwd = 2, cex=2) ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-50-1.png) Extract VIPs. ``` r #extract treatment_VIP <- PLSDA.VIP(MyResult.plsda) treatment_VIP_df <- as.data.frame(treatment_VIP[["tab"]]) treatment_VIP_df ``` ## VIP ## FUN_001654 2.04371343 ## FUN_035010 1.91874001 ## FUN_017777 1.89086516 ## FUN_000579 1.86082866 ## FUN_025792 1.82334001 ## FUN_014637 1.80193055 ## FUN_037441 1.78513795 ## FUN_014530 1.77358793 ## FUN_031950 1.75447339 ## FUN_009656 1.73643481 ## FUN_037573 1.72334419 ## FUN_028200 1.72121655 ## FUN_041721 1.70189895 ## FUN_016069 1.69792099 ## FUN_006002 1.67112680 ## FUN_041963 1.66980231 ## FUN_032542 1.65902365 ## FUN_025802 1.65711102 ## FUN_001898 1.64430532 ## FUN_034015 1.63572527 ## FUN_037566 1.63363079 ## FUN_037230 1.61602942 ## FUN_028956 1.60956485 ## FUN_000960 1.60646531 ## FUN_038172 1.60243195 ## FUN_015978 1.59665486 ## FUN_026025 1.59102797 ## FUN_008660 1.57362280 ## FUN_004852 1.57361709 ## FUN_036193 1.56739917 ## FUN_043381 1.56339769 ## FUN_016131 1.55733878 ## FUN_010668 1.55123119 ## FUN_016073 1.54678914 ## FUN_016422 1.54588316 ## FUN_016506 1.54390448 ## FUN_021112 1.53386984 ## FUN_024761 1.51377395 ## FUN_032288 1.51036429 ## FUN_023379 1.50925849 ## FUN_016614 1.50815640 ## FUN_002579 1.50577438 ## FUN_033845 1.48778207 ## FUN_041860 1.48772081 ## FUN_031522 1.47553871 ## FUN_017842 1.47395322 ## FUN_000856 1.45605637 ## FUN_035467 1.45150719 ## FUN_001193 1.45144025 ## FUN_029276 1.45126068 ## FUN_012349 1.45071673 ## FUN_004576 1.44656127 ## FUN_023698 1.44135253 ## FUN_006439 1.43649090 ## FUN_043244 1.43579420 ## FUN_040784 1.43120113 ## FUN_006248 1.43002075 ## FUN_041901 1.42560075 ## FUN_015994 1.42550600 ## FUN_014560 1.42149120 ## FUN_041962 1.41506723 ## FUN_024564 1.41422015 ## FUN_014750 1.41395571 ## FUN_015038 1.41370772 ## FUN_014904 1.41186889 ## FUN_025580 1.40661465 ## FUN_008485 1.39895482 ## FUN_024679 1.39514738 ## FUN_042013 1.39306327 ## FUN_001179 1.39081623 ## FUN_009017 1.38905274 ## FUN_008702 1.38780127 ## FUN_014473 1.38749089 ## FUN_038476 1.38655960 ## FUN_035066 1.38407189 ## FUN_025425 1.38100932 ## FUN_032380 1.38037782 ## FUN_012547 1.37830249 ## FUN_005739 1.37665354 ## FUN_006428 1.37598098 ## FUN_032108 1.37573528 ## FUN_033844 1.37460954 ## FUN_016052 1.37458710 ## FUN_018008 1.36999046 ## FUN_022662 1.36570646 ## FUN_039946 1.36158425 ## FUN_023365 1.35870859 ## FUN_010639 1.35533614 ## FUN_043347 1.35150407 ## FUN_010664 1.35053030 ## FUN_023401 1.34256790 ## FUN_024414 1.34204591 ## FUN_037052 1.34196365 ## FUN_008561 1.33740236 ## FUN_043497 1.33677153 ## FUN_027281 1.33541173 ## FUN_005992 1.33252995 ## FUN_042624 1.32662365 ## FUN_029550 1.32542576 ## FUN_001905 1.32510509 ## FUN_028981 1.32332146 ## FUN_035191 1.32090488 ## FUN_013364 1.32018551 ## FUN_001356 1.31966487 ## FUN_005220 1.31906428 ## FUN_036022 1.31461219 ## FUN_000437 1.31458298 ## FUN_017872 1.31402730 ## FUN_039776 1.31235384 ## FUN_026150 1.31048722 ## FUN_042911 1.30949482 ## FUN_016433 1.29822190 ## FUN_039893 1.29454773 ## FUN_034116 1.29329564 ## FUN_041212 1.28930234 ## FUN_024387 1.28185013 ## FUN_041861 1.28168047 ## FUN_015238 1.28006361 ## FUN_032382 1.27763887 ## FUN_029078 1.27611920 ## FUN_001063 1.27434043 ## FUN_018123 1.27225405 ## FUN_015070 1.27132106 ## 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FUN_008227 0.49715806 ## FUN_003806 0.49059064 ## FUN_023241 0.48859453 ## FUN_001794 0.48172953 ## FUN_033715 0.48018299 ## FUN_028292 0.48013070 ## FUN_026281 0.47661665 ## FUN_026231 0.47215758 ## FUN_013590 0.46924324 ## FUN_003797 0.46648571 ## FUN_041900 0.46355470 ## FUN_036113 0.46255595 ## FUN_016075 0.45991478 ## FUN_006729 0.45947000 ## FUN_009449 0.45945281 ## FUN_012116 0.45497593 ## FUN_012473 0.45192116 ## FUN_008949 0.44974421 ## FUN_013262 0.44799088 ## FUN_001201 0.44663577 ## FUN_024891 0.44574533 ## FUN_032623 0.44373828 ## FUN_017854 0.44239672 ## FUN_038459 0.44193878 ## FUN_004647 0.43957631 ## FUN_016615 0.43793234 ## FUN_008569 0.43537601 ## FUN_011714 0.43457188 ## FUN_016967 0.43391438 ## FUN_017712 0.43225417 ## FUN_012770 0.41382266 ## FUN_004817 0.41180521 ## FUN_024881 0.40997410 ## FUN_016620 0.40869627 ## FUN_036847 0.40679097 ## FUN_014958 0.40259309 ## FUN_008409 0.40159511 ## FUN_000956 0.40037165 ## FUN_027280 0.39647879 ## FUN_022857 0.39373263 ## FUN_023925 0.37949082 ## FUN_024173 0.37159119 ## FUN_024066 0.36965458 ## FUN_000371 0.36590883 ## FUN_012094 0.36564361 ## FUN_016623 0.36146628 ## FUN_023465 0.35699457 ## FUN_022248 0.35223456 ## FUN_023024 0.35016846 ## FUN_026286 0.35002677 ## FUN_015356 0.34573068 ## FUN_026290 0.34206657 ## FUN_032626 0.33054420 ## FUN_012468 0.32558702 ## FUN_003686 0.32483019 ## FUN_012068 0.32449792 ## FUN_036858 0.32086399 ## FUN_043408 0.31716780 ## FUN_015354 0.31558725 ## FUN_032273 0.31517301 ## FUN_001205 0.31472423 ## FUN_043411 0.31062978 ## FUN_002405 0.30897438 ## FUN_005216 0.30466181 ## FUN_038232 0.30356792 ## FUN_001202 0.29448687 ## FUN_003799 0.29381949 ## FUN_041373 0.28781314 ## FUN_038050 0.27644399 ## FUN_028151 0.27587743 ## FUN_009443 0.27197982 ## FUN_004100 0.27062194 ## FUN_031477 0.26946063 ## FUN_012095 0.26183183 ## FUN_043410 0.25977916 ## FUN_016966 0.25331880 ## FUN_003902 0.24106421 ## FUN_007813 0.23660193 ## FUN_032272 0.23512865 ## FUN_039965 0.22466474 ## FUN_012084 0.21995860 ## FUN_012077 0.21626816 ## FUN_000582 0.21322574 ## FUN_026277 0.21084913 ## FUN_031851 0.20890825 ## FUN_017015 0.20010560 ## FUN_010828 0.19559449 ## FUN_005249 0.18747897 ## FUN_023361 0.17996025 ## FUN_034345 0.16458680 ## FUN_027948 0.16417703 ## FUN_010623 0.15403782 ## FUN_005251 0.14784279 ## FUN_003689 0.13881715 ## FUN_012096 0.12474177 ## FUN_029293 0.10010444 ## FUN_034855 0.07708862 ## FUN_042618 0.07056620 ## FUN_022444 0.06161346 ## FUN_015353 0.02912804 ``` r # Converting row names to column treatment_VIP_table <- rownames_to_column(treatment_VIP_df, var = "Gene") #filter for VIP > 1 treatment_VIP_1 <- treatment_VIP_table %>% filter(VIP >= 1.5) #plot VIP_list_plot<-treatment_VIP_1 %>% arrange(VIP) %>% ggplot( aes(x = VIP, y = reorder(Gene,VIP,sum))) + geom_point() + ylab("Gene") + xlab("VIP Score") + ggtitle("Lipolysis") + theme_bw() + theme(panel.border = element_rect(linetype = "solid", color = "black"), panel.grid.major = element_blank(), #Makes background theme white panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"));VIP_list_plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-51-1.png) Gene FUN_001654 is the most important - plot this. ``` r plot<-data3%>% ggplot(aes(x=timepoint, y=FUN_001654, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-52-1.png) Plot second most important ``` r plot<-data3%>% ggplot(aes(x=timepoint, y=FUN_035010, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-53-1.png) Plot third most important ``` r plot<-data3%>% ggplot(aes(x=timepoint, y=FUN_017777, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-54-1.png) Look at a PCA of the differentiating genes. ``` r #extract list of VIPs vip_genes<-treatment_VIP_1%>%pull(Gene) #turn to wide format with pca_data_vips<-pca_data_cleaned%>%dplyr::select(all_of(c("timepoint", "colony_id_corr", vip_genes))) ``` ``` r scaled.pca<-prcomp(pca_data_vips%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_vips%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_vips, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_vips, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 640.67 0.40142 7.8239 0.001 *** ## Residual 35 955.33 0.59858 ## Total 38 1596.00 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in lipolysis gene expression profile between time points. View by species ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_vips, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ ggtitle("Lipolysis")+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-58-1.png) Pull out PC1 score for each sample for GO term. ``` r scores1 <- scaled.pca$x scores1<-as.data.frame(scores1) scores1<-scores1%>%dplyr::select(PC1) scores1$sample<-pca_data_vips$colony_id_corr scores1$timepoint<-pca_data_vips$timepoint scores1<-scores1%>% rename(lipolysis=PC1) scores<-left_join(scores, scores1) ``` ## Joining with `by = join_by(sample, timepoint)` ``` r head(scores) ``` ## glycolysis sample timepoint gluconeogenesis lipolysis ## 1 -1.190941 ACR-139 TP1 -1.7424776 0.2958143 ## 2 -3.751002 ACR-139 TP2 -7.5823907 3.4220239 ## 3 -0.367541 ACR-139 TP3 -3.6596187 -0.1048175 ## 4 1.435906 ACR-139 TP4 0.5056709 -4.8568009 ## 5 4.333006 ACR-145 TP1 4.3200803 -4.9876692 ## 6 -1.959780 ACR-145 TP2 0.8176864 2.9345918 # Gene set 4: Fatty acid beta oxidation Load in gene set generated by Apul-energy-go script ``` r ffa_go<-read_table(file="D-Apul/output/23-Apul-energy-GO/Apul_blastp-GO:0006635_out.tab")%>%pull(var=1) ``` ## ## ── Column specification ──────────────────────────────────────────────────────── ## cols( ## `FUN_000270-T1` = col_character(), ## `sp|Q06497|ANT1_YEAST` = col_character(), ## `24.667` = col_double(), ## `300` = col_double(), ## `191` = col_double(), ## `9` = col_double(), ## `86` = col_double(), ## `358` = col_double(), ## `4` = col_double(), ## `295` = col_double(), ## `2.11e-21` = col_double(), ## `89.4` = col_double() ## ) ``` r ffa_go <- str_remove(ffa_go, "-T1$") ffa_go <- str_remove(ffa_go, "-T2$") ffa_go <- str_remove(ffa_go, "-T3$") ffa_go <- str_remove(ffa_go, "-T4$") ``` Subset gene count matrix for this gene set. ``` r ffa_genes<-Apul_genes%>% filter(rownames(.) %in% ffa_go) ``` Calculate the sum of the total gene set for each sample. ``` r ffa_genes<-as.data.frame(t(ffa_genes)) ffa_genes$Sample<-rownames(ffa_genes) ffa_genes<-ffa_genes %>% rowwise() %>% mutate(ffa_count = sum(c_across(where(is.numeric)))) %>% ungroup()%>% as.data.frame() ``` Merge into master data frame with metadata and physiology as a new column called “glycolysis”. ``` r data4<-left_join(phys, ffa_genes) ``` ## Joining with `by = join_by(Sample)` Plot over timepoints. ``` r plot<-data4%>% ggplot(aes(x=timepoint, y=ffa_count, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-64-1.png) Plot as a PCA. ``` r pca_data <- data4 %>% dplyr::select(c(starts_with("FUN"), colony_id_corr, timepoint)) # Identify numeric columns numeric_cols <- sapply(pca_data, is.numeric) # Among numeric columns, find those with non-zero sum non_zero_cols <- colSums(pca_data[, numeric_cols]) != 0 # Combine non-numeric columns with numeric columns that have non-zero sum pca_data_cleaned <- cbind( pca_data[, !numeric_cols], # All non-numeric columns pca_data[, numeric_cols][, non_zero_cols] # Numeric columns with non-zero sum ) ``` ``` r scaled.pca<-prcomp(pca_data_cleaned%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_cleaned%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_cleaned, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_cleaned, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 1115.7 0.1717 2.4185 0.005 ** ## Residual 35 5382.3 0.8283 ## Total 38 6498.0 1.0000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in ffa beta oxidation gene expression profile between time points. View by timepoint ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_cleaned, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=FALSE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ ggtitle("Fatty Acid Beta Oxidation")+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-68-1.png) Which genes are driving this? Run PLSDA and VIP. ``` r #assigning datasets X <- pca_data_cleaned levels(as.factor(X$timepoint)) ``` ## [1] "TP1" "TP2" "TP3" "TP4" ``` r Y <- as.factor(X$timepoint) #select treatment names Y ``` ## [1] TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 ## [20] TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 ## [39] TP3 ## Levels: TP1 TP2 TP3 TP4 ``` r X<-X%>%dplyr::select(where(is.numeric)) #pull only data columns # run PLSDA MyResult.plsda <- plsda(X,Y) # 1 Run the method plotIndiv(MyResult.plsda, ind.names = FALSE, legend=TRUE, legend.title = "FFA Beta Oxidation", ellipse = FALSE, title="", style = "graphics", centroid=FALSE, point.lwd = 2, cex=2) ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-69-1.png) Extract VIPs. ``` r #extract treatment_VIP <- PLSDA.VIP(MyResult.plsda) treatment_VIP_df <- as.data.frame(treatment_VIP[["tab"]]) treatment_VIP_df ``` ## VIP ## FUN_035010 1.8151412 ## FUN_014637 1.7366020 ## FUN_031950 1.6979498 ## FUN_028200 1.6596555 ## FUN_037441 1.5895312 ## FUN_037573 1.5699398 ## FUN_006166 1.5617067 ## FUN_004852 1.5609563 ## FUN_015978 1.5607498 ## FUN_036193 1.5578459 ## FUN_025802 1.5184411 ## FUN_024761 1.4919580 ## FUN_041963 1.4691465 ## FUN_006002 1.4594129 ## FUN_016614 1.4401038 ## FUN_026303 1.4389972 ## FUN_021112 1.3946011 ## FUN_040784 1.3594429 ## FUN_012547 1.3551729 ## FUN_016052 1.3221061 ## FUN_024414 1.3193785 ## FUN_042911 1.3032349 ## FUN_004576 1.3014122 ## FUN_012349 1.2966669 ## FUN_024387 1.2667838 ## FUN_010664 1.2643408 ## FUN_041861 1.2571469 ## FUN_034963 1.2499537 ## FUN_025580 1.2447375 ## FUN_026897 1.2420945 ## FUN_009773 1.2377943 ## FUN_034780 1.2344885 ## FUN_010945 1.2282393 ## FUN_001356 1.2212570 ## FUN_008223 1.2207007 ## FUN_038464 1.2164290 ## FUN_043493 1.2143489 ## FUN_035264 1.2100673 ## FUN_016468 1.2084229 ## FUN_036108 1.1923910 ## FUN_029550 1.1883533 ## FUN_008056 1.1871254 ## FUN_039893 1.1863349 ## FUN_037826 1.1527877 ## FUN_035058 1.1517941 ## FUN_011931 1.1408998 ## FUN_037837 1.1304897 ## FUN_024187 1.1298383 ## FUN_008188 1.1244891 ## FUN_006864 1.1230696 ## FUN_016163 1.1032858 ## FUN_005307 1.1004481 ## FUN_009772 1.0969191 ## FUN_043014 1.0962638 ## FUN_008588 1.0885723 ## FUN_025315 1.0781057 ## FUN_040059 1.0771597 ## FUN_040783 1.0717177 ## FUN_022354 1.0630991 ## FUN_001341 1.0626578 ## FUN_007846 1.0619158 ## FUN_040076 1.0612142 ## FUN_002433 1.0569867 ## FUN_025670 1.0531757 ## FUN_009771 1.0528243 ## FUN_008649 1.0519347 ## FUN_036897 1.0476028 ## FUN_023593 1.0432104 ## FUN_011959 1.0349626 ## FUN_032202 1.0304729 ## FUN_004350 1.0260460 ## FUN_043128 1.0193377 ## FUN_009488 1.0172585 ## FUN_004270 1.0153360 ## FUN_024385 1.0146626 ## FUN_038285 1.0071381 ## FUN_008661 0.9918792 ## FUN_023246 0.9873853 ## FUN_037862 0.9752850 ## FUN_032424 0.9714100 ## FUN_023045 0.9703007 ## FUN_014771 0.9549454 ## FUN_040243 0.9548968 ## FUN_022664 0.9546674 ## FUN_022622 0.9530681 ## FUN_018501 0.9387361 ## FUN_012640 0.9322468 ## FUN_035233 0.9304000 ## FUN_006107 0.9285255 ## FUN_033668 0.9283612 ## FUN_016444 0.9282828 ## FUN_014992 0.9272458 ## FUN_008976 0.9231921 ## FUN_023922 0.9223540 ## FUN_014993 0.9194623 ## FUN_004752 0.9194480 ## FUN_034120 0.9099031 ## FUN_031635 0.9086182 ## FUN_032423 0.9074737 ## FUN_041890 0.9045182 ## FUN_040089 0.8990358 ## FUN_009777 0.8951522 ## FUN_008015 0.8946762 ## FUN_043005 0.8725140 ## FUN_014169 0.8722965 ## FUN_035420 0.8564048 ## FUN_001418 0.8479976 ## FUN_040091 0.8446246 ## FUN_038505 0.8372441 ## FUN_043621 0.8308241 ## FUN_029093 0.8272519 ## FUN_036919 0.8173218 ## FUN_026074 0.8157218 ## FUN_030643 0.8123377 ## FUN_001160 0.7990356 ## FUN_034965 0.7968421 ## FUN_002767 0.7966702 ## FUN_009778 0.7913995 ## FUN_038220 0.7625005 ## FUN_025514 0.7624689 ## FUN_040090 0.7568451 ## FUN_035891 0.7533633 ## FUN_029413 0.7493082 ## FUN_025463 0.7405062 ## FUN_034834 0.7378321 ## FUN_002697 0.7346088 ## FUN_026166 0.7310831 ## FUN_006927 0.7203157 ## FUN_031929 0.6905645 ## FUN_032271 0.6876610 ## FUN_022926 0.6678300 ## FUN_032379 0.6575695 ## FUN_008937 0.6572857 ## FUN_012022 0.6566782 ## FUN_017004 0.6505165 ## FUN_035273 0.6485735 ## FUN_041607 0.6455548 ## FUN_017955 0.6373960 ## FUN_012229 0.6307569 ## FUN_012330 0.6190651 ## FUN_036898 0.6071270 ## FUN_043012 0.6028417 ## FUN_038016 0.6017030 ## FUN_006654 0.5936217 ## FUN_008006 0.5835191 ## FUN_011847 0.5557131 ## FUN_008794 0.5540298 ## FUN_041758 0.5336068 ## FUN_009776 0.5035445 ## FUN_025512 0.4850126 ## FUN_012770 0.4719807 ## FUN_004647 0.4613174 ## FUN_036113 0.4497875 ## FUN_024891 0.4482494 ## FUN_017712 0.4457879 ## FUN_038459 0.4407002 ## FUN_024881 0.4391793 ## FUN_033715 0.4384217 ## FUN_017776 0.4330903 ## FUN_010837 0.3849289 ## FUN_016615 0.3777422 ## FUN_014908 0.3579730 ## FUN_038232 0.3353657 ## FUN_022248 0.3261494 ## FUN_028151 0.3184505 ## FUN_023925 0.2816879 ## FUN_032273 0.2781605 ## FUN_016620 0.2659223 ## FUN_032272 0.2463378 ## FUN_005249 0.1926112 ## FUN_039965 0.1586100 ``` r # Converting row names to column treatment_VIP_table <- rownames_to_column(treatment_VIP_df, var = "Gene") #filter for VIP > 1 treatment_VIP_1 <- treatment_VIP_table %>% filter(VIP >= 1) #plot VIP_list_plot<-treatment_VIP_1 %>% arrange(VIP) %>% ggplot( aes(x = VIP, y = reorder(Gene,VIP,sum))) + geom_point() + ylab("Gene") + xlab("VIP Score") + ggtitle("Beta Oxidation") + theme_bw() + theme(panel.border = element_rect(linetype = "solid", color = "black"), panel.grid.major = element_blank(), #Makes background theme white panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"));VIP_list_plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-70-1.png) Gene FUN_035010 is the most important - plot this. ``` r plot<-data3%>% ggplot(aes(x=timepoint, y=FUN_035010, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-71-1.png) Plot second most important ``` r plot<-data3%>% ggplot(aes(x=timepoint, y=FUN_014637, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-72-1.png) Plot third most important ``` r plot<-data3%>% ggplot(aes(x=timepoint, y=FUN_031950, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-73-1.png) Look at a PCA of the differentiating genes. ``` r #extract list of VIPs vip_genes<-treatment_VIP_1%>%pull(Gene) #turn to wide format with pca_data_vips<-pca_data_cleaned%>%dplyr::select(all_of(c("timepoint", "colony_id_corr", vip_genes))) ``` ``` r scaled.pca<-prcomp(pca_data_vips%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_vips%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_vips, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_vips, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 777.28 0.26914 4.2963 0.001 *** ## Residual 35 2110.72 0.73086 ## Total 38 2888.00 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in fatty acid beta oxidation gene expression profile between time points. View by species ``` r plot2<-ggplot2::autoplot(scaled.pca, data=pca_data_vips, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ ggtitle("Lipolysis")+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot2 ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-77-1.png) Pull out PC1 score for each sample for GO term. ``` r scores1 <- scaled.pca$x scores1<-as.data.frame(scores1) scores1<-scores1%>%dplyr::select(PC1) scores1$sample<-pca_data_vips$colony_id_corr scores1$timepoint<-pca_data_vips$timepoint scores1<-scores1%>% rename(fa_beta=PC1) scores<-left_join(scores, scores1) ``` ## Joining with `by = join_by(sample, timepoint)` ``` r head(scores) ``` ## glycolysis sample timepoint gluconeogenesis lipolysis fa_beta ## 1 -1.190941 ACR-139 TP1 -1.7424776 0.2958143 -0.2148238 ## 2 -3.751002 ACR-139 TP2 -7.5823907 3.4220239 5.5510011 ## 3 -0.367541 ACR-139 TP3 -3.6596187 -0.1048175 8.1317320 ## 4 1.435906 ACR-139 TP4 0.5056709 -4.8568009 0.8125497 ## 5 4.333006 ACR-145 TP1 4.3200803 -4.9876692 -1.4244745 ## 6 -1.959780 ACR-145 TP2 0.8176864 2.9345918 -3.9665474 # Gene set 5: Starvation Load in gene set generated by Apul-energy-go script ``` r starve_go<-read_table(file="D-Apul/output/23-Apul-energy-GO/Apul_blastp-GO:0042594_out.tab")%>%pull(var=1) ``` ## ## ── Column specification ──────────────────────────────────────────────────────── ## cols( ## `FUN_000184-T1` = col_character(), ## `sp|Q6P5W5|S39A4_HUMAN` = col_character(), ## `30.458` = col_double(), ## `568` = col_double(), ## `329` = col_double(), ## `13` = col_double(), ## `155` = col_double(), ## `716` = col_double(), ## `138` = col_double(), ## `645` = col_double(), ## `3.51e-64` = col_double(), ## `222` = col_double() ## ) ``` r starve_go <- str_remove(starve_go, "-T1$") starve_go <- str_remove(starve_go, "-T2$") starve_go <- str_remove(starve_go, "-T3$") starve_go <- str_remove(starve_go, "-T4$") ``` Subset gene count matrix for this gene set. ``` r starve_genes<-Apul_genes%>% filter(rownames(.) %in% starve_go) ``` Calculate the sum of the total gene set for each sample. ``` r starve_genes<-as.data.frame(t(starve_genes)) starve_genes$Sample<-rownames(starve_genes) starve_genes<-starve_genes %>% rowwise() %>% mutate(starve_count = sum(c_across(where(is.numeric)))) %>% ungroup()%>% as.data.frame() ``` Merge into master data frame with metadata and physiology as a new column called “glycolysis”. ``` r data5<-left_join(phys, starve_genes) ``` ## Joining with `by = join_by(Sample)` Plot over timepoints. ``` r plot<-data5%>% ggplot(aes(x=timepoint, y=starve_count, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-83-1.png) Plot as a PCA. ``` r pca_data <- data5 %>% dplyr::select(c(starts_with("FUN"), colony_id_corr, timepoint)) # Identify numeric columns numeric_cols <- sapply(pca_data, is.numeric) # Among numeric columns, find those with non-zero sum non_zero_cols <- colSums(pca_data[, numeric_cols]) != 0 # Combine non-numeric columns with numeric columns that have non-zero sum pca_data_cleaned <- cbind( pca_data[, !numeric_cols], # All non-numeric columns pca_data[, numeric_cols][, non_zero_cols] # Numeric columns with non-zero sum ) ``` ``` r scaled.pca<-prcomp(pca_data_cleaned%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_cleaned%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_cleaned, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_cleaned, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 12602 0.16177 2.2515 0.004 ** ## Residual 35 65298 0.83823 ## Total 38 77900 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in ffa beta oxidation gene expression profile between time points. View by timepoint ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_cleaned, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=FALSE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-87-1.png) Which genes are driving this? Run PLSDA and VIP. ``` r #assigning datasets X <- pca_data_cleaned levels(as.factor(X$timepoint)) ``` ## [1] "TP1" "TP2" "TP3" "TP4" ``` r Y <- as.factor(X$timepoint) #select treatment names Y ``` ## [1] TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 ## [20] TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 ## [39] TP3 ## Levels: TP1 TP2 TP3 TP4 ``` r X<-X%>%dplyr::select(where(is.numeric)) #pull only data columns # run PLSDA MyResult.plsda <- plsda(X,Y) # 1 Run the method plotIndiv(MyResult.plsda, ind.names = FALSE, legend=TRUE, legend.title = "Starvation", ellipse = FALSE, title="", style = "graphics", centroid=FALSE, point.lwd = 2, cex=2) ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-88-1.png) Extract VIPs. ``` r #extract treatment_VIP <- PLSDA.VIP(MyResult.plsda) treatment_VIP_df <- as.data.frame(treatment_VIP[["tab"]]) treatment_VIP_df ``` ## VIP ## FUN_014754 2.04812312 ## FUN_001192 2.00627240 ## FUN_010519 1.99473285 ## FUN_043392 1.97122950 ## FUN_037940 1.95842923 ## FUN_014486 1.93726140 ## FUN_024155 1.93333990 ## FUN_024778 1.92025124 ## FUN_018530 1.91779685 ## FUN_009857 1.91304722 ## FUN_028109 1.86202731 ## FUN_001654 1.84288813 ## FUN_014637 1.82956600 ## FUN_017777 1.81933535 ## FUN_025581 1.81545454 ## FUN_023368 1.81475213 ## FUN_025792 1.80336318 ## FUN_014530 1.78606486 ## FUN_016644 1.78359834 ## FUN_037377 1.78285932 ## FUN_040138 1.77883974 ## FUN_012871 1.77752812 ## FUN_032610 1.76037372 ## FUN_029103 1.76030507 ## FUN_012596 1.75013481 ## FUN_016110 1.73404300 ## FUN_009656 1.72459017 ## FUN_038245 1.71585237 ## FUN_015487 1.71422411 ## FUN_004003 1.71195867 ## FUN_004690 1.70930694 ## FUN_007888 1.70823097 ## FUN_022823 1.70806541 ## FUN_038292 1.70623053 ## FUN_037573 1.70097531 ## FUN_023634 1.69612314 ## FUN_008426 1.69353889 ## FUN_006002 1.69141524 ## FUN_032542 1.69042022 ## FUN_016069 1.68537180 ## FUN_033937 1.67643170 ## FUN_041496 1.67445734 ## FUN_037917 1.67435396 ## FUN_001616 1.67416467 ## FUN_031950 1.67347740 ## FUN_038166 1.65892868 ## FUN_043195 1.65821185 ## FUN_010614 1.65511524 ## FUN_026108 1.65463721 ## FUN_001272 1.65421300 ## FUN_001898 1.65053729 ## FUN_032607 1.64973193 ## FUN_028200 1.64694565 ## FUN_012969 1.64620444 ## FUN_038247 1.63734453 ## FUN_008084 1.63220076 ## FUN_034091 1.62805176 ## FUN_038325 1.62045653 ## FUN_043565 1.61660361 ## FUN_037230 1.61562266 ## FUN_031975 1.61236107 ## FUN_028956 1.61233464 ## FUN_008660 1.60578953 ## FUN_012767 1.60474098 ## FUN_024006 1.60460347 ## FUN_009835 1.60407675 ## FUN_006852 1.59989414 ## FUN_043131 1.59830078 ## FUN_029665 1.59821658 ## FUN_024906 1.59274541 ## FUN_036506 1.59159166 ## FUN_023044 1.59062876 ## FUN_026058 1.59003413 ## FUN_000960 1.58500672 ## FUN_006940 1.58251910 ## FUN_000576 1.58059914 ## FUN_038291 1.57493246 ## FUN_038034 1.57452316 ## FUN_025661 1.57419013 ## FUN_000529 1.57153136 ## FUN_016073 1.55929636 ## FUN_001249 1.55487535 ## FUN_015337 1.55434523 ## FUN_032535 1.54864662 ## FUN_006018 1.54835357 ## FUN_028299 1.53690543 ## FUN_036174 1.53439946 ## FUN_012560 1.53220879 ## FUN_017759 1.52905022 ## FUN_038124 1.52794444 ## FUN_032199 1.52659437 ## FUN_032314 1.52341492 ## FUN_016422 1.52297833 ## FUN_035833 1.52269545 ## FUN_040206 1.52043812 ## FUN_007860 1.51450286 ## FUN_038766 1.50349266 ## FUN_013155 1.50014642 ## FUN_041630 1.49720761 ## FUN_043182 1.49514052 ## FUN_024679 1.49511864 ## FUN_016506 1.49156514 ## FUN_010818 1.49085775 ## FUN_005324 1.48891943 ## FUN_014613 1.48887409 ## FUN_036042 1.48830286 ## FUN_038373 1.48754357 ## FUN_004466 1.48095054 ## FUN_032754 1.47939404 ## FUN_001193 1.47708908 ## FUN_035467 1.47689484 ## FUN_010779 1.47461955 ## FUN_043391 1.46770538 ## FUN_011984 1.46565454 ## FUN_011737 1.46520988 ## FUN_017842 1.46408469 ## FUN_038637 1.46104028 ## FUN_037295 1.45840608 ## FUN_021108 1.45826275 ## FUN_014904 1.45776138 ## FUN_012610 1.45724799 ## FUN_005455 1.45127612 ## FUN_041565 1.44685170 ## FUN_024222 1.44630772 ## FUN_010885 1.44590061 ## FUN_023353 1.44487557 ## FUN_004896 1.44455285 ## FUN_029339 1.44246942 ## FUN_007955 1.43889767 ## FUN_040039 1.43414305 ## FUN_015284 1.43270481 ## FUN_004895 1.43175092 ## FUN_015994 1.43172041 ## FUN_042848 1.43067426 ## FUN_041897 1.42967058 ## FUN_036172 1.42835053 ## FUN_043113 1.42722686 ## FUN_001562 1.42546019 ## FUN_037488 1.42327670 ## FUN_031644 1.42219247 ## FUN_036925 1.42182388 ## FUN_036027 1.42135093 ## FUN_014505 1.42105468 ## FUN_026511 1.42086763 ## FUN_028938 1.41645750 ## FUN_032394 1.41638974 ## FUN_021478 1.41562222 ## FUN_024564 1.41532525 ## FUN_022512 1.41472865 ## FUN_035701 1.41443003 ## FUN_010613 1.41411048 ## FUN_036923 1.41349356 ## FUN_016052 1.41209618 ## FUN_031645 1.40901990 ## FUN_023698 1.40859537 ## FUN_032245 1.40762697 ## FUN_038814 1.40752045 ## FUN_006619 1.40478179 ## FUN_041837 1.40405270 ## FUN_014752 1.40380528 ## FUN_001344 1.40377917 ## FUN_000857 1.40339354 ## FUN_004917 1.40320475 ## FUN_041872 1.40310754 ## FUN_015038 1.40047644 ## FUN_008418 1.40017677 ## FUN_035032 1.39885474 ## FUN_029193 1.39834876 ## FUN_024216 1.39752432 ## FUN_019346 1.39350377 ## FUN_029708 1.39348017 ## FUN_038418 1.38999140 ## FUN_032308 1.38870581 ## FUN_035819 1.38820813 ## FUN_016314 1.38732248 ## FUN_012676 1.38679114 ## FUN_037744 1.38644172 ## FUN_016720 1.38579186 ## FUN_022013 1.38578033 ## FUN_022631 1.38052463 ## FUN_022398 1.38032240 ## FUN_025425 1.38018052 ## FUN_022671 1.37936927 ## FUN_023365 1.37829963 ## FUN_024136 1.37740084 ## FUN_012547 1.37680336 ## FUN_037327 1.37653741 ## FUN_032380 1.37433315 ## FUN_032220 1.37380276 ## FUN_011066 1.37264474 ## FUN_038264 1.37213099 ## FUN_043027 1.37192587 ## FUN_023401 1.37101942 ## FUN_037309 1.36955217 ## FUN_014987 1.36940096 ## FUN_031062 1.36907402 ## FUN_034962 1.36854447 ## FUN_022633 1.36797941 ## FUN_036204 1.36693904 ## FUN_038054 1.36661901 ## FUN_036096 1.36637297 ## FUN_027850 1.36572008 ## FUN_004346 1.36509411 ## FUN_006428 1.36450659 ## FUN_029109 1.36163778 ## FUN_004590 1.35870249 ## FUN_004891 1.35524807 ## FUN_002810 1.35517498 ## FUN_005032 1.35517052 ## FUN_039946 1.35484048 ## FUN_001665 1.35179315 ## FUN_008485 1.35000753 ## FUN_006165 1.34918658 ## FUN_029099 1.34905247 ## FUN_003165 1.34794076 ## FUN_000914 1.34793776 ## FUN_023670 1.34769289 ## FUN_002454 1.34705721 ## FUN_024905 1.34700554 ## FUN_026513 1.34686991 ## FUN_037906 1.34660293 ## FUN_016486 1.34638941 ## FUN_014449 1.34604722 ## FUN_014954 1.34595586 ## FUN_036887 1.34584972 ## FUN_006038 1.34532681 ## FUN_035773 1.34444487 ## FUN_038550 1.34312291 ## FUN_038476 1.34048815 ## FUN_022662 1.33729372 ## FUN_004736 1.33726335 ## FUN_024001 1.33718168 ## FUN_029260 1.33521977 ## FUN_029276 1.33503144 ## FUN_029664 1.33423493 ## FUN_016349 1.33368362 ## FUN_005474 1.33365231 ## FUN_027983 1.33201297 ## FUN_034486 1.33109220 ## FUN_023795 1.32906845 ## FUN_025767 1.32775765 ## FUN_001594 1.32738199 ## FUN_043347 1.32601601 ## FUN_038315 1.32334699 ## FUN_012349 1.32295571 ## FUN_001595 1.32258519 ## FUN_001356 1.32184223 ## FUN_043573 1.32158017 ## FUN_012350 1.32060252 ## FUN_038439 1.31921946 ## FUN_034111 1.31875837 ## FUN_038432 1.31850880 ## FUN_039776 1.31572451 ## FUN_039793 1.31570777 ## FUN_000482 1.31488134 ## FUN_024378 1.31206735 ## FUN_023159 1.31188824 ## FUN_005470 1.31156390 ## FUN_015287 1.31108114 ## FUN_035882 1.31084290 ## FUN_028940 1.30983605 ## FUN_005883 1.30956265 ## FUN_014981 1.30863563 ## FUN_033968 1.30719166 ## FUN_002102 1.30582198 ## FUN_041962 1.30445084 ## FUN_043231 1.30113670 ## FUN_035758 1.30052877 ## FUN_017872 1.29983897 ## FUN_001593 1.29877265 ## FUN_025752 1.29604780 ## FUN_032099 1.29530908 ## FUN_035733 1.29485163 ## FUN_025459 1.29479708 ## FUN_014844 1.29442569 ## FUN_022780 1.29404234 ## FUN_034993 1.29271037 ## FUN_026631 1.29252949 ## FUN_032384 1.29185591 ## FUN_006652 1.29172758 ## FUN_043119 1.29130910 ## FUN_004028 1.29078396 ## FUN_011963 1.28990552 ## FUN_034376 1.28955276 ## FUN_023383 1.28941443 ## FUN_041861 1.28907504 ## FUN_008906 1.28877813 ## FUN_001453 1.28876891 ## FUN_023947 1.28820741 ## FUN_001063 1.28740719 ## FUN_008215 1.28733100 ## FUN_035728 1.28726118 ## FUN_011048 1.28562980 ## FUN_001396 1.28394122 ## FUN_029485 1.28329102 ## FUN_012475 1.28327024 ## FUN_004293 1.28325182 ## FUN_022385 1.28258172 ## FUN_014604 1.28210235 ## FUN_026897 1.28034815 ## FUN_009826 1.28031823 ## FUN_016468 1.28001078 ## FUN_014490 1.27971957 ## FUN_002806 1.27968353 ## FUN_001081 1.27939179 ## FUN_000247 1.27765416 ## FUN_018008 1.27750507 ## FUN_032382 1.27731949 ## FUN_005313 1.27710782 ## FUN_031606 1.27689364 ## FUN_022527 1.27668543 ## FUN_032456 1.27630093 ## FUN_011848 1.27602891 ## FUN_015290 1.27562775 ## FUN_000230 1.27530877 ## FUN_012571 1.27408586 ## FUN_015292 1.27372464 ## FUN_023379 1.27333694 ## FUN_000437 1.27333288 ## FUN_035093 1.27328845 ## FUN_024387 1.27296783 ## FUN_029042 1.27293176 ## FUN_000323 1.27268314 ## 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FUN_027751 0.54994970 ## FUN_029709 0.54992372 ## FUN_012167 0.54968963 ## FUN_013262 0.54959665 ## FUN_017967 0.54805785 ## FUN_025728 0.54707939 ## FUN_019650 0.54532016 ## FUN_032466 0.54493485 ## FUN_003627 0.54388027 ## FUN_037170 0.54267846 ## FUN_016868 0.54226007 ## FUN_037978 0.54223225 ## FUN_036911 0.54136504 ## FUN_026141 0.54048044 ## FUN_022312 0.54043061 ## FUN_036036 0.53887140 ## FUN_028939 0.53703053 ## FUN_032644 0.53642451 ## FUN_025226 0.53388544 ## FUN_008411 0.53320233 ## FUN_008414 0.53236315 ## FUN_027789 0.53208284 ## FUN_003719 0.53185390 ## FUN_041667 0.53140418 ## FUN_040381 0.53137989 ## FUN_008243 0.53106764 ## FUN_015409 0.53043000 ## FUN_026381 0.53008560 ## FUN_016484 0.53000277 ## FUN_002878 0.52879182 ## FUN_035468 0.52742522 ## FUN_004713 0.52679581 ## FUN_025771 0.52436261 ## FUN_041619 0.52341541 ## FUN_006488 0.52337833 ## FUN_032886 0.52236109 ## FUN_022875 0.52231117 ## FUN_016678 0.52050879 ## FUN_016761 0.51875409 ## FUN_018387 0.51817591 ## FUN_013519 0.51769705 ## FUN_028391 0.51756499 ## FUN_032301 0.51544738 ## FUN_042100 0.51469059 ## FUN_001794 0.51423155 ## FUN_008717 0.51331257 ## FUN_040266 0.51300485 ## FUN_005993 0.51279855 ## FUN_005330 0.51223670 ## FUN_033934 0.51186941 ## FUN_012094 0.50961260 ## FUN_020968 0.50868098 ## FUN_025463 0.50839041 ## FUN_015467 0.50743999 ## FUN_034122 0.50716268 ## FUN_013545 0.50693887 ## FUN_033940 0.50532637 ## FUN_009449 0.50496119 ## FUN_006364 0.50355627 ## FUN_009842 0.50351886 ## FUN_038682 0.50289470 ## FUN_037381 0.50255984 ## FUN_000898 0.50222713 ## FUN_004099 0.50194010 ## FUN_028355 0.50177939 ## FUN_033715 0.50063207 ## FUN_014624 0.49984679 ## FUN_044015 0.49855365 ## FUN_038038 0.49824832 ## FUN_038800 0.49584133 ## FUN_025522 0.49538904 ## FUN_015349 0.49464387 ## FUN_034245 0.49454509 ## FUN_025561 0.49442467 ## FUN_031478 0.49425966 ## FUN_026632 0.49164133 ## FUN_012185 0.49155982 ## FUN_016116 0.48850754 ## FUN_032895 0.48733097 ## FUN_000939 0.48705192 ## FUN_016687 0.48660037 ## FUN_041665 0.48571715 ## FUN_038426 0.48530117 ## FUN_005159 0.48249364 ## FUN_035857 0.48213324 ## FUN_017858 0.48186948 ## FUN_024354 0.48055925 ## FUN_029640 0.48016750 ## FUN_015153 0.47998978 ## FUN_024160 0.47849978 ## FUN_026148 0.47805667 ## FUN_006729 0.47801701 ## FUN_042033 0.47748755 ## FUN_023385 0.47551146 ## FUN_008478 0.47492486 ## FUN_016623 0.46917814 ## FUN_028354 0.46781429 ## FUN_026439 0.46727356 ## FUN_016618 0.46691292 ## FUN_009456 0.46554201 ## FUN_002935 0.46347961 ## FUN_038459 0.46270309 ## FUN_010765 0.46232389 ## FUN_019367 0.46157022 ## FUN_043143 0.46139104 ## FUN_015655 0.46090256 ## FUN_017854 0.45872802 ## FUN_009833 0.45797282 ## FUN_026281 0.45777005 ## FUN_023369 0.45663840 ## FUN_008776 0.45547498 ## FUN_036936 0.45386251 ## FUN_012430 0.45319701 ## FUN_005974 0.45246947 ## FUN_008409 0.45156210 ## FUN_025465 0.44960418 ## FUN_025355 0.44944225 ## FUN_003806 0.44761449 ## FUN_000575 0.44746236 ## FUN_035761 0.44665477 ## FUN_014475 0.44097676 ## FUN_002080 0.44075625 ## FUN_004647 0.43917393 ## FUN_036813 0.43917393 ## FUN_000621 0.43903203 ## FUN_022267 0.43899112 ## FUN_023356 0.43859466 ## FUN_008772 0.43549681 ## FUN_002814 0.43548808 ## FUN_008569 0.43523989 ## FUN_038782 0.43467429 ## FUN_011715 0.43360455 ## FUN_000371 0.43215619 ## FUN_003686 0.43131539 ## FUN_010600 0.43110712 ## FUN_022364 0.43059800 ## FUN_036879 0.43038330 ## FUN_003996 0.42986596 ## FUN_019620 0.42733961 ## FUN_008499 0.42522156 ## FUN_031747 0.42492246 ## FUN_031973 0.42482948 ## FUN_023115 0.42471373 ## FUN_026655 0.42281005 ## FUN_017897 0.42253144 ## FUN_012305 0.42189543 ## FUN_011768 0.42138070 ## FUN_016908 0.42104140 ## FUN_040205 0.41875740 ## FUN_001372 0.41812759 ## FUN_016967 0.41764187 ## FUN_003797 0.41648571 ## FUN_015404 0.41629417 ## FUN_038263 0.41304833 ## FUN_036173 0.41281651 ## FUN_022579 0.41216410 ## FUN_012066 0.41183129 ## FUN_031851 0.41043686 ## FUN_012116 0.41039522 ## FUN_025595 0.40964587 ## FUN_016205 0.40941601 ## FUN_001376 0.40904444 ## FUN_028454 0.40563566 ## FUN_016690 0.40521310 ## FUN_013483 0.40475965 ## FUN_002100 0.40370417 ## FUN_009845 0.40199865 ## FUN_010828 0.40182311 ## FUN_029324 0.40057820 ## FUN_041634 0.39939589 ## FUN_012230 0.39846477 ## FUN_027851 0.39799813 ## FUN_023949 0.39753405 ## FUN_038337 0.39408113 ## FUN_022857 0.39343160 ## FUN_024417 0.39291538 ## FUN_014881 0.39267676 ## FUN_020838 0.39176142 ## FUN_043245 0.39130356 ## FUN_001682 0.38818117 ## FUN_000480 0.38773558 ## FUN_014939 0.38523836 ## FUN_013508 0.38336389 ## FUN_005982 0.38306340 ## FUN_008692 0.38188197 ## FUN_039965 0.38017376 ## FUN_026081 0.37838874 ## FUN_028016 0.37819103 ## FUN_004469 0.37704958 ## FUN_017768 0.37191051 ## FUN_026646 0.37132554 ## FUN_041681 0.36985264 ## FUN_000599 0.36918829 ## FUN_011714 0.36403841 ## FUN_041845 0.36400740 ## FUN_006365 0.36183193 ## FUN_014746 0.36139685 ## FUN_001142 0.36139386 ## FUN_026928 0.36028746 ## FUN_032894 0.35929474 ## FUN_028292 0.35903063 ## FUN_043406 0.35855379 ## FUN_028461 0.35766003 ## FUN_026286 0.35559118 ## FUN_000632 0.35439628 ## FUN_028452 0.35403448 ## FUN_000629 0.35294191 ## FUN_040405 0.35237808 ## FUN_006361 0.34835236 ## FUN_027797 0.34647885 ## FUN_035540 0.34225359 ## FUN_026894 0.34141999 ## FUN_031754 0.34093078 ## FUN_026824 0.33980469 ## FUN_016076 0.33915057 ## FUN_024066 0.33896294 ## FUN_000474 0.33886161 ## FUN_010760 0.33726259 ## FUN_019371 0.33655020 ## FUN_038187 0.33003572 ## FUN_008325 0.32693775 ## FUN_022222 0.32669480 ## FUN_014705 0.32665629 ## FUN_036773 0.32562762 ## FUN_029325 0.31950737 ## FUN_013515 0.31847836 ## FUN_009013 0.31822304 ## FUN_043408 0.31794797 ## FUN_010687 0.31749647 ## FUN_032429 0.31721454 ## FUN_005381 0.31638109 ## FUN_032855 0.31488905 ## FUN_005216 0.31363830 ## FUN_040002 0.31355389 ## FUN_025447 0.31264444 ## FUN_013476 0.31246805 ## FUN_007813 0.31205080 ## FUN_026457 0.31065572 ## FUN_016815 0.31026598 ## FUN_005249 0.30952789 ## FUN_040241 0.30838472 ## FUN_011776 0.30788652 ## FUN_025679 0.30689086 ## FUN_040107 0.30653346 ## FUN_038089 0.29856067 ## FUN_029184 0.29609427 ## FUN_018939 0.29500427 ## FUN_016966 0.29479445 ## FUN_005251 0.29456401 ## FUN_023009 0.29326917 ## FUN_043410 0.29030497 ## FUN_008244 0.29001783 ## FUN_027778 0.28960000 ## FUN_034345 0.28774793 ## FUN_039983 0.28712316 ## FUN_012077 0.28375718 ## FUN_036939 0.28130734 ## FUN_038778 0.28071133 ## FUN_016200 0.28056918 ## FUN_016719 0.27974004 ## FUN_026128 0.27732486 ## FUN_022587 0.27457752 ## FUN_019354 0.27168242 ## FUN_016259 0.27149764 ## FUN_043429 0.27094789 ## FUN_038153 0.27030143 ## FUN_016265 0.26674569 ## FUN_005371 0.26499826 ## FUN_023323 0.26243095 ## FUN_023022 0.26090728 ## FUN_000478 0.26048567 ## FUN_033871 0.25948924 ## FUN_012794 0.25395007 ## FUN_016681 0.25278044 ## FUN_000637 0.25092614 ## FUN_026822 0.24965256 ## FUN_003799 0.24833157 ## FUN_022268 0.24831206 ## FUN_032629 0.24474486 ## FUN_025826 0.24284956 ## FUN_028151 0.24188242 ## FUN_004465 0.23815945 ## FUN_000941 0.23354662 ## FUN_027802 0.22969870 ## FUN_028317 0.22316375 ## FUN_013478 0.22247966 ## FUN_013520 0.22056776 ## FUN_041844 0.21680072 ## FUN_041384 0.21487811 ## FUN_012177 0.21471096 ## FUN_016356 0.21381261 ## FUN_024078 0.21277132 ## FUN_006926 0.21183421 ## FUN_013147 0.21100695 ## FUN_012084 0.20638271 ## FUN_026526 0.20621763 ## FUN_022775 0.20419198 ## FUN_032775 0.20212699 ## FUN_013346 0.19404405 ## FUN_022488 0.19026495 ## FUN_000200 0.18871235 ## FUN_026277 0.18423386 ## FUN_043411 0.18027954 ## FUN_022444 0.17764972 ## FUN_012096 0.17615063 ## FUN_012095 0.17493940 ## FUN_005830 0.17401127 ## FUN_025570 0.17146343 ## FUN_015341 0.16877703 ## FUN_012359 0.16739707 ## FUN_034994 0.16617809 ## FUN_028246 0.15716952 ## FUN_023219 0.15597618 ## FUN_023358 0.15588720 ## FUN_003902 0.14633967 ## FUN_022892 0.12976765 ## FUN_009386 0.11885252 ## FUN_035697 0.11279798 ## FUN_023981 0.10769746 ## FUN_003689 0.10675016 ## FUN_013333 0.10021931 ## FUN_024388 0.09856106 ## FUN_043790 0.09054469 ## FUN_028247 0.08623361 ## FUN_023254 0.07492177 ## FUN_033999 0.03118423 ## FUN_010920 0.01950579 ``` r # Converting row names to column treatment_VIP_table <- rownames_to_column(treatment_VIP_df, var = "Gene") #filter for VIP > 1 treatment_VIP_1 <- treatment_VIP_table %>% filter(VIP >= 1) #plot VIP_list_plot<-treatment_VIP_1 %>% arrange(VIP) %>% ggplot( aes(x = VIP, y = reorder(Gene,VIP,sum))) + geom_point() + ylab("Gene") + xlab("VIP Score") + ggtitle("Starvation") + theme_bw() + theme(panel.border = element_rect(linetype = "solid", color = "black"), panel.grid.major = element_blank(), #Makes background theme white panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"));VIP_list_plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-89-1.png) Gene FUN_035010 is the most important - plot this. ``` r plot<-data5%>% ggplot(aes(x=timepoint, y=FUN_034029, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-90-1.png) Plot second most important ``` r plot<-data5%>% ggplot(aes(x=timepoint, y=FUN_014794, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-91-1.png) Plot third most important ``` r plot<-data5%>% ggplot(aes(x=timepoint, y=FUN_014886, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-92-1.png) Look at a PCA of the differentiating genes. ``` r #extract list of VIPs vip_genes<-treatment_VIP_1%>%pull(Gene) #turn to wide format with pca_data_vips<-pca_data_cleaned%>%dplyr::select(all_of(c("timepoint", "colony_id_corr", vip_genes))) ``` ``` r scaled.pca<-prcomp(pca_data_vips%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_vips%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_vips, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_vips, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 8675 0.24209 3.7266 0.001 *** ## Residual 35 27159 0.75791 ## Total 38 35834 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in fatty acid beta oxidation gene expression profile between time points. View by species ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_vips, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ ggtitle("Starvation")+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-96-1.png) Pull out PC1 score for each sample for GO term. ``` r scores1 <- scaled.pca$x scores1<-as.data.frame(scores1) scores1<-scores1%>%dplyr::select(PC1) scores1$sample<-pca_data_vips$colony_id_corr scores1$timepoint<-pca_data_vips$timepoint scores1<-scores1%>% rename(starve=PC1) scores<-left_join(scores, scores1) ``` ## Joining with `by = join_by(sample, timepoint)` ``` r head(scores) ``` ## glycolysis sample timepoint gluconeogenesis lipolysis fa_beta ## 1 -1.190941 ACR-139 TP1 -1.7424776 0.2958143 -0.2148238 ## 2 -3.751002 ACR-139 TP2 -7.5823907 3.4220239 5.5510011 ## 3 -0.367541 ACR-139 TP3 -3.6596187 -0.1048175 8.1317320 ## 4 1.435906 ACR-139 TP4 0.5056709 -4.8568009 0.8125497 ## 5 4.333006 ACR-145 TP1 4.3200803 -4.9876692 -1.4244745 ## 6 -1.959780 ACR-145 TP2 0.8176864 2.9345918 -3.9665474 ## starve ## 1 -13.2465720 ## 2 -32.2038389 ## 3 -9.4273718 ## 4 -0.7986395 ## 5 11.4822275 ## 6 3.1539286 # Gene set 6: Lipid biosynthesis Load in gene set generated by Apul-energy-go script ``` r lipid_go<-read_table(file="D-Apul/output/23-Apul-energy-GO/Apul_blastp-GO:0008610_out.tab")%>%pull(var=1) ``` ## ## ── Column specification ──────────────────────────────────────────────────────── ## cols( ## `FUN_000194-T1` = col_character(), ## `sp|P70182|PI51A_MOUSE` = col_character(), ## `60.579` = col_double(), ## `449` = col_double(), ## `126` = col_double(), ## `6` = col_double(), ## `56` = col_double(), ## `496` = col_double(), ## `43` = col_double(), ## `448` = col_double(), ## `0.0` = col_double(), ## `547` = col_double() ## ) ``` r lipid_go <- str_remove(lipid_go, "-T1$") lipid_go <- str_remove(lipid_go, "-T2$") lipid_go <- str_remove(lipid_go, "-T3$") lipid_go <- str_remove(lipid_go, "-T4$") ``` Subset gene count matrix for this gene set. ``` r lipid_genes<-Apul_genes%>% filter(rownames(.) %in% lipid_go) ``` Calculate the sum of the total gene set for each sample. ``` r lipid_genes<-as.data.frame(t(lipid_genes)) lipid_genes$Sample<-rownames(lipid_genes) lipid_genes<-lipid_genes %>% rowwise() %>% mutate(lipid_count = sum(c_across(where(is.numeric)))) %>% ungroup()%>% as.data.frame() ``` Merge into master data frame with metadata and physiology as a new column called “glycolysis”. ``` r data6<-left_join(phys, lipid_genes) ``` ## Joining with `by = join_by(Sample)` Plot over timepoints. ``` r plot<-data6%>% ggplot(aes(x=timepoint, y=lipid_count, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-102-1.png) Plot as a PCA. ``` r pca_data <- data6 %>% dplyr::select(c(starts_with("FUN"), colony_id_corr, timepoint)) # Identify numeric columns numeric_cols <- sapply(pca_data, is.numeric) # Among numeric columns, find those with non-zero sum non_zero_cols <- colSums(pca_data[, numeric_cols]) != 0 # Combine non-numeric columns with numeric columns that have non-zero sum pca_data_cleaned <- cbind( pca_data[, !numeric_cols], # All non-numeric columns pca_data[, numeric_cols][, non_zero_cols] # Numeric columns with non-zero sum ) ``` ``` r scaled.pca<-prcomp(pca_data_cleaned%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_cleaned%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_cleaned, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_cleaned, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 9468 0.15779 2.1858 0.005 ** ## Residual 35 50534 0.84221 ## Total 38 60002 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in ffa beta oxidation gene expression profile between time points. View by timepoint ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_cleaned, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=FALSE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-106-1.png) Which genes are driving this? Run PLSDA and VIP. ``` r #assigning datasets X <- pca_data_cleaned levels(as.factor(X$timepoint)) ``` ## [1] "TP1" "TP2" "TP3" "TP4" ``` r Y <- as.factor(X$timepoint) #select treatment names Y ``` ## [1] TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 ## [20] TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 ## [39] TP3 ## Levels: TP1 TP2 TP3 TP4 ``` r X<-X%>%dplyr::select(where(is.numeric)) #pull only data columns # run PLSDA MyResult.plsda <- plsda(X,Y) # 1 Run the method plotIndiv(MyResult.plsda, ind.names = FALSE, legend=TRUE, legend.title = "Lipid Synthesis", ellipse = FALSE, title="", style = "graphics", centroid=FALSE, point.lwd = 2, cex=2) ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-107-1.png) Extract VIPs. ``` r #extract treatment_VIP <- PLSDA.VIP(MyResult.plsda) treatment_VIP_df <- as.data.frame(treatment_VIP[["tab"]]) treatment_VIP_df ``` ## VIP ## FUN_035821 2.07797899 ## FUN_006730 2.05068124 ## FUN_010519 2.02158971 ## FUN_034874 1.97592592 ## FUN_003718 1.92602584 ## FUN_031669 1.91459234 ## FUN_000579 1.91015204 ## FUN_007988 1.90368110 ## FUN_016644 1.89723674 ## FUN_025581 1.89090969 ## FUN_004062 1.86954178 ## FUN_014639 1.84719966 ## FUN_014637 1.84670801 ## FUN_032610 1.82488785 ## FUN_014599 1.81715370 ## FUN_012871 1.80597230 ## FUN_035536 1.79852356 ## FUN_016110 1.79011295 ## FUN_022580 1.77351032 ## FUN_028970 1.77336151 ## FUN_011845 1.77080736 ## FUN_006002 1.76942913 ## FUN_023217 1.76169339 ## FUN_026025 1.74183205 ## FUN_038245 1.73979091 ## FUN_015487 1.73797558 ## FUN_006940 1.73173575 ## FUN_034880 1.72536511 ## FUN_008388 1.71375505 ## FUN_033718 1.70832165 ## FUN_041963 1.68813755 ## FUN_011997 1.68180869 ## FUN_033937 1.67941613 ## FUN_001616 1.67547045 ## FUN_022526 1.67422472 ## FUN_014657 1.67280364 ## FUN_042806 1.67050762 ## FUN_016493 1.66387284 ## FUN_023647 1.66147357 ## FUN_010685 1.65759061 ## FUN_000329 1.65530586 ## FUN_026156 1.65303950 ## FUN_026108 1.65141813 ## FUN_005823 1.64811278 ## FUN_010613 1.63763810 ## FUN_015978 1.63361278 ## FUN_037566 1.63053076 ## FUN_006018 1.62633134 ## FUN_031667 1.62498997 ## FUN_038034 1.62292067 ## FUN_017756 1.62274172 ## FUN_028198 1.62118382 ## FUN_015008 1.61813793 ## FUN_034162 1.61553410 ## FUN_001735 1.60959336 ## FUN_009835 1.60811130 ## FUN_007116 1.60610826 ## FUN_040086 1.60479222 ## FUN_041574 1.59398334 ## FUN_038766 1.58911782 ## FUN_036174 1.57331968 ## FUN_008370 1.56336031 ## FUN_008544 1.55774986 ## FUN_004852 1.55189340 ## FUN_004359 1.53731896 ## FUN_040042 1.53487469 ## FUN_032365 1.52787092 ## FUN_040784 1.52415130 ## FUN_013617 1.52389187 ## FUN_036193 1.52052565 ## FUN_038387 1.51818911 ## FUN_036042 1.51669437 ## FUN_036044 1.51362796 ## FUN_035467 1.51070442 ## FUN_021112 1.49838071 ## FUN_042044 1.49719506 ## FUN_016052 1.49606668 ## FUN_021108 1.49431714 ## FUN_010676 1.49336458 ## FUN_041565 1.49179921 ## FUN_032132 1.48887535 ## FUN_025823 1.48858799 ## FUN_035833 1.48235954 ## FUN_023379 1.48089646 ## FUN_033859 1.47760787 ## FUN_001079 1.47616497 ## FUN_025518 1.47403007 ## FUN_010818 1.47113070 ## FUN_003163 1.46948613 ## FUN_029276 1.46662656 ## FUN_034962 1.46601236 ## FUN_032288 1.46474293 ## FUN_005474 1.46458431 ## FUN_038205 1.46329957 ## FUN_034385 1.45863375 ## FUN_012709 1.45652248 ## FUN_007955 1.45650548 ## FUN_037484 1.45518276 ## FUN_015022 1.45239347 ## FUN_041860 1.45174119 ## FUN_026155 1.45149663 ## FUN_035805 1.44799530 ## FUN_026303 1.44782454 ## FUN_039878 1.44568106 ## FUN_043347 1.44494552 ## FUN_025405 1.44011867 ## FUN_032754 1.43850180 ## FUN_004895 1.43845484 ## FUN_031522 1.43789281 ## FUN_024201 1.43741860 ## FUN_004916 1.43366260 ## FUN_015284 1.43352400 ## FUN_031644 1.43128722 ## FUN_029438 1.43079432 ## FUN_035402 1.42452586 ## FUN_024223 1.42346606 ## FUN_032245 1.41960704 ## FUN_041703 1.41599894 ## FUN_029339 1.41351308 ## FUN_023140 1.41162257 ## FUN_015041 1.40958050 ## FUN_022013 1.40507722 ## FUN_004896 1.40405812 ## FUN_019346 1.39522962 ## FUN_016486 1.39483350 ## FUN_002454 1.39327204 ## FUN_023365 1.39272525 ## FUN_038500 1.39213256 ## FUN_011811 1.38651444 ## FUN_004346 1.38583216 ## FUN_033863 1.38415940 ## FUN_024414 1.38326192 ## FUN_002579 1.38317955 ## FUN_010664 1.38202374 ## FUN_022662 1.38061215 ## FUN_034232 1.37886943 ## FUN_008954 1.37872392 ## FUN_029550 1.37724303 ## FUN_012265 1.37637893 ## FUN_023670 1.37534007 ## FUN_027961 1.37287928 ## FUN_036022 1.36810592 ## FUN_016840 1.36735535 ## FUN_037862 1.36470898 ## FUN_000247 1.36409867 ## FUN_032010 1.36359346 ## FUN_013383 1.36272914 ## FUN_012436 1.36201362 ## FUN_024216 1.36132927 ## FUN_024524 1.35997090 ## FUN_001053 1.35802966 ## FUN_032099 1.35583967 ## FUN_031296 1.35399798 ## FUN_016720 1.35211146 ## FUN_008194 1.35029696 ## FUN_014473 1.34999804 ## FUN_001344 1.34712542 ## FUN_031668 1.34568541 ## FUN_042911 1.34532497 ## FUN_022671 1.34421700 ## FUN_007994 1.34325177 ## FUN_017872 1.34263562 ## FUN_001656 1.34245754 ## FUN_023376 1.34166532 ## FUN_003587 1.34162744 ## FUN_025425 1.34037138 ## FUN_008702 1.33544319 ## FUN_012547 1.33536183 ## FUN_036096 1.33169418 ## FUN_029405 1.33151528 ## FUN_032380 1.33045082 ## FUN_024136 1.32954383 ## FUN_014987 1.32771219 ## FUN_015443 1.32669315 ## FUN_031489 1.32540611 ## FUN_032220 1.32436794 ## FUN_022049 1.32374167 ## FUN_014750 1.32372150 ## FUN_008122 1.32142952 ## FUN_001080 1.31996186 ## FUN_007870 1.31995169 ## FUN_027983 1.31912906 ## FUN_001267 1.31898155 ## FUN_034486 1.31841367 ## FUN_014442 1.31604072 ## FUN_003887 1.31251766 ## FUN_025315 1.31225741 ## FUN_002667 1.31186008 ## FUN_008237 1.31138871 ## FUN_000482 1.31067793 ## FUN_043231 1.31038897 ## FUN_014809 1.30991894 ## FUN_028145 1.30968667 ## FUN_010794 1.30942806 ## FUN_028981 1.30694252 ## FUN_023220 1.30690766 ## FUN_007926 1.30611326 ## FUN_040235 1.30593578 ## FUN_015287 1.30423023 ## FUN_018035 1.30404570 ## FUN_012590 1.30152334 ## FUN_036841 1.29816270 ## FUN_008524 1.29610104 ## FUN_024001 1.29600209 ## FUN_014449 1.29304457 ## FUN_036165 1.29290762 ## FUN_038774 1.29262801 ## FUN_023054 1.29223714 ## FUN_001356 1.29220718 ## FUN_023064 1.28615583 ## FUN_022205 1.28577384 ## FUN_036255 1.28554186 ## FUN_016433 1.28493292 ## FUN_022771 1.28483374 ## FUN_006021 1.28452695 ## FUN_033837 1.28404219 ## FUN_032456 1.28403918 ## FUN_007047 1.28321150 ## FUN_035233 1.28288115 ## FUN_038393 1.28076682 ## FUN_031686 1.28027909 ## FUN_019138 1.28004304 ## FUN_035932 1.27600226 ## FUN_039793 1.27539535 ## FUN_009777 1.27532501 ## FUN_026051 1.27491643 ## FUN_031920 1.27401566 ## FUN_032352 1.27398604 ## FUN_035462 1.27390380 ## FUN_000323 1.27367008 ## FUN_014806 1.27342329 ## FUN_021608 1.27342060 ## FUN_002378 1.27296422 ## FUN_005883 1.27232117 ## FUN_043573 1.27181116 ## FUN_004428 1.27146934 ## FUN_004413 1.27040223 ## FUN_018123 1.26998971 ## FUN_034450 1.26960117 ## FUN_033040 1.26950901 ## FUN_026917 1.26823793 ## FUN_033512 1.26719129 ## FUN_038757 1.26640301 ## FUN_022606 1.26628710 ## FUN_023694 1.26553317 ## FUN_010597 1.26546683 ## FUN_038772 1.26056367 ## FUN_028451 1.26029954 ## FUN_024576 1.25793163 ## FUN_039893 1.25659557 ## FUN_020837 1.25628344 ## FUN_016141 1.25557083 ## FUN_011307 1.25193136 ## FUN_002102 1.25191439 ## FUN_015290 1.25157466 ## FUN_007004 1.25090805 ## FUN_033968 1.25078266 ## FUN_028229 1.24987922 ## FUN_032384 1.24986058 ## FUN_008994 1.24927116 ## FUN_032191 1.24898867 ## FUN_001081 1.24852903 ## FUN_011933 1.24610285 ## FUN_035665 1.24570519 ## FUN_032382 1.24546900 ## FUN_025640 1.24527179 ## FUN_036897 1.24526920 ## FUN_026897 1.24377129 ## FUN_035170 1.24362364 ## FUN_024069 1.24117846 ## FUN_007957 1.24055362 ## FUN_007961 1.24009231 ## FUN_024387 1.24004750 ## FUN_022315 1.23955423 ## FUN_041654 1.23820997 ## FUN_008188 1.23787136 ## FUN_034116 1.23786272 ## FUN_037589 1.23768522 ## FUN_024371 1.23745194 ## FUN_043322 1.23677936 ## FUN_029042 1.23669446 ## FUN_038507 1.23527315 ## FUN_011048 1.23513609 ## FUN_041656 1.23506812 ## FUN_008588 1.23328082 ## FUN_037923 1.23249095 ## FUN_008489 1.23179048 ## FUN_037970 1.23150115 ## FUN_022609 1.23118294 ## FUN_016468 1.22998489 ## FUN_041638 1.22995288 ## FUN_033510 1.22968787 ## FUN_012144 1.22782509 ## FUN_022527 1.22680808 ## FUN_022936 1.22600453 ## FUN_025305 1.22563694 ## FUN_012317 1.22386412 ## FUN_004919 1.22357477 ## FUN_038535 1.22208049 ## FUN_028014 1.22169478 ## FUN_000440 1.22110865 ## FUN_004979 1.22068464 ## FUN_035244 1.21885265 ## FUN_036016 1.21863424 ## FUN_023983 1.21744858 ## FUN_008519 1.21696608 ## FUN_015292 1.21674264 ## FUN_041494 1.21615202 ## FUN_033114 1.21448049 ## FUN_035043 1.21379944 ## FUN_008056 1.21353943 ## FUN_016408 1.21241712 ## FUN_006869 1.21207128 ## FUN_032268 1.21137611 ## FUN_016508 1.20989309 ## FUN_043448 1.20940901 ## FUN_014610 1.20766329 ## FUN_042912 1.20685299 ## FUN_009006 1.20665144 ## FUN_022668 1.20612019 ## FUN_013596 1.20578362 ## FUN_036523 1.20564399 ## FUN_039827 1.20377357 ## FUN_041932 1.20330514 ## FUN_028982 1.20329688 ## FUN_037808 1.20288908 ## FUN_010807 1.20229152 ## FUN_032367 1.20167903 ## FUN_022649 1.20092569 ## FUN_004498 1.19899764 ## FUN_026686 1.19720112 ## FUN_037794 1.19706983 ## FUN_027211 1.19678160 ## FUN_035736 1.19661960 ## FUN_042814 1.19631724 ## FUN_014951 1.19573624 ## FUN_043285 1.19524730 ## FUN_035683 1.19513917 ## FUN_043240 1.19480039 ## FUN_024133 1.19378010 ## FUN_034387 1.19370822 ## FUN_006578 1.19338688 ## FUN_040965 1.18957395 ## FUN_009450 1.18783703 ## FUN_038533 1.18781018 ## FUN_043176 1.18744062 ## FUN_009054 1.18689551 ## FUN_011912 1.18625728 ## FUN_032252 1.18586368 ## FUN_008121 1.18404625 ## FUN_009773 1.18395037 ## FUN_007705 1.18309585 ## FUN_031232 1.18232058 ## FUN_040493 1.18110167 ## FUN_004421 1.18108146 ## FUN_025835 1.17937888 ## FUN_038297 1.17928881 ## FUN_042610 1.17922257 ## FUN_028484 1.17875906 ## FUN_001518 1.17872581 ## FUN_032370 1.17853317 ## FUN_015149 1.17850919 ## FUN_004264 1.17804218 ## FUN_035058 1.17787938 ## FUN_000489 1.17738707 ## FUN_016296 1.17728254 ## FUN_004078 1.17665333 ## FUN_032781 1.17662406 ## FUN_036230 1.17462827 ## FUN_016064 1.17296578 ## FUN_043108 1.17291337 ## FUN_004000 1.17096184 ## FUN_010701 1.17071862 ## FUN_027127 1.17019193 ## FUN_038510 1.17007428 ## FUN_001535 1.16915543 ## FUN_004468 1.16721847 ## FUN_041815 1.16712351 ## FUN_016368 1.16649814 ## FUN_016722 1.16576231 ## FUN_031251 1.16496460 ## FUN_028174 1.16482099 ## FUN_008758 1.16440207 ## FUN_025256 1.16338734 ## FUN_009044 1.16265939 ## FUN_038464 1.16204082 ## FUN_007704 1.16140569 ## FUN_014900 1.16132275 ## FUN_009673 1.16061547 ## FUN_006864 1.16041517 ## FUN_034859 1.16014821 ## FUN_033836 1.15960600 ## FUN_024339 1.15952456 ## FUN_009023 1.15762285 ## FUN_037196 1.15726746 ## FUN_000932 1.15663950 ## FUN_016587 1.15643301 ## FUN_001271 1.15475066 ## FUN_026160 1.15464882 ## FUN_004248 1.15409600 ## FUN_016444 1.15402877 ## FUN_034383 1.15301082 ## FUN_008020 1.15229683 ## FUN_043493 1.15101136 ## FUN_012374 1.15067514 ## FUN_007115 1.15058184 ## FUN_035229 1.15055271 ## FUN_019347 1.15042498 ## FUN_006872 1.15011370 ## FUN_012303 1.14961038 ## FUN_004225 1.14942198 ## FUN_008408 1.14935502 ## FUN_028312 1.14881591 ## FUN_032447 1.14863355 ## FUN_031983 1.14814428 ## FUN_025878 1.14808289 ## FUN_043014 1.14800700 ## FUN_008190 1.14790070 ## FUN_024724 1.14711544 ## FUN_001211 1.14674127 ## FUN_035655 1.14642732 ## FUN_010717 1.14590174 ## FUN_014980 1.14557941 ## FUN_019134 1.14455653 ## FUN_029644 1.14438752 ## FUN_012245 1.14364287 ## FUN_043378 1.14355426 ## FUN_040326 1.14325126 ## FUN_040071 1.14284690 ## FUN_016063 1.14080480 ## FUN_012758 1.14037497 ## FUN_029119 1.13953904 ## FUN_035628 1.13920331 ## FUN_028316 1.13917156 ## FUN_022529 1.13905627 ## FUN_013340 1.13872218 ## FUN_006007 1.13835920 ## FUN_043674 1.13823705 ## FUN_032372 1.13787851 ## FUN_025254 1.13781675 ## FUN_016091 1.13760075 ## FUN_037496 1.13759307 ## FUN_025252 1.13729881 ## FUN_014733 1.13718988 ## FUN_016651 1.13675212 ## FUN_035060 1.13575879 ## FUN_035264 1.13522207 ## FUN_008225 1.13463031 ## FUN_022902 1.13451473 ## FUN_001246 1.13378946 ## FUN_029541 1.13371395 ## FUN_003852 1.13343144 ## FUN_013418 1.13249696 ## FUN_032452 1.13158229 ## FUN_007995 1.13020404 ## FUN_011931 1.12968473 ## FUN_004862 1.12956803 ## FUN_004247 1.12816759 ## FUN_038919 1.12761650 ## FUN_037795 1.12759570 ## FUN_012181 1.12720387 ## FUN_040091 1.12652642 ## FUN_032202 1.12543958 ## FUN_010631 1.12539985 ## FUN_010814 1.12461017 ## FUN_029410 1.12450603 ## FUN_031887 1.12359154 ## FUN_036826 1.12334056 ## FUN_008615 1.12326978 ## FUN_000931 1.12324628 ## FUN_009268 1.12083623 ## FUN_012457 1.12034053 ## FUN_000977 1.12001612 ## FUN_026241 1.11985482 ## FUN_022937 1.11966733 ## FUN_002433 1.11944347 ## FUN_032605 1.11906512 ## FUN_034120 1.11845225 ## FUN_032364 1.11832009 ## FUN_032453 1.11736175 ## FUN_008551 1.11702036 ## FUN_004275 1.11678078 ## FUN_034951 1.11651677 ## FUN_022532 1.11604480 ## FUN_021928 1.11593352 ## FUN_035256 1.11573843 ## FUN_039323 1.11510885 ## FUN_025842 1.11505474 ## FUN_032260 1.11488883 ## FUN_014561 1.11415381 ## FUN_028092 1.11375449 ## FUN_035933 1.11316265 ## FUN_007161 1.11308012 ## FUN_022056 1.11263504 ## FUN_017919 1.11239780 ## FUN_015099 1.11215161 ## FUN_034321 1.11052032 ## FUN_041938 1.11024967 ## FUN_035255 1.11016154 ## FUN_002682 1.10998595 ## FUN_016051 1.10951569 ## FUN_029113 1.10948919 ## FUN_022082 1.10874598 ## FUN_036103 1.10835587 ## FUN_026294 1.10807748 ## FUN_008661 1.10777972 ## FUN_004511 1.10760839 ## FUN_015233 1.10744801 ## FUN_034303 1.10574823 ## FUN_000301 1.10490348 ## FUN_014707 1.10441030 ## FUN_041552 1.10398241 ## FUN_008512 1.10335897 ## FUN_015042 1.10203240 ## FUN_006329 1.10170250 ## FUN_022978 1.10140859 ## FUN_014606 1.10114838 ## FUN_014928 1.10005527 ## FUN_037685 1.09997052 ## FUN_005200 1.09832881 ## FUN_035818 1.09815145 ## FUN_022654 1.09768470 ## FUN_000968 1.09745564 ## FUN_033672 1.09736282 ## FUN_011054 1.09736205 ## FUN_023311 1.09559141 ## FUN_013456 1.09536614 ## FUN_022850 1.09442737 ## FUN_004270 1.09377113 ## FUN_009026 1.09281341 ## FUN_007846 1.09007541 ## FUN_034384 1.08929884 ## FUN_014932 1.08926085 ## FUN_007908 1.08822936 ## FUN_041700 1.08698185 ## FUN_023246 1.08697333 ## FUN_042034 1.08682751 ## FUN_037861 1.08505657 ## FUN_010716 1.08467953 ## FUN_009772 1.08467354 ## FUN_008407 1.08398596 ## FUN_024035 1.08352261 ## FUN_014674 1.08346804 ## FUN_025977 1.08339979 ## FUN_006704 1.08293909 ## FUN_034846 1.08265275 ## FUN_043439 1.08170638 ## FUN_029182 1.08025900 ## FUN_026909 1.07943216 ## FUN_027215 1.07918839 ## FUN_013354 1.07911927 ## FUN_029451 1.07894009 ## FUN_040492 1.07759333 ## FUN_043365 1.07655356 ## FUN_038756 1.07601538 ## FUN_042908 1.07512467 ## FUN_030663 1.07477650 ## FUN_023243 1.07434386 ## FUN_008075 1.07393171 ## FUN_029426 1.07370373 ## FUN_033673 1.07366237 ## FUN_004350 1.07289105 ## FUN_031561 1.07274257 ## FUN_032165 1.07268679 ## FUN_017901 1.07214696 ## FUN_042854 1.07195489 ## FUN_040243 1.07191839 ## FUN_031734 1.07164738 ## FUN_028315 1.07154243 ## FUN_006223 1.06899494 ## FUN_008099 1.06891981 ## FUN_010785 1.06878566 ## FUN_022664 1.06855468 ## FUN_000231 1.06793645 ## FUN_029151 1.06753948 ## FUN_014169 1.06745127 ## FUN_037455 1.06704634 ## FUN_035895 1.06699942 ## FUN_015997 1.06651248 ## FUN_038270 1.06574362 ## FUN_015984 1.06550227 ## FUN_034319 1.06533956 ## FUN_001733 1.06502998 ## FUN_004890 1.06477920 ## FUN_029318 1.06407320 ## FUN_016723 1.06406523 ## FUN_025266 1.06379091 ## FUN_006602 1.06361747 ## FUN_034115 1.06249814 ## FUN_031888 1.06242053 ## FUN_023056 1.06231249 ## FUN_002379 1.06202782 ## FUN_006821 1.06197623 ## FUN_040140 1.06147458 ## FUN_008085 1.06088291 ## FUN_029024 1.06072784 ## FUN_035828 1.05981269 ## FUN_018010 1.05881965 ## FUN_008261 1.05863188 ## FUN_026486 1.05786048 ## FUN_031525 1.05722394 ## FUN_040061 1.05656892 ## FUN_042973 1.05653355 ## 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FUN_003859 0.56305035 ## FUN_027789 0.56259033 ## FUN_005317 0.56246401 ## FUN_016198 0.56242542 ## FUN_037413 0.56188615 ## FUN_005988 0.56169180 ## FUN_032803 0.56060149 ## FUN_015351 0.56054631 ## FUN_024139 0.56046896 ## FUN_013546 0.56005461 ## FUN_008243 0.55989625 ## FUN_043562 0.55939376 ## FUN_013287 0.55819985 ## FUN_042878 0.55818079 ## FUN_007183 0.55763371 ## FUN_003919 0.55710194 ## FUN_006845 0.55647633 ## FUN_028351 0.55610383 ## FUN_010598 0.55470966 ## FUN_012185 0.55468508 ## FUN_022445 0.55462323 ## FUN_012430 0.55190380 ## FUN_001794 0.55078688 ## FUN_009456 0.54951688 ## FUN_040133 0.54888927 ## FUN_019367 0.54618457 ## FUN_025584 0.54606430 ## FUN_002878 0.54555574 ## FUN_028335 0.54206399 ## FUN_026515 0.54113609 ## FUN_004370 0.54016588 ## FUN_013462 0.53687982 ## FUN_017938 0.53614714 ## FUN_023241 0.53532385 ## FUN_016156 0.53516378 ## FUN_028355 0.53260355 ## FUN_003450 0.53175497 ## FUN_034245 0.53144804 ## FUN_033981 0.53083183 ## FUN_033491 0.52999493 ## FUN_009776 0.52970831 ## FUN_018092 0.52959052 ## FUN_042989 0.52929337 ## FUN_035876 0.52902779 ## FUN_035625 0.52860980 ## FUN_019693 0.52762631 ## FUN_008946 0.52754181 ## FUN_032561 0.52686218 ## FUN_031246 0.52535366 ## FUN_016636 0.52339700 ## FUN_037422 0.52252205 ## FUN_016617 0.51951883 ## FUN_026281 0.51943110 ## FUN_024459 0.51906805 ## FUN_028391 0.51768827 ## FUN_031919 0.51667293 ## FUN_041619 0.51467325 ## FUN_005822 0.51450916 ## FUN_037414 0.50936467 ## FUN_029709 0.50912597 ## FUN_019650 0.50593081 ## FUN_000939 0.50479130 ## FUN_031478 0.50440077 ## FUN_039803 0.50429075 ## FUN_006729 0.50228260 ## FUN_043239 0.50181277 ## FUN_033509 0.50136943 ## FUN_009449 0.50030242 ## FUN_025577 0.49942315 ## FUN_018533 0.49846295 ## FUN_013262 0.49844924 ## FUN_018399 0.49840500 ## FUN_025406 0.49282222 ## FUN_010789 0.49220130 ## FUN_031304 0.49134436 ## FUN_013519 0.49112876 ## FUN_042976 0.48737380 ## FUN_008309 0.48476396 ## FUN_026225 0.48340500 ## FUN_038800 0.48207631 ## FUN_026927 0.48194469 ## FUN_038459 0.48129628 ## FUN_012068 0.48123785 ## FUN_032563 0.48095592 ## FUN_004312 0.47857548 ## FUN_007079 0.47683039 ## FUN_022437 0.47402678 ## FUN_015074 0.47346588 ## FUN_004647 0.47241930 ## FUN_012776 0.47125933 ## FUN_007686 0.46932408 ## FUN_007685 0.46932408 ## FUN_007038 0.46932408 ## FUN_002080 0.46920598 ## FUN_020838 0.46880439 ## FUN_035068 0.46800850 ## FUN_025595 0.46736298 ## FUN_041665 0.46549482 ## FUN_003856 0.46264263 ## FUN_037829 0.46194911 ## FUN_008945 0.46189829 ## FUN_009833 0.46178265 ## FUN_016908 0.46058026 ## FUN_036113 0.45986600 ## FUN_008227 0.45654447 ## FUN_038782 0.45475447 ## FUN_010678 0.45445325 ## FUN_015655 0.45261978 ## FUN_026439 0.45229998 ## FUN_028354 0.45225028 ## FUN_025465 0.45214643 ## FUN_028846 0.45105724 ## FUN_019371 0.44889414 ## FUN_015356 0.44593576 ## FUN_001201 0.44352432 ## FUN_018528 0.44324028 ## FUN_036173 0.44321884 ## FUN_004060 0.44207759 ## FUN_003921 0.44167250 ## FUN_017854 0.44147001 ## FUN_003869 0.44126965 ## FUN_002935 0.44095160 ## FUN_026928 0.43690772 ## FUN_001202 0.43580243 ## FUN_035259 0.43417378 ## FUN_013545 0.43314228 ## FUN_006290 0.43170175 ## FUN_020968 0.42771140 ## FUN_033785 0.42580470 ## FUN_002100 0.42462876 ## FUN_031973 0.42338573 ## FUN_007172 0.41632544 ## FUN_013483 0.41491979 ## FUN_028454 0.41323029 ## FUN_032806 0.41281065 ## FUN_026894 0.41083848 ## FUN_017858 0.41070608 ## FUN_023182 0.41000167 ## FUN_010926 0.40916392 ## FUN_019620 0.40291535 ## FUN_022267 0.40093523 ## FUN_004817 0.39928557 ## FUN_022364 0.39794757 ## FUN_036029 0.39721239 ## FUN_037317 0.39310766 ## FUN_008795 0.39146830 ## FUN_010795 0.38933293 ## FUN_015078 0.38922234 ## FUN_000606 0.38749995 ## FUN_014939 0.38583408 ## FUN_027797 0.38412626 ## FUN_039965 0.38246209 ## FUN_003917 0.38108342 ## FUN_026148 0.37926233 ## FUN_017897 0.37827505 ## FUN_028452 0.37743684 ## FUN_025982 0.37632474 ## FUN_041634 0.37553633 ## FUN_016053 0.37397694 ## FUN_036773 0.37391438 ## FUN_016065 0.37230823 ## FUN_041681 0.37012094 ## FUN_017840 0.36985594 ## FUN_014475 0.36970804 ## FUN_040205 0.36961261 ## FUN_022579 0.36619123 ## FUN_024066 0.36323252 ## FUN_004414 0.36178353 ## FUN_007989 0.36114438 ## FUN_015354 0.35581428 ## FUN_026824 0.35547504 ## FUN_015082 0.35306906 ## FUN_028461 0.35031853 ## FUN_038092 0.35027992 ## FUN_022637 0.34838236 ## FUN_013508 0.34399897 ## FUN_036956 0.34086153 ## FUN_007813 0.34063348 ## FUN_015081 0.34023103 ## FUN_036847 0.33811730 ## FUN_032273 0.33640873 ## FUN_022248 0.33626360 ## FUN_016205 0.33114944 ## FUN_024173 0.33026892 ## FUN_004230 0.32695187 ## FUN_038778 0.32596233 ## FUN_013515 0.32198707 ## FUN_026457 0.31820358 ## FUN_010620 0.31459337 ## FUN_031666 0.31148907 ## FUN_010670 0.31060706 ## FUN_033039 0.30919747 ## FUN_002405 0.30757016 ## FUN_027802 0.30634937 ## FUN_003868 0.30602499 ## FUN_029184 0.30564108 ## FUN_026290 0.30487155 ## FUN_028317 0.29381523 ## FUN_039881 0.29080126 ## FUN_013476 0.29037853 ## FUN_010450 0.28398112 ## FUN_005249 0.28262902 ## FUN_025981 0.27589362 ## FUN_003867 0.27491646 ## FUN_001205 0.27339157 ## FUN_022587 0.27226397 ## FUN_015084 0.27105002 ## FUN_012794 0.26964841 ## FUN_000582 0.26886883 ## FUN_007683 0.26432624 ## FUN_000941 0.25620143 ## FUN_013478 0.25355810 ## FUN_016801 0.24745508 ## FUN_004465 0.24737651 ## FUN_026822 0.24242072 ## FUN_009443 0.24102603 ## FUN_040081 0.23967779 ## FUN_022268 0.23897550 ## FUN_019354 0.23828601 ## FUN_005131 0.23656877 ## FUN_016719 0.23145718 ## FUN_004100 0.22818933 ## FUN_003871 0.22638217 ## FUN_032454 0.22004988 ## FUN_003863 0.21926774 ## FUN_012889 0.21596090 ## FUN_017015 0.21306624 ## FUN_013520 0.21257359 ## FUN_023981 0.20926560 ## FUN_004368 0.20710408 ## FUN_026526 0.20440186 ## FUN_028486 0.20045744 ## FUN_005989 0.19771847 ## FUN_040263 0.19330326 ## FUN_028151 0.18961369 ## FUN_031843 0.18695189 ## FUN_012177 0.18538395 ## FUN_015259 0.18206407 ## FUN_026277 0.17408309 ## FUN_013346 0.17286832 ## FUN_032272 0.17256239 ## FUN_033507 0.16979977 ## FUN_032257 0.16560862 ## FUN_010493 0.15827979 ## FUN_042618 0.12407658 ## FUN_005123 0.12315864 ## FUN_012670 0.12040833 ## FUN_022444 0.11980974 ## FUN_029045 0.11597122 ## FUN_000220 0.11561685 ## FUN_023774 0.11268672 ## FUN_003861 0.11133127 ## FUN_041373 0.10057215 ## FUN_013333 0.09808045 ## FUN_015353 0.06785427 ## FUN_029293 0.04234620 ``` r # Converting row names to column treatment_VIP_table <- rownames_to_column(treatment_VIP_df, var = "Gene") #filter for VIP > 1 treatment_VIP_1 <- treatment_VIP_table %>% filter(VIP >= 1.5) #plot VIP_list_plot<-treatment_VIP_1 %>% arrange(VIP) %>% ggplot( aes(x = VIP, y = reorder(Gene,VIP,sum))) + geom_point() + ylab("Gene") + xlab("VIP Score") + ggtitle("Lipid Catabolism") + theme_bw() + theme(panel.border = element_rect(linetype = "solid", color = "black"), panel.grid.major = element_blank(), #Makes background theme white panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"));VIP_list_plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-108-1.png) Gene FUN_035821 is the most important - plot this. ``` r plot<-data6%>% ggplot(aes(x=timepoint, y=FUN_035821, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-109-1.png) Plot second most important ``` r plot<-data6%>% ggplot(aes(x=timepoint, y=FUN_006730, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-110-1.png) Plot third most important ``` r plot<-data6%>% ggplot(aes(x=timepoint, y=FUN_010519, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-111-1.png) Look at a PCA of the differentiating genes. ``` r #extract list of VIPs vip_genes<-treatment_VIP_1%>%pull(Gene) #turn to wide format with pca_data_vips<-pca_data_cleaned%>%dplyr::select(all_of(c("timepoint", "colony_id_corr", vip_genes))) ``` ``` r scaled.pca<-prcomp(pca_data_vips%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_vips%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_vips, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_vips, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 1108 0.39404 7.5864 0.001 *** ## Residual 35 1704 0.60596 ## Total 38 2812 1.00000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in lipid synthesis gene expression profile between time points. View by species ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_vips, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ ggtitle("Lipid Synthesis")+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-115-1.png) Pull out PC1 score for each sample for GO term. ``` r scores1 <- scaled.pca$x scores1<-as.data.frame(scores1) scores1<-scores1%>%dplyr::select(PC1) scores1$sample<-pca_data_vips$colony_id_corr scores1$timepoint<-pca_data_vips$timepoint scores1<-scores1%>% rename(lipids=PC1) scores<-left_join(scores, scores1) ``` ## Joining with `by = join_by(sample, timepoint)` ``` r head(scores) ``` ## glycolysis sample timepoint gluconeogenesis lipolysis fa_beta ## 1 -1.190941 ACR-139 TP1 -1.7424776 0.2958143 -0.2148238 ## 2 -3.751002 ACR-139 TP2 -7.5823907 3.4220239 5.5510011 ## 3 -0.367541 ACR-139 TP3 -3.6596187 -0.1048175 8.1317320 ## 4 1.435906 ACR-139 TP4 0.5056709 -4.8568009 0.8125497 ## 5 4.333006 ACR-145 TP1 4.3200803 -4.9876692 -1.4244745 ## 6 -1.959780 ACR-145 TP2 0.8176864 2.9345918 -3.9665474 ## starve lipids ## 1 -13.2465720 -0.59117164 ## 2 -32.2038389 -3.86270823 ## 3 -9.4273718 -0.06921135 ## 4 -0.7986395 5.49471831 ## 5 11.4822275 6.75836754 ## 6 3.1539286 -5.20932641 # Gene set 7: Protein catabolic process Load in gene set generated by Apul-energy-go script ``` r protein_go<-read_table(file="D-Apul/output/23-Apul-energy-GO/Apul_blastp-GO:0030163_out.tab")%>%pull(var=1) ``` ## ## ── Column specification ──────────────────────────────────────────────────────── ## cols( ## `FUN_000185-T1` = col_character(), ## `sp|Q9JJ22|ERAP1_RAT` = col_character(), ## `34.755` = col_double(), ## `938` = col_double(), ## `537` = col_double(), ## `18` = col_double(), ## `55` = col_double(), ## `944` = col_double(), ## `13` = col_double(), ## `923` = col_double(), ## `0.0` = col_double(), ## `587` = col_double() ## ) ``` r protein_go <- str_remove(protein_go, "-T1$") protein_go <- str_remove(protein_go, "-T2$") protein_go <- str_remove(protein_go, "-T3$") protein_go <- str_remove(protein_go, "-T4$") ``` Subset gene count matrix for this gene set. ``` r protein_genes<-Apul_genes%>% filter(rownames(.) %in% protein_go) ``` Calculate the sum of the total gene set for each sample. ``` r protein_genes<-as.data.frame(t(protein_genes)) protein_genes$Sample<-rownames(protein_genes) protein_genes<-protein_genes %>% rowwise() %>% mutate(protein_count = sum(c_across(where(is.numeric)))) %>% ungroup()%>% as.data.frame() ``` Merge into master data frame with metadata and physiology as a new column called “glycolysis”. ``` r data7<-left_join(phys, protein_genes) ``` ## Joining with `by = join_by(Sample)` Plot over timepoints. ``` r plot<-data7%>% ggplot(aes(x=timepoint, y=protein_count, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-121-1.png) Plot as a PCA. ``` r pca_data <- data7 %>% dplyr::select(c(starts_with("FUN"), colony_id_corr, timepoint)) # Identify numeric columns numeric_cols <- sapply(pca_data, is.numeric) # Among numeric columns, find those with non-zero sum non_zero_cols <- colSums(pca_data[, numeric_cols]) != 0 # Combine non-numeric columns with numeric columns that have non-zero sum pca_data_cleaned <- cbind( pca_data[, !numeric_cols], # All non-numeric columns pca_data[, numeric_cols][, non_zero_cols] # Numeric columns with non-zero sum ) ``` ``` r scaled.pca<-prcomp(pca_data_cleaned%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_cleaned%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_cleaned, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_cleaned, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 12758 0.155 2.1401 0.011 * ## Residual 35 69550 0.845 ## Total 38 82308 1.000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in ffa beta oxidation gene expression profile between time points. View by timepoint ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_cleaned, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=FALSE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-125-1.png) Which genes are driving this? Run PLSDA and VIP. ``` r #assigning datasets X <- pca_data_cleaned levels(as.factor(X$timepoint)) ``` ## [1] "TP1" "TP2" "TP3" "TP4" ``` r Y <- as.factor(X$timepoint) #select treatment names Y ``` ## [1] TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 ## [20] TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 TP3 TP4 TP1 TP2 ## [39] TP3 ## Levels: TP1 TP2 TP3 TP4 ``` r X<-X%>%dplyr::select(where(is.numeric)) #pull only data columns # run PLSDA MyResult.plsda <- plsda(X,Y) # 1 Run the method plotIndiv(MyResult.plsda, ind.names = FALSE, legend=TRUE, legend.title = "Protein Catabolism", ellipse = FALSE, title="", style = "graphics", centroid=FALSE, point.lwd = 2, cex=2) ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-126-1.png) Extract VIPs. ``` r #extract treatment_VIP <- PLSDA.VIP(MyResult.plsda) treatment_VIP_df <- as.data.frame(treatment_VIP[["tab"]]) treatment_VIP_df ``` ## VIP ## FUN_014754 2.06863970 ## FUN_001192 2.06765926 ## FUN_004464 2.02781740 ## FUN_024155 1.97855247 ## FUN_014486 1.96707057 ## FUN_035010 1.91363067 ## FUN_008078 1.88931072 ## FUN_001654 1.88376442 ## FUN_013329 1.88149485 ## FUN_008023 1.84920641 ## FUN_017777 1.84538017 ## FUN_025581 1.84350260 ## FUN_040138 1.84112402 ## FUN_016644 1.83639505 ## FUN_022722 1.81635455 ## FUN_022519 1.80181079 ## FUN_014530 1.80128929 ## FUN_015448 1.79984798 ## FUN_015344 1.79630160 ## FUN_032610 1.79478035 ## FUN_037441 1.79274769 ## FUN_032637 1.76917783 ## FUN_037573 1.75292207 ## FUN_035888 1.75291643 ## FUN_037843 1.75090790 ## FUN_024588 1.74556723 ## FUN_038415 1.74163764 ## FUN_025412 1.73976827 ## FUN_004690 1.73634174 ## FUN_038457 1.73585903 ## FUN_004003 1.73245965 ## FUN_038033 1.73068051 ## FUN_038532 1.72384311 ## FUN_008422 1.71836698 ## FUN_038265 1.71504253 ## FUN_034363 1.71373608 ## FUN_025361 1.71204074 ## FUN_034143 1.71061837 ## FUN_029296 1.70119381 ## FUN_031950 1.69408963 ## FUN_006636 1.69347628 ## FUN_004661 1.69179819 ## FUN_037917 1.68915462 ## FUN_028193 1.68863681 ## FUN_042955 1.68689161 ## FUN_028181 1.68500950 ## FUN_026025 1.67802106 ## FUN_008660 1.66827926 ## FUN_022827 1.66645696 ## FUN_001898 1.66540929 ## FUN_028200 1.66486900 ## FUN_004083 1.66313852 ## FUN_032607 1.65823474 ## FUN_028301 1.65476167 ## FUN_009661 1.65249875 ## FUN_008028 1.64874708 ## FUN_010606 1.64577069 ## FUN_008383 1.64451177 ## FUN_038808 1.64120457 ## FUN_036900 1.64098509 ## FUN_014656 1.64062806 ## FUN_006940 1.63957543 ## FUN_033718 1.63957366 ## FUN_043565 1.63842569 ## FUN_005347 1.62930792 ## FUN_034015 1.62773807 ## FUN_031667 1.62611676 ## FUN_006731 1.62598583 ## FUN_034918 1.62554993 ## FUN_028956 1.62520328 ## FUN_043505 1.62472091 ## FUN_042012 1.61858825 ## FUN_037230 1.61857231 ## FUN_024006 1.61117158 ## FUN_009835 1.60925612 ## FUN_038034 1.60750785 ## FUN_027900 1.60483346 ## FUN_038168 1.60310771 ## FUN_026708 1.59373580 ## FUN_012110 1.59059907 ## FUN_000329 1.59034449 ## FUN_015337 1.58486398 ## FUN_023181 1.58151335 ## FUN_043412 1.57986890 ## FUN_009335 1.57569713 ## FUN_006018 1.57358782 ## FUN_032791 1.57148897 ## FUN_028299 1.57000396 ## FUN_024761 1.56978669 ## FUN_022195 1.56710571 ## FUN_012531 1.55731109 ## FUN_032314 1.55688672 ## FUN_031210 1.55497471 ## FUN_004989 1.55211740 ## FUN_024679 1.55133651 ## FUN_007116 1.55058041 ## FUN_010698 1.54817627 ## FUN_017759 1.54509993 ## FUN_015008 1.54326137 ## FUN_008104 1.54153144 ## FUN_024394 1.54075102 ## FUN_010661 1.53864942 ## FUN_016422 1.53863412 ## FUN_037060 1.53271367 ## FUN_036174 1.53060714 ## FUN_012109 1.52761196 ## FUN_004561 1.52722652 ## FUN_010779 1.52646435 ## FUN_028498 1.52595074 ## FUN_035844 1.52503749 ## FUN_041760 1.52120657 ## FUN_013138 1.51994132 ## FUN_035444 1.51736846 ## FUN_016614 1.51158322 ## FUN_037963 1.50631631 ## FUN_012012 1.50318696 ## FUN_010818 1.49744433 ## FUN_037295 1.49682309 ## FUN_034012 1.49587425 ## FUN_004466 1.48706366 ## FUN_043251 1.48666998 ## FUN_034808 1.48558721 ## FUN_004407 1.48505872 ## 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FUN_028461 0.35589713 ## FUN_002663 0.35401666 ## FUN_028452 0.35324538 ## FUN_036939 0.35218290 ## FUN_029705 0.35107842 ## FUN_035079 0.35035202 ## FUN_032894 0.34842389 ## FUN_002330 0.34802363 ## FUN_026824 0.34674682 ## FUN_002328 0.34625698 ## FUN_025445 0.34619935 ## FUN_020920 0.34500463 ## FUN_001846 0.34483295 ## FUN_019371 0.34429489 ## FUN_029535 0.34326547 ## FUN_004002 0.34317077 ## FUN_017770 0.34242542 ## FUN_009336 0.34235964 ## FUN_024715 0.34156279 ## FUN_040002 0.34117548 ## FUN_017768 0.34034766 ## FUN_000632 0.33999155 ## FUN_033812 0.33952912 ## FUN_029589 0.33934564 ## FUN_008956 0.33248277 ## FUN_025350 0.33076938 ## FUN_029674 0.32637915 ## FUN_031666 0.32589573 ## FUN_016355 0.32479362 ## FUN_004768 0.32317206 ## FUN_040241 0.32216690 ## FUN_010760 0.32197428 ## FUN_027752 0.32169055 ## FUN_026290 0.31951118 ## FUN_042953 0.31920152 ## FUN_026457 0.31824314 ## FUN_037871 0.31773878 ## FUN_037559 0.31065454 ## FUN_040107 0.31054486 ## FUN_036163 0.30926013 ## FUN_025472 0.30899212 ## FUN_022292 0.30747752 ## FUN_015354 0.30743242 ## FUN_026270 0.30342773 ## FUN_040088 0.30109436 ## FUN_033015 0.30098054 ## FUN_036228 0.30078997 ## FUN_019354 0.29610420 ## FUN_036169 0.29248873 ## FUN_004781 0.29084367 ## FUN_032397 0.28927891 ## FUN_005249 0.28826678 ## FUN_024215 0.28806209 ## FUN_027778 0.28713373 ## FUN_017889 0.28536721 ## FUN_027802 0.28247788 ## FUN_033012 0.27988289 ## FUN_001205 0.27869311 ## FUN_028317 0.27410854 ## FUN_033813 0.27357625 ## FUN_022487 0.26820340 ## FUN_036962 0.26638420 ## FUN_010557 0.26516646 ## FUN_032526 0.25685180 ## FUN_026143 0.25569077 ## FUN_026128 0.25379002 ## FUN_023790 0.25301055 ## FUN_000941 0.24937346 ## FUN_037563 0.24893335 ## FUN_010767 0.24560518 ## FUN_038153 0.24211469 ## FUN_000637 0.23574559 ## FUN_028151 0.23519667 ## FUN_039081 0.23409223 ## FUN_022587 0.23228511 ## FUN_041617 0.23173781 ## FUN_009825 0.22970480 ## FUN_013346 0.22736116 ## FUN_032787 0.21787859 ## FUN_022549 0.21627266 ## FUN_032304 0.21541370 ## FUN_016356 0.21508836 ## FUN_031086 0.21338999 ## FUN_039983 0.21329173 ## FUN_004465 0.21205234 ## FUN_026526 0.20996622 ## FUN_000552 0.20870540 ## FUN_012177 0.20712190 ## FUN_000553 0.20154401 ## FUN_036899 0.19431641 ## FUN_026277 0.19363772 ## FUN_015341 0.18663851 ## FUN_022091 0.18428520 ## FUN_009329 0.18348511 ## FUN_014178 0.18147265 ## FUN_001609 0.17943411 ## FUN_027779 0.17829756 ## FUN_040263 0.17534756 ## FUN_001332 0.16879947 ## FUN_034065 0.16522379 ## FUN_012359 0.16517236 ## FUN_042902 0.16063923 ## FUN_001908 0.15984121 ## FUN_027756 0.15632670 ## FUN_026047 0.15361233 ## FUN_016341 0.14898611 ## FUN_014484 0.14632146 ## FUN_028242 0.13909021 ## FUN_041503 0.13526531 ## FUN_040227 0.13382636 ## FUN_023112 0.12552238 ## FUN_035109 0.12467301 ## FUN_028447 0.11660413 ## FUN_033723 0.11368661 ## FUN_028015 0.09907032 ## FUN_009386 0.09124355 ## FUN_043537 0.08715281 ## FUN_005267 0.08628626 ## FUN_033725 0.08542253 ## FUN_013333 0.07945065 ## FUN_015353 0.06394600 ## FUN_041616 0.05972050 ## FUN_035082 0.02831813 ``` r # Converting row names to column treatment_VIP_table <- rownames_to_column(treatment_VIP_df, var = "Gene") #filter for VIP > 1 treatment_VIP_1 <- treatment_VIP_table %>% filter(VIP >= 1) #plot VIP_list_plot<-treatment_VIP_1 %>% arrange(VIP) %>% ggplot( aes(x = VIP, y = reorder(Gene,VIP,sum))) + geom_point() + ylab("Gene") + xlab("VIP Score") + ggtitle("Protein Catabolism") + theme_bw() + theme(panel.border = element_rect(linetype = "solid", color = "black"), panel.grid.major = element_blank(), #Makes background theme white panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"));VIP_list_plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-127-1.png) Gene FUN_035867 is the most important - plot this. ``` r plot<-data7%>% ggplot(aes(x=timepoint, y=FUN_035867, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-128-1.png) Plot second most important ``` r plot<-data7%>% ggplot(aes(x=timepoint, y=FUN_011924, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-129-1.png) Plot third most important ``` r plot<-data7%>% ggplot(aes(x=timepoint, y=FUN_010717, group=colony_id_corr))+ facet_wrap(~species)+ geom_point()+ geom_line()+ theme_classic();plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-130-1.png) Look at a PCA of the differentiating genes. ``` r #extract list of VIPs vip_genes<-treatment_VIP_1%>%pull(Gene) #turn to wide format with pca_data_vips<-pca_data_cleaned%>%dplyr::select(all_of(c("timepoint", "colony_id_corr", vip_genes))) ``` ``` r scaled.pca<-prcomp(pca_data_vips%>%dplyr::select(where(is.numeric)), scale=TRUE, center=TRUE) ``` Prepare a PCA plot ``` r # scale data vegan <- scale(pca_data_vips%>%dplyr::select(where(is.numeric))) # PerMANOVA permanova<-adonis2(vegan ~ timepoint, data = pca_data_vips, method='eu') permanova ``` ## Permutation test for adonis under reduced model ## Terms added sequentially (first to last) ## Permutation: free ## Number of permutations: 999 ## ## adonis2(formula = vegan ~ timepoint, data = pca_data_vips, method = "eu") ## Df SumOfSqs R2 F Pr(>F) ## timepoint 3 8642 0.2288 3.4613 0.001 *** ## Residual 35 29130 0.7712 ## Total 38 37772 1.0000 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Significant differences in fatty acid beta oxidation gene expression profile between time points. View by species ``` r plot<-ggplot2::autoplot(scaled.pca, data=pca_data_vips, loadings=FALSE, colour="timepoint", loadings.label.colour="black", loadings.colour="black", loadings.label=FALSE, frame=TRUE, loadings.label.size=5, loadings.label.vjust=-1, size=5) + theme_classic()+ ggtitle("Protein Catabolism")+ theme(legend.text = element_text(size=18), legend.position="right", plot.background = element_blank(), legend.title = element_text(size=18, face="bold"), axis.text = element_text(size=18), axis.title = element_text(size=18, face="bold"));plot ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-134-1.png) Pull out PC1 score for each sample for GO term. ``` r scores1 <- scaled.pca$x scores1<-as.data.frame(scores1) scores1<-scores1%>%dplyr::select(PC1) scores1$sample<-pca_data_vips$colony_id_corr scores1$timepoint<-pca_data_vips$timepoint scores1<-scores1%>% rename(protein=PC1) scores<-left_join(scores, scores1) ``` ## Joining with `by = join_by(sample, timepoint)` ``` r head(scores) ``` ## glycolysis sample timepoint gluconeogenesis lipolysis fa_beta ## 1 -1.190941 ACR-139 TP1 -1.7424776 0.2958143 -0.2148238 ## 2 -3.751002 ACR-139 TP2 -7.5823907 3.4220239 5.5510011 ## 3 -0.367541 ACR-139 TP3 -3.6596187 -0.1048175 8.1317320 ## 4 1.435906 ACR-139 TP4 0.5056709 -4.8568009 0.8125497 ## 5 4.333006 ACR-145 TP1 4.3200803 -4.9876692 -1.4244745 ## 6 -1.959780 ACR-145 TP2 0.8176864 2.9345918 -3.9665474 ## starve lipids protein ## 1 -13.2465720 -0.59117164 -12.460811 ## 2 -32.2038389 -3.86270823 -30.611104 ## 3 -9.4273718 -0.06921135 -8.232598 ## 4 -0.7986395 5.49471831 -2.797053 ## 5 11.4822275 6.75836754 9.963967 ## 6 3.1539286 -5.20932641 5.752924 # Output PC1 scores for each GO term for each sample ``` r head(scores) ``` ## glycolysis sample timepoint gluconeogenesis lipolysis fa_beta ## 1 -1.190941 ACR-139 TP1 -1.7424776 0.2958143 -0.2148238 ## 2 -3.751002 ACR-139 TP2 -7.5823907 3.4220239 5.5510011 ## 3 -0.367541 ACR-139 TP3 -3.6596187 -0.1048175 8.1317320 ## 4 1.435906 ACR-139 TP4 0.5056709 -4.8568009 0.8125497 ## 5 4.333006 ACR-145 TP1 4.3200803 -4.9876692 -1.4244745 ## 6 -1.959780 ACR-145 TP2 0.8176864 2.9345918 -3.9665474 ## starve lipids protein ## 1 -13.2465720 -0.59117164 -12.460811 ## 2 -32.2038389 -3.86270823 -30.611104 ## 3 -9.4273718 -0.06921135 -8.232598 ## 4 -0.7986395 5.49471831 -2.797053 ## 5 11.4822275 6.75836754 9.963967 ## 6 3.1539286 -5.20932641 5.752924 ``` r scores<-scores%>% rename(colony_id_corr=sample) ``` # Correlate PC scores to physiology metrics ``` r #merge scores into physiology data frame head(phys) ``` ## colony_id_corr timepoint species site Host_AFDW.mg.cm2 Sym_AFDW.mg.cm2 ## 1 ACR-139 TP1 Acropora Manava 1.7591018 0.7954199 ## 2 ACR-139 TP2 Acropora Manava 1.2115782 0.5452102 ## 3 ACR-139 TP3 Acropora Manava 0.6641778 0.4933892 ## 4 ACR-139 TP4 Acropora Manava 1.1328361 0.5380971 ## 5 ACR-145 TP1 Acropora Manava 1.1008131 0.3124889 ## 6 ACR-145 TP2 Acropora Manava 1.1518671 0.7818735 ## Am AQY Rd Ik Ic calc.umol.cm2.hr ## 1 1.0423651 0.002247138 0.4110558 463.8635 199.05551 0.00696294 ## 2 0.7502141 0.001797326 0.3126811 417.4057 191.38564 0.00000000 ## 3 0.5297574 0.001605656 0.3435224 329.9320 281.04264 0.29804663 ## 4 0.4480059 0.001089380 0.1397578 411.2486 135.02956 0.00000000 ## 5 0.7092019 0.002476605 0.2058895 286.3605 86.87529 0.00000000 ## 6 1.3689889 0.003914251 0.4722326 349.7448 128.53368 0.00000000 ## cells.mgAFDW prot_mg.mgafdw Ratio_AFDW.mg.cm2 Total_Chl Total_Chl_cell ## 1 357004.8 0.3305641 0.3113772 3.0301449 8.487686e-06 ## 2 432916.7 0.2441992 0.3103448 1.3906667 3.212320e-06 ## 3 398214.3 0.5820417 0.4262295 2.2954762 5.764425e-06 ## 4 190625.0 0.1845620 0.3220339 0.6833594 3.584836e-06 ## 5 622222.2 0.2806709 0.2211055 2.7291398 4.386118e-06 ## 6 823232.3 0.2684692 0.4043321 2.9794444 3.619202e-06 ## cre.umol.mgafdw Sample ## 1 0.02538140 ACR-139_TP1 ## 2 NA ACR-139_TP2 ## 3 0.16866813 ACR-139_TP3 ## 4 0.06861166 ACR-139_TP4 ## 5 0.03220156 ACR-145_TP1 ## 6 NA ACR-145_TP2 ``` r head(scores) ``` ## glycolysis colony_id_corr timepoint gluconeogenesis lipolysis fa_beta ## 1 -1.190941 ACR-139 TP1 -1.7424776 0.2958143 -0.2148238 ## 2 -3.751002 ACR-139 TP2 -7.5823907 3.4220239 5.5510011 ## 3 -0.367541 ACR-139 TP3 -3.6596187 -0.1048175 8.1317320 ## 4 1.435906 ACR-139 TP4 0.5056709 -4.8568009 0.8125497 ## 5 4.333006 ACR-145 TP1 4.3200803 -4.9876692 -1.4244745 ## 6 -1.959780 ACR-145 TP2 0.8176864 2.9345918 -3.9665474 ## starve lipids protein ## 1 -13.2465720 -0.59117164 -12.460811 ## 2 -32.2038389 -3.86270823 -30.611104 ## 3 -9.4273718 -0.06921135 -8.232598 ## 4 -0.7986395 5.49471831 -2.797053 ## 5 11.4822275 6.75836754 9.963967 ## 6 3.1539286 -5.20932641 5.752924 ``` r main<-left_join(phys, scores) ``` ## Joining with `by = join_by(colony_id_corr, timepoint)` ``` r head(main) ``` ## colony_id_corr timepoint species site Host_AFDW.mg.cm2 Sym_AFDW.mg.cm2 ## 1 ACR-139 TP1 Acropora Manava 1.7591018 0.7954199 ## 2 ACR-139 TP2 Acropora Manava 1.2115782 0.5452102 ## 3 ACR-139 TP3 Acropora Manava 0.6641778 0.4933892 ## 4 ACR-139 TP4 Acropora Manava 1.1328361 0.5380971 ## 5 ACR-145 TP1 Acropora Manava 1.1008131 0.3124889 ## 6 ACR-145 TP2 Acropora Manava 1.1518671 0.7818735 ## Am AQY Rd Ik Ic calc.umol.cm2.hr ## 1 1.0423651 0.002247138 0.4110558 463.8635 199.05551 0.00696294 ## 2 0.7502141 0.001797326 0.3126811 417.4057 191.38564 0.00000000 ## 3 0.5297574 0.001605656 0.3435224 329.9320 281.04264 0.29804663 ## 4 0.4480059 0.001089380 0.1397578 411.2486 135.02956 0.00000000 ## 5 0.7092019 0.002476605 0.2058895 286.3605 86.87529 0.00000000 ## 6 1.3689889 0.003914251 0.4722326 349.7448 128.53368 0.00000000 ## cells.mgAFDW prot_mg.mgafdw Ratio_AFDW.mg.cm2 Total_Chl Total_Chl_cell ## 1 357004.8 0.3305641 0.3113772 3.0301449 8.487686e-06 ## 2 432916.7 0.2441992 0.3103448 1.3906667 3.212320e-06 ## 3 398214.3 0.5820417 0.4262295 2.2954762 5.764425e-06 ## 4 190625.0 0.1845620 0.3220339 0.6833594 3.584836e-06 ## 5 622222.2 0.2806709 0.2211055 2.7291398 4.386118e-06 ## 6 823232.3 0.2684692 0.4043321 2.9794444 3.619202e-06 ## cre.umol.mgafdw Sample glycolysis gluconeogenesis lipolysis fa_beta ## 1 0.02538140 ACR-139_TP1 -1.190941 -1.7424776 0.2958143 -0.2148238 ## 2 NA ACR-139_TP2 -3.751002 -7.5823907 3.4220239 5.5510011 ## 3 0.16866813 ACR-139_TP3 -0.367541 -3.6596187 -0.1048175 8.1317320 ## 4 0.06861166 ACR-139_TP4 1.435906 0.5056709 -4.8568009 0.8125497 ## 5 0.03220156 ACR-145_TP1 4.333006 4.3200803 -4.9876692 -1.4244745 ## 6 NA ACR-145_TP2 -1.959780 0.8176864 2.9345918 -3.9665474 ## starve lipids protein ## 1 -13.2465720 -0.59117164 -12.460811 ## 2 -32.2038389 -3.86270823 -30.611104 ## 3 -9.4273718 -0.06921135 -8.232598 ## 4 -0.7986395 5.49471831 -2.797053 ## 5 11.4822275 6.75836754 9.963967 ## 6 3.1539286 -5.20932641 5.752924 ``` r main<-main%>% dplyr::select(where(is.numeric)) head(main) ``` ## Host_AFDW.mg.cm2 Sym_AFDW.mg.cm2 Am AQY Rd Ik ## 1 1.7591018 0.7954199 1.0423651 0.002247138 0.4110558 463.8635 ## 2 1.2115782 0.5452102 0.7502141 0.001797326 0.3126811 417.4057 ## 3 0.6641778 0.4933892 0.5297574 0.001605656 0.3435224 329.9320 ## 4 1.1328361 0.5380971 0.4480059 0.001089380 0.1397578 411.2486 ## 5 1.1008131 0.3124889 0.7092019 0.002476605 0.2058895 286.3605 ## 6 1.1518671 0.7818735 1.3689889 0.003914251 0.4722326 349.7448 ## Ic calc.umol.cm2.hr cells.mgAFDW prot_mg.mgafdw Ratio_AFDW.mg.cm2 ## 1 199.05551 0.00696294 357004.8 0.3305641 0.3113772 ## 2 191.38564 0.00000000 432916.7 0.2441992 0.3103448 ## 3 281.04264 0.29804663 398214.3 0.5820417 0.4262295 ## 4 135.02956 0.00000000 190625.0 0.1845620 0.3220339 ## 5 86.87529 0.00000000 622222.2 0.2806709 0.2211055 ## 6 128.53368 0.00000000 823232.3 0.2684692 0.4043321 ## Total_Chl Total_Chl_cell cre.umol.mgafdw glycolysis gluconeogenesis ## 1 3.0301449 8.487686e-06 0.02538140 -1.190941 -1.7424776 ## 2 1.3906667 3.212320e-06 NA -3.751002 -7.5823907 ## 3 2.2954762 5.764425e-06 0.16866813 -0.367541 -3.6596187 ## 4 0.6833594 3.584836e-06 0.06861166 1.435906 0.5056709 ## 5 2.7291398 4.386118e-06 0.03220156 4.333006 4.3200803 ## 6 2.9794444 3.619202e-06 NA -1.959780 0.8176864 ## lipolysis fa_beta starve lipids protein ## 1 0.2958143 -0.2148238 -13.2465720 -0.59117164 -12.460811 ## 2 3.4220239 5.5510011 -32.2038389 -3.86270823 -30.611104 ## 3 -0.1048175 8.1317320 -9.4273718 -0.06921135 -8.232598 ## 4 -4.8568009 0.8125497 -0.7986395 5.49471831 -2.797053 ## 5 -4.9876692 -1.4244745 11.4822275 6.75836754 9.963967 ## 6 2.9345918 -3.9665474 3.1539286 -5.20932641 5.752924 Compute correlations ``` r # Compute correlations with rcorr corr_result <- rcorr(as.matrix(main), type = "pearson") # Extract correlation matrix and p-values cor_matrix <- corr_result$r p_matrix <- corr_result$P # Get correlation between sets of interest only terms<-c("glycolysis", "gluconeogenesis", "lipolysis", "fa_beta", "starve", "protein", "lipids") phys_var<-c("Host_AFDW.mg.cm2", "Sym_AFDW.mg.cm2", "Am", "AQY", "Rd", "Ik", "Ic", "calc.umol.cm2.hr", "cells.mgAFDW", "prot_mg.mgafdw", "Ratio_AFDW.mg.cm2", "Total_Chl", "Total_Chl_cell", "cre.umol.mgafdw") # Subset the correlation matrix subset_cor <- cor_matrix[terms, phys_var, drop = FALSE] # Subset the p-value matrix subset_p <- p_matrix[terms, phys_var, drop = FALSE] ``` Plot heatmap ``` r # Create matrix of formatted p-values p_labels <- matrix(sprintf("%.3f", subset_p), nrow = nrow(subset_p)) # Plot with corrplot corrplot(subset_cor, method = "color", # Fill squares with color col = colorRampPalette(c("blue", "white", "red"))(200), type = "full", tl.col = "black", # Text label color tl.cex = 1.1, # Label size addCoef.col = "black", # Show correlation values (optional) number.cex = 0.8, # Text size for numbers p.mat = subset_p, # p-value matrix insig = "blank", # Only show significant diag = FALSE # Hide diagonal ) ``` ![](23-Apul-energetic-state_files/figure-gfm/unnamed-chunk-139-1.png) ``` r # Overlay p-values as text manually (optional if not showing coef) #text(x = rep(1:ncol(subset_p), each = nrow(subset_p)), # y = rep(nrow(subset_p):1, ncol(subset_p)), # labels = p_labels, # cex = 0.8) ```