GObayesC Report
Last updated: 2023-11-28
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Knit directory: dgrp-starve/
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#setwd("..")
#Correlation Coefficient Paths
uniF <- readRDS('snake/data/go/40_all/sexf/partData.Rds')
uniM <- readRDS('snake/data/go/40_all/sexm/partData.Rds')#reorders 50 X m table to a (50*m) X 2 table
#converts method into factor to retain order
ggTidy <- function(data){
for(i in 1:dim(data)[2]){
name <- colnames(data)[i]
temp <- cbind(rep(name, dim(data)[1]), data[,i, with=FALSE])
if(i==1){
hold <- temp
} else{
hold <- rbind(hold, temp, use.names=FALSE)
}
}
colnames(hold) <- c("method", "cor")
hold$method <- factor(hold$method, levels=unique(hold$method))
return(hold)
}
#wrapper for ggplot call to custom fill sex, title, and y axis label
ggMake <- function(data, sex, custom.title, custom.Ylab){
plothole <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y=custom.Ylab, tag=sex, title=custom.title) +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
return(plothole)
}
corSummary <- function(dataList, outPath){
hold <- readRDS(dataList[1])
for (i in 2:length(dataList)) {
#print(i)
hold <- rbind(hold, readRDS(dataList[i]))
}
colnames(hold) <- c('sex', 'rmax', 'rgo', 'term', 'cor')
saveRDS(hold, outPath)
}partMake <- function(data, sex, yint1, cutoff, lower, custom.title, custom.Xlab, custom.Ylab){
plothole <- ggplot(data,aes(y=cor,x=term))+
geom_point(color=viridis(1, begin=0.5))+
geom_text(aes(label=ifelse(cor>cutoff, as.character(term),'')), hjust=-0.1, size=2, angle=90)+
geom_text(aes(label=ifelse(cor<lower, as.character(term),'')), hjust=-0.1, size=2, angle=90)+
geom_hline(yintercept = yint1) +
geom_hline(yintercept = cutoff) +
theme_minimal() +
labs(x=custom.Xlab,y=custom.Ylab, tag=sex, title=custom.title) +
theme(text=element_text(size=10),
plot.tag = element_text(size=15))
return(plothole)
}
dMake <- function(data, sex, custom.title, custom.Xlab, custom.Ylab){
plothole <- ggplot(data, aes(x= index, y=cor, label=gene))+
geom_point(color=viridis(1, begin=0.5))+
geom_text(aes(label=ifelse(cor>0.5, as.character(gene),'')),hjust=1,vjust=0, angle=90)+
theme_minimal() +
labs(x=custom.Xlab,y=custom.Ylab, tag=sex, title=custom.title) +
theme(text=element_text(size=10),
plot.tag = element_text(size=15))
return(plothole)
}allNames <- read.table(file="snake/goPost/dataList", sep="\n")
dataList <- allNames[,1]
front <- 'snake/data/go/33_metric/sexf/rmax0.8/rgo0.01/'
end <- '/rowData.Rds'
finalList <- paste0(front, dataList, end)
outPath <- 'snake/data/go/40_all/sexf/partData.Rds'
temp <- na.omit(readRDS(outPath))
facs <- matrix(as.factor(unlist(temp[,1:4])), ncol=4)
cors <- as.numeric(unlist(temp[,5]))
data <- data.table(facs, cors)
colnames(data) <- c('sex', 'rmax', 'rgo', 'term', 'cor')
dataF <- data[data$sex=='f',]
yintData1 <- readRDS('snake/data/sr/33_metric/go/sexf/rmax0.8/rgo0/term1/rowData.Rds')
yF <- as.numeric(yintData1[5])
#
gg[[1]] <- partMake(dataF, 'F', yF, 0.36, 0.27, 'Effect of GO Annotations in Bayesian models', 'GO Term', 'Prediction Accuracy')
dataFOF <- dataF[order(cor),]
#post processing
temp99 <- readRDS("snake/data/go/40_all/sexf/partData.Rds")
subF <- dataF[cor>0.36,4:5]
subF <- subF[order(-cor),]
plot(x=1:dim(dataFOF)[1], y=dataFOF$cor)
#snake/
allNames <- read.table(file="snake/goPost/dataList", sep="\n")
dataList <- allNames[,1]
front <- 'snake/data/go/33_metric/sexm/rmax0.8/rgo0.01/'
end <- '/rowData.Rds'
finalList <- paste0(front, dataList, end)
outPath <- 'snake/data/go/40_all/sexm/partData.Rds'
temp <- na.omit(readRDS(outPath))
facs <- matrix(as.factor(unlist(temp[,1:4])), ncol=4)
cors <- as.numeric(unlist(temp[,5]))
data <- data.table(facs, cors)
colnames(data) <- c('sex', 'rmax', 'rgo', 'term', 'cor')
dataM <- data[data$sex=='m',]
yintData1 <- readRDS('snake/data/sr/33_metric/go/sexm/rmax0.8/rgo0/term1/rowData.Rds')
yM <- as.numeric(yintData1[5])
#
gg[[2]] <- partMake(dataM, 'M', yF, 0.48, 0.40, 'Effect of GO Annotations in Bayesian models', 'GO Term', 'Prediction Accuracy')
dataMOM <- dataM[order(cor),]
#post processing
#temp99 <- readRDS("snake/data/go/40_all/sexm/partData.Rds")
subM <- dataM[cor>0.48,4:5]
subM <- subM[order(-cor),]
#plot(x=1:dim(dataFOF)[1], y=dataFOF$cor)#ftop
#0045819
#0033500
#0055088
#
#mtop
#0035008
#0140042
#0007485
term <- 'GO.0045819'
idPath <- paste0('snake/data/go/03_goterms/sexf/', term, '.Rds')
fitPath <- paste0('snake/data/go/25_fit/sexf/', term, '/bayesFull.Rds')
xp <- readRDS('snake/data/01_matched/f_starvation.Rds')
genes <- colnames(xp[,-1])
id1 <- readRDS(idPath)
model1 <- readRDS(fitPath)
fit1 <- model1$fit
inlay <- fit1$ETA[[1]]$d
outlay <- fit1$ETA[[2]]$d
#genes[id1]
fitDataF <- data.table(index=c(1:length(inlay)), cor=inlay, gene=genes[id1])
gg[[3]] <- dMake(fitDataF, 'F', "Effect...", 'Index', 'PIP')#ftop
#0045819
#0033500
#0055088
#
#mtop
#0035008
#0140042
#0007485
termReader <- function(term, sex)
{
idPath <- paste0('snake/data/go/03_goterms/sex', sex,'/', term, '.Rds')
fitPath <- paste0('snake/data/go/25_fit/sex', sex,'/', term, '/bayesFull.Rds')
xpPath <- paste0('snake/data/01_matched/', sex,'_starvation.Rds')
xp <- readRDS(xpPath)
genes <- colnames(xp[,-1])
id1 <- readRDS(idPath)
model1 <- readRDS(fitPath)
fit1 <- model1$fit
inlay <- fit1$ETA[[1]]$d
outlay <- fit1$ETA[[2]]$d
fitDataM <- data.table(index=c(1:length(inlay)), cor=inlay, gene=genes[id1])
plothole <- dMake(fitDataM, toupper(sex), paste0("Posterior Inclusion Probability of Genes in ", term), 'Index', 'PIP')
return(plothole)
}
f1 <- termReader('GO.0045819', 'f')
f2 <- termReader('GO.0033500', 'f')
f3 <- termReader('GO.0055088', 'f')
f4 <- termReader('GO.0042675', 'f')
m1 <- termReader('GO.0035008', 'm')
m2 <- termReader('GO.0140042', 'm')
m3 <- termReader('GO.0007485', 'm')
m4 <- termReader('GO.0005811', 'm')Female
plot_grid(gg[[1]], ncol=1)
plot_grid(f1, f2, f3, f4, ncol=2)
The low female outlier is GO:0031408 responsible for oxylipin synthesis.
We then translated the top GO terms into human readable categories to assess our findings. Below are the top ten ordered by correlation:
id: GO:0045819 name: positive regulation of glycogen catabolic process – id: GO:0033500 name: carbohydrate homeostasis – id: GO:0055088 name: lipid homeostasis – id: GO:0042675 name: compound eye cone cell differentiation – id: GO:0042277 name: peptide binding – id: GO:0008586 name: imaginal disc-derived wing vein morphogenesis – id: GO:0016042 name: lipid catabolic process – id: GO:0007368 name: determination of left/right symmetry – id: GO:0007638 name: mechanosensory behavior – id: GO:0006644 name: phospholipid metabolic process
Male
plot_grid(gg[[2]], ncol=1)
plot_grid(m1, m2, m3, m4, ncol=2)
No extreme outlier was found in males.
We then translated the top GO terms into human readable categories to assess our findings. Below are the top ten ordered by correlation:
id: GO:0035008 name: positive regulation of melanization defense response – id: GO:0140042 name: lipid droplet formation – id: GO:0007485 name: imaginal disc-derived male genitalia development – id: GO:0005811 name: lipid droplet – id: GO:0045819 name: positive regulation of glycogen catabolic process – id: GO:0042461 name: photoreceptor cell development – id: GO:0033500 name: carbohydrate homeostasis – id: GO:0016327 name: apicolateral plasma membrane – id: GO:0045186 name: zonula adherens assembly – id: GO:0006044 name: N-acetylglucosamine metabolic process
geneMatchF <- readRDS('snake/goPost/finalDataF.Rds')
geneMatchM <- readRDS('snake/goPost/finalDataM.Rds')
hitF <- geneMatchF[which(count>2),]
hitM <- geneMatchM[which(count>2),]
d1 <- hitF
d2 <- hitM
knitr::kable(
list(d1, d2),
caption = 'Top Female and Male Genes',
booktabs = TRUE, valign = 't', "simple"
)| flybase | count | gene |
|---|---|---|
| FBgn0025595 | 8 | AkhR |
| FBgn0000575 | 7 | emc |
| FBgn0004552 | 4 | Akh |
| FBgn0283499 | 4 | InR |
| FBgn0003731 | 4 | Egfr |
| FBgn0000490 | 4 | dpp |
| FBgn0003205 | 4 | Ras85D |
| FBgn0262738 | 4 | norpA |
| FBgn0010303 | 3 | hep |
| FBgn0015279 | 3 | Pi3K92E |
| FBgn0033799 | 3 | GLaz |
| FBgn0036449 | 3 | bmm |
| FBgn0003463 | 3 | sog |
| FBgn0003719 | 3 | tld |
| flybase | count | gene |
|---|---|---|
| FBgn0265778 | 5 | PDZ-GEF |
| FBgn0025595 | 5 | AkhR |
| FBgn0261873 | 5 | sdt |
| FBgn0036046 | 5 | Ilp2 |
| FBgn0283499 | 5 | InR |
| FBgn0086687 | 4 | Desat1 |
| FBgn0036449 | 3 | bmm |
| FBgn0004552 | 3 | Akh |
| FBgn0003205 | 3 | Ras85D |
| FBgn0015279 | 3 | Pi3K92E |
| FBgn0067864 | 3 | Patj |
| FBgn0261854 | 3 | aPKC |
| FBgn0263289 | 3 | scrib |
| FBgn0024248 | 3 | chico |
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.5 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/libs/gnu9/openblas/0.3.7/lib/libopenblasp-r0.3.7.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.43 reshape2_1.4.4 melt_1.10.0 ggcorrplot_0.1.4.1
[5] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.0 purrr_1.0.1
[9] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 tidyverse_2.0.0
[13] scales_1.2.1 viridis_0.6.4 viridisLite_0.4.2 qqman_0.1.9
[17] cowplot_1.1.1 ggplot2_3.4.4 data.table_1.14.8 dplyr_1.1.3
[21] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.11 getPass_0.2-2 ps_1.7.5 rprojroot_2.0.3
[5] digest_0.6.33 utf8_1.2.3 plyr_1.8.9 R6_2.5.1
[9] evaluate_0.21 highr_0.10 httr_1.4.7 pillar_1.9.0
[13] rlang_1.1.1 rstudioapi_0.15.0 whisker_0.4.1 callr_3.7.3
[17] jquerylib_0.1.4 rmarkdown_2.23 labeling_0.4.3 munsell_0.5.0
[21] compiler_4.1.2 httpuv_1.6.12 xfun_0.39 pkgconfig_2.0.3
[25] htmltools_0.5.5 tidyselect_1.2.0 gridExtra_2.3 fansi_1.0.4
[29] calibrate_1.7.7 tzdb_0.4.0 withr_2.5.0 later_1.3.1
[33] MASS_7.3-60 grid_4.1.2 jsonlite_1.8.7 gtable_0.3.4
[37] lifecycle_1.0.3 git2r_0.32.0 magrittr_2.0.3 cli_3.6.1
[41] stringi_1.7.12 cachem_1.0.8 farver_2.1.1 fs_1.6.3
[45] promises_1.2.0.1 bslib_0.5.0 generics_0.1.3 vctrs_0.6.4
[49] tools_4.1.2 glue_1.6.2 hms_1.1.3 processx_3.8.2
[53] fastmap_1.1.1 yaml_2.3.7 timechange_0.2.0 colorspace_2.1-0
[57] sass_0.4.7