GObayesC Report
Last updated: 2023-12-05
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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, psize){
plothole <- ggplot(data, aes(x= index, y=cor, label=gene))+
geom_point(color=viridis(1, begin=0.5), size=psize)+
geom_text(aes(label=ifelse(cor>0.5, as.character(gene),'')),hjust=1,vjust=0, angle=90, size=3)+
theme_minimal() +
labs(x=custom.Xlab,y=custom.Ylab, tag=sex, title=custom.title) +
theme(text=element_text(size=8),
plot.tag = element_text(size=15))
return(plothole)
}Initial results showed that Gene Ontology association could be used to improve prediction accuracy of a Bayesian model by assigning genes of interest a separate portion of variance. The following results are from selecting GO terms with five or more associated genes and scoring their prediction accuracy as a mean of 25 replicates apiece.
allNames <- read.table(file="snake/goPost/dataList", sep="")
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$cor)#snake/
allNames <- read.table(file="snake/goPost/dataList", sep="")
dataList <- allNames[,1]
front <- 'snake/data/go/33_metric/sexm/rmax0.8/rgo0.01/'
end <- '/rowData.Rds'
finalList <- paste0(front, dataList, end)
outPath <- 'snake/data/go/40_all/sexm/partData.Rds'
temp <- na.omit(readRDS(outPath))
facs <- matrix(as.factor(unlist(temp[,1:4])), ncol=4)
cors <- as.numeric(unlist(temp[,5]))
data <- data.table(facs, cors)
colnames(data) <- c('sex', 'rmax', 'rgo', 'term', 'cor')
dataM <- data[data$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', yM, 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$cor)termReader <- function(term, sex)
{
idPath <- paste0('snake/data/go/03_goterms/sex', sex,'/', term, '.Rds')
fitPath <- paste0('snake/data/go/25_fit/sex', sex,'/', term, '/bayesFull.Rds')
xpPath <- paste0('snake/data/01_matched/', sex,'_starvation.Rds')
xp <- readRDS(xpPath)
genes <- colnames(xp[,-1])
id1 <- readRDS(idPath)
model1 <- readRDS(fitPath)
fit1 <- model1$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(term, " PIP"), 'Index', 'PIP', 1)
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')nonReader <- 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(outlay)), cor=outlay, gene=genes[-id1])
plothole <- dMake(fitDataM, toupper(sex), paste0(term, " PIP"), 'Index', 'PIP', 0.1)
return(plothole)
}
nf1 <- nonReader('GO.0045819', 'f')
nf2 <- nonReader('GO.0033500', 'f')
nf3 <- nonReader('GO.0055088', 'f')
nf4 <- nonReader('GO.0042675', 'f')
nm1 <- nonReader('GO.0035008', 'm')
nm2 <- nonReader('GO.0140042', 'm')
nm3 <- nonReader('GO.0007485', 'm')
nm4 <- nonReader('GO.0005811', 'm')Female
Top Results
print(subF) term cor
1: GO.0045819 0.4382497
2: GO.0033500 0.4231229
3: GO.0055088 0.4178735
4: GO.0042675 0.4014416
5: GO.0042277 0.3994211
6: GO.0008586 0.3984704
7: GO.0016042 0.3919625
8: GO.0007368 0.3873729
9: GO.0007638 0.3856025
10: GO.0006644 0.3799043
11: GO.0010898 0.3798619
12: GO.0046488 0.3777651
13: GO.0034389 0.3753829
14: GO.0017056 0.3736580
15: GO.0006865 0.3734180
16: GO.0016056 0.3729896
17: GO.0009267 0.3709695
18: GO.0070328 0.3692209
19: GO.0061883 0.3691317
20: GO.0007458 0.3686037
21: GO.0030381 0.3684766
22: GO.0007313 0.3681358
23: GO.0008188 0.3658592
24: GO.0005518 0.3644649
25: GO.0045466 0.3643595
26: GO.0000164 0.3641153
27: GO.0046673 0.3614228
28: GO.0000055 0.3606683
29: GO.0038004 0.3605215
30: GO.0032922 0.3600324
term corPosterior Inclusion Probability of GO portion
plot_grid(f1, f2, f3, f4, ncol=2)
Posterior Inclusion Probability of non-GO portion
plot_grid(nf1, nf2, nf3, nf4, ncol=2)
A detailed look at all PIP plots can be found here.
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
Overall
plot_grid(gg[[2]], ncol=1)
Top Results
print(subM) term cor
1: GO.0035008 0.5116379
2: GO.0140042 0.5064311
3: GO.0007485 0.5062906
4: GO.0005811 0.5009641
5: GO.0045819 0.5000330
6: GO.0042461 0.4984272
7: GO.0033500 0.4984207
8: GO.0016327 0.4978650
9: GO.0035003 0.4966652
10: GO.0045186 0.4927519
11: GO.0006044 0.4924000
12: GO.0040018 0.4923077
13: GO.0042593 0.4918994
14: GO.0004402 0.4893254
15: GO.0001738 0.4882313
16: GO.0016042 0.4875109
17: GO.0045196 0.4866070
18: GO.0034333 0.4857293
19: GO.0005912 0.4853594
20: GO.0007265 0.4849572
21: GO.0006646 0.4847660
22: GO.0070328 0.4844510
23: GO.0045176 0.4840628
24: GO.0052650 0.4835012
25: GO.0045793 0.4831989
26: GO.0002009 0.4830880
27: GO.0042277 0.4812196
term corPosterior Inclusion Probability of GO portion
plot_grid(m1, m2, m3, m4, ncol=2)
Posterior Inclusion Probability of non-GO portion
plot_grid(nm1, nm2, nm3, nm4, ncol=2)
A detailed look at all PIP plots can be found here.
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:0035003 name: subapical complex
Post-processing
Beyond this, we took the models to determine if certain genes were enriched in the GO terms of interest. From the terms above the cutoff, we pooled the associated genes and totaled gene occurrence.
Females: Out of 323 genes, 37 appeared more than once and 14 appeared twice or more.
Males: Out of 448 genes, 50 appeared more than once and 14 appeared twice or more.
After establishing unique genes, we translated the FlyBase gene codes to human-readable genes.
geneMatchF <- readRDS('snake/goPost/finalDataF.Rds')
geneMatchM <- readRDS('snake/goPost/finalDataM.Rds')
hitF <- geneMatchF[which(count>2),]
hitM <- geneMatchM[which(count>2),]
parm <- dim(hitM)[1] - dim(hitF)[1]
filler <- data.table(matrix(rep(NA, 3*parm), ncol=3))
hitF <- rbind(hitF, filler, use.names=FALSE)
d1 <- cbind(hitF, hitM)
colnames(d1) <- c('Female Gene', 'Count', 'Gene', 'Male Gene', 'Count', 'Gene')
options(knitr.kable.NA = '')
kable(d1, caption = 'Top Female and Male Genes', "simple")| Female Gene | Count | Gene | Male Gene | Count | Gene |
|---|---|---|---|---|---|
| FBgn0025595 | 8 | AkhR | FBgn0261873 | 9 | sdt |
| FBgn0000575 | 7 | emc | FBgn0025595 | 6 | AkhR |
| FBgn0004552 | 4 | Akh | FBgn0067864 | 6 | Patj |
| FBgn0283499 | 4 | InR | FBgn0261854 | 6 | aPKC |
| FBgn0003731 | 4 | Egfr | FBgn0265778 | 5 | PDZ-GEF |
| FBgn0000490 | 4 | dpp | FBgn0036046 | 5 | Ilp2 |
| FBgn0003205 | 4 | Ras85D | FBgn0283499 | 5 | InR |
| FBgn0262738 | 4 | norpA | FBgn0026192 | 5 | par-6 |
| FBgn0010303 | 3 | hep | FBgn0259685 | 4 | crb |
| FBgn0015279 | 3 | Pi3K92E | FBgn0263289 | 4 | scrib |
| FBgn0033799 | 3 | GLaz | FBgn0003391 | 4 | shg |
| FBgn0036449 | 3 | bmm | FBgn0086687 | 4 | Desat1 |
| FBgn0003463 | 3 | sog | FBgn0036449 | 3 | bmm |
| FBgn0003719 | 3 | tld | FBgn0004552 | 3 | Akh |
| FBgn0003205 | 3 | Ras85D | |||
| FBgn0015279 | 3 | Pi3K92E | |||
| FBgn0041191 | 3 | Rheb | |||
| FBgn0002121 | 3 | l(2)gl | |||
| FBgn0011661 | 3 | Moe | |||
| FBgn0000163 | 3 | baz | |||
| FBgn0024248 | 3 | chico |
The following six genes are implicated in both male and female prediction.
Adipokinetic hormone + receptor:
Insulin Receptor pathway:
Others:
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