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#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])
      hold <- temp
    } else{
      hold <- rbind(hold, temp, use.names=FALSE)
  colnames(hold) <- c("method", "cor")
  hold$method <- factor(hold$method, levels=unique(hold$method))

#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) +
          axis.text.x = element_text(angle = -45, size=10),
          plot.tag = element_text(size=15)) +
    scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)

corSummary <- function(dataList, outPath){
  hold <- readRDS(dataList[1])
  for (i in 2:length(dataList)) {
    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) +
          plot.tag = element_text(size=15))

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) +
          plot.tag = element_text(size=15))
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)

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', 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')

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')


plot_grid(gg[[1]], ncol=1)

Version Author Date
8ab4763 nklimko 2023-11-28
print("Top terms: \n")
[1] "Top terms: \n"
          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       cor
plot_grid(f1, f2, f3, f4, ncol=2)

Version Author Date
8ab4763 nklimko 2023-11-28

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


plot_grid(gg[[2]], ncol=1)

Version Author Date
12d6df4 nklimko 2023-11-28
8ab4763 nklimko 2023-11-28
print("Top terms: \n")
[1] "Top terms: \n"
          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.0045186 0.4927519
10: GO.0006044 0.4924000
11: GO.0040018 0.4923077
12: GO.0042593 0.4918994
13: GO.0016042 0.4875109
14: GO.0045196 0.4866070
15: GO.0034333 0.4857293
16: GO.0005912 0.4853594
17: GO.0007265 0.4849572
18: GO.0006646 0.4847660
19: GO.0070328 0.4844510
20: GO.0052650 0.4835012
21: GO.0045793 0.4831989
          term       cor
plot_grid(m1, m2, m3, m4, ncol=2)

Version Author Date
8ab4763 nklimko 2023-11-28

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


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),]
d1 <- cbind(hitF, hitM)

colnames(d1) <- c('Female Gene', 'Count', 'Gene', 'Male Gene', 'Count', 'Gene')

kable(d1, caption = 'Top Female and Male Genes', "simple")
Top Female and Male Genes
Female Gene Count Gene Male Gene Count Gene
FBgn0025595 8 AkhR FBgn0265778 5 PDZ-GEF
FBgn0000575 7 emc FBgn0025595 5 AkhR
FBgn0004552 4 Akh FBgn0261873 5 sdt
FBgn0283499 4 InR FBgn0036046 5 Ilp2
FBgn0003731 4 Egfr FBgn0283499 5 InR
FBgn0000490 4 dpp FBgn0086687 4 Desat1
FBgn0003205 4 Ras85D FBgn0036449 3 bmm
FBgn0262738 4 norpA FBgn0004552 3 Akh
FBgn0010303 3 hep FBgn0003205 3 Ras85D
FBgn0015279 3 Pi3K92E FBgn0015279 3 Pi3K92E
FBgn0033799 3 GLaz FBgn0067864 3 Patj
FBgn0036449 3 bmm FBgn0261854 3 aPKC
FBgn0003463 3 sog FBgn0263289 3 scrib
FBgn0003719 3 tld FBgn0024248 3 chico

The following five genes are implicated in both male and female prediction. All are related to some form of lipid processing

Adipokinetic hormone + receptor:

Insulin Receptor pathway:

Brummer: triglyceride lipase:

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/

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=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