Last updated: 2022-11-22
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Knit directory: dgrp-starve/
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| /data/morgante_lab/nklimko/rep/dgrp-starve/data/starve.csv | data/starve.csv |
| /data/morgante_lab/nklimko/rep/dgrp-starve/data/INPUT.txt | data/INPUT.txt |
| /data/morgante_lab/nklimko/rep/dgrp-starve/data/CHANGE.txt | data/CHANGE.txt |
| /data/morgante_lab/nklimko/rep/dgrp-starve/data/snpList.txt | data/snpList.txt |
| /data/morgante_lab/nklimko/rep/dgrp-starve/data/geneHits.txt | data/geneHits.txt |
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##### Libraries
library(dplyr)
library(tidyr)
### Function: create tibble from data path
antiNull <- function(csvPath){
#Read in table
allRaw <- tibble(read.csv(csvPath))
#filter for non-null values
starveRaw <- allRaw %>% select(line,starvation) %>% filter(!is.na(starvation))
return(starveRaw)
}
#Female data
csvPathF <- "/data/morgante_lab/data/dgrp/phenotypes/eQTL_traits_females.csv"
starveF <- antiNull(csvPathF)
#Male data
csvPathM <- "/data/morgante_lab/data/dgrp/phenotypes/eQTL_traits_males.csv"
starveM <- antiNull(csvPathM)
#rename columns for combination
colnames(starveF) <- c("line", "f")
colnames(starveM) <- c("line", "m")
#Combine data
starve <- starveF %>% mutate(m = starveM$m)
#compute difference and average
starve <- starve %>% select(line, f, m) %>% mutate(dif = f - m, avg = (f+m)/2)
#Sort by difference, cosmetic
starve <- arrange(starve, starve$dif)
#Save, colnames are lin,f,m,dif,avg
write.csv(starve, "/data/morgante_lab/nklimko/rep/dgrp-starve/data/starve.csv")#Libraries and setup
library(dplyr)
library(data.table)
library(tidyr)
starve <- tibble(read.csv("/data/morgante_lab/nklimko/rep/dgrp-starve/data/starve.csv")) %>% select(-X)
x <- starve$f
y <- starve$m
#plot parameters
par(mfrow=c(1,2))
#Female Histogram
hist(x,
col="red",
border="black",
prob=TRUE,
xlab = "Starvation Resistance",
main = "Female Lines")
lines(density(x),
lwd = 2,
col = "black")
#Male Histogram
hist(y,
col="blue",
border="black",
prob=TRUE,
xlab = "Starvation Resistance",
main = "Male Lines")
lines(density(y),
lwd = 2,
col = "black")
#Summary stats
cbind(summary(x), summary(y))
# Comparative Boxplot
boxplot(x,
y,
col = c("red","blue"),
names=c("Female", "Male"),
main="Sex Comparison of Starvation Resistance",
xlab="Starvation Resistance",
ylab="Sex",
horizontal = TRUE)
# Scatter Plot
plot(x,
y,
col="black",
xlab="Female Starvation Resistance",
ylab="Male Starvation Resistance",
abline(reg=lm(y~x),
col="purple"))
text(x = 85, y = 25,
"y = 14.2347x + 0.5192",
cex = 0.75)
text(x = 85, y = 22.5,
"R=0.4693, p-value < 2.2e-16",
cex = 0.75)
#Trendline parameter determination
summary(lm(y~x))
# QQ Plots
#plot parameters
par(mfrow=c(1,2))
qqnorm(x, main="Female Distribution")
qqline(x)
qqnorm(y, main="Male Distribution")
qqline(y)
#Shapiro-Wilk Normality Tests
Female_Starvation <- x
Male_Starvation <- y
shapiro.test(Female_Starvation)
shapiro.test(Male_Starvation)
### Group selection
#plot parameters
par(mfrow=c(1,2))
#Difference
hist(starve$dif,
col="purple",
border="black",
xlab = "Change in Starvation Resistance",
main = "Difference of Female and Male Lines")
#Average
hist(starve$avg,
col="pink",
border="black",
xlab = "Average Starvation Resistance",
main = "Avg of Female and Male Lines")
### Scatter Plot
plot(starve$avg,
starve$dif,
col="black",
xlab="Line Average",
ylab="Intersex Difference",
abline(reg=lm(starve$dif~starve$avg),
col="purple"))
text(x = 72, y = -12.5,
"y = 0.3258 - 2.3984",
cex = 0.75)
text(x = 72, y = -15,
"R=0.1293, p-value < 1.37e-7",
cex = 0.75)
#Trendline parameter determination
summary(lm(starve$dif~starve$avg))##### Libraries
library(dplyr)
library(tidyr)
library(data.table)
#Read in data
starve <- tibble(read.csv("data/starve.csv")) %>% select(-X)
#Manual inspection of data to create cutoffs for ingroups
print(arrange(starve, starve$dif), n=205)
print(arrange(starve, starve$avg), n=205)
# Lines with male SR high than female SR
difMinus <- starve %>% filter(dif < 0) %>% arrange(line) %>% select(line)
difMinus <- as.data.table(difMinus)
fwrite(difMinus, "../data/difMinus.txt")
# Lines with largest gap from male SR to female SR
difPlus <- starve %>% filter(dif >= 30) %>% arrange(line) %>% select(line)
difPlus <- as.data.table(difPlus)
fwrite(difPlus, "../data/difPlus.txt")
# Lowest average SR between males and females
avgMinus <- starve %>% filter(avg < 40) %>% arrange(line) %>% select(line)
avgMinus <- as.data.table(avgMinus)
fwrite(avgMinus, "../data/avgMinus.txt")
# Highest average SR between males and females
avgPlus <- starve %>% filter(dif >= 30) %>% arrange(line) %>% select(line)
avgPlus <- as.data.table(avgPlus)
fwrite(avgPlus, "../data/avgPlus.txt")### Libraries
library("data.table")
library("dplyr")
##change per job
dataPath <- "/data/morgante_lab/data/dgrp/genotypes/dgrp2_tgeno_filtered_meanimputed.txt"
targetPath <- "/data/morgante_lab/nklimko/rep/dgrp-starve/data/INPUT.txt"
finalPath <- "/data/morgante_lab/nklimko/rep/dgrp-starve/data/CHANGE.txt"
#number of results to save
finalCount <- 200
#files read in
data <- fread(dataPath)
inGroup <- fread(targetPath)
#modification of inGroup to vector type, clunky
inGroup <- inGroup[,line]
#IDs saved and removed
var_id <- data[,var_id]
data <- data[, var_id:=NULL]
#data partitioned by column
inData <- data[,colnames(data) %in% inGroup, with=FALSE]
outData <- data[,!(colnames(data) %in% inGroup), with=FALSE]
#means calculated for every row
inMean <- rowMeans(inData)
outMean <- rowMeans(outData)
#table constructed of marker IDs and group means
phaseA <- data.table(var_id, inMean, outMean)
#calculates difference of group means
trueData <- phaseA[, result:=inMean - outMean]
#trueData <- phaseA[, result:=inMean / outMean]
#orders data by absolute value of difference, largest first
trueSort <- trueData[order(-abs(result))]
#index controls number of top results to save
finalSet<- trueSort[1:finalCount]
#writes top (finalCount) to designated path
fwrite(finalSet, finalPath)### Libraries
library(data.table)
library(dplyr)
library(stringr)
#input paths
genePath <- "/data/databases/flybase/fb-r5.57/fb-r5.57.clean.gene.bound"
snpPath <- "/data/morgante_lab/nklimko/rep/dgrp-starve/data/snpList.txt"
#read data in
genBank <- fread(genePath, col.names = c("leg","start", "stop", "strand","gene"))
snpList <- fread(snpPath)
#split SNP tag to search gene data correctly
snpList[, arm := tstrsplit(var_id, split="_",fixed = TRUE)[1]]
snpList[, pos := tstrsplit(var_id, split="_",fixed = TRUE)[2]]
snpList[, pos := as.numeric(pos)]
#set start and stop to numeric type
genBank[, ':='(start=as.numeric(start), stop=as.numeric(stop))]
#extract gene where position contained and arm matches
geneHits <- genBank[snpList, on = .(start <= pos, stop >= pos,leg==arm), .(ori, geneFind = gene)][!is.na(geneFind)]
#save file
fwrite(geneHits, "/data/morgante_lab/nklimko/rep/dgrp-starve/data/geneHits.txt")### Libraries
library(data.table)
library(dplyr)
#input paths
goPath <- "/data/databases/flybase/fb-r5.57/gene_go.table"
genePath <- "/data/morgante_lab/nklimko/rep/dgrp-starve/data/geneHits.txt"
#read data in
genList <- fread(genePath, col.names = c("ori", "gene"))
goBank <- fread(goPath, col.names = c("gene", "mf", "bp", "cc"))
#match go terms to gene
genList <- goBank[genList, on = .(gene)]
#save
fwrite(genList, "./goGroups.txt")
#No origin
table(genList[,mf])
table(genList[,cc])
table(genList[,bp])
##This one
table(genList[,gene, by=ori])
table(genList[,mf, by=ori])
table(genList[,cc, by=ori])
table(genList[,bp, by=ori])
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/Software/openblas_0.3.10/lib/libopenblas_haswellp-r0.3.10.dev.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] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 highr_0.9 bslib_0.3.1 jquerylib_0.1.4
[5] compiler_4.0.3 pillar_1.7.0 later_1.3.0 git2r_0.30.1
[9] tools_4.0.3 getPass_0.2-2 digest_0.6.29 jsonlite_1.8.0
[13] evaluate_0.15 tibble_3.1.6 lifecycle_1.0.1 pkgconfig_2.0.3
[17] rlang_1.0.4 cli_3.3.0 rstudioapi_0.13 yaml_2.3.5
[21] xfun_0.30 fastmap_1.1.0 httr_1.4.2 stringr_1.4.0
[25] knitr_1.38 sass_0.4.1 fs_1.5.2 vctrs_0.4.1
[29] rprojroot_2.0.3 glue_1.6.2 R6_2.5.1 processx_3.5.3
[33] fansi_1.0.3 rmarkdown_2.16 callr_3.7.0 magrittr_2.0.3
[37] whisker_0.4 ps_1.6.0 promises_1.2.0.1 htmltools_0.5.2
[41] ellipsis_0.3.2 httpuv_1.6.5 utf8_1.2.2 stringi_1.7.6
[45] crayon_1.5.1