Starvation Resistance Analysis
Last updated: 2022-11-10
Checks: 5 2
Knit directory: dgrp-starve/
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| /data/morgante_lab/nklimko/rep/dgrp-starve/data/snpList.txt | data/snpList.txt |
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| File | Version | Author | Date | Message |
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| Rmd | 81037cc | nklimko | 2022-11-09 | html reworking, snp success |
| html | 81037cc | nklimko | 2022-11-09 | html reworking, snp success |
| Rmd | 41de4a8 | nklimko | 2022-11-08 | difMinus success, reframe for all four genotype analysis |
| html | 41de4a8 | nklimko | 2022-11-08 | difMinus success, reframe for all four genotype analysis |
| Rmd | d2abea6 | nklimko | 2022-11-05 | 11/5 Phase plan, data prep/analysis nested steps and pseudo for script |
| html | d2abea6 | nklimko | 2022-11-05 | 11/5 Phase plan, data prep/analysis nested steps and pseudo for script |
| Rmd | d0a5cb1 | nklimko | 2022-11-05 | Baseline analysis 11/5, horrendous formatting |
About
details background information and the premise of this project
Starvation Resistance Curation
Gathering of starvation resistance data from female and male lines and column calculation
Data was extracted from two tables of eQTL male/female traits.
First, each file was filtered for non-null values
# csvPath dependent upon female/male file
allRaw <- tibble(read.csv(csvPath))
starveRaw <- allRaw %>% select(line,starvation) %>% filter(!is.na(starvation))Two tables, starveF and starveM, were then joined into one. Difference and average of each line were calculated and added
#Combine mf
starve <- starveF %>% mutate(m = starveM$m)
#compute difference and average
starve <- starve %>% select(line, f, m) %>% mutate(dif = f - m, avg = (f+m)/2)Analysis of SR by Sex
Standard analysis of distribution, normality, and trends within the base data
Histograms of Starvation Resistance
First impressions on histograms are that both are unimodal with light skew to the right.
Comparative Boxplot
Plotting the distributions on the same axis gives a more accurate representation of distribution between sexes. The average female has a much greate starvation resistance of 60.665 compared to the average male 45.732. Outliers to the right of both boxplots indicate right skew.
Scatter Plot
The trendline is a Simple Linear Regression ‘reg=lm(y~x)’ with R and p values extracted from summary statistics. The slope indicates a positive correlation between male and female starvation resistance while the R value of 0.4693 indicates that the correlation is not close to linear.
QQ Plots
There is some systematic deviation from linearity on both tails in Females and on the upper tail in Males. Eyeballing is not an exhaustive measure of discerning normality. Quantitative methods are needed:
Shapiro-Wilk Tests for Normality
The Shapiro-Wilk method was chosen as it retains the best power
The test statistic formula is: \[ W = \frac{(\sum_{i=1}^{n}a_ix_{(i)})^{2}}{\sum_{i=1}^{n}(x_i-\overline x)^{2}} \]
More about the Shapiro-Wilk test statistic can be found here.
\(W\) values closer to 1 indicate normality, with \(W = 1\) being perfectly normal.
Shapiro-Wilk normality test
data: Female_Starvation
W = 0.97662, p-value = 0.001826
Shapiro-Wilk normality test
data: Male_Starvation
W = 0.98745, p-value = 0.07023At an \(\alpha\) level of 0.05, we have sufficient evidence to reject that the Female population is normally distributed as p < \(\alpha\) (0.001826 < 0.05).
At an \(\alpha\) level of 0.05, we do not have sufficient evidence to reject that the Male population is normally distributed as p > \(\alpha\) (0.07023 > 0.05). Notably the correlation is not strong as the p-value is close to 0.05.
Group selection
The two groups of interest were extreme values on both ends.
Scatter Plot
Along with this, I wanted to look at the highest and lowest averages. Twenty lines from the top and bottom were partitioned for further analysis. With these four groups, I sought to look for difference in genotype and fold-change in gene expression hereLINK to PHASE ??? not sure yet
Selecting Ingroups
Manual selection of groups of interest for futher comparison using additional data layers
Lines were manually selected and hard coded into the following extractions.
#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")SNP Extraction
Selection of SNPs by comparison of prevalence in chosen sample versus remaining lines
##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)SNP Curation
Combination of all sample SNPs to streamline analysis
# Set max number of results
index <- 50
#read in "index" number of results, set above
dM <- fread("../data/difMinus-result.txt", nrows = index)
dP <- fread("../data/difPlus-result.txt", nrows = index)
aM <- fread("../data/avgMinus-result.txt", nrows = index)
aP <- fread("../data/avgPlus-result.txt", nrows = index)
#mark origin of SNP
dM[,ori:="difMinus"]
dP[,ori:="difPlus"]
aM[,ori:="avgMinus"]
aP[,ori:="avgPlus"]
#combine tables
snpList <- rbind(dM, dP, aM, aP)
#remove data columns by assigning to NULL
snpList[,':='(inMean=NULL, outMean=NULL, result=NULL)]
#Save file
fwrite(snpList, "/data/morgante_lab/nklimko/rep/dgrp-starve/data/snpList.txt")Gene Extraction
Mapping extracted SNPs to genes using flybase annotations
### 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)]
#write file
fwrite(geneHits, "/data/morgante_lab/nklimko/rep/dgrp-starve/data/geneHits.txt")GO Term Extraction
Compilation of GO terms from affected genes to measure impact
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] tidyr_1.2.0 stringr_1.4.0 data.table_1.14.2 dplyr_1.0.8
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 pillar_1.7.0 compiler_4.0.3 bslib_0.3.1
[5] later_1.3.0 jquerylib_0.1.4 git2r_0.30.1 highr_0.9
[9] workflowr_1.7.0 tools_4.0.3 digest_0.6.29 jsonlite_1.8.0
[13] evaluate_0.15 lifecycle_1.0.1 tibble_3.1.6 pkgconfig_2.0.3
[17] rlang_1.0.4 DBI_1.1.2 cli_3.3.0 rstudioapi_0.13
[21] yaml_2.3.5 xfun_0.30 fastmap_1.1.0 knitr_1.38
[25] generics_0.1.2 fs_1.5.2 vctrs_0.4.1 sass_0.4.1
[29] tidyselect_1.1.2 rprojroot_2.0.3 glue_1.6.2 R6_2.5.1
[33] fansi_1.0.3 rmarkdown_2.16 purrr_0.3.4 magrittr_2.0.3
[37] whisker_0.4 promises_1.2.0.1 ellipsis_0.3.2 htmltools_0.5.2
[41] assertthat_0.2.1 httpuv_1.6.5 utf8_1.2.2 stringi_1.7.6
[45] crayon_1.5.1