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Multiple Comparison

Starvation was plotted against every other trait and correlations were calculated. Ideally, strong correlation between traits can be used to increase the impact of PCA as traits with strong correlations can be used to reduce dimensions. Strong correlations allow for calculated columns.

Female Traits

# Read in tables
dtf <- fread("data/eQTL_traits_females.csv")

#Change column order to line, starvation, everything else
setcolorder(dtf, c(1,10,2:9,11:19))

#Storage structures 
corKeep <- rep(0,18)
gg <- vector(mode='list', length=18)

# x axis, starvation
x <- dtf[,2,with=FALSE]

# x Label
xL <- "starvation"

# Determine average of each trait
for(i in 3:19)
{
  #grab column of interest
  y <- dtf[,i,with=FALSE]

  #grab trait name from column name
  yL <- colnames(dtf[, i, with=FALSE])

  #extract non NULL paired data, set to data table, rename columns
  raw <- na.omit(cbind(x,y))
  clean <- setDT(raw)
  colnames(clean) <- c("starvation", "trait")

  #grab correlation coefficient using cor.test
  corCoeff <- str_sub(as.character(cor.test(clean$starvation, clean$trait)[4]), 8,16)
  corKeep[i-1] <- as.numeric(corCoeff)

  #create graph title using y label and include correlation coefficient
  graphTitle <- paste0(yL,": R=",corCoeff)

  #gg plot commands: starvation vs trait, plot dots, trend line, and labels
  gg[[i-1]] <- ggplot(clean, aes(x=starvation, y=trait)) +
  geom_point(color="red") +
  geom_smooth(formula = y ~ x, method=lm) +
  labs(title = graphTitle, x=xL, y=yL)
}

#reorder plots by absolute value of correlation coefficient
gg <- cbind(gg,corKeep)
gg <- gg[order(abs(corKeep),decreasing=TRUE),]
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Male Traits

# Read in tables
dtm <- fread("data/eQTL_traits_males.csv")

#Change column order to line, starvation, everything else
setcolorder(dtm, c(1,11,3:10,12:20,2))

#Storage structures 
corKeep <- rep(0,18)
gg <- vector(mode='list', length=18)

# x axis, starvation
x <- dtm[,2,with=FALSE]

# x Label
xL <- "starvation"

# Determine average of each trait
for(i in 3:20)
{
  #grab column of interest
  y <- dtm[,i,with=FALSE]

  #grab trait name from column name
  yL <- colnames(dtm[, i, with=FALSE])

  #extract non NULL paired data, set to data table, rename columns
  raw <- na.omit(cbind(x,y))
  clean <- setDT(raw)
  colnames(clean) <- c("starvation", "trait")

  #grab correlation coefficient using cor.test
  corCoeff <- str_sub(as.character(cor.test(clean$starvation, clean$trait)[4]), 8,16)
  corKeep[i-1] <- as.numeric(corCoeff)

  #create graph title using y label and include correlation coefficient
  graphTitle <- paste0(yL,": R=",corCoeff)

  #gg plot commands: starvation vs trait, plot dots, trend line, and labels
  gg[[i-1]] <- ggplot(clean, aes(x=starvation, y=trait)) +
  geom_point() +
  geom_smooth(formula = y ~ x, method=lm) +
  labs(title = graphTitle, x=xL, y=yL)
}

#reorder plots by absolute value of correlation coefficient
gg <- cbind(gg,corKeep)
gg <- gg[order(abs(corKeep),decreasing=TRUE),]
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Mean Expression

#read in data
xpf <- fread("data/xp-f.txt")

#remove line, non numeric
xpf <- xpf[,-1]

#colMeans for means of all columns
xpfMean <- colMeans(xpf, na.rm=TRUE)

#sapply to calculate variance of every column
xpfVar <- sapply(xpf, var)

#store final info
xpfStats <- data.table(xpfMean, xpfVar)

#selection of upper regions of histograms
ffilt <- xpfStats[order(xpfStats$xpfVar, decreasing=TRUE)]

ftrim <- ffilt[1:250]
#read in data
xpm <- fread("data/xp-m.txt")

#remove line, non numeric
xpm <- xpm[,-1]

#colMeans for means of all columns
xpmMean <- colMeans(xpm, na.rm=TRUE)

#sapply to calculate variance of every column
xpmVar <- sapply(xpm, var)

#store final info
xpmStats <- data.table(xpmMean, xpmVar)

#selection of upper regions of histograms
mfilt <- xpmStats[order(xpmStats$xpmVar, decreasing=TRUE)]

mtrim <- mfilt[1:250]

Gene Counts

#list input for venn diagram
A <- list('Female'=colnames(xpf), 'Male'=colnames(xpm))

#Venn diagram of gene counts
ggvenn(A, show_percentage = FALSE)

# means converted to data tables for easier join
fMeans <- as.data.table(xpfMean, keep.rownames = TRUE)
mMeans <- as.data.table(xpmMean, keep.rownames = TRUE)

#joined means, nulls omitted
aMeans <- fMeans[mMeans, on = .(rn), nomatch=NULL]

#correlation of male to female expression
corCoeff <- str_sub(as.character(cor.test(aMeans$xpfMean, aMeans$xpmMean)[4]), 8,16)

#gg plot commands: starvation vs trait, plot dots, trend line, and labels
ggplot(aMeans, aes(x=xpfMean, y=xpmMean)) +
geom_point(color="purple") +
geom_smooth(formula = y ~ x, method=lm) +
labs(x="Female Expression", y="Female Expression", subtitle=paste0("R: ",corCoeff)) +
ggtitle("Male vs Female Mean Expression")
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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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] ggvenn_0.1.9      cowplot_1.1.1     ggplot2_3.3.5     data.table_1.14.2
[5] stringr_1.4.0     dplyr_1.0.8       workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.2 xfun_0.30        bslib_0.3.1      purrr_0.3.4     
 [5] lattice_0.20-45  splines_4.0.3    colorspace_2.0-3 vctrs_0.4.1     
 [9] generics_0.1.2   htmltools_0.5.2  mgcv_1.8-40      yaml_2.3.5      
[13] utf8_1.2.2       rlang_1.0.4      later_1.3.0      pillar_1.7.0    
[17] jquerylib_0.1.4  withr_2.5.0      glue_1.6.2       DBI_1.1.2       
[21] lifecycle_1.0.1  munsell_0.5.0    gtable_0.3.0     evaluate_0.15   
[25] labeling_0.4.2   knitr_1.38       callr_3.7.0      fastmap_1.1.0   
[29] httpuv_1.6.5     ps_1.6.0         fansi_1.0.3      highr_0.9       
[33] Rcpp_1.0.8.3     promises_1.2.0.1 scales_1.2.0     jsonlite_1.8.0  
[37] farver_2.1.0     fs_1.5.2         digest_0.6.29    stringi_1.7.6   
[41] processx_3.5.3   getPass_0.2-2    rprojroot_2.0.3  cli_3.3.0       
[45] tools_4.0.3      magrittr_2.0.3   sass_0.4.1       tibble_3.1.6    
[49] crayon_1.5.1     whisker_0.4      pkgconfig_2.0.3  Matrix_1.4-1    
[53] ellipsis_0.3.2   assertthat_0.2.1 rmarkdown_2.16   httr_1.4.2      
[57] rstudioapi_0.13  R6_2.5.1         nlme_3.1-157     git2r_0.30.1    
[61] compiler_4.0.3