Last updated: 2020-02-27

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Knit directory: Blancetal-1/analysis/

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    Modified:   analysis/Drought-genes.Rmd
    Modified:   analysis/Identifying_quaint.Rmd

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Rmd c018f1d Em 2020-02-26 added drought info
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Intro

I’m rerunning the code to identify selected genes with the Quaint package

### function for testing all genes
testAllGenes <- function(myTissue){
# Read in mean-centered expression values
df1 <- read.table(paste("../data/Mean_centered_expression/",myTissue,".txt",sep=""))
# Read in tissue specific kinship matrix 
myF <- read.table(paste('../data/Kinship_matrices/F_',myTissue,'.txt',sep=""))

## Get Eigen Values and Vectors 
myE <- eigen(myF)
E_vectors <- myE$vectors
E_values <- myE$values

## Testing for selection on first 5 PCs
myM = 1:5

## Using the last 1/2 of PCs to estimate Va
myL = 6:dim(myF)[1]

## test for selection on each gene
allGeneOutput <- sapply(1:ncol(df1), function(x){
myQpc = calcQpc(myZ = df1[,x], myU = E_vectors, myLambdas = E_values, myL = myL, myM = myM)
return(myQpc)
})

return(allGeneOutput)
}

###run on all genes
alltissues = c('GRoot',"Kern","LMAD26","LMAN26", "L3Tip","GShoot","L3Base","LMAD8","LMAN8")
alltissueresults = lapply(alltissues, testAllGenes)
names(alltissueresults) <- alltissues



##look at sig results
sigresults = lapply(1:length(alltissues), function(i){
# extract the pvalues
pvals = matrix(unlist(alltissueresults[[i]][4,]), ncol=5, byrow=TRUE) #each row corresponds to a gene, columns are to PCs

##look at pvalue distributions
par(mfrow=c(3,2), mar=c(4,4,1,1))
sapply(1:5, function(x){
hist(pvals[,x], border="white", col = "darkgray", main="", breaks = 20, xlab = paste('PC ',x,' ',alltissues[i],sep=""))
})

## test fpr sogmofocamce
pfdr = data.frame(apply(pvals,2, function(x){p.adjust(x, method='fdr')}))

## how many are significant?
numsig <- apply(pfdr, 2, function(x){sum(x<0.1)})
numsig
})

###make a big image of how many sig results we have
sigTable = as.data.frame(matrix(unlist(sigresults), ncol=5, byrow=T))
names(sigTable) = c('PC1','PC2','PC3','PC4','PC5')
sigTable$tissue = alltissues
sigLong = tidyr::gather(sigTable, 'variable','value', -tissue)
sigLong[sigLong == 0] <- NA

pl <- ggplot(data=sigLong,aes(x=variable,y=tissue)) + 
  geom_tile(aes(fill=value),color='black') + scale_fill_gradient(low = 'lightyellow', high = "#CC79A7", guide = FALSE, na.value = "white") + theme_bw() + xlab("\n") + ylab("Tissue") + 
  theme(axis.ticks=element_blank(),panel.border=element_blank(),panel.grid.major = element_blank(), axis.text.y = element_text(size=10,angle=0), axis.title.y = element_text(size=16),axis.title.x  = element_text(size=16),axis.text.x = element_text(angle = 0, hjust = 0.5,size=14)) + geom_text(aes(label=value),colour="grey15",size=3.5)

pl
Warning: Removed 29 rows containing missing values (geom_text).

I think the main difference is the PCs that I’m using to estimate Va are different from Jennifer’s (she uses 11:20). Let’s check.

### function for testing all genes
testAllGenes1120 <- function(myTissue){
# Read in mean-centered expression values
df1 <- read.table(paste("../data/Mean_centered_expression/",myTissue,".txt",sep=""))
# Read in tissue specific kinship matrix 
myF <- read.table(paste('../data/Kinship_matrices/F_',myTissue,'.txt',sep=""))

## Get Eigen Values and Vectors 
myE <- eigen(myF)
E_vectors <- myE$vectors
E_values <- myE$values

## Testing for selection on first 5 PCs
myM = 1:5

## Using the last 1/2 of PCs to estimate Va
myL = 11:20

## test for selection on each gene
allGeneOutput <- sapply(1:ncol(df1), function(x){
myQpc = calcQpc(myZ = df1[,x], myU = E_vectors, myLambdas = E_values, myL = myL, myM = myM)
return(myQpc)
})

return(allGeneOutput)
}



alltissueresults1120 = lapply(alltissues, testAllGenes1120)
names(alltissueresults1120) <- alltissues



##look at sig results
sigresults1120 = lapply(1:length(alltissues), function(i){
# extract the pvalues
pvals = matrix(unlist(alltissueresults1120[[i]][4,]), ncol=5, byrow=TRUE) #each row corresponds to a gene, columns are to PCs

##look at pvalue distributions
par(mfrow=c(3,2), mar=c(4,4,1,1))
sapply(1:5, function(x){
hist(pvals[,x], border="white", col = "darkgray", main="", breaks = 20, xlab = paste('PC ',x,' ',alltissues[i],sep=""))
})

## test fpr sogmofocamce
pfdr = data.frame(apply(pvals,2, function(x){p.adjust(x, method='fdr')}))

## how many are significant?
numsig <- apply(pfdr, 2, function(x){sum(x<0.1)})
numsig
})

###make a big image of how many sig results we have
sigTable1120 = as.data.frame(matrix(unlist(sigresults1120), ncol=5, byrow=T))
names(sigTable1120) = c('PC1','PC2','PC3','PC4','PC5')
sigTable1120$tissue = alltissues
sigLong1120 = tidyr::gather(sigTable1120, 'variable','value', -tissue)


pl <- ggplot(data=sigLong1120,aes(x=variable,y=tissue)) + 
  geom_tile(aes(fill=value),color='black') + scale_fill_gradient(low = 'lightyellow', high = "#CC79A7", guide = FALSE, na.value = "white") + theme_bw() + xlab("\n") + ylab("Tissue") + 
  theme(axis.ticks=element_blank(),panel.border=element_blank(),panel.grid.major = element_blank(), axis.text.y = element_text(size=10,angle=0), axis.title.y = element_text(size=16),axis.title.x  = element_text(size=16),axis.text.x = element_text(angle = 0, hjust = 0.5,size=14)) + geom_text(aes(label=value),colour="grey15",size=3.5)

pl


sessionInfo()
R version 3.6.2 (2019-12-12)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

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_1.0.2       quaint_0.0.0.9000 ggpubr_0.2.5      magrittr_1.5     
[5] reshape2_1.4.3    ggplot2_3.2.1    

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3       plyr_1.8.5       compiler_3.6.2   pillar_1.4.3    
 [5] later_1.0.0      git2r_0.26.1     highr_0.8        workflowr_1.6.0 
 [9] tools_3.6.2      digest_0.6.25    evaluate_0.14    lifecycle_0.1.0 
[13] tibble_2.1.3     gtable_0.3.0     pkgconfig_2.0.3  rlang_0.4.4     
[17] yaml_2.2.1       xfun_0.12        withr_2.1.2      stringr_1.4.0   
[21] dplyr_0.8.4      knitr_1.28       vctrs_0.2.3      fs_1.3.1        
[25] rprojroot_1.3-2  grid_3.6.2       tidyselect_1.0.0 glue_1.3.1      
[29] R6_2.4.1         rmarkdown_2.1    farver_2.0.3     purrr_0.3.3     
[33] whisker_0.4      ellipsis_0.3.0   backports_1.1.5  scales_1.1.0    
[37] promises_1.1.0   htmltools_0.4.0  assertthat_0.2.1 colorspace_1.4-1
[41] ggsignif_0.6.0   httpuv_1.5.2     stringi_1.4.6    lazyeval_0.2.2  
[45] munsell_0.5.0    crayon_1.3.4