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Sex_bias_edgeR.R
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Sex_bias_edgeR.R
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##############################################################################
### install
##source("http://bioconductor.org/biocLite.R")
## biocLite("edgeR")
##############################################################################
### library
library("edgeR")
library("VennDiagram")
library(gridExtra)
library(grid)
library(ggplot2)
library(lattice)
library(cowplot)
library(stringr)
library(gtable)
library(pheatmap)
library(RColorBrewer)
require(vegan)
library(pvclust)
library(raster)
library("SuperExactTest")
print (sessionInfo())
# R version 3.4.1 (2017-06-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.3
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# attached base packages:
# [1] grid stats graphics grDevices utils datasets methods base
# other attached packages:
# [1] SuperExactTest_0.99.4 raster_2.5-8 sp_1.2-5 pvclust_2.0-0 vegan_2.4-4 permute_0.9-4 RColorBrewer_1.1-2 pheatmap_1.0.8 gtable_0.2.0 stringr_1.2.0 cowplot_0.8.0
# [12] lattice_0.20-35 ggplot2_2.2.1 gridExtra_2.3 VennDiagram_1.6.17 futile.logger_1.4.3 edgeR_3.18.1 limma_3.32.10
# loaded via a namespace (and not attached):
# [1] Rcpp_0.12.13 cluster_2.0.6 magrittr_1.5 MASS_7.3-47 munsell_0.4.3 colorspace_1.3-2 rlang_0.1.6 plyr_1.8.4 tools_3.4.1 parallel_3.4.1 nlme_3.1-131
# [12] mgcv_1.8-22 lambda.r_1.2 lazyeval_0.2.1 tibble_1.3.4 Matrix_1.2-11 futile.options_1.0.0 stringi_1.1.5 compiler_3.4.1 scales_0.5.0 locfit_1.5-9.1
# >
##############################################################################
# Data
rawdata_Tbi_RBBH <- read.csv("Data/Read_counts/Tbi_Tte_RBBH_orth_counts_Arthropoda+Mixed+NOBLASTHIT_K2edge.counts", check.names=FALSE, stringsAsFactors=FALSE)
rawdata_Tce_RBBH <- read.csv("Data/Read_counts/Tce_Tms_RBBH_orth_counts_Arthropoda+Mixed+NOBLASTHIT_K2edge.counts", check.names=FALSE, stringsAsFactors=FALSE)
rawdata_Tcm_RBBH <- read.csv("Data/Read_counts/Tcm_Tsi_RBBH_orth_counts_Arthropoda+Mixed+NOBLASTHIT_K2edge.counts", check.names=FALSE, stringsAsFactors=FALSE)
rawdata_Tpa_RBBH <- read.csv("Data/Read_counts/Tpa_Tge_RBBH_orth_counts_Arthropoda+Mixed+NOBLASTHIT_K2edge.counts", check.names=FALSE, stringsAsFactors=FALSE)
rawdata_Tps_RBBH <- read.csv("Data/Read_counts/Tps_Tdi_RBBH_orth_counts_Arthropoda+Mixed+NOBLASTHIT_K2edge.counts", check.names=FALSE, stringsAsFactors=FALSE)
head(rawdata_Tbi_RBBH)
head(rawdata_Tce_RBBH)
head(rawdata_Tcm_RBBH)
head(rawdata_Tpa_RBBH)
head(rawdata_Tps_RBBH)
colnames(rawdata_Tbi_RBBH)
length(colnames(rawdata_Tbi_RBBH))
dir.create("Output")
dir.create("Output/DE")
setwd("Output/DE")
###### DE analysis DOING for each tissue seperatly
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
#### WB
## for SB
y_WB_SB_RBBH_Tbi_UF <- DGEList(counts=rawdata_Tbi_RBBH[,c("Tbi_SF_WB_Md_Re1","Tbi_SF_WB_Md_Re2","Tbi_SF_WB_Md_Re3","Tbi_SM_WB_Md_Re1","Tbi_SM_WB_Md_Re2","Tbi_SM_WB_Md_Re3")], genes=rawdata_Tbi_RBBH[,1:1])
y_WB_SB_RBBH_Tce_UF <- DGEList(counts=rawdata_Tce_RBBH[,c("Tce_SF_WB_Md_Re1","Tce_SF_WB_Md_Re2","Tce_SF_WB_Md_Re3","Tce_SM_WB_Md_Re1","Tce_SM_WB_Md_Re2","Tce_SM_WB_Md_Re3")], genes=rawdata_Tce_RBBH[,1:1])
y_WB_SB_RBBH_Tcm_UF <- DGEList(counts=rawdata_Tcm_RBBH[,c("Tcm_SF_WB_Md_Re1","Tcm_SF_WB_Md_Re2","Tcm_SF_WB_Md_Re3","Tcm_SM_WB_Md_Re1","Tcm_SM_WB_Md_Re2","Tcm_SM_WB_Md_Re3")], genes=rawdata_Tcm_RBBH[,1:1])
y_WB_SB_RBBH_Tpa_UF <- DGEList(counts=rawdata_Tpa_RBBH[,c("Tpa_SF_WB_Md_Re1","Tpa_SF_WB_Md_Re2","Tpa_SF_WB_Md_Re3","Tpa_SM_WB_Md_Re1","Tpa_SM_WB_Md_Re2","Tpa_SM_WB_Md_Re3")], genes=rawdata_Tpa_RBBH[,1:1])
y_WB_SB_RBBH_Tps_UF <- DGEList(counts=rawdata_Tps_RBBH[,c("Tps_SF_WB_Md_Re1","Tps_SF_WB_Md_Re2","Tps_SF_WB_Md_Re3","Tps_SM_WB_Md_Re1","Tps_SM_WB_Md_Re2","Tps_SM_WB_Md_Re3")], genes=rawdata_Tps_RBBH[,1:1])
## for sex-asex
y_WB_SA_RBBH_Tbi_UF <- DGEList(counts=rawdata_Tbi_RBBH[,c("Tbi_SF_WB_Md_Re1","Tbi_SF_WB_Md_Re2","Tbi_SF_WB_Md_Re3","Tte_AF_WB_Vi_Re1","Tte_AF_WB_Vi_Re2","Tte_AF_WB_Vi_Re3")], genes=rawdata_Tbi_RBBH[,1:1])
y_WB_SA_RBBH_Tce_UF <- DGEList(counts=rawdata_Tce_RBBH[,c("Tce_SF_WB_Md_Re1","Tce_SF_WB_Md_Re2","Tce_SF_WB_Md_Re3","Tms_AF_WB_Vi_Re1","Tms_AF_WB_Vi_Re2","Tms_AF_WB_Vi_Re3")], genes=rawdata_Tce_RBBH[,1:1])
y_WB_SA_RBBH_Tcm_UF <- DGEList(counts=rawdata_Tcm_RBBH[,c("Tcm_SF_WB_Md_Re1","Tcm_SF_WB_Md_Re2","Tcm_SF_WB_Md_Re3","Tsi_AF_WB_Vi_Re1","Tsi_AF_WB_Vi_Re2","Tsi_AF_WB_Vi_Re3")], genes=rawdata_Tcm_RBBH[,1:1])
y_WB_SA_RBBH_Tpa_UF <- DGEList(counts=rawdata_Tpa_RBBH[,c("Tpa_SF_WB_Md_Re1","Tpa_SF_WB_Md_Re2","Tpa_SF_WB_Md_Re3","Tge_AF_WB_Vi_Re1","Tge_AF_WB_Vi_Re2","Tge_AF_WB_Vi_Re3")], genes=rawdata_Tpa_RBBH[,1:1])
y_WB_SA_RBBH_Tps_UF <- DGEList(counts=rawdata_Tps_RBBH[,c("Tps_SF_WB_Md_Re1","Tps_SF_WB_Md_Re2","Tps_SF_WB_Md_Re3","Tdi_AF_WB_Vi_Re1","Tdi_AF_WB_Vi_Re2","Tdi_AF_WB_Vi_Re3")], genes=rawdata_Tps_RBBH[,1:1])
#### RT
## for SB
y_RT_SB_RBBH_Tbi_UF <- DGEList(counts=rawdata_Tbi_RBBH[,c("Tbi_SF_RT_Md_Re1","Tbi_SF_RT_Md_Re2","Tbi_SF_RT_Md_Re3","Tbi_SM_RT_Md_Re1","Tbi_SM_RT_Md_Re2","Tbi_SM_RT_Md_Re3")], genes=rawdata_Tbi_RBBH[,1:1])
y_RT_SB_RBBH_Tce_UF <- DGEList(counts=rawdata_Tce_RBBH[,c("Tce_SF_RT_Md_Re1","Tce_SF_RT_Md_Re2","Tce_SF_RT_Md_Re3","Tce_SM_RT_Md_Re1","Tce_SM_RT_Md_Re2","Tce_SM_RT_Md_Re3")], genes=rawdata_Tce_RBBH[,1:1])
y_RT_SB_RBBH_Tcm_UF <- DGEList(counts=rawdata_Tcm_RBBH[,c("Tcm_SF_RT_Md_Re1","Tcm_SF_RT_Md_Re2","Tcm_SF_RT_Md_Re3","Tcm_SM_RT_Md_Re1","Tcm_SM_RT_Md_Re2","Tcm_SM_RT_Md_Re3")], genes=rawdata_Tcm_RBBH[,1:1])
y_RT_SB_RBBH_Tpa_UF <- DGEList(counts=rawdata_Tpa_RBBH[,c("Tpa_SF_RT_Md_Re1","Tpa_SF_RT_Md_Re2","Tpa_SF_RT_Md_Re3","Tpa_SM_RT_Md_Re1","Tpa_SM_RT_Md_Re2","Tpa_SM_RT_Md_Re3")], genes=rawdata_Tpa_RBBH[,1:1])
y_RT_SB_RBBH_Tps_UF <- DGEList(counts=rawdata_Tps_RBBH[,c("Tps_SF_RT_Md_Re1","Tps_SF_RT_Md_Re2","Tps_SF_RT_Md_Re3","Tps_SM_RT_Md_Re1","Tps_SM_RT_Md_Re2","Tps_SM_RT_Md_Re3")], genes=rawdata_Tps_RBBH[,1:1])
## for sex-asex
y_RT_SA_RBBH_Tbi_UF <- DGEList(counts=rawdata_Tbi_RBBH[,c("Tbi_SF_RT_Md_Re1","Tbi_SF_RT_Md_Re2","Tbi_SF_RT_Md_Re3","Tte_AF_RT_Vi_Re1","Tte_AF_RT_Vi_Re2","Tte_AF_RT_Vi_Re3")], genes=rawdata_Tbi_RBBH[,1:1])
y_RT_SA_RBBH_Tce_UF <- DGEList(counts=rawdata_Tce_RBBH[,c("Tce_SF_RT_Md_Re1","Tce_SF_RT_Md_Re2","Tce_SF_RT_Md_Re3","Tms_AF_RT_Vi_Re1","Tms_AF_RT_Vi_Re2","Tms_AF_RT_Vi_Re3")], genes=rawdata_Tce_RBBH[,1:1])
y_RT_SA_RBBH_Tcm_UF <- DGEList(counts=rawdata_Tcm_RBBH[,c("Tcm_SF_RT_Md_Re1","Tcm_SF_RT_Md_Re2","Tcm_SF_RT_Md_Re3","Tsi_AF_RT_Vi_Re1","Tsi_AF_RT_Vi_Re2","Tsi_AF_RT_Vi_Re3")], genes=rawdata_Tcm_RBBH[,1:1])
y_RT_SA_RBBH_Tpa_UF <- DGEList(counts=rawdata_Tpa_RBBH[,c("Tpa_SF_RT_Md_Re1","Tpa_SF_RT_Md_Re2","Tpa_SF_RT_Md_Re3","Tge_AF_RT_Vi_Re1","Tge_AF_RT_Vi_Re2","Tge_AF_RT_Vi_Re3")], genes=rawdata_Tpa_RBBH[,1:1])
y_RT_SA_RBBH_Tps_UF <- DGEList(counts=rawdata_Tps_RBBH[,c("Tps_SF_RT_Md_Re1","Tps_SF_RT_Md_Re2","Tps_SF_RT_Md_Re3","Tdi_AF_RT_Vi_Re1","Tdi_AF_RT_Vi_Re2","Tdi_AF_RT_Vi_Re3")], genes=rawdata_Tps_RBBH[,1:1])
#### LG
## for SB
y_LG_SB_RBBH_Tbi_UF <- DGEList(counts=rawdata_Tbi_RBBH[,c("Tbi_SF_LG_Md_Re1","Tbi_SF_LG_Md_Re2","Tbi_SF_LG_Md_Re3","Tbi_SM_LG_Md_Re1","Tbi_SM_LG_Md_Re2","Tbi_SM_LG_Md_Re3")], genes=rawdata_Tbi_RBBH[,1:1])
y_LG_SB_RBBH_Tce_UF <- DGEList(counts=rawdata_Tce_RBBH[,c("Tce_SF_LG_Md_Re1","Tce_SF_LG_Md_Re2","Tce_SF_LG_Md_Re3","Tce_SM_LG_Md_Re1","Tce_SM_LG_Md_Re2","Tce_SM_LG_Md_Re3")], genes=rawdata_Tce_RBBH[,1:1])
y_LG_SB_RBBH_Tcm_UF <- DGEList(counts=rawdata_Tcm_RBBH[,c("Tcm_SF_LG_Md_Re1","Tcm_SF_LG_Md_Re2","Tcm_SF_LG_Md_Re3","Tcm_SM_LG_Md_Re1","Tcm_SM_LG_Md_Re2","Tcm_SM_LG_Md_Re3")], genes=rawdata_Tcm_RBBH[,1:1])
y_LG_SB_RBBH_Tpa_UF <- DGEList(counts=rawdata_Tpa_RBBH[,c("Tpa_SF_LG_Md_Re1","Tpa_SF_LG_Md_Re2","Tpa_SF_LG_Md_Re3","Tpa_SM_LG_Md_Re1","Tpa_SM_LG_Md_Re2","Tpa_SM_LG_Md_Re3")], genes=rawdata_Tpa_RBBH[,1:1])
y_LG_SB_RBBH_Tps_UF <- DGEList(counts=rawdata_Tps_RBBH[,c("Tps_SF_LG_Md_Re1","Tps_SF_LG_Md_Re2","Tps_SF_LG_Md_Re3","Tps_SM_LG_Md_Re1","Tps_SM_LG_Md_Re2","Tps_SM_LG_Md_Re3")], genes=rawdata_Tps_RBBH[,1:1])
## for sex-asex
y_LG_SA_RBBH_Tbi_UF <- DGEList(counts=rawdata_Tbi_RBBH[,c("Tbi_SF_LG_Md_Re1","Tbi_SF_LG_Md_Re2","Tbi_SF_LG_Md_Re3","Tte_AF_LG_Vi_Re1","Tte_AF_LG_Vi_Re2","Tte_AF_LG_Vi_Re3")], genes=rawdata_Tbi_RBBH[,1:1])
y_LG_SA_RBBH_Tce_UF <- DGEList(counts=rawdata_Tce_RBBH[,c("Tce_SF_LG_Md_Re1","Tce_SF_LG_Md_Re2","Tce_SF_LG_Md_Re3","Tms_AF_LG_Vi_Re1","Tms_AF_LG_Vi_Re2","Tms_AF_LG_Vi_Re3")], genes=rawdata_Tce_RBBH[,1:1])
y_LG_SA_RBBH_Tcm_UF <- DGEList(counts=rawdata_Tcm_RBBH[,c("Tcm_SF_LG_Md_Re1","Tcm_SF_LG_Md_Re2","Tcm_SF_LG_Md_Re3","Tsi_AF_LG_Vi_Re1","Tsi_AF_LG_Vi_Re2","Tsi_AF_LG_Vi_Re3")], genes=rawdata_Tcm_RBBH[,1:1])
y_LG_SA_RBBH_Tpa_UF <- DGEList(counts=rawdata_Tpa_RBBH[,c("Tpa_SF_LG_Md_Re1","Tpa_SF_LG_Md_Re2","Tpa_SF_LG_Md_Re3","Tge_AF_LG_Vi_Re1","Tge_AF_LG_Vi_Re2","Tge_AF_LG_Vi_Re3")], genes=rawdata_Tpa_RBBH[,1:1])
y_LG_SA_RBBH_Tps_UF <- DGEList(counts=rawdata_Tps_RBBH[,c("Tps_SF_LG_Md_Re1","Tps_SF_LG_Md_Re2","Tps_SF_LG_Md_Re3","Tdi_AF_LG_Vi_Re1","Tdi_AF_LG_Vi_Re2","Tdi_AF_LG_Vi_Re3")], genes=rawdata_Tps_RBBH[,1:1])
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
#### filter and normalisation (code)
filt_and_norm <- function(y,cpm_cut,cut_in_Nsams){
cat("\nNumber number of genes / samples in orig data\n")
print(dim(y)) ### number of genes / samples
head(cpm(y))
keep <- rowSums(cpm(y)>cpm_cut) >= cut_in_Nsams
y <- y[keep,]
y$samples$lib.size <- colSums(y$counts) # if filter recalc lib size
y <- calcNormFactors(y)
cat("\nLib norm factors\n")
print(y$samples)
cat("\nNumber number of genes / samples after filtering\n")
print(dim(y))
return(y)
}
filt_and_norm_maj <- function(y,cpm_cut,cut_in_Nsams){
cat("\nNumber number of genes / samples in orig data\n")
print(dim(y)) ### number of genes / samples
head(cpm(y))
keep <-
rowSums(cpm(y[,1:3])> cpm_cut) >= cut_in_Nsams &
rowSums(cpm(y[,4:6])> cpm_cut) >= cut_in_Nsams
y <- y[keep,]
y$samples$lib.size <- colSums(y$counts) # if filter recalc lib size
y <- calcNormFactors(y)
cat("\nLib norm factors\n")
print(y$samples)
cat("\nNumber number of genes / samples after filtering\n")
print(dim(y))
return(y)
}
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
### DE
#### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
#### filter and normalisation (samples)
## at least 0.5 cpm in 2/3 reps in all samps (i.e. NO sex- or sp- specific genes)
#### WB
y_WB_SB_RBBH_Tbi <- filt_and_norm_maj(y_WB_SB_RBBH_Tbi_UF,0.5, 2)
y_WB_SB_RBBH_Tce <- filt_and_norm_maj(y_WB_SB_RBBH_Tce_UF,0.5, 2)
y_WB_SB_RBBH_Tcm <- filt_and_norm_maj(y_WB_SB_RBBH_Tcm_UF,0.5, 2)
y_WB_SB_RBBH_Tpa <- filt_and_norm_maj(y_WB_SB_RBBH_Tpa_UF,0.5, 2)
y_WB_SB_RBBH_Tps <- filt_and_norm_maj(y_WB_SB_RBBH_Tps_UF,0.5, 2)
# Number number of genes / samples in orig data
# [1] 17583 6
# [1] 17170 6
# [1] 15500 6
# [1] 16113 6
# [1] 15932 6
# Number number of genes / samples after filtering
# [1] 15401 6
# [1] 15082 6
# [1] 14154 6
# [1] 14111 6
# [1] 13994 6
y_WB_SA_RBBH_Tbi <- filt_and_norm_maj(y_WB_SA_RBBH_Tbi_UF,0.5, 2)
y_WB_SA_RBBH_Tce <- filt_and_norm_maj(y_WB_SA_RBBH_Tce_UF,0.5, 2)
y_WB_SA_RBBH_Tcm <- filt_and_norm_maj(y_WB_SA_RBBH_Tcm_UF,0.5, 2)
y_WB_SA_RBBH_Tpa <- filt_and_norm_maj(y_WB_SA_RBBH_Tpa_UF,0.5, 2)
y_WB_SA_RBBH_Tps <- filt_and_norm_maj(y_WB_SA_RBBH_Tps_UF,0.5, 2)
# Number number of genes / samples in orig data
# [1] 17583 6
# [1] 17170 6
# [1] 15500 6
# [1] 16113 6
# [1] 15932
# Number number of genes / samples after filtering
# [1] 15793 6
# [1] 15308 6
# [1] 14354 6
# [1] 14205 6
# [1] 14257 6
#### RT
y_RT_SB_RBBH_Tbi <- filt_and_norm_maj(y_RT_SB_RBBH_Tbi_UF,0.5, 2)
y_RT_SB_RBBH_Tce <- filt_and_norm_maj(y_RT_SB_RBBH_Tce_UF,0.5, 2)
y_RT_SB_RBBH_Tcm <- filt_and_norm_maj(y_RT_SB_RBBH_Tcm_UF,0.5, 2)
y_RT_SB_RBBH_Tpa <- filt_and_norm_maj(y_RT_SB_RBBH_Tpa_UF,0.5, 2)
y_RT_SB_RBBH_Tps <- filt_and_norm_maj(y_RT_SB_RBBH_Tps_UF,0.5, 2)
# Number number of genes / samples in orig data
# [1] 17583 6
# [1] 17170 6
# [1] 15500 6
# [1] 16113 6
# [1] 15932 6
# Number number of genes / samples after filtering
# [1] 13068 6
# [1] 12484 6
# [1] 11656 6
# [1] 11404 6
# [1] 11977 6
y_RT_SA_RBBH_Tbi <- filt_and_norm_maj(y_RT_SA_RBBH_Tbi_UF,0.5, 2)
y_RT_SA_RBBH_Tce <- filt_and_norm_maj(y_RT_SA_RBBH_Tce_UF,0.5, 2)
y_RT_SA_RBBH_Tcm <- filt_and_norm_maj(y_RT_SA_RBBH_Tcm_UF,0.5, 2)
y_RT_SA_RBBH_Tpa <- filt_and_norm_maj(y_RT_SA_RBBH_Tpa_UF,0.5, 2)
y_RT_SA_RBBH_Tps <- filt_and_norm_maj(y_RT_SA_RBBH_Tps_UF,0.5, 2)
# # Number number of genes / samples in orig data
# [1] 17583 6
# [1] 17170 6
# [1] 15500 6
# [1] 16113 6
# [1] 15932 6
# Number number of genes / samples after filtering
# [1] 13196 6
# [1] 12599 6
# [1] 11654 6
# [1] 11322 6
# [1] 11990 6
#### LG
y_LG_SB_RBBH_Tbi <- filt_and_norm_maj(y_LG_SB_RBBH_Tbi_UF,0.5, 2)
y_LG_SB_RBBH_Tce <- filt_and_norm_maj(y_LG_SB_RBBH_Tce_UF,0.5, 2)
y_LG_SB_RBBH_Tcm <- filt_and_norm_maj(y_LG_SB_RBBH_Tcm_UF,0.5, 2)
y_LG_SB_RBBH_Tpa <- filt_and_norm_maj(y_LG_SB_RBBH_Tpa_UF,0.5, 2)
y_LG_SB_RBBH_Tps <- filt_and_norm_maj(y_LG_SB_RBBH_Tps_UF,0.5, 2)
# Number number of genes / samples in orig data
# [1] 17583 6
# [1] 17170 6
# [1] 15500 6
# [1] 16113 6
# [1] 15932 6
# Number number of genes / samples after filtering
# [1] 12335 6
# [1] 11120 6
# [1] 11349 6
# [1] 10565 6
# [1] 11483 6
y_LG_SA_RBBH_Tbi <- filt_and_norm_maj(y_LG_SA_RBBH_Tbi_UF,0.5, 2)
y_LG_SA_RBBH_Tce <- filt_and_norm_maj(y_LG_SA_RBBH_Tce_UF,0.5, 2)
y_LG_SA_RBBH_Tcm <- filt_and_norm_maj(y_LG_SA_RBBH_Tcm_UF,0.5, 2)
y_LG_SA_RBBH_Tpa <- filt_and_norm_maj(y_LG_SA_RBBH_Tpa_UF,0.5, 2)
y_LG_SA_RBBH_Tps <- filt_and_norm_maj(y_LG_SA_RBBH_Tps_UF,0.5, 2)
# Number number of genes / samples in orig data
# [1] 17583 6
# [1] 17170 6
# [1] 15500 6
# [1] 16113 6
# [1] 15932 6
# Number number of genes / samples after filtering
# [1] 12302 6
# [1] 11068 6
# [1] 11223 6
# [1] 10057 6
# [1] 11313 6
#################################################################################################
### get DE_gene_table (code)
## returns full table and sig DE genes as a vector
get_DE_genes <- function(fita,FDRa){
TT1 = topTags(fita, n =3000000000)
TT2 = TT1$table
temp_sig <- subset(TT2, TT2$FDR <= FDRa)
sig_genes = temp_sig$genes
sig_logFC = temp_sig$logFC
N_sig_genes <- length(sig_genes)
cat("Number of sig genes: ", N_sig_genes )
r_list <- list("table" = TT2, "S_gene_list" = sig_genes, "S_logFC_list" = sig_logFC )
return(r_list)
}
##################################################################################################
####### get SB_genes
####### design matrix
## using 0M for male and 1F for female samples
sex_SB = factor(c(
"1F","1F","1F","0M","0M","0M"
))
data.frame(Sample=colnames(y_WB_SB_RBBH_Tbi),sex_SB)
### design matrix WB
design_WB_SB_RBBH_Tbi <- model.matrix(~sex_SB)
rownames(design_WB_SB_RBBH_Tbi) <- colnames(y_WB_SB_RBBH_Tbi)
design_WB_SB_RBBH_Tbi
### design matrix RT
design_RT_SB_RBBH_Tbi <- model.matrix(~sex_SB)
rownames(design_RT_SB_RBBH_Tbi ) <- colnames(y_RT_SB_RBBH_Tbi)
design_RT_SB_RBBH_Tbi
### design matrix LG
design_LG_SB_RBBH_Tbi <- model.matrix(~sex_SB )
rownames(design_LG_SB_RBBH_Tbi ) <- colnames(y_LG_SB_RBBH_Tbi )
design_LG_SB_RBBH_Tbi
###### Est dispersion (samples)
y_WB_SB_RBBH_Tbi <- estimateDisp(y_WB_SB_RBBH_Tbi, design_WB_SB_RBBH_Tbi)
y_RT_SB_RBBH_Tbi <- estimateDisp(y_RT_SB_RBBH_Tbi, design_RT_SB_RBBH_Tbi)
y_LG_SB_RBBH_Tbi <- estimateDisp(y_LG_SB_RBBH_Tbi, design_LG_SB_RBBH_Tbi)
### design matrix WB
design_WB_SB_RBBH_Tce <- model.matrix(~sex_SB)
rownames(design_WB_SB_RBBH_Tce) <- colnames(y_WB_SB_RBBH_Tce)
design_WB_SB_RBBH_Tce
### design matrix RT
design_RT_SB_RBBH_Tce <- model.matrix(~sex_SB)
rownames(design_RT_SB_RBBH_Tce ) <- colnames(y_RT_SB_RBBH_Tce)
design_RT_SB_RBBH_Tce
### design matrix LG
design_LG_SB_RBBH_Tce <- model.matrix(~sex_SB )
rownames(design_LG_SB_RBBH_Tce ) <- colnames(y_LG_SB_RBBH_Tce )
design_LG_SB_RBBH_Tce
###### Est dispersion (samples)
y_WB_SB_RBBH_Tce <- estimateDisp(y_WB_SB_RBBH_Tce, design_WB_SB_RBBH_Tce)
y_RT_SB_RBBH_Tce <- estimateDisp(y_RT_SB_RBBH_Tce, design_RT_SB_RBBH_Tce)
y_LG_SB_RBBH_Tce <- estimateDisp(y_LG_SB_RBBH_Tce, design_LG_SB_RBBH_Tce)
### design matrix WB
design_WB_SB_RBBH_Tcm <- model.matrix(~sex_SB)
rownames(design_WB_SB_RBBH_Tcm) <- colnames(y_WB_SB_RBBH_Tcm)
design_WB_SB_RBBH_Tcm
### design matrix RT
design_RT_SB_RBBH_Tcm <- model.matrix(~sex_SB)
rownames(design_RT_SB_RBBH_Tcm ) <- colnames(y_RT_SB_RBBH_Tcm)
design_RT_SB_RBBH_Tcm
### design matrix LG
design_LG_SB_RBBH_Tcm <- model.matrix(~sex_SB )
rownames(design_LG_SB_RBBH_Tcm ) <- colnames(y_LG_SB_RBBH_Tcm )
design_LG_SB_RBBH_Tcm
###### Est dispersion (samples)
y_WB_SB_RBBH_Tcm <- estimateDisp(y_WB_SB_RBBH_Tcm, design_WB_SB_RBBH_Tcm)
y_RT_SB_RBBH_Tcm <- estimateDisp(y_RT_SB_RBBH_Tcm, design_RT_SB_RBBH_Tcm)
y_LG_SB_RBBH_Tcm <- estimateDisp(y_LG_SB_RBBH_Tcm, design_LG_SB_RBBH_Tcm)
### design matrix WB
design_WB_SB_RBBH_Tpa <- model.matrix(~sex_SB)
rownames(design_WB_SB_RBBH_Tpa) <- colnames(y_WB_SB_RBBH_Tpa)
design_WB_SB_RBBH_Tpa
### design matrix RT
design_RT_SB_RBBH_Tpa <- model.matrix(~sex_SB)
rownames(design_RT_SB_RBBH_Tpa ) <- colnames(y_RT_SB_RBBH_Tpa)
design_RT_SB_RBBH_Tpa
### design matrix LG
design_LG_SB_RBBH_Tpa <- model.matrix(~sex_SB )
rownames(design_LG_SB_RBBH_Tpa ) <- colnames(y_LG_SB_RBBH_Tpa )
design_LG_SB_RBBH_Tpa
###### Est dispersion (samples)
y_WB_SB_RBBH_Tpa <- estimateDisp(y_WB_SB_RBBH_Tpa, design_WB_SB_RBBH_Tpa)
y_RT_SB_RBBH_Tpa <- estimateDisp(y_RT_SB_RBBH_Tpa, design_RT_SB_RBBH_Tpa)
y_LG_SB_RBBH_Tpa <- estimateDisp(y_LG_SB_RBBH_Tpa, design_LG_SB_RBBH_Tpa)
### design matrix WB
design_WB_SB_RBBH_Tps <- model.matrix(~sex_SB)
rownames(design_WB_SB_RBBH_Tps) <- colnames(y_WB_SB_RBBH_Tps)
design_WB_SB_RBBH_Tps
### design matrix RT
design_RT_SB_RBBH_Tps <- model.matrix(~sex_SB)
rownames(design_RT_SB_RBBH_Tps ) <- colnames(y_RT_SB_RBBH_Tps)
design_RT_SB_RBBH_Tps
### design matrix LG
design_LG_SB_RBBH_Tps <- model.matrix(~sex_SB )
rownames(design_LG_SB_RBBH_Tps ) <- colnames(y_LG_SB_RBBH_Tps )
design_LG_SB_RBBH_Tps
###### Est dispersion (samples)
y_WB_SB_RBBH_Tps <- estimateDisp(y_WB_SB_RBBH_Tps, design_WB_SB_RBBH_Tps)
y_RT_SB_RBBH_Tps <- estimateDisp(y_RT_SB_RBBH_Tps, design_RT_SB_RBBH_Tps)
y_LG_SB_RBBH_Tps <- estimateDisp(y_LG_SB_RBBH_Tps, design_LG_SB_RBBH_Tps)
#####################################################################################################################################################################
###### GET BCV ## this is the (common) BCV
BCV_SB_df <- as.data.frame(cbind(
c("Tbi","Tce", "Tcm", "Tpa", "Tps"),
c(
sqrt(y_WB_SB_RBBH_Tbi$common.dispersion),
sqrt(y_WB_SB_RBBH_Tce$common.dispersion),
sqrt(y_WB_SB_RBBH_Tcm$common.dispersion),
sqrt(y_WB_SB_RBBH_Tpa$common.dispersion),
sqrt(y_WB_SB_RBBH_Tps$common.dispersion)),
c(
sqrt(y_RT_SB_RBBH_Tbi$common.dispersion),
sqrt(y_RT_SB_RBBH_Tce$common.dispersion),
sqrt(y_RT_SB_RBBH_Tcm$common.dispersion),
sqrt(y_RT_SB_RBBH_Tpa$common.dispersion),
sqrt(y_RT_SB_RBBH_Tps$common.dispersion)),
c(
sqrt(y_LG_SB_RBBH_Tbi$common.dispersion),
sqrt(y_LG_SB_RBBH_Tce$common.dispersion),
sqrt(y_LG_SB_RBBH_Tcm$common.dispersion),
sqrt(y_LG_SB_RBBH_Tpa$common.dispersion),
sqrt(y_LG_SB_RBBH_Tps$common.dispersion))))
colnames(BCV_SB_df) <- c("sp","WB","RT","LG")
write.csv(BCV_SB_df, "BCV_SB_df.csv", row.names = F)
#####################################################################################################################################################################
###### fit glm
fit_WB_SB_RBBH_Tbi <- glmFit(y_WB_SB_RBBH_Tbi, design_WB_SB_RBBH_Tbi,robust=TRUE)
fit_RT_SB_RBBH_Tbi <- glmFit(y_RT_SB_RBBH_Tbi, design_RT_SB_RBBH_Tbi,robust=TRUE)
fit_LG_SB_RBBH_Tbi <- glmFit(y_LG_SB_RBBH_Tbi, design_LG_SB_RBBH_Tbi,robust=TRUE)
fit_WB_SB_RBBH_Tce <- glmFit(y_WB_SB_RBBH_Tce, design_WB_SB_RBBH_Tce,robust=TRUE)
fit_RT_SB_RBBH_Tce <- glmFit(y_RT_SB_RBBH_Tce, design_RT_SB_RBBH_Tce,robust=TRUE)
fit_LG_SB_RBBH_Tce <- glmFit(y_LG_SB_RBBH_Tce, design_LG_SB_RBBH_Tce,robust=TRUE)
fit_WB_SB_RBBH_Tcm <- glmFit(y_WB_SB_RBBH_Tcm, design_WB_SB_RBBH_Tcm,robust=TRUE)
fit_RT_SB_RBBH_Tcm <- glmFit(y_RT_SB_RBBH_Tcm, design_RT_SB_RBBH_Tcm,robust=TRUE)
fit_LG_SB_RBBH_Tcm <- glmFit(y_LG_SB_RBBH_Tcm, design_LG_SB_RBBH_Tcm,robust=TRUE)
fit_WB_SB_RBBH_Tpa <- glmFit(y_WB_SB_RBBH_Tpa, design_WB_SB_RBBH_Tpa,robust=TRUE)
fit_RT_SB_RBBH_Tpa <- glmFit(y_RT_SB_RBBH_Tpa, design_RT_SB_RBBH_Tpa,robust=TRUE)
fit_LG_SB_RBBH_Tpa <- glmFit(y_LG_SB_RBBH_Tpa, design_LG_SB_RBBH_Tpa,robust=TRUE)
fit_WB_SB_RBBH_Tps <- glmFit(y_WB_SB_RBBH_Tps, design_WB_SB_RBBH_Tps,robust=TRUE)
fit_RT_SB_RBBH_Tps <- glmFit(y_RT_SB_RBBH_Tps, design_RT_SB_RBBH_Tps,robust=TRUE)
fit_LG_SB_RBBH_Tps <- glmFit(y_LG_SB_RBBH_Tps, design_LG_SB_RBBH_Tps,robust=TRUE)
colnames(fit_LG_SB_RBBH_Tps)
colnames(fit_WB_SB_RBBH_Tbi)
# [1] "(Intercept)" "sex_SB1F"
## get SB genes ### NOTE +ve FC = higher experssion in females (female-biased)
fit_WB_SB_RBBH_Tbi_c <- glmLRT(fit_WB_SB_RBBH_Tbi,coef=2)
fit_RT_SB_RBBH_Tbi_c <- glmLRT(fit_RT_SB_RBBH_Tbi,coef=2)
fit_LG_SB_RBBH_Tbi_c <- glmLRT(fit_LG_SB_RBBH_Tbi,coef=2)
TTT_WB_SB_RBBH_Tbi_sex_bias <- get_DE_genes(fit_WB_SB_RBBH_Tbi_c , 0.05)
TTT_RT_SB_RBBH_Tbi_sex_bias <- get_DE_genes(fit_RT_SB_RBBH_Tbi_c , 0.05)
TTT_LG_SB_RBBH_Tbi_sex_bias <- get_DE_genes(fit_LG_SB_RBBH_Tbi_c , 0.05)
fit_WB_SB_RBBH_Tce_c <- glmLRT(fit_WB_SB_RBBH_Tce,coef=2)
fit_RT_SB_RBBH_Tce_c <- glmLRT(fit_RT_SB_RBBH_Tce,coef=2)
fit_LG_SB_RBBH_Tce_c <- glmLRT(fit_LG_SB_RBBH_Tce,coef=2)
TTT_WB_SB_RBBH_Tce_sex_bias <- get_DE_genes(fit_WB_SB_RBBH_Tce_c , 0.05)
TTT_RT_SB_RBBH_Tce_sex_bias <- get_DE_genes(fit_RT_SB_RBBH_Tce_c , 0.05)
TTT_LG_SB_RBBH_Tce_sex_bias <- get_DE_genes(fit_LG_SB_RBBH_Tce_c , 0.05)
fit_WB_SB_RBBH_Tcm_c <- glmLRT(fit_WB_SB_RBBH_Tcm,coef=2)
fit_RT_SB_RBBH_Tcm_c <- glmLRT(fit_RT_SB_RBBH_Tcm,coef=2)
fit_LG_SB_RBBH_Tcm_c <- glmLRT(fit_LG_SB_RBBH_Tcm,coef=2)
TTT_WB_SB_RBBH_Tcm_sex_bias <- get_DE_genes(fit_WB_SB_RBBH_Tcm_c , 0.05)
TTT_RT_SB_RBBH_Tcm_sex_bias <- get_DE_genes(fit_RT_SB_RBBH_Tcm_c , 0.05)
TTT_LG_SB_RBBH_Tcm_sex_bias <- get_DE_genes(fit_LG_SB_RBBH_Tcm_c , 0.05)
fit_WB_SB_RBBH_Tpa_c <- glmLRT(fit_WB_SB_RBBH_Tpa,coef=2)
fit_RT_SB_RBBH_Tpa_c <- glmLRT(fit_RT_SB_RBBH_Tpa,coef=2)
fit_LG_SB_RBBH_Tpa_c <- glmLRT(fit_LG_SB_RBBH_Tpa,coef=2)
TTT_WB_SB_RBBH_Tpa_sex_bias <- get_DE_genes(fit_WB_SB_RBBH_Tpa_c , 0.05)
TTT_RT_SB_RBBH_Tpa_sex_bias <- get_DE_genes(fit_RT_SB_RBBH_Tpa_c , 0.05)
TTT_LG_SB_RBBH_Tpa_sex_bias <- get_DE_genes(fit_LG_SB_RBBH_Tpa_c , 0.05)
fit_WB_SB_RBBH_Tps_c <- glmLRT(fit_WB_SB_RBBH_Tps,coef=2)
fit_RT_SB_RBBH_Tps_c <- glmLRT(fit_RT_SB_RBBH_Tps,coef=2)
fit_LG_SB_RBBH_Tps_c <- glmLRT(fit_LG_SB_RBBH_Tps,coef=2)
TTT_WB_SB_RBBH_Tps_sex_bias <- get_DE_genes(fit_WB_SB_RBBH_Tps_c , 0.05)
TTT_RT_SB_RBBH_Tps_sex_bias <- get_DE_genes(fit_RT_SB_RBBH_Tps_c , 0.05)
TTT_LG_SB_RBBH_Tps_sex_bias <- get_DE_genes(fit_LG_SB_RBBH_Tps_c , 0.05)
####### export full tables
write.table(TTT_WB_SB_RBBH_Tbi_sex_bias$table, "TTT_lrt_Tbi_sex_bias_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SB_RBBH_Tbi_sex_bias$table, "TTT_lrt_Tbi_sex_bias_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SB_RBBH_Tbi_sex_bias$table, "TTT_lrt_Tbi_sex_bias_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_WB_SB_RBBH_Tce_sex_bias$table, "TTT_lrt_Tce_sex_bias_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SB_RBBH_Tce_sex_bias$table, "TTT_lrt_Tce_sex_bias_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SB_RBBH_Tce_sex_bias$table, "TTT_lrt_Tce_sex_bias_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_WB_SB_RBBH_Tcm_sex_bias$table, "TTT_lrt_Tcm_sex_bias_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SB_RBBH_Tcm_sex_bias$table, "TTT_lrt_Tcm_sex_bias_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SB_RBBH_Tcm_sex_bias$table, "TTT_lrt_Tcm_sex_bias_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_WB_SB_RBBH_Tpa_sex_bias$table, "TTT_lrt_Tpa_sex_bias_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SB_RBBH_Tpa_sex_bias$table, "TTT_lrt_Tpa_sex_bias_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SB_RBBH_Tpa_sex_bias$table, "TTT_lrt_Tpa_sex_bias_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_WB_SB_RBBH_Tps_sex_bias$table, "TTT_lrt_Tps_sex_bias_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SB_RBBH_Tps_sex_bias$table, "TTT_lrt_Tps_sex_bias_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SB_RBBH_Tps_sex_bias$table, "TTT_lrt_Tps_sex_bias_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
##################################################################################################
####### get (sex-asex) SA_genes
####### design matrix
## using 0SF for sexual female and 1AF for asexual female samples
RM_SA = factor(c(
"0SF","0SF","0SF","1AF","1AF","1AF"
))
data.frame(Sample=colnames(y_WB_SA_RBBH_Tbi),RM_SA)
### design matrix WB
design_WB_SA_RBBH_Tbi <- model.matrix(~RM_SA)
rownames(design_WB_SA_RBBH_Tbi) <- colnames(y_WB_SA_RBBH_Tbi)
design_WB_SA_RBBH_Tbi
### design matrix RT
design_RT_SA_RBBH_Tbi <- model.matrix(~RM_SA)
rownames(design_RT_SA_RBBH_Tbi ) <- colnames(y_RT_SA_RBBH_Tbi)
design_RT_SA_RBBH_Tbi
### design matrix LG
design_LG_SA_RBBH_Tbi <- model.matrix(~RM_SA )
rownames(design_LG_SA_RBBH_Tbi ) <- colnames(y_LG_SA_RBBH_Tbi )
design_LG_SA_RBBH_Tbi
###### Est dispersion (samples)
y_WB_SA_RBBH_Tbi <- estimateDisp(y_WB_SA_RBBH_Tbi, design_WB_SA_RBBH_Tbi)
y_RT_SA_RBBH_Tbi <- estimateDisp(y_RT_SA_RBBH_Tbi, design_RT_SA_RBBH_Tbi)
y_LG_SA_RBBH_Tbi <- estimateDisp(y_LG_SA_RBBH_Tbi, design_LG_SA_RBBH_Tbi)
### design matrix WB
design_WB_SA_RBBH_Tce <- model.matrix(~RM_SA)
rownames(design_WB_SA_RBBH_Tce) <- colnames(y_WB_SA_RBBH_Tce)
design_WB_SA_RBBH_Tce
### design matrix RT
design_RT_SA_RBBH_Tce <- model.matrix(~RM_SA)
rownames(design_RT_SA_RBBH_Tce ) <- colnames(y_RT_SA_RBBH_Tce)
design_RT_SA_RBBH_Tce
### design matrix LG
design_LG_SA_RBBH_Tce <- model.matrix(~RM_SA )
rownames(design_LG_SA_RBBH_Tce ) <- colnames(y_LG_SA_RBBH_Tce )
design_LG_SA_RBBH_Tce
###### Est dispersion (samples)
y_WB_SA_RBBH_Tce <- estimateDisp(y_WB_SA_RBBH_Tce, design_WB_SA_RBBH_Tce)
y_RT_SA_RBBH_Tce <- estimateDisp(y_RT_SA_RBBH_Tce, design_RT_SA_RBBH_Tce)
y_LG_SA_RBBH_Tce <- estimateDisp(y_LG_SA_RBBH_Tce, design_LG_SA_RBBH_Tce)
### design matrix WB
design_WB_SA_RBBH_Tcm <- model.matrix(~RM_SA)
rownames(design_WB_SA_RBBH_Tcm) <- colnames(y_WB_SA_RBBH_Tcm)
design_WB_SA_RBBH_Tcm
### design matrix RT
design_RT_SA_RBBH_Tcm <- model.matrix(~RM_SA)
rownames(design_RT_SA_RBBH_Tcm ) <- colnames(y_RT_SA_RBBH_Tcm)
design_RT_SA_RBBH_Tcm
### design matrix LG
design_LG_SA_RBBH_Tcm <- model.matrix(~RM_SA )
rownames(design_LG_SA_RBBH_Tcm ) <- colnames(y_LG_SA_RBBH_Tcm )
design_LG_SA_RBBH_Tcm
###### Est dispersion (samples)
y_WB_SA_RBBH_Tcm <- estimateDisp(y_WB_SA_RBBH_Tcm, design_WB_SA_RBBH_Tcm)
y_RT_SA_RBBH_Tcm <- estimateDisp(y_RT_SA_RBBH_Tcm, design_RT_SA_RBBH_Tcm)
y_LG_SA_RBBH_Tcm <- estimateDisp(y_LG_SA_RBBH_Tcm, design_LG_SA_RBBH_Tcm)
### design matrix WB
design_WB_SA_RBBH_Tpa <- model.matrix(~RM_SA)
rownames(design_WB_SA_RBBH_Tpa) <- colnames(y_WB_SA_RBBH_Tpa)
design_WB_SA_RBBH_Tpa
### design matrix RT
design_RT_SA_RBBH_Tpa <- model.matrix(~RM_SA)
rownames(design_RT_SA_RBBH_Tpa ) <- colnames(y_RT_SA_RBBH_Tpa)
design_RT_SA_RBBH_Tpa
### design matrix LG
design_LG_SA_RBBH_Tpa <- model.matrix(~RM_SA )
rownames(design_LG_SA_RBBH_Tpa ) <- colnames(y_LG_SA_RBBH_Tpa )
design_LG_SA_RBBH_Tpa
###### Est dispersion (samples)
y_WB_SA_RBBH_Tpa <- estimateDisp(y_WB_SA_RBBH_Tpa, design_WB_SA_RBBH_Tpa)
y_RT_SA_RBBH_Tpa <- estimateDisp(y_RT_SA_RBBH_Tpa, design_RT_SA_RBBH_Tpa)
y_LG_SA_RBBH_Tpa <- estimateDisp(y_LG_SA_RBBH_Tpa, design_LG_SA_RBBH_Tpa)
### design matrix WB
design_WB_SA_RBBH_Tps <- model.matrix(~RM_SA)
rownames(design_WB_SA_RBBH_Tps) <- colnames(y_WB_SA_RBBH_Tps)
design_WB_SA_RBBH_Tps
### design matrix RT
design_RT_SA_RBBH_Tps <- model.matrix(~RM_SA)
rownames(design_RT_SA_RBBH_Tps ) <- colnames(y_RT_SA_RBBH_Tps)
design_RT_SA_RBBH_Tps
### design matrix LG
design_LG_SA_RBBH_Tps <- model.matrix(~RM_SA )
rownames(design_LG_SA_RBBH_Tps ) <- colnames(y_LG_SA_RBBH_Tps )
design_LG_SA_RBBH_Tps
###### Est dispersion (samples)
y_WB_SA_RBBH_Tps <- estimateDisp(y_WB_SA_RBBH_Tps, design_WB_SA_RBBH_Tps)
y_RT_SA_RBBH_Tps <- estimateDisp(y_RT_SA_RBBH_Tps, design_RT_SA_RBBH_Tps)
y_LG_SA_RBBH_Tps <- estimateDisp(y_LG_SA_RBBH_Tps, design_LG_SA_RBBH_Tps)
#####################################################################################################################################################################
###### fit glm
fit_WB_SA_RBBH_Tbi <- glmFit(y_WB_SA_RBBH_Tbi, design_WB_SA_RBBH_Tbi,robust=TRUE)
fit_RT_SA_RBBH_Tbi <- glmFit(y_RT_SA_RBBH_Tbi, design_RT_SA_RBBH_Tbi,robust=TRUE)
fit_LG_SA_RBBH_Tbi <- glmFit(y_LG_SA_RBBH_Tbi, design_LG_SA_RBBH_Tbi,robust=TRUE)
fit_WB_SA_RBBH_Tce <- glmFit(y_WB_SA_RBBH_Tce, design_WB_SA_RBBH_Tce,robust=TRUE)
fit_RT_SA_RBBH_Tce <- glmFit(y_RT_SA_RBBH_Tce, design_RT_SA_RBBH_Tce,robust=TRUE)
fit_LG_SA_RBBH_Tce <- glmFit(y_LG_SA_RBBH_Tce, design_LG_SA_RBBH_Tce,robust=TRUE)
fit_WB_SA_RBBH_Tcm <- glmFit(y_WB_SA_RBBH_Tcm, design_WB_SA_RBBH_Tcm,robust=TRUE)
fit_RT_SA_RBBH_Tcm <- glmFit(y_RT_SA_RBBH_Tcm, design_RT_SA_RBBH_Tcm,robust=TRUE)
fit_LG_SA_RBBH_Tcm <- glmFit(y_LG_SA_RBBH_Tcm, design_LG_SA_RBBH_Tcm,robust=TRUE)
fit_WB_SA_RBBH_Tpa <- glmFit(y_WB_SA_RBBH_Tpa, design_WB_SA_RBBH_Tpa,robust=TRUE)
fit_RT_SA_RBBH_Tpa <- glmFit(y_RT_SA_RBBH_Tpa, design_RT_SA_RBBH_Tpa,robust=TRUE)
fit_LG_SA_RBBH_Tpa <- glmFit(y_LG_SA_RBBH_Tpa, design_LG_SA_RBBH_Tpa,robust=TRUE)
fit_WB_SA_RBBH_Tps <- glmFit(y_WB_SA_RBBH_Tps, design_WB_SA_RBBH_Tps,robust=TRUE)
fit_RT_SA_RBBH_Tps <- glmFit(y_RT_SA_RBBH_Tps, design_RT_SA_RBBH_Tps,robust=TRUE)
fit_LG_SA_RBBH_Tps <- glmFit(y_LG_SA_RBBH_Tps, design_LG_SA_RBBH_Tps,robust=TRUE)
colnames(fit_LG_SA_RBBH_Tps)
colnames(fit_WB_SA_RBBH_Tbi)
# [1] "(Intercept)" "RM_SA1AF"
## get Sex-asex genes ### NOTE +ve FC = higher experssion in ASEX
fit_WB_SA_RBBH_Tbi_c <- glmLRT(fit_WB_SA_RBBH_Tbi,coef=2)
fit_RT_SA_RBBH_Tbi_c <- glmLRT(fit_RT_SA_RBBH_Tbi,coef=2)
fit_LG_SA_RBBH_Tbi_c <- glmLRT(fit_LG_SA_RBBH_Tbi,coef=2)
TTT_WB_SA_RBBH_Tbi_sex_asex <- get_DE_genes(fit_WB_SA_RBBH_Tbi_c , 0.05)
TTT_RT_SA_RBBH_Tbi_sex_asex <- get_DE_genes(fit_RT_SA_RBBH_Tbi_c , 0.05)
TTT_LG_SA_RBBH_Tbi_sex_asex <- get_DE_genes(fit_LG_SA_RBBH_Tbi_c , 0.05)
fit_WB_SA_RBBH_Tce_c <- glmLRT(fit_WB_SA_RBBH_Tce,coef=2)
fit_RT_SA_RBBH_Tce_c <- glmLRT(fit_RT_SA_RBBH_Tce,coef=2)
fit_LG_SA_RBBH_Tce_c <- glmLRT(fit_LG_SA_RBBH_Tce,coef=2)
TTT_WB_SA_RBBH_Tce_sex_asex <- get_DE_genes(fit_WB_SA_RBBH_Tce_c , 0.05)
TTT_RT_SA_RBBH_Tce_sex_asex <- get_DE_genes(fit_RT_SA_RBBH_Tce_c , 0.05)
TTT_LG_SA_RBBH_Tce_sex_asex <- get_DE_genes(fit_LG_SA_RBBH_Tce_c , 0.05)
fit_WB_SA_RBBH_Tcm_c <- glmLRT(fit_WB_SA_RBBH_Tcm,coef=2)
fit_RT_SA_RBBH_Tcm_c <- glmLRT(fit_RT_SA_RBBH_Tcm,coef=2)
fit_LG_SA_RBBH_Tcm_c <- glmLRT(fit_LG_SA_RBBH_Tcm,coef=2)
TTT_WB_SA_RBBH_Tcm_sex_asex <- get_DE_genes(fit_WB_SA_RBBH_Tcm_c , 0.05)
TTT_RT_SA_RBBH_Tcm_sex_asex <- get_DE_genes(fit_RT_SA_RBBH_Tcm_c , 0.05)
TTT_LG_SA_RBBH_Tcm_sex_asex <- get_DE_genes(fit_LG_SA_RBBH_Tcm_c , 0.05)
fit_WB_SA_RBBH_Tpa_c <- glmLRT(fit_WB_SA_RBBH_Tpa,coef=2)
fit_RT_SA_RBBH_Tpa_c <- glmLRT(fit_RT_SA_RBBH_Tpa,coef=2)
fit_LG_SA_RBBH_Tpa_c <- glmLRT(fit_LG_SA_RBBH_Tpa,coef=2)
TTT_WB_SA_RBBH_Tpa_sex_asex <- get_DE_genes(fit_WB_SA_RBBH_Tpa_c , 0.05)
TTT_RT_SA_RBBH_Tpa_sex_asex <- get_DE_genes(fit_RT_SA_RBBH_Tpa_c , 0.05)
TTT_LG_SA_RBBH_Tpa_sex_asex <- get_DE_genes(fit_LG_SA_RBBH_Tpa_c , 0.05)
fit_WB_SA_RBBH_Tps_c <- glmLRT(fit_WB_SA_RBBH_Tps,coef=2)
fit_RT_SA_RBBH_Tps_c <- glmLRT(fit_RT_SA_RBBH_Tps,coef=2)
fit_LG_SA_RBBH_Tps_c <- glmLRT(fit_LG_SA_RBBH_Tps,coef=2)
TTT_WB_SA_RBBH_Tps_sex_asex <- get_DE_genes(fit_WB_SA_RBBH_Tps_c , 0.05)
TTT_RT_SA_RBBH_Tps_sex_asex <- get_DE_genes(fit_RT_SA_RBBH_Tps_c , 0.05)
TTT_LG_SA_RBBH_Tps_sex_asex <- get_DE_genes(fit_LG_SA_RBBH_Tps_c , 0.05)
####### export full tables
write.table(TTT_WB_SA_RBBH_Tbi_sex_asex$table, "TTT_lrt_Tbi_sex_asex_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SA_RBBH_Tbi_sex_asex$table, "TTT_lrt_Tbi_sex_asex_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SA_RBBH_Tbi_sex_asex$table, "TTT_lrt_Tbi_sex_asex_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_WB_SA_RBBH_Tce_sex_asex$table, "TTT_lrt_Tce_sex_asex_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SA_RBBH_Tce_sex_asex$table, "TTT_lrt_Tce_sex_asex_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SA_RBBH_Tce_sex_asex$table, "TTT_lrt_Tce_sex_asex_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_WB_SA_RBBH_Tcm_sex_asex$table, "TTT_lrt_Tcm_sex_asex_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SA_RBBH_Tcm_sex_asex$table, "TTT_lrt_Tcm_sex_asex_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SA_RBBH_Tcm_sex_asex$table, "TTT_lrt_Tcm_sex_asex_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_WB_SA_RBBH_Tpa_sex_asex$table, "TTT_lrt_Tpa_sex_asex_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SA_RBBH_Tpa_sex_asex$table, "TTT_lrt_Tpa_sex_asex_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SA_RBBH_Tpa_sex_asex$table, "TTT_lrt_Tpa_sex_asex_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_WB_SA_RBBH_Tps_sex_asex$table, "TTT_lrt_Tps_sex_asex_WB.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_RT_SA_RBBH_Tps_sex_asex$table, "TTT_lrt_Tps_sex_asex_RT.csv", sep = ',', quote = FALSE, row.names = FALSE)
write.table(TTT_LG_SA_RBBH_Tps_sex_asex$table, "TTT_lrt_Tps_sex_asex_LG.csv", sep = ',', quote = FALSE, row.names = FALSE)
#######################################################################################################################
##### summarise the data a bit
## N sex-biased genes
N_sex_bias_genes <- as.data.frame(c(
length(TTT_WB_SB_RBBH_Tbi_sex_bias$S_gene_list),
length(TTT_WB_SB_RBBH_Tce_sex_bias$S_gene_list),
length(TTT_WB_SB_RBBH_Tcm_sex_bias$S_gene_list),
length(TTT_WB_SB_RBBH_Tpa_sex_bias$S_gene_list),
length(TTT_WB_SB_RBBH_Tps_sex_bias$S_gene_list),
length(TTT_RT_SB_RBBH_Tbi_sex_bias$S_gene_list),
length(TTT_RT_SB_RBBH_Tce_sex_bias$S_gene_list),
length(TTT_RT_SB_RBBH_Tcm_sex_bias$S_gene_list),
length(TTT_RT_SB_RBBH_Tpa_sex_bias$S_gene_list),
length(TTT_RT_SB_RBBH_Tps_sex_bias$S_gene_list),
length(TTT_LG_SB_RBBH_Tbi_sex_bias$S_gene_list),
length(TTT_LG_SB_RBBH_Tce_sex_bias$S_gene_list),
length(TTT_LG_SB_RBBH_Tcm_sex_bias$S_gene_list),
length(TTT_LG_SB_RBBH_Tpa_sex_bias$S_gene_list),
length(TTT_LG_SB_RBBH_Tps_sex_bias$S_gene_list)
))
N_sex_bias_genes <- as.data.frame(cbind(N_sex_bias_genes,c(
length(TTT_WB_SB_RBBH_Tbi_sex_bias$table[,1]),
length(TTT_WB_SB_RBBH_Tce_sex_bias$table[,1]),
length(TTT_WB_SB_RBBH_Tcm_sex_bias$table[,1]),
length(TTT_WB_SB_RBBH_Tpa_sex_bias$table[,1]),
length(TTT_WB_SB_RBBH_Tps_sex_bias$table[,1]),
length(TTT_RT_SB_RBBH_Tbi_sex_bias$table[,1]),
length(TTT_RT_SB_RBBH_Tce_sex_bias$table[,1]),
length(TTT_RT_SB_RBBH_Tcm_sex_bias$table[,1]),
length(TTT_RT_SB_RBBH_Tpa_sex_bias$table[,1]),
length(TTT_RT_SB_RBBH_Tps_sex_bias$table[,1]),
length(TTT_LG_SB_RBBH_Tbi_sex_bias$table[,1]),
length(TTT_LG_SB_RBBH_Tce_sex_bias$table[,1]),
length(TTT_LG_SB_RBBH_Tcm_sex_bias$table[,1]),
length(TTT_LG_SB_RBBH_Tpa_sex_bias$table[,1]),
length(TTT_LG_SB_RBBH_Tps_sex_bias$table[,1])
)))
names(N_sex_bias_genes) <- c("N_sex_bias_genes", "N_genes")
N_sex_bias_genes$sp <- c("Tbi","Tce","Tcm","Tpa","Tps","Tbi","Tce","Tcm","Tpa","Tps","Tbi","Tce","Tcm","Tpa","Tps")
N_sex_bias_genes$tiss <- c("WB","WB","WB","WB","WB","RT","RT","RT","RT","RT","LG","LG","LG","LG","LG")
N_sex_bias_genes$group <- paste(N_sex_bias_genes$sp, N_sex_bias_genes$tiss, sep = "_")
N_sex_bias_genes$group_ordered <- ordered(N_sex_bias_genes$group, levels=c(
"Tbi_WB","Tbi_RT","Tbi_LG",
"Tce_WB","Tce_RT","Tce_LG",
"Tps_WB","Tps_RT","Tps_LG",
"Tcm_WB","Tcm_RT","Tcm_LG",
"Tpa_WB","Tpa_RT","Tpa_LG"
))
N_sex_bias_genes$perc_sex_bias_genes <- N_sex_bias_genes$N_sex_bias_genes / N_sex_bias_genes$N_genes * 100
write.csv(N_sex_bias_genes, "N_sex_bias_genes_RBBH.csv")
p3 <- ggplot(N_sex_bias_genes, aes(factor(group_ordered), N_sex_bias_genes, fill = tiss )) +
geom_bar(stat="identity", position = "dodge") +
theme_bw() +
xlab ("Species pair") +
ylab ("Number of sex biased genes, FDR < 0.05") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim=c(-0,8000))
png(filename = "N_sex_bias_genes.png",
width = 6, height = 7, units = "in", pointsize = 12,
bg = "white", res = 300)
p3 + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_fill_manual(values=c("firebrick3", "#56B4E9", "black"))
dev.off()
getwd() ## where has my plot gone....?
p3a <- ggplot(N_sex_bias_genes, aes(factor(group_ordered), perc_sex_bias_genes, fill = tiss )) +
geom_bar(stat="identity", position = "dodge") +
theme_bw() +
xlab ("Species pair") +
ylab ("Percentage of sex biased genes, FDR < 0.05") +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim=c(-0,60))
png(filename = "Perc_sex_bias_genes.png",
width = 6, height = 7, units = "in", pointsize = 12,
bg = "white", res = 300)
p3a + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_fill_manual(values=c("firebrick3", "#56B4E9", "black"))
dev.off()
getwd() ## where has my plot gone....?
########################################################################################################################################################################
####### output session info
print (sessionInfo())
writeLines(capture.output(sessionInfo()), "sex_bias_edgeR_sessionInfo.txt")