library(limma)
library(edgeR)
library(magrittr)
library(janitor)
library(dplyr)
library(tibble)
library(rtracklayer)
library(venn)
library(UpSetR)
Change the working directory to where the project is located.
setwd("~/Documents/Kelly_2020/Research/Six_M_Work_2020/Jo_RNAseq/Kelly_JoRNAseq_DEG_Parthway_R_codes")
load("count_jo_Bos_taurus.ARS-UCD1.2.99.rda")
load("annotation_jo_Bos_taurus.ARS-UCD1.2.99.rda")
Hereford_ARSgtf_EST <- ebi_anno_gtf_gene_as_df
Load the counts for genes from the featureCounts
to identify the DEGs.
genes <- countsensembl$counts
x <- list()
# Set the count
x$counts <- genes%>%apply(.,MARGIN = 2,FUN = as.numeric)%>%
set_rownames(rownames(genes))
colnames(x$counts) <- gsub(".R1.all.ARS.sorted.bam", "", colnames(x$counts ))
x$anno <- subset(Hereford_ARSgtf_EST, gene_id %in% rownames(x$counts))%>%subset(feature %in% "gene")
x$genes <- data.frame(gene_id = x$anno$gene_id,
chr = x$anno$chr,
gene_name = x$anno$gene_name,
gene_biotype = x$anno$gene_biotype)
#set expression level cutoff, can change from 0.5 to 1.
sel <- rowSums(cpm(x$counts) > 1) >= 3
x$counts <- x$counts[sel,]
x$genes <- x$genes[sel,]
x$anno <- x$anno[sel,]
sampleinfo<- read.csv("sampleinfo.csv")
sampleinfo
## rna_id Subject Phenotype Tissue_type
## 1 546FB 546 horned forebrain
## 2 546FS 546 horned frontal_skin
## 3 546HB 546 horned hornbud
## 4 546MB 546 horned midbrain
## 5 618FB 618 horned forebrain
## 6 618FS 618 horned frontal_skin
## 7 618HB 618 horned hornbud
## 8 618MB 618 horned midbrain
## 9 667FB 667 polled forebrain
## 10 667FS 667 polled frontal_skin
## 11 667HB 667 polled hornbud
## 12 667MB 667 polled midbrain
## 13 668FB 668 horned forebrain
## 14 668FS 668 horned frontal_skin
## 15 668HB 668 horned hornbud
## 16 668MB 668 horned midbrain
## 17 698FB 698 polled forebrain
## 18 698FS 698 polled frontal_skin
## 19 698HB 698 polled hornbud
## 20 698MB 698 polled midbrain
## 21 709FB 709 polled forebrain
## 22 709FS 709 polled frontal_skin
## 23 709HB 709 polled hornbud
## 24 709MB 709 polled midbrain
## 25 736FB 736 horned forebrain
## 26 736FS 736 horned frontal_skin
## 27 736HB 736 horned hornbud
## 28 736MB 736 horned midbrain
x$counts <- x$counts[,match(sampleinfo[,1],colnames(x$counts))]
colnames(x$counts)
## [1] "546FB" "546FS" "546HB" "546MB" "618FB" "618FS" "618HB" "618MB" "667FB"
## [10] "667FS" "667HB" "667MB" "668FB" "668FS" "668HB" "668MB" "698FB" "698FS"
## [19] "698HB" "698MB" "709FB" "709FS" "709HB" "709MB" "736FB" "736FS" "736HB"
## [28] "736MB"
Subject <- as.factor(sampleinfo[,2])
phenotype <- as.factor(sampleinfo[,3])
tissues <- as.factor(sampleinfo[,4])
Treat <- factor(paste(phenotype,tissues,sep="."))
x <- new("DGEList", x)
dim(x)
## [1] 20297 28
x <- calcNormFactors(x, method="TMM")
#set design matrix
design <- model.matrix(~0+Treat)
colnames(design) <- levels(Treat)
xtreat <- estimateDisp(x,design)
#plot biological coefficient variation
plotBCV(xtreat)
sqrt(xtreat$common.dispersion)
## [1] 0.3368676
vtreat <- voomWithQualityWeights(xtreat,design=design,normalize.method = "none",
plot=T,col=as.numeric(Treat))
cols <- rep("red",28)
cols[phenotype=="horned"] <- "blue"
#mds plot with batch included
plotMDS(vtreat,label=tissues,col=cols,dim.plot=c(1,2),main="horned and polled RNA-seq MDSplot")
cols <- rep("red",28)
cols[tissues=="forebrain"] <- "blue"
cols[tissues=="frontal_skin"] <- "black"
cols[tissues=="hornbud"] <- "red"
cols[tissues=="midbrain"] <- "green"
#mds plot with batch included
plotMDS(vtreat,label=phenotype,col=cols,dim.plot=c(1,2),main="horned and polled RNA-seq MDSplot(colored by tissues)")
cols <- rep("red",28)
cols[Subject=="546"] <- "blue"
cols[Subject=="618"] <- "black"
cols[Subject=="667"] <- "red"
cols[Subject=="668"] <- "green"
cols[Subject=="698"] <- "yellow"
cols[Subject=="709"] <- "orange"
cols[Subject=="736"] <- "grey"
#mds plot with batch included
plotMDS(vtreat,label=tissues,col=cols,dim.plot=c(1,2),main="horned and polled RNA-seq MDSplot(colored by tissues)")
#### Comparisions & results
# estimate the correlation between measurements made on the same subject
corfit <- duplicateCorrelation(vtreat,design,block=sampleinfo$Subject)
corfit$consensus
## [1] 0.254882
vfittreat <- lmFit(vtreat,design,block=sampleinfo$Subject,correlation=corfit$consensus)
cm <- makeContrasts(
polledvshornedForforebrain = polled.forebrain-horned.forebrain,
polledvshornedForfrontal_skin = polled.frontal_skin-horned.frontal_skin,
polledvshornedForhornbud = polled.hornbud-horned.hornbud,
polledvshornedFormidbrain = polled.midbrain-horned.midbrain,
forebrainvsfrontal_skinForpolled = polled.forebrain-polled.frontal_skin,
forebrainvsfrontal_skinForhorned = horned.forebrain-horned.frontal_skin,
forebrainvshornbudForpolled = polled.forebrain-polled.hornbud,
forebrainvshornbudForhorned = horned.forebrain-horned.hornbud,
midbrainvsforebrainForpolled = polled.midbrain-polled.forebrain,
midbrainvsforebrainForhorned = horned.midbrain-horned.forebrain,
hornbudvsfrontal_skinForpolled = polled.hornbud-polled.frontal_skin,
hornbudvsfrontal_skinForhorned = horned.hornbud-horned.frontal_skin,
midbrainvsfrontal_skinForpolled = polled.midbrain-polled.frontal_skin,
midbrainvsfrontal_skinForhorned = horned.midbrain-horned.frontal_skin,
midbrainvshornbudForpolled = polled.midbrain-polled.hornbud,
midbrainvshornbudForhorned = horned.midbrain-horned.hornbud,levels=design)
vfittreat2 <- contrasts.fit(vfittreat, cm)
vfittreat2 <- eBayes(vfittreat2)
# outcome of each hypothesis test
resulttreat <- decideTests(vfittreat2,p.value=0.05, lfc = 1)
# Venn diagram showing numbers of genes significant in each comparison
#venn(as.data.frame(resulttreat)%>%abs(), zcolor = "style")
summary(decideTests(vfittreat2,p.value=0.05,lfc = 1))
## polledvshornedForforebrain polledvshornedForfrontal_skin
## Down 1480 1122
## NotSig 15703 13285
## Up 3114 5890
## polledvshornedForhornbud polledvshornedFormidbrain
## Down 1214 1451
## NotSig 12711 16897
## Up 6372 1949
## forebrainvsfrontal_skinForpolled forebrainvsfrontal_skinForhorned
## Down 161 294
## NotSig 20014 17607
## Up 122 2396
## forebrainvshornbudForpolled forebrainvshornbudForhorned
## Down 114 740
## NotSig 20181 15069
## Up 2 4488
## midbrainvsforebrainForpolled midbrainvsforebrainForhorned
## Down 3 36
## NotSig 20291 20200
## Up 3 61
## hornbudvsfrontal_skinForpolled hornbudvsfrontal_skinForhorned
## Down 0 43
## NotSig 20295 20200
## Up 2 54
## midbrainvsfrontal_skinForpolled midbrainvsfrontal_skinForhorned
## Down 174 302
## NotSig 19961 16135
## Up 162 3860
## midbrainvshornbudForpolled midbrainvshornbudForhorned
## Down 176 772
## NotSig 20017 14002
## Up 104 5523
summary(decideTests(vfittreat2,p.value=0.05,lfc = 1))[,1:4]
## polledvshornedForforebrain polledvshornedForfrontal_skin
## Down 1480 1122
## NotSig 15703 13285
## Up 3114 5890
## polledvshornedForhornbud polledvshornedFormidbrain
## Down 1214 1451
## NotSig 12711 16897
## Up 6372 1949
venn(as.data.frame(resulttreat)[,1:4]%>%abs(), zcolor = "style")
# transfer the data format
Upset_input <- c(
polledvshornedForforebrain = 4594,
polledvshornedForfrontal_skin = 7012,
polledvshornedForhornbud = 7586,
polledvshornedFormidbrain = 3400,
"polledvshornedForforebrain&polledvshornedForfrontal_skin" = 3232,
"polledvshornedForforebrain&polledvshornedForhornbud" = 3377,
"polledvshornedForforebrain&polledvshornedFormidbrain" = 2173,
"polledvshornedForfrontal_skin&polledvshornedForhornbud" = 6015,
"polledvshornedForfrontal_skin&polledvshornedFormidbrain" = 2268,
"polledvshornedForhornbud&polledvshornedFormidbrain" = 2315,
"polledvshornedForhornbud&polledvshornedForforebrain&polledvshornedForfrontal_skin" = 2980,
"polledvshornedForfrontal_skin&polledvshornedFormidbrain&polledvshornedForforebrain" = 1491,
"polledvshornedForhornbud&polledvshornedFormidbrain&polledvshornedForfrontal_skin" = 2016,
"polledvshornedForhornbud&polledvshornedFormidbrain&polledvshornedForforebrain" = 1558,
"polledvshornedForhornbud&polledvshornedFormidbrain&polledvshornedForfrontal_skin&polledvshornedForforebrain" = 1353)
# plot
upset(fromExpression(Upset_input),
nintersects = NA,
nsets = 4,
mb.ratio = c(0.6, 0.4),
number.angles = 0,
text.scale = 1.1,
point.size = 2.8,
line.size = 1,
main.bar.color = "black")
###### venn for forebrainvsfrontal_skin
venn(as.data.frame(resulttreat)[,c(5,6)]%>%abs(), zcolor = "style")
venn(as.data.frame(resulttreat)[,c(7,8)]%>%abs(), zcolor = "style")
###### venn for midbrainvsforebrain
venn(as.data.frame(resulttreat)[,c(9,10)]%>%abs(), zcolor = "style")
###### venn for hornbudvsfrontal_skin
venn(as.data.frame(resulttreat)[,c(11,12)]%>%abs(), zcolor = "style")
###### venn for midbrainvsfrontal_skin
venn(as.data.frame(resulttreat)[,c(13,14)]%>%abs(), zcolor = "style")
###### venn for midbrainvshornbud
venn(as.data.frame(resulttreat)[,c(15,16)]%>%abs(), zcolor = "style")
summary(decideTests(vfittreat2,p.value=0.05,lfc = 1))[,c(5,7,9,11,13,15)]
## forebrainvsfrontal_skinForpolled forebrainvshornbudForpolled
## Down 161 114
## NotSig 20014 20181
## Up 122 2
## midbrainvsforebrainForpolled hornbudvsfrontal_skinForpolled
## Down 3 0
## NotSig 20291 20295
## Up 3 2
## midbrainvsfrontal_skinForpolled midbrainvshornbudForpolled
## Down 174 176
## NotSig 19961 20017
## Up 162 104
venn(as.data.frame(resulttreat)[,c(5,7,9,11,13,15)]%>%abs(), zcolor = "style")
# transfer the data format
Upset_input <- c(
forebrainvsfrontal_skinForpolled = 283,
forebrainvshornbudForpolled = 116,
midbrainvsforebrainForpolled = 6,
hornbudvsfrontal_skinForpolled = 2,
midbrainvsfrontal_skinForpolled = 336,
midbrainvshornbudForpolled = 280,
"forebrainvsfrontal_skinForpolled&forebrainvshornbudForpolled" = 68,
"forebrainvsfrontal_skinForpolled&midbrainvsforebrainForpolled" = 1,
"forebrainvsfrontal_skinForpolled&hornbudvsfrontal_skinForpolled" = 1,
"forebrainvsfrontal_skinForpolled&midbrainvsfrontal_skinForpolled" = 106,
"forebrainvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 58,
"forebrainvshornbudForpolled&midbrainvsforebrainForpolled" = 3,
"forebrainvshornbudForpolled&hornbudvsfrontal_skinForpolled" = 0,
"forebrainvshornbudForpolled&midbrainvsfrontal_skinForpolled" = 41,
"forebrainvshornbudForpolled&midbrainvshornbudForpolled" = 56,
"midbrainvsforebrainForpolled&hornbudvsfrontal_skinForpolled" = 0,
"midbrainvsforebrainForpolled&midbrainvsfrontal_skinForpolled" = 6,
"midbrainvsforebrainForpolled&midbrainvshornbudForpolled" = 3,
"hornbudvsfrontal_skinForpolled&midbrainvsfrontal_skinForpolled" = 1,
"hornbudvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 0,
"midbrainvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 120,
"forebrainvsfrontal_skinForpolled&forebrainvshornbudForpolled&midbrainvsforebrainForpolled" = 1,
"forebrainvsfrontal_skinForpolled&forebrainvshornbudForpolled&hornbudvsfrontal_skinForpolled" = 0,
"forebrainvsfrontal_skinForpolled&forebrainvshornbudForpolled&midbrainvsfrontal_skinForpolled" = 34,
"forebrainvsfrontal_skinForpolled&forebrainvshornbudForpolled&midbrainvshornbudForpolled" = 39,
"forebrainvsfrontal_skinForpolled&midbrainvsforebrainForpolled&hornbudvsfrontal_skinForpolled" = 0,
"forebrainvsfrontal_skinForpolled&midbrainvsforebrainForpolled&midbrainvsfrontal_skinForpolled" = 1,
"forebrainvsfrontal_skinForpolled&midbrainvsforebrainForpolled&midbrainvshornbudForpolled" = 1,
"forebrainvsfrontal_skinForpolled&hornbudvsfrontal_skinForpolled&midbrainvsfrontal_skinForpolled" = 1,
"forebrainvsfrontal_skinForpolled&hornbudvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 0,
"forebrainvsfrontal_skinForpolled&midbrainvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 50,
"forebrainvshornbudForpolled&midbrainvsforebrainForpolled&hornbudvsfrontal_skinForpolled" = 0,
"forebrainvshornbudForpolled&midbrainvsforebrainForpolled&midbrainvsfrontal_skinForpolled" = 3,
"forebrainvshornbudForpolled&midbrainvsforebrainForpolled&midbrainvshornbudForpolled" = 2,
"forebrainvshornbudForpolled&hornbudvsfrontal_skinForpolled&midbrainvsfrontal_skinForpolled" = 0,
"forebrainvshornbudForpolled&hornbudvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 0,
"forebrainvshornbudForpolled&midbrainvsfrontal_skinForpolled&midbrainvshornbudForpolled" =36,
"midbrainvsforebrainForpolled&hornbudvsfrontal_skinForpolled&midbrainvsfrontal_skinForpolled" = 0,
"midbrainvsforebrainForpolled&hornbudvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 0,
"midbrainvsforebrainForpolled&midbrainvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 3,
"hornbudvsfrontal_skinForpolled&midbrainvsfrontal_skinForpolled&midbrainvshornbudForpolled" = 0)
# plot
upset(fromExpression(Upset_input),
nintersects = NA,
nsets = 6,
mb.ratio = c(0.6, 0.4),
number.angles = 0,
text.scale = 1.1,
point.size = 2.8,
line.size = 1,
main.bar.color = "red")
summary(decideTests(vfittreat2,p.value=0.05,lfc = 1))[,c(6,8,10,12,14,16)]
## forebrainvsfrontal_skinForhorned forebrainvshornbudForhorned
## Down 294 740
## NotSig 17607 15069
## Up 2396 4488
## midbrainvsforebrainForhorned hornbudvsfrontal_skinForhorned
## Down 36 43
## NotSig 20200 20200
## Up 61 54
## midbrainvsfrontal_skinForhorned midbrainvshornbudForhorned
## Down 302 772
## NotSig 16135 14002
## Up 3860 5523
venn(as.data.frame(resulttreat)[,c(6,8,10,12,14,16)]%>%abs(), zcolor = "style")
# transfer the data format
Upset_input <- c(
forebrainvsfrontal_skinForhorned = 2690,
forebrainvshornbudForhorned = 5228,
midbrainvsforebrainForhorned = 97,
hornbudvsfrontal_skinForhorned = 97,
midbrainvsfrontal_skinForhorned = 4162,
midbrainvshornbudForhorned = 6295,
"forebrainvsfrontal_skinForhorned&forebrainvshornbudForhorned" = 2292,
"forebrainvsfrontal_skinForhorned&midbrainvsforebrainForhorned" = 18,
"forebrainvsfrontal_skinForhorned&hornbudvsfrontal_skinForhorned" = 18,
"forebrainvsfrontal_skinForhorned&midbrainvsfrontal_skinForhorned" = 2381,
"forebrainvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 2359,
"forebrainvshornbudForhorned&midbrainvsforebrainForhorned" = 33,
"forebrainvshornbudForhorned&hornbudvsfrontal_skinForhorned" = 60,
"forebrainvshornbudForhorned&midbrainvsfrontal_skinForhorned" = 3100,
"forebrainvshornbudForhorned&midbrainvshornbudForhorned" = 4737,
"midbrainvsforebrainForhorned&hornbudvsfrontal_skinForhorned" = 1,
"midbrainvsforebrainForhorned&midbrainvsfrontal_skinForhorned" = 64,
"midbrainvsforebrainForhorned&midbrainvshornbudForhorned" = 66,
"hornbudvsfrontal_skinForhorned&midbrainvsfrontal_skinForhorned" = 20,
"hornbudvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 58,
"midbrainvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 3800,
"forebrainvsfrontal_skinForhorned&forebrainvshornbudForhorned&midbrainvsforebrainForhorned" = 13,
"forebrainvsfrontal_skinForhorned&forebrainvshornbudForhorned&hornbudvsfrontal_skinForhorned" = 4,
"forebrainvsfrontal_skinForhorned&forebrainvshornbudForhorned&midbrainvsfrontal_skinForhorned" = 2075,
"forebrainvsfrontal_skinForhorned&forebrainvshornbudForhorned&midbrainvshornbudForhorned" = 2187,
"forebrainvsfrontal_skinForhorned&midbrainvsforebrainForhorned&hornbudvsfrontal_skinForhorned" = 0,
"forebrainvsfrontal_skinForhorned&midbrainvsforebrainForhorned&midbrainvsfrontal_skinForhorned" = 6,
"forebrainvsfrontal_skinForhorned&midbrainvsforebrainForhorned&midbrainvshornbudForhorned" = 9,
"forebrainvsfrontal_skinForhorned&hornbudvsfrontal_skinForhorned&midbrainvsfrontal_skinForhorned" = 13,
"forebrainvsfrontal_skinForhorned&hornbudvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 4,
"forebrainvsfrontal_skinForhorned&midbrainvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 2221,
"forebrainvshornbudForhorned&midbrainvsforebrainForhorned&hornbudvsfrontal_skinForhorned" = 1,
"forebrainvshornbudForhorned&midbrainvsforebrainForhorned&midbrainvsfrontal_skinForhorned" = 16,
"forebrainvshornbudForhorned&midbrainvsforebrainForhorned&midbrainvshornbudForhorned" = 14,
"forebrainvshornbudForhorned&hornbudvsfrontal_skinForhorned&midbrainvsfrontal_skinForhorned" = 6,
"forebrainvshornbudForhorned&hornbudvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 49,
"forebrainvshornbudForhorned&midbrainvsfrontal_skinForhorned&midbrainvshornbudForhorned" =3068,
"midbrainvsforebrainForhorned&hornbudvsfrontal_skinForhorned&midbrainvsfrontal_skinForhorned" = 0,
"midbrainvsforebrainForhorned&hornbudvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 0,
"midbrainvsforebrainForhorned&midbrainvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 58,
"hornbudvsfrontal_skinForhorned&midbrainvsfrontal_skinForhorned&midbrainvshornbudForhorned" = 2)
# plot
upset(fromExpression(Upset_input),
nintersects = NA,
nsets = 6,
mb.ratio = c(0.6, 0.4),
number.angles = 0,
text.scale = 1.1,
point.size = 2.8,
line.size = 1,
main.bar.color = "blue")
Results_list <- list(topTable(vfittreat2,n=Inf,coef="polledvshornedForforebrain",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="polledvshornedForfrontal_skin",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="polledvshornedForhornbud",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="polledvshornedFormidbrain",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="forebrainvsfrontal_skinForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="forebrainvsfrontal_skinForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="forebrainvshornbudForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="forebrainvshornbudForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="midbrainvsforebrainForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="midbrainvsforebrainForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="hornbudvsfrontal_skinForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="hornbudvsfrontal_skinForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="midbrainvsfrontal_skinForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="midbrainvsfrontal_skinForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="midbrainvshornbudForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),
topTable(vfittreat2,n=Inf,coef="midbrainvshornbudForhorned",p.value=0.05,lfc = 1)%>%remove_rownames())%>%set_names(cm%>%colnames())
write.csv(topTable(vfittreat2,n=Inf,coef="polledvshornedForforebrain",p.value=0.05,lfc = 1)%>%remove_rownames(),file="polledvshornedForforebrain-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="polledvshornedForfrontal_skin",p.value=0.05,lfc = 1)%>%remove_rownames(),file="polledvshornedForfrontal_skin-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="polledvshornedForhornbud",p.value=0.05,lfc = 1)%>%remove_rownames(),file="polledvshornedForhornbud-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="polledvshornedFormidbrain",p.value=0.05,lfc = 1)%>%remove_rownames(),file="polledvshornedFormidbrain-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="forebrainvsfrontal_skinForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),file="forebrainvsfrontal_skinForpolled-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="forebrainvsfrontal_skinForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),file="forebrainvsfrontal_skinForhorned-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="forebrainvshornbudForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),file="forebrainvshornbudForpolled-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="forebrainvshornbudForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),file="forebrainvshornbudForhorned-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvsforebrainForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),file="midbrainvsforebrainForpolled-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvsforebrainForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),file="midbrainvsforebrainForhorned-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="hornbudvsfrontal_skinForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),file="hornbudvsfrontal_skinForpolled-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="hornbudvsfrontal_skinForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),file="hornbudvsfrontal_skinForhorned-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvsfrontal_skinForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),file="midbrainvsfrontal_skinForpolled-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvsfrontal_skinForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),file="midbrainvsfrontal_skinForhorned-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvshornbudForpolled",p.value=0.05,lfc = 1)%>%remove_rownames(),file="midbrainvshornbudForpolled-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvshornbudForhorned",p.value=0.05,lfc = 1)%>%remove_rownames(),file="midbrainvshornbudForhorned-ARS_jo-DEG_p0.05_lfc1.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="polledvshornedForforebrain")%>%remove_rownames(),file="polledvshornedForforebrain-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="polledvshornedForfrontal_skin")%>%remove_rownames(),file="polledvshornedForfrontal_skin-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="polledvshornedForhornbud")%>%remove_rownames(),file="polledvshornedForhornbud-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="polledvshornedFormidbrain")%>%remove_rownames(),file="polledvshornedFormidbrain-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="forebrainvsfrontal_skinForpolled")%>%remove_rownames(),file="forebrainvsfrontal_skinForpolled-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="forebrainvsfrontal_skinForhorned")%>%remove_rownames(),file="forebrainvsfrontal_skinForhorned-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="forebrainvshornbudForpolled")%>%remove_rownames(),file="forebrainvshornbudForpolled-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="forebrainvshornbudForhorned")%>%remove_rownames(),file="forebrainvshornbudForhorned-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvsforebrainForpolled")%>%remove_rownames(),file="midbrainvsforebrainForpolled-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvsforebrainForhorned")%>%remove_rownames(),file="midbrainvsforebrainForhorned-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="hornbudvsfrontal_skinForpolled")%>%remove_rownames(),file="hornbudvsfrontal_skinForpolled-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="hornbudvsfrontal_skinForhorned")%>%remove_rownames(),file="hornbudvsfrontal_skinForhorned-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvsfrontal_skinForpolled")%>%remove_rownames(),file="midbrainvsfrontal_skinForpolled-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvsfrontal_skinForhorned")%>%remove_rownames(),file="midbrainvsfrontal_skinForhorned-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvshornbudForpolled")%>%remove_rownames(),file="midbrainvshornbudForpolled-ARS_jo-DEG_all.csv")
write.csv(topTable(vfittreat2,n=Inf,coef="midbrainvshornbudForhorned")%>%remove_rownames(),file="midbrainvshornbudForhorned-ARS_jo-DEG_all.csv")
sessionInfo()
## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] UpSetR_1.4.0 venn_1.9 rtracklayer_1.48.0
## [4] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2 IRanges_2.22.2
## [7] S4Vectors_0.26.1 BiocGenerics_0.34.0 tibble_3.0.3
## [10] dplyr_1.0.2 janitor_2.0.1 magrittr_1.5
## [13] edgeR_3.30.3 limma_3.44.3
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 locfit_1.5-9.4
## [3] lubridate_1.7.9 lattice_0.20-41
## [5] Rsamtools_2.4.0 Biostrings_2.56.0
## [7] digest_0.6.25 R6_2.4.1
## [9] plyr_1.8.6 evaluate_0.14
## [11] ggplot2_3.3.2 pillar_1.4.6
## [13] zlibbioc_1.34.0 rlang_0.4.7
## [15] Matrix_1.2-18 rmarkdown_2.3
## [17] labeling_0.3 splines_4.0.2
## [19] statmod_1.4.34 BiocParallel_1.22.0
## [21] stringr_1.4.0 RCurl_1.98-1.2
## [23] munsell_0.5.0 DelayedArray_0.14.1
## [25] compiler_4.0.2 xfun_0.16
## [27] pkgconfig_2.0.3 htmltools_0.5.0
## [29] tidyselect_1.1.0 SummarizedExperiment_1.18.2
## [31] gridExtra_2.3 GenomeInfoDbData_1.2.3
## [33] bookdown_0.20 matrixStats_0.56.0
## [35] XML_3.99-0.5 crayon_1.3.4
## [37] GenomicAlignments_1.24.0 bitops_1.0-6
## [39] grid_4.0.2 gtable_0.3.0
## [41] lifecycle_0.2.0 scales_1.1.1
## [43] stringi_1.4.6 farver_2.0.3
## [45] XVector_0.28.0 snakecase_0.11.0
## [47] ellipsis_0.3.1 admisc_0.8
## [49] generics_0.0.2 vctrs_0.3.2
## [51] tools_4.0.2 Biobase_2.48.0
## [53] glue_1.4.1 purrr_0.3.4
## [55] yaml_2.2.1 colorspace_1.4-1
## [57] knitr_1.29