#/////////////////////////////////////////////////////////////////////////////
# LIBRARIES AND CUSTOM FUNCTIONS
#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
library(edgeR)
## Loading required package: limma
library(car)
library(gplots)
## KernSmooth 2.23 loaded
## Copyright M. P. Wand 1997-2009
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess
library(reshape)
library(stringr)
library(RColorBrewer)
library(limma)
# Create folder for results files
setwd("~/Projects/2013-10_HTL_RNASeq/RSEM/")
# Run once #################################################################################################
library(biomaRt)
ensMart<-useMart("ensembl")
ensembl_hs_mart <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
ensembl_df <- getBM(attributes=c("ensembl_gene_id", "refseq_mrna",
"hgnc_symbol","chromosome_name", "ensembl_transcript_id"),mart=ensembl_hs_mart)
head(ensembl_df)
## ensembl_gene_id refseq_mrna hgnc_symbol chromosome_name
## 1 ENSG00000261657 SLC25A26 HG991_PATCH
## 2 ENSG00000261657 SLC25A26 HG991_PATCH
## 3 ENSG00000261657 SLC25A26 HG991_PATCH
## 4 ENSG00000261657 SLC25A26 HG991_PATCH
## 5 ENSG00000261657 SLC25A26 HG991_PATCH
## 6 ENSG00000223116 13
## ensembl_transcript_id
## 1 ENST00000566782
## 2 ENST00000562780
## 3 ENST00000569579
## 4 ENST00000568242
## 5 ENST00000565530
## 6 ENST00000411184
# /Run once #################################################################################################
#/////////////////////////////////////////////////////////////////////////////
# Read in isform data ------------------------------------------------------------
#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
## Read in the data making the row names the first column
counttable_isform <- read.table("~/Projects/2013-10_HTL_RNASeq/RSEM/RSEM.gencode18_isoforms.20140616.tsv", header=T, row.names=1)
#head(counttable_all)
colnames(counttable_isform)<-sapply(strsplit(colnames(counttable_isform), "\\."), `[`, 2)
#/////////////////////////////////////////////////////////////////////////////
# Bring in metadata
#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
meta <- read.csv("~/Projects/2013-10_HTL_RNASeq/Metadata/20131020_phenotypes_from_intranet.csv",row.names=1,sep="\t")
#rownames(meta) <- paste("Sample",meta$SampleID,meta$Treatment,sep="_")
meta$Glycemic<-factor(recode(meta$Hba1c,"0:6='Normoglycemic';6:6.5='IGT';6.5:1000='T2D'"))
meta$Glycemic <- relevel(meta$Glycemic,ref="Normoglycemic")
meta$Donor <- rownames(meta)
#/////////////////////////////////////////////////////////////////////////////
# Bring in alfa/beta
#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
AlfaBeta <- read.csv("~/Projects//2013-09_Corrado_group/ins_glucagon.csv",sep="\t")
MetaAlfaBeta<-merge(meta,AlfaBeta,by.x="Donor",by.y="DONOR",all.x=T)
rownames(MetaAlfaBeta) <- MetaAlfaBeta$Donor
#/////////////////////////////////////////////////////////////////////////////
# Add information for high glucose
#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
highcl <- colnames(counttable_isform)[grep("High",colnames(counttable_isform))]
#HighSamples <- sapply(strsplit(highcl, "\\_"), `[`, 1)
HighSamples <- substr(highcl,1,7)
MetaAlfaBeta$Treatment <- "Low"
MetaAlfaBeta$InGlucosePair <- 0
MetaAlfaBeta[HighSamples,]$InGlucosePair <- 1
MetaHigh <- MetaAlfaBeta[HighSamples,]
rownames(MetaHigh) <- highcl
MetaHigh$Treatment <- "High"
MetaComplete <- rbind(MetaAlfaBeta,MetaHigh)
MetaSeqSamples <- MetaComplete[intersect(colnames(counttable_isform),rownames(MetaComplete)),]
#/////////////////////////////////////////////////////////////////////////////
# Basic qc and remove bad samples
#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
GoodSamples <- read.csv("/home/petter/Projects/2014-03-05_GlucoseTreatedIslets/Goodsamples.csv",row.names=1)
GoodSamples$y <- gsub("_","",GoodSamples$x)
CounttableQCisform <- counttable_isform[,as.character(GoodSamples$y)]
MetaSeqSamplesQC <- MetaSeqSamples[intersect(colnames(CounttableQCisform),rownames(MetaComplete)),]
setdiff(colnames(CounttableQCisform),rownames(MetaSeqSamplesQC))
## character(0)
MetaSeqSamplesQC$Treatment<-factor(MetaSeqSamplesQC$Treatment)
MetaSeqSamplesQC$Treatment <- relevel(MetaSeqSamplesQC$Treatment,ref="Low")
MetaSeqSamplesQC$Age <- as.numeric(substr(MetaSeqSamplesQC$Isolated,1,4))-MetaSeqSamplesQC$Year.of.birth
# FILTER
library(genefilter)
##
## Attaching package: 'genefilter'
##
## The following object is masked from 'package:car':
##
## Anova
f1 <- kOverA(5, 1)
ffun <- filterfun(f1)
wh1 <- genefilter(CounttableQCisform, ffun)
table(wh1)
## wh1
## FALSE TRUE
## 134319 62201
CounttableQCisform_expressionfilter <- CounttableQCisform[wh1,]
design <- model.matrix(~Donor+Treatment,data=MetaSeqSamplesQC)
fit <- lmFit(as.matrix(log2(CounttableQCisform_expressionfilter+.1)),design)
fit <- eBayes(fit)
tt_isform <- topTable(fit,coef="TreatmentHigh",n=16000000,sort="p")
table(Signifikant=tt_isform$adj.P.Val < .05,Uppreg=tt_isform$logFC>0)
## Uppreg
## Signifikant FALSE TRUE
## FALSE 31327 25612
## TRUE 3471 1791
tt_isform$ensembl_tid <- substr(rownames(tt_isform),1,15)
tt_isform$orginal_id <- rownames(tt_isform)
tt_isform = cbind(ensembl_df[match(tt_isform$ensembl_tid, ensembl_df$ensembl_transcript_id),],tt_isform)
#my_isforms_ann <- my_genes_ann[order(my_isforms_ann$meanexpression,decreasing=T),]
head(tt_isform,n=32)
## ensembl_gene_id refseq_mrna hgnc_symbol chromosome_name
## 210875 ENSG00000143333 NM_002928 RGS16 1
## 189782 ENSG00000108551 NM_016084 RASD1 17
## 187749 ENSG00000121351 IAPP 12
## 88365 ENSG00000175426 NM_000439 PCSK1 5
## 57511 ENSG00000259796 14
## 138042 ENSG00000165621 NM_080818 OXGR1 13
## 187747 ENSG00000121351 NM_000415 IAPP 12
## 137045 ENSG00000154277 NM_004181 UCHL1 4
## 82770 ENSG00000172020 NM_002045 GAP43 3
## 211149 ENSG00000116690 NM_001127710 PRG4 1
## 79601 ENSG00000135447 NM_006741 PPP1R1A 12
## 81519 ENSG00000070985 TRPM5 11
## 208679 ENSG00000139973 NM_031914 SYT16 14
## 187748 ENSG00000121351 IAPP 12
## 187750 ENSG00000121351 IAPP 12
## 79600 ENSG00000135447 PPP1R1A 12
## 124211 ENSG00000101605 NM_019856 MYOM1 18
## 30271 ENSG00000155849 NM_001039459 ELMO1 7
## 77171 ENSG00000136750 GAD2 10
## 171072 ENSG00000148120 C9orf3 9
## 171077 ENSG00000148120 C9orf3 9
## 193487 ENSG00000150687 PRSS23 11
## 103380 ENSG00000198535 NM_207322 C2CD4A 15
## 80953 ENSG00000170577 NM_016932 SIX2 2
## 67530 ENSG00000179222 NM_006986 MAGED1 X
## 146118 ENSG00000104722 NM_005382 NEFM 8
## 95486 ENSG00000096433 NM_002224 ITPR3 6
## 185598 ENSG00000149571 NM_032531 KIRREL3 11
## 30268 ENSG00000155849 NM_130442 ELMO1 7
## 91418 ENSG00000167588 NM_001257199 GPD1 12
## 193486 ENSG00000150687 NM_007173 PRSS23 11
## 136625 ENSG00000091664 NM_020346 SLC17A6 11
## ensembl_transcript_id logFC AveExpr t P.Value adj.P.Val
## 210875 ENST00000367558 1.0343 3.8816 16.062 7.464e-16 4.643e-11
## 189782 ENST00000225688 1.0214 7.6748 13.947 2.725e-14 8.476e-10
## 187749 ENST00000537593 2.5318 3.7826 12.824 2.183e-13 4.393e-09
## 88365 ENST00000311106 1.2174 8.1324 12.690 2.825e-13 4.393e-09
## 57511 ENST00000568344 1.8346 4.1499 12.477 4.267e-13 5.022e-09
## 138042 ENST00000298440 1.2984 0.7611 12.412 4.845e-13 5.022e-09
## 187747 ENST00000240652 2.1565 10.3565 12.179 7.658e-13 6.141e-09
## 137045 ENST00000284440 0.6238 7.5659 12.164 7.898e-13 6.141e-09
## 82770 ENST00000305124 0.8874 2.0420 11.562 2.658e-12 1.689e-08
## 211149 ENST00000367485 0.9543 5.1050 11.552 2.716e-12 1.689e-08
## 79601 ENST00000257905 0.7422 7.8349 11.479 3.153e-12 1.783e-08
## 81519 ENST00000533881 2.5315 -0.2589 11.166 6.050e-12 3.136e-08
## 208679 ENST00000430451 1.8045 2.6787 11.000 8.596e-12 4.113e-08
## 187748 ENST00000542023 2.3579 3.0587 10.926 1.005e-11 4.467e-08
## 187750 ENST00000535428 2.5459 3.6270 10.683 1.693e-11 7.022e-08
## 79600 ENST00000547431 0.8859 1.0788 10.628 1.905e-11 7.408e-08
## 124211 ENST00000356443 1.3933 3.4424 10.423 2.977e-11 1.038e-07
## 30271 ENST00000396040 1.0322 4.8704 10.419 3.005e-11 1.038e-07
## 77171 ENST00000428517 1.2577 6.1718 10.172 5.183e-11 1.697e-07
## 171072 ENST00000473778 1.3337 1.4255 9.992 7.746e-11 2.365e-07
## 171077 ENST00000478603 1.6857 1.4868 9.979 7.983e-11 2.365e-07
## 193487 ENST00000532234 -1.3877 1.2939 -9.897 9.611e-11 2.717e-07
## 103380 ENST00000355522 0.9170 6.5410 9.823 1.136e-10 3.072e-07
## 80953 ENST00000303077 -0.9908 3.3297 -9.772 1.274e-10 3.203e-07
## 67530 ENST00000326587 0.5244 7.0397 9.768 1.287e-10 3.203e-07
## 146118 ENST00000437366 1.3151 2.8197 9.618 1.812e-10 4.335e-07
## 95486 ENST00000605930 -0.5314 5.4939 -9.599 1.893e-10 4.361e-07
## 185598 ENST00000525144 0.7077 2.7885 9.535 2.196e-10 4.832e-07
## 30268 ENST00000396045 0.6240 5.3735 9.523 2.257e-10 4.832e-07
## 91418 ENST00000301149 0.8830 2.7830 9.509 2.330e-10 4.832e-07
## 193486 ENST00000280258 -0.5911 4.3015 -9.466 2.576e-10 5.109e-07
## 136625 ENST00000263160 0.7762 2.7446 9.457 2.628e-10 5.109e-07
## B ensembl_tid orginal_id
## 210875 24.75 ENST00000367558 ENST00000367558.5
## 189782 21.71 ENST00000225688 ENST00000225688.3
## 187749 19.89 ENST00000537593 ENST00000537593.1
## 88365 19.66 ENST00000311106 ENST00000311106.3
## 57511 19.30 ENST00000568344 ENST00000568344.1
## 138042 19.18 ENST00000298440 ENST00000298440.1
## 187747 18.78 ENST00000240652 ENST00000240652.3
## 137045 18.75 ENST00000284440 ENST00000284440.4
## 82770 17.66 ENST00000305124 ENST00000305124.6
## 211149 17.64 ENST00000367485 ENST00000367485.4
## 79601 17.51 ENST00000257905 ENST00000257905.8
## 81519 16.92 ENST00000533881 ENST00000533881.1
## 208679 16.60 ENST00000430451 ENST00000430451.2
## 187748 16.46 ENST00000542023 ENST00000542023.1
## 187750 15.98 ENST00000535428 ENST00000535428.1
## 79600 15.87 ENST00000547431 ENST00000547431.1
## 124211 15.46 ENST00000356443 ENST00000356443.4
## 30271 15.45 ENST00000396040 ENST00000396040.2
## 77171 14.95 ENST00000428517 ENST00000428517.2
## 171072 14.58 ENST00000473778 ENST00000473778.1
## 171077 14.56 ENST00000478603 ENST00000478603.1
## 193487 14.38 ENST00000532234 ENST00000532234.1
## 103380 14.23 ENST00000355522 ENST00000355522.5
## 80953 14.12 ENST00000303077 ENST00000303077.6
## 67530 14.11 ENST00000326587 ENST00000326587.7
## 146118 13.80 ENST00000437366 ENST00000437366.2
## 95486 13.76 ENST00000605930 ENST00000605930.1
## 185598 13.62 ENST00000525144 ENST00000525144.2
## 30268 13.59 ENST00000396045 ENST00000396045.3
## 91418 13.56 ENST00000301149 ENST00000301149.3
## 193486 13.47 ENST00000280258 ENST00000280258.5
## 136625 13.45 ENST00000263160 ENST00000263160.3
for (i in 1:10) {
boxplot(unlist(CounttableQCisform_expressionfilter[tt_isform[i,"orginal_id"],])~MetaSeqSamplesQC$Treatment,las=2,ylab=tt_isform[i,"hgnc_symbol"])
}
#/////////////////////////////////////////////////////////////////////////////
# GENE
#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
## Read in the data making the row names the first column
counttable_gene <- read.table("~/Projects/2013-10_HTL_RNASeq/RSEM/RSEM.gene.20140618.tsv", header=T, row.names=1)
#head(counttable_gene)
colnames(counttable_gene)<-sapply(strsplit(colnames(counttable_gene), "\\."), `[`, 2)
CounttableQCGene <- counttable_gene[-1,as.character(GoodSamples$y)]
MetaSeqSamplesQCGene <- MetaSeqSamples[intersect(colnames(CounttableQCGene),rownames(MetaComplete)),]
setdiff(colnames(CounttableQCGene),rownames(MetaSeqSamplesQCGene))
## character(0)
MetaSeqSamplesQCGene$Treatment<-factor(MetaSeqSamplesQCGene$Treatment)
MetaSeqSamplesQCGene$Treatment <- relevel(MetaSeqSamplesQCGene$Treatment,ref="Low")
MetaSeqSamplesQCGene$Age <- as.numeric(substr(MetaSeqSamplesQCGene$Isolated,1,4))-MetaSeqSamplesQCGene$Year.of.birth
# FILTER
library(genefilter)
f1 <- kOverA(5, 1)
ffun <- filterfun(f1)
wh1 <- genefilter(CounttableQCGene, ffun)
table(wh1)
## wh1
## FALSE TRUE
## 39330 18489
CounttableQCGene_expressionfilter <- CounttableQCGene[wh1,]
design <- model.matrix(~Donor+Treatment,data=MetaSeqSamplesQCGene)
fit <- lmFit(as.matrix(log2(CounttableQCGene_expressionfilter+.1)),design)
fit <- eBayes(fit)
ttGene <- topTable(fit,coef="TreatmentHigh",n=16000000,sort="p")
table(Signifikant=ttGene$adj.P.Val < .05,Uppreg=ttGene$logFC>0)
## Uppreg
## Signifikant FALSE TRUE
## FALSE 7363 5047
## TRUE 4461 1618
ttGene$ensembl_tid <- substr(rownames(ttGene),1,15)
tt_gene_annotated = cbind(ensembl_df[match(ttGene$ensembl_tid, ensembl_df$ensembl_gene_id),],ttGene)
#my_genes_ann <- my_genes_ann[order(my_genes_ann$meanexpression,decreasing=T),]
merge <- merge(tt_isform,tt_gene_annotated,by="ensembl_gene_id",suffixes=c("_isoform","_gene"))
library(dplyr)
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following object is masked from '.env':
##
## n
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
hitme <- filter(merge, adj.P.Val_isoform < 0.05 & adj.P.Val_gene > .5 )
dim(hitme)
## [1] 253 24
hitme<-hitme[order(hitme$adj.P.Val_isoform),]
head(hitme,n=20)
## ensembl_gene_id refseq_mrna_isoform hgnc_symbol_isoform
## 18 ENSG00000070087 NM_053024 PFN2
## 58 ENSG00000108840 HDAC5
## 161 ENSG00000164941 INTS8
## 141 ENSG00000156113 KCNMA1
## 13 ENSG00000049449 RCN1
## 33 ENSG00000089248 NM_001034025 ERP29
## 232 ENSG00000254772 EEF1G
## 138 ENSG00000153914 SREK1
## 185 ENSG00000177963 NM_001286134 RIC8A
## 100 ENSG00000132142 ACACA
## 226 ENSG00000231312
## 70 ENSG00000114867 EIF4G1
## 8 ENSG00000015568 RGPD5
## 116 ENSG00000137337 MDC1
## 96 ENSG00000131389 SLC6A6
## 95 ENSG00000131389 SLC6A6
## 201 ENSG00000196498 NCOR2
## 191 ENSG00000182979 MTA1
## 77 ENSG00000118363 NM_014752 SPCS2
## 125 ENSG00000141551 CSNK1D
## chromosome_name_isoform ensembl_transcript_id_isoform logFC_isoform
## 18 3 ENST00000452853 -0.3185
## 58 17 ENST00000591714 0.7105
## 161 8 ENST00000524333 0.3428
## 141 10 ENST00000428546 -0.9235
## 13 11 ENST00000506388 0.6765
## 33 12 ENST00000455836 -0.5645
## 232 11 ENST00000378019 0.5156
## 138 5 ENST00000519259 -0.2822
## 185 11 ENST00000526104 0.2513
## 100 17 ENST00000588142 0.7431
## 226 2 ENST00000449569 -0.4164
## 70 3 ENST00000442406 -0.9828
## 8 2 ENST00000493394 -0.4270
## 116 6 ENST00000376405 -0.3522
## 96 3 ENST00000427436 -0.8231
## 95 3 ENST00000435095 -1.0247
## 201 12 ENST00000420698 -0.6242
## 191 14 ENST00000438610 -0.3338
## 77 11 ENST00000263672 0.3660
## 125 17 ENST00000584377 0.9105
## AveExpr_isoform t_isoform P.Value_isoform adj.P.Val_isoform B_isoform
## 18 5.6610 -7.047 1.018e-07 3.726e-05 7.836
## 58 2.0049 6.385 5.924e-07 1.289e-04 6.157
## 161 2.8791 6.208 9.553e-07 1.851e-04 5.700
## 141 0.3108 -5.842 2.583e-06 3.632e-04 4.749
## 13 -0.5239 5.831 2.662e-06 3.677e-04 4.720
## 33 4.1997 -5.806 2.851e-06 3.798e-04 4.654
## 232 2.2471 5.791 2.968e-06 3.911e-04 4.616
## 138 1.0489 -5.635 4.560e-06 5.234e-04 4.205
## 185 4.1844 5.602 4.986e-06 5.480e-04 4.119
## 100 1.0076 5.514 6.358e-06 6.647e-04 3.887
## 226 0.9772 -5.458 7.414e-06 7.390e-04 3.740
## 70 -0.2125 -5.434 7.921e-06 7.771e-04 3.677
## 8 1.3883 -5.354 9.860e-06 9.082e-04 3.467
## 116 2.4248 -5.332 1.049e-05 9.439e-04 3.408
## 96 2.2197 -5.131 1.826e-05 1.380e-03 2.878
## 95 0.5088 -5.038 2.361e-05 1.617e-03 2.632
## 201 -0.5692 -4.939 3.106e-05 1.973e-03 2.370
## 191 2.8975 -4.926 3.215e-05 2.026e-03 2.337
## 77 5.0606 4.917 3.303e-05 2.058e-03 2.312
## 125 3.0814 4.916 3.310e-05 2.061e-03 2.310
## ensembl_tid_isoform orginal_id refseq_mrna_gene
## 18 ENST00000452853 ENST00000452853.2 NM_053024
## 58 ENST00000591714 ENST00000591714.1
## 161 ENST00000524333 ENST00000524333.1
## 141 ENST00000428546 ENST00000428546.1 NM_001271518
## 13 ENST00000506388 ENST00000506388.2
## 33 ENST00000455836 ENST00000455836.1 NM_006817
## 232 ENST00000378019 ENST00000378019.3 NM_001404
## 138 ENST00000519259 ENST00000519259.1
## 185 ENST00000526104 ENST00000526104.1 NM_001286134
## 100 ENST00000588142 ENST00000588142.1 NM_198834
## 226 ENST00000449569 ENST00000449569.1
## 70 ENST00000442406 ENST00000442406.1 NM_198241
## 8 ENST00000493394 ENST00000493394.1 NM_032260
## 116 ENST00000376405 ENST00000376405.2 NM_014641
## 96 ENST00000427436 ENST00000427436.1
## 95 ENST00000435095 ENST00000435095.1
## 201 ENST00000420698 ENST00000420698.1
## 191 ENST00000438610 ENST00000438610.1 NM_004689
## 77 ENST00000263672 ENST00000263672.6 NM_014752
## 125 ENST00000584377 ENST00000584377.1
## hgnc_symbol_gene chromosome_name_gene ensembl_transcript_id_gene
## 18 PFN2 3 ENST00000452853
## 58 HDAC5 17 ENST00000225983
## 161 INTS8 8 ENST00000522171
## 141 KCNMA1 10 ENST00000604624
## 13 RCN1 11 ENST00000530348
## 33 ERP29 12 ENST00000261735
## 232 EEF1G 11 ENST00000329251
## 138 SREK1 5 ENST00000521691
## 185 RIC8A 11 ENST00000526104
## 100 ACACA 17 ENST00000353139
## 226 2 ENST00000426083
## 70 EIF4G1 3 ENST00000346169
## 8 RGPD5 2 ENST00000393283
## 116 MDC1 6 ENST00000376406
## 96 SLC6A6 3 ENST00000490683
## 95 SLC6A6 3 ENST00000490683
## 201 NCOR2 12 ENST00000405201
## 191 MTA1 14 ENST00000331320
## 77 SPCS2 11 ENST00000263672
## 125 CSNK1D 17 ENST00000584672
## logFC_gene AveExpr_gene t_gene P.Value_gene adj.P.Val_gene B_gene
## 18 -0.0109072 7.153 -0.29471 0.7703 0.8506 -6.692
## 58 0.0270983 5.947 0.62923 0.5341 0.6604 -6.535
## 161 0.0302609 4.980 0.71990 0.4773 0.6098 -6.474
## 141 0.0352283 5.372 0.57964 0.5666 0.6886 -6.565
## 13 0.0172743 6.599 0.60208 0.5518 0.6761 -6.552
## 33 0.0264859 7.578 0.81332 0.4227 0.5602 -6.402
## 232 -0.0126942 10.194 -0.52277 0.6051 0.7220 -6.597
## 138 -0.0098432 5.802 -0.14792 0.8834 0.9287 -6.725
## 185 0.0069339 5.667 0.21089 0.8344 0.8947 -6.713
## 100 -0.0032013 4.577 -0.07886 0.9377 0.9617 -6.733
## 226 0.0233143 3.634 0.39171 0.6981 0.7949 -6.658
## 70 0.0048792 7.071 0.11375 0.9102 0.9449 -6.729
## 8 -0.0047076 3.468 -0.02804 0.9778 0.9872 -6.735
## 116 -0.0096035 4.226 -0.19600 0.8460 0.9020 -6.716
## 96 0.0101283 6.199 0.15482 0.8780 0.9252 -6.724
## 95 0.0101283 6.199 0.15482 0.8780 0.9252 -6.724
## 201 -0.0418395 5.996 -0.62517 0.5367 0.6626 -6.538
## 191 -0.0375801 5.605 -0.79211 0.4347 0.5711 -6.419
## 77 0.0167471 7.577 0.46165 0.6478 0.7562 -6.627
## 125 -0.0007099 7.036 -0.01974 0.9844 0.9912 -6.736
## ensembl_tid_gene
## 18 ENSG00000070087
## 58 ENSG00000108840
## 161 ENSG00000164941
## 141 ENSG00000156113
## 13 ENSG00000049449
## 33 ENSG00000089248
## 232 ENSG00000254772
## 138 ENSG00000153914
## 185 ENSG00000177963
## 100 ENSG00000132142
## 226 ENSG00000231312
## 70 ENSG00000114867
## 8 ENSG00000015568
## 116 ENSG00000137337
## 96 ENSG00000131389
## 95 ENSG00000131389
## 201 ENSG00000196498
## 191 ENSG00000182979
## 77 ENSG00000118363
## 125 ENSG00000141551
for (i in 1:5) {
par(mfrow=c(2,1))
boxplot(unlist(CounttableQCGene_expressionfilter[hitme[i,"ensembl_gene_id"],])~MetaSeqSamplesQCGene$Treatment,
main=paste(hitme[i,"ensembl_gene_id"],"\n Adj p-val",prettyNum(hitme[i,"adj.P.Val_gene"],digits=2)),
ylab=hitme[i,"hgnc_symbol_isoform"])
boxplot(unlist(CounttableQCisform_expressionfilter[hitme[i,"orginal_id"],])~MetaSeqSamplesQCGene$Treatment,
main=paste(hitme[i,"ensembl_tid_isoform"],"\n",prettyNum(hitme[i,"adj.P.Val_isoform"],digits=2)))
}
