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RNAseq-Meth-Integration_NeuroDiff.Rmd
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---
title: "Integration DNAm and expression - control samples"
author: "MS"
date: "8/19/2020"
output: html_document
---
```{r setup,include=FALSE}
knitr::opts_chunk$set(include = TRUE, root.dir ="/Integration", echo = TRUE)
library("readr")
#devtools::install_github("PF2-pasteur-fr/SARTools", build_opts="--no-resave-data")
library(SARTools)
library("DESeq2")
library(ggplot2)
library(tidyverse)
library(ggthemes)
#library("ReportingTools")
#BiocManager::install("EnhancedVolcano")
library("EnhancedVolcano")
#BiocManager::install('grimbough/biomaRt')
packageVersion('biomaRt')
library(biomaRt)
library(minfi)
#devtools::install_bitbucket("ConesaLab/more")
library(MORE)
library(devtools)
#install_github("achilleasNP/IlluminaHumanMethylationEPICmanifest")
#install_github("achilleasNP/IlluminaHumanMethylationEPICanno.ilm10b5.hg38")
library(IlluminaHumanMethylationEPICanno.ilm10b5.hg38)
library(IlluminaHumanMethylationEPICmanifest)
#install.packages("data.table")
library(data.table)
#BiocManager::install("ComplexHeatmap")
library("ComplexHeatmap")
library(latticeExtra)
library(lattice)
library("scales")
#install.packages("ggsci")
library("ggsci")
library("gridExtra")
library(dendsort)
#install.packages("colorspace")
library("colorspace")
```
# Load data
```{r load data}
##----Methylation data-------
load("/MethData/analysis/DMresults.RData")
load("/MethData/analysis/mSetSqFlt.RData")
mVals = getM(mSetSqFlt)
betas = getBeta(mSetSqFlt)
rm(mSetSqFlt)
#------RNAseq data-----------
#Day 0 vs 7
D0D7= read.table("RNAseq/30_SARTools_Ctrl/tables/Ctrl7vsCtrl0.complete.txt", header = TRUE)
D0D7 = D0D7[order(D0D7$pvalue),]
sum(D0D7$padj < 0.01, na.rm=TRUE)
sigD0D7 = D0D7[which(D0D7$padj < 0.01),]
sum(sigD0D7$log2FoldChange < 0)#Downregulated
sum(sigD0D7$log2FoldChange > 0) #Upregulated
#Day 0 vs 13
D0D13= read.table("RNAseq/30_SARTools_Ctrl/tables/Ctrl13vsCtrl0.complete.txt", header = TRUE)
D0D13 = D0D13[order(D0D13$pvalue),]
sum(D0D13$padj < 0.01, na.rm=TRUE)
sigD0D13 = D0D13[which(D0D13$padj < 0.01),]
sum(sigD0D13$log2FoldChange < 0)#Downregulated
sum(sigD0D13$log2FoldChange > 0) #Upregulated
#Day 0 vs 20
D0D20= read.table("RNAseq/30_SARTools_Ctrl/tables/Ctrl20vsCtrl0.complete.txt", header = TRUE)
D0D20 = D0D20[order(D0D20$pvalue),]
sum(D0D20$padj < 0.01, na.rm=TRUE)
sigD0D20 = D0D20[which(D0D20$padj < 0.01),]
sum(sigD0D20$log2FoldChange < 0)#Downregulated
sum(sigD0D20$log2FoldChange > 0) #Upregulated
#Day 7 vs 13
D13D7= read.table("RNAseq/30_SARTools_Ctrl/tables/Ctrl7vsCtrl13.complete.txt", header = TRUE)
D13D7 = D13D7[order(D13D7$pvalue),]
sum(D13D7$padj < 0.01, na.rm=TRUE)
#Data for D13 vs D7
#Change FC sign for all genes D7 vs D13
D7D13 <- D13D7 %>% dplyr::select(Id,log2FoldChange, pvalue, padj) %>% mutate(log2FoldChange2 = log2FoldChange * -1) #New column with correct sign for day 13-day 7
sum(D7D13$padj < 0.01, na.rm=TRUE)
D7D13 = D7D13 %>% dplyr::select(Id,log2FoldChange2, pvalue, padj)
colnames(D7D13) = c("Id","log2FoldChange", "pvalue", "padj")
#D7D13[which(D7D13$log2FoldChange2 < 0),]
#D7D13[which(D7D13$log2FoldChange2 > 0),]
sigD7D13 = D7D13[which(D7D13$padj < 0.01),]
sum(sigD7D13$log2FoldChange < 0)#Downregulated
sum(sigD7D13$log2FoldChange > 0) #Upregulated
#Day 7 vs 20
D20D7= read.table("RNAseq/30_SARTools_Ctrl/tables/Ctrl7vsCtrl20.complete.txt", header = TRUE)
D20D7 = D20D7[order(D20D7$pvalue),]
sum(D20D7$padj < 0.01, na.rm=TRUE)
#Data for D20 vs D7
#Change FC sign for all genes D7 vs D20
D7D20 <- D20D7 %>% dplyr::select(Id,log2FoldChange, pvalue, padj) %>% mutate(log2FoldChange2 = log2FoldChange * -1) #New column with correct sign for day 20-day 7
sum(D7D20$padj < 0.01, na.rm=TRUE)
D7D20 = D7D20 %>% dplyr::select(Id,log2FoldChange2, pvalue, padj)
colnames(D7D20) = c("Id","log2FoldChange", "pvalue", "padj")
#sigD7D20[which(sigD7D20$log2FoldChange2 < 0),]
#sigD7D20[which(sigD7D20$log2FoldChange2 > 0),]
sigD7D20 = D7D20[which(D7D20$padj < 0.01),]
sum(sigD7D20$log2FoldChange < 0)#Downregulated
sum(sigD7D20$log2FoldChange > 0) #Upregulated
#Day 13 vs 20
D13D20= read.table("RNAseq/30_SARTools_Ctrl/tables/Ctrl20vsCtrl13.complete.txt", header = TRUE)
D13D20 = D13D20[order(D13D20$pvalue),]
sum(D13D20$padj < 0.01, na.rm=TRUE)
sigD13D20 = D13D20[which(D13D20$padj < 0.05),]
sum(sigD13D20$log2FoldChange < 0)#Downregulated
sum(sigD13D20$log2FoldChange > 0) #Upregulated
```
```{r normalized counts}
#Make normalized count matrix
targetFile <- "RNAseq/Analysis/design-control.txt" # path to the design/target file
rawDir <- "RNAseq/20_featureCounts"
varInt <- "groups" # factor of interest
condRef <- "Ctrl0" # reference biological condition
# loading target file
target <- loadTargetFile(targetFile=targetFile, varInt=varInt, condRef=condRef, batch=NULL)
# loading counts
counts <- round(loadCountData(target=target, rawDir=rawDir, featuresToRemove=NULL))
#design = as.formula(~ groups)
target$groups = factor(target$groups)
design = as.formula(~ groups)
levels(target$groups)
dds <- DESeqDataSetFromMatrix(countData = counts,
colData = target,
design = design)
dds = DESeq(dds, fitType = "parametric")
resultsNames(dds)
normCounts = counts(dds, normalized = TRUE)
#save(normCounts, file = "normCounts.Match.rData") #For heatmat
normcounts.df = as.data.frame(normCounts)
write.table(normcounts.df, file="normcounts.control.csv", sep="\t")
```
```{r Annotation of ensembl#}
library(biomaRt)
#biomartHuman = useMart("ensembl",dataset="hsapiens_gene_ensembl")
#listFilters(biomartHuman)
#listAttributes(biomartHuman)
#myannot = getBM(attributes = c("external_gene_name", "ensembl_gene_id"),
# filters = "ensembl_gene_id", values=rownames(counts),
# mart=biomartHuman)
#save(myannot, file = "Integration/myannot.RData")
load("Integration/myannot.RData")
```
```{r Volcano plots control}
colnames(myannot)[2] = "Id"
D0D7 = merge(D0D7, myannot, by = "Id")
D0D7 = D0D7[order(D0D7$pvalue),]
#write.csv(D0D7, file="D0D7_results.csv")
sum(D0D7$padj < 0.01, na.rm=TRUE)
annot = D0D7 %>% dplyr::filter(padj<10^-10) %>% arrange(desc(abs(log2FoldChange)))
annotUP = annot %>% dplyr::filter(log2FoldChange > 0)
annotUP = annotUP[1:200,]
annotUP = annotUP[order(annotUP$padj),]
annotDOWN = annot %>% dplyr::filter(log2FoldChange < 0)
annotDOWN = annotDOWN[1:200,]
annotDOWN = annotDOWN[order(annotDOWN$padj),]
#Remove LCRNAs and alternative trascripts
annot = c("ZEB1", "PRSS23", "FOXG1", "NCALD", "NRG1", "SMOC1", "ZEB2", "CDH11", "GLIS3", "ME3", "ZFHX4", "FGF9", "DACH1", "EYA4", "WNT5B", "SOX5", "ELOVL2", "DCLK2", "TRPM8", "DGKI", "PRTG", "LHX2", "KCNK10", "KCNC1", "ADAMTS9", "AKAP6", "TBC1D9", "HHAT", "RAX", "PTPRN2", "PLEKHG1", "ADAM12", "SOX6", "DUSP4", "SULF1", "FAM13C", "MPPED1", "HPSE2", "IGFBP3", "CLVS1", "LRP2", "COL2A1", "MAP6", "PHYHIPL", "MEIS1", "SCD5", "GABRG3", "DNAH6", "GRIA1", "NR2F2", "CDH20", "EPHA4", "FBLN7", "CCDC92", "C6orf118", "TMEM255A", "FAM222A", "NKAIN2", "ADGRG1", "GPR85", "SEZ6L", "RGS20", "HMGCLL1", "ANKRD1", "MECOM", "SIX6", "CDH10", "NPY2R", "MEG3", "FABP7", "TSHZ2", "PRDM16", "FLRT3", "MARCHF1", "VAX1", "BMP3", "NDP", "ILDR2", "PCDH17", "PAX6", "GAD2", "SLC1A4", "ZBTB16", "MAPK10", "C5", "ULBP1", "DIRAS2", "STOML3", "ACTA2", "FRRS1L", "ATP8B4", "NR2F1", "CCDC177", "SLITRK1", "EBF3", "F2RL2", "IGFN1", "KCNJ13", "DNAH7", "FILIP1L",
"EPCAM", "PHC1", "CDH1", "PRDM14", "CD9", "TRDN", "NANOG", "SCNN1A", "MARVELD3", "CRYBG1", "LCK", "FLT1", "LAMC2", "ESRP2", "AP1M2", "IRF6", "RUNX1T1", "CCDC141", "BCL11A", "EPHA1", "FAM83F", "CDCP1", "GRID2", "GRHL2", "SYT2", "BRINP1", "NKX1-2", "HTR7", "CALB1", "TDGF1", "TTN", "GAL", "CCDC69", "GLB1L3", "RBM47", "NANOGP8", "RAB17", "C13orf42", "CARD11", "CHST4", "POU5F1", "SCGB3A2", "MTARC2", "CTCFL", "ESRP1", "A2ML1", "PMEPA1", "LAD1", "FOXD3-AS1", "CLDN7", "ABHD12B", "PPIC", "TRIML2", "CRB3", "HPAT5", "HHLA1", "SYK", "FGF19", "IDO1", "SLC17A9", "RARRES2", "GABRP", "SH2D3A", "C1orf210", "FOXD3", "MGAT4C", "FOXH1", "GDF3", "TNFRSF8", "USP43", "OR52A1", "BICDL2", "ZNF488", "LAMC3", "TMEM30B", "CCDC172", "SHISA3", "ITLN2", "MYCT1", "TM4SF18", "CCL26", "POU3F4", "SLC15A1", "RIT2", "TACR1", "BCL2L14", "KLK13", "AADACL3", "SEMG1", "TMC1", "TINAGL1", "PRR15", "BSPRY", "OVOL2", "UCMA", "VAV1", "LEFTY2", "SLC51A", "FGF4", "KCTD8", "TFAP2C", "IL20RA")
keyvals.colour <- ifelse(
D0D7$padj >= 0.01 | D0D7$padj == "NA", 'grey30',
ifelse(D0D7$padj < 0.01, 'red2',
'grey30'))
keyvals.colour[is.na(keyvals.colour)] <- 'grey30'
names(keyvals.colour)[keyvals.colour == 'red2'] <- 'Significant'
names(keyvals.colour)[keyvals.colour == 'black'] <- '?'
names(keyvals.colour)[keyvals.colour == 'grey30'] <- 'Not significant'
pdf("CtrlDay0vsDay7.EnhVolA.pdf", width = 12, height = 12)
EnhancedVolcano(D0D7,
lab = D0D7$external_gene_name,
selectLab =annot,
labSize = 2,
x = 'log2FoldChange',
y = 'pvalue',
hline = NULL,
FCcutoff = 0.5,
cutoffLineType = "blank",
colCustom = keyvals.colour,
title = "Day 0 vs day 7",
caption = "")
dev.off()
##DAY 0 vs 13 -------------------------------------------------------
sum(D0D13$padj < 0.01, na.rm=TRUE)
D0D13 = merge(D0D13, myannot, by = "Id")
D0D13 = D0D13[order(D0D13$pvalue),]
#write.csv(D0D13, file="D0D13_results.csv")
annot = D0D13%>% dplyr::filter(padj<10^-10) %>% arrange(desc(abs(log2FoldChange)))
annotUP = annot %>% dplyr::filter(log2FoldChange > 0)
annotUP = annotUP[1:200,]
annotUP = annotUP[order(annotUP$padj),]
annotDOWN = annot %>% dplyr::filter(log2FoldChange < 0)
annotDOWN = annotDOWN[1:200,]
annotDOWN = annotDOWN[order(annotDOWN$padj),]
#Remove LCRNAs and alternative trascripts
annot = c("ZEB1", "FOXG1", "PCDH19", "DCLK2", "EYA4", "NPAS3", "POU3F3", "MAP6", "SMOC1", "TBC1D9", "SOX6", "PRDM16", "ZEB2", "PTPRN2", "WNT5B", "MPPED1", "TMEM255A", "FGF9", "PHYHIPL", "SOX5", "CNR1", "LHX2", "GRIA1", "FAM13C", "KCNC1", "MYO16", "MEIS1", "NOG", "VAX1", "RSPO1", "PCDH9", "GPR85", "ANKRD1", "CCDC92", "ISLR2", "COL2A1", "AMER2", "PCDH17", "CDH10", "DLX6-AS1", "MECOM", "SCD5", "ST8SIA4", "CDH20", "MMRN1", "ADGRG1", "GPR50", "LRP2", "OTX2-AS1", "DIO3", "ZIC4", "DLX1", "ZBTB16", "NHLH1", "PRRX1", "FBLN7", "NKX2-1", "DLX6", "ISL1", "FLRT3", "SCN3A", "RTL1", "CRB1", "LGR5", "MEG3", "EMX2OS", "NR2F1", "VCAM1", "SOX1", "GAD2", "SFTA3", "UNC13C", "CHL1", "DCT", "CNTN6", "ST18", "OLIG2", "NR2F2", "F2RL2", "FILIP1L", "STMN4", "CELF3", "EMX2", "FOXG1-AS1", "LHX9", "HES5", "ASCL1", "HDC", "SLC30A4", "DLX2", "GASK1B", "DMTN", "DLX5", "ZIC1", "TLR4", "NR2E1", "SCRG1", "C21orf62","FEZF2", "FGF8", "PAX6", "PLCH2","NDST4", "ONECUT3", "SHH", "HCRT", "NEUROD1", "NNMT", "WNT7B", "DLK1",
"CDH1", "NANOG", "CNMD", "FLT1", "VRTN", "TRDN", "CRYBG1", "ADGRG2", "AP1M2", "CXCL5", "TC2N", "GRID2", "FAM160A1", "RAB17", "MARVELD3", "GAL", "ESRP1", "SPINT1", "RBM47", "LRRC2", "IRF6", "B3GNT7", "HTR7", "NTS", "TRIML2", "LRRK1", "TDGF1", "POU5F1", "ESRP2", "NANOGP8", "CHST4", "ZFP42", "ZSCAN10", "FGD5", "KCNK6", "CLDN7", "SCGB3A2", "CTCFL", "GRB7", "CARD11", "NKX1-2", "SCNN1A", "TUBA4A", "ABHD12B", "DHRS3", "VIL1", "LCK", "ARHGEF35", "PRDM14", "GRHL2", "NOS1", "CDCP1", "FOXD3-AS1", "EPHA1", "IDO1", "COL6A3", "HHLA1", "HPAT5", "PRSS8", "GABRP", "C13orf42", "KLRG2", "NR5A2", "TMEM92", "FOXL2NB", "SYK", "LAD1", "AIF1", "CRB3", "A2ML1", "GRM4", "TNFSF11", "ACTN3", "TMEM30B", "FUT2", "FOXD3", "FOXH1", "GDF3", "ST14", "DAZL", "SH2D3A", "MLPH", "C1orf210", "KDF1", "OR52A1", "C3", "CCDC172", "ACP5", "TMEM125", "CCL26", "TINAGL1", "RIT2", "LTBR", "LY75", "FAM124B", "TNFRSF8", "FGF4", "BSPRY", "C1orf116", "PRR15",
"PRSS23", "EPCAM", "PODXL", "MAP7", "DCLK2", "NCALD")
#Tatt ut opp: "NKX2-1-AS1", "FAM71F2", "AMBN", , "ANKFN1", "DNAH10OS", "DIO3OS" "GUCY1A1", "TRIM67", "TKTL1", "SLC40A1",
keyvals.colour <- ifelse(
D0D13$padj >= 0.01 | D0D13$padj == "NA", 'grey30',
ifelse(D0D13$padj < 0.01, 'red2',
'grey30'))
keyvals.colour[is.na(keyvals.colour)] <- 'grey30'
names(keyvals.colour)[keyvals.colour == 'red2'] <- 'Significant'
names(keyvals.colour)[keyvals.colour == 'black'] <- '?'
names(keyvals.colour)[keyvals.colour == 'grey30'] <- 'Not significant'
pdf("CtrlDay0vsDay13.EnhVolA.pdf", width = 12, height = 12)
EnhancedVolcano(D0D13,
lab = D0D13$external_gene_name,
selectLab = annot,
labSize = 2.0,
x = 'log2FoldChange',
y = 'pvalue',
FCcutoff = 0.5,
cutoffLineType = "blank",
colCustom = keyvals.colour,
title = "Day 0 vs day 13",
caption = "")
dev.off()
### DAY 0 vs 20 -------------------------------------------------------
sum(D0D20$padj < 0.01, na.rm=TRUE)
D0D20 = merge(D0D20, myannot, by = "Id")
D0D20 = D0D20[order(D0D20$pvalue),]
#write.csv(D0D20, file="D0D20_results.csv")
annot = D0D20%>% dplyr::filter(padj<10^-10) %>% arrange(desc(abs(log2FoldChange)))
annotUP = annot %>% dplyr::filter(log2FoldChange > 0)
annotUP = annotUP[1:200,]
annotUP = annotUP[order(annotUP$padj),]
annotDOWN = annot %>% dplyr::filter(log2FoldChange < 0)
annotDOWN = annotDOWN[1:200,]
annotDOWN = annotDOWN[order(annotDOWN$padj),]
#Remove LCRNAs and alternative trascripts
annot = c("STMN2", "ZEB1", "FOXG1", "CNR1", "NPAS3", "MAP6", "DCLK2", "POU3F3", "EYA4", "SOX6", "ZEB2", "ISL1", "AMER2", "GPR85", "RSPO1", "MYO16", "NSG2", "ISLR2", "ZNF536", "ZIC4", "NOG", "LHX2", "SCN3A", "CCDC92", "VAX1", "DLX1", "SAMD5", "CDH10", "DLX6-AS1", "PRRX1", "OTX2-AS1", "MPPED1", "CNTNAP4", "CELF3", "SEZ6L", "CHL1", "CRB1", "MMRN1", "NKX2-1", "STMN4", "EMX2OS", "ONECUT3", "CDH12", "INSRR", "ST18", "SFTA3", "ZBTB16", "DCT", "LHX9", "NTRK1", "ASCL1", "C21orf62", "CDK5R2", "EMX2", "DLX2", "GAD2", "SOX1", "TRIM67", "MYT1L", "DMTN", "ANKFN1", "KLHL1", "GRIA2", "SLC30A4", "DDC", "HDC", "CNTN6", "DLX6", "ZIC1", "OLIG2", "FOXG1-AS1", "NKX2-1-AS1", "HES5", "DCC", "FEZF2", "CNGA3", "INSM2", "LRRTM2", "NR2F1", "CNGB1", "NEUROD1", "C22orf42", "MKRN3", "APC2", "IL13RA2", "NDST4", "GPR22", "SMIM18", "IFI44L", "CYSLTR2", "ZNF804A", "NEUROG1", "CBLN2", "DLX5", "GRIN3A", "ARX", "ADGRA1", "FGF8", "TNR", "TFAP2B",
"EPCAM", "CGNL1", "CDH1", "NANOG", "APELA", "FLT1", "CRYBG1", "POLR3G", "TDGF1", "RAB17", "AP1M2", "EPHA1", "CXCL5", "TC2N", "CYP2S1", "RBM47", "NKX1-2", "LRRK1", "ESRP1", "HTR7", "MARVELD3", "L1TD1", "TRDN", "NTS", "LRRC2", "POU5F1", "ESRP2", "NANOGP8", "IRF6", "SPINT1", "ZFP42", "SCNN1A", "VRTN", "ABHD12B", "FOXA3", "TFCP2L1", "SCGB3A2", "GAL", "DHRS3", "ARHGEF35", "CHST4", "CARD11", "PRDM14", "GRB7", "LCK", "CLDN7", "TRIML2", "GRHL2", "HHLA1", "IDO1", "ZSCAN10", "CTCFL", "TUBA4A", "FOXD3-AS1", "CRB3", "TMEM92", "GABRP", "PRSS8", "HPAT5", "C13orf42", "LAD1", "LHX4", "ADGRE2", "DAZL", "ACTN3", "C1orf210", "TNFRSF8", "TMEM30B", "FUT2", "TNFSF11", "FOXD3", "GDF3", "A2ML1", "TNNT2", "NPR1", "KDF1", "LTBR", "OR52A1", "C3", "BCL2L14", "TMEM125", "VSIG1", "CCDC172", "ACP5", "DPEP3", "OVOL2", "FOXH1", "KLHDC7A", "M1AP", "SLC51A", "SMIM24", "LY75", "EMX1", "SH2D3A", "ATP8B1", "TMPRSS11E", "COL21A1", "BSPRY", "MUC16", "AIF1",
"RTN1", "ST8SIA2", "ZFHX3", "NCAM1", "KLHDC8A", "ASTN1", "PODXL", "F11R"
)
#Manuelt tatt ut
keyvals.colour <- ifelse(
D0D20$padj >= 0.01 | D0D20$padj == "NA", 'grey30',
ifelse(D0D20$padj < 0.01, 'red2',
'grey30'))
keyvals.colour[is.na(keyvals.colour)] <- 'grey30'
names(keyvals.colour)[keyvals.colour == 'red2'] <- 'Significant'
names(keyvals.colour)[keyvals.colour == 'black'] <- '?'
names(keyvals.colour)[keyvals.colour == 'grey30'] <- 'Not significant'
pdf("CtrlDay0vsDay20.EnhVolA.pdf", width = 12, height = 12)
EnhancedVolcano(D0D20,
lab = D0D20$external_gene_name,
selectLab = annot,
x = 'log2FoldChange',
y = 'pvalue',
labSize = 2.0,
FCcutoff = 0.5,
cutoffLineType = "blank",
colCustom = keyvals.colour,
title = "Day 0 vs day 20",
caption = "")
dev.off()
## Day 7 vs day 13 -------------------------------------
sum(D7D13$padj < 0.01, na.rm=TRUE)
D7D13 = merge(D7D13, myannot, by = "Id")
D7D13 = D7D13[order(D7D13$pvalue),]
#write.csv(D7D13, file="D7D13_results.csv")
annot = D7D13%>% dplyr::filter(padj<10^-10) %>% arrange(desc(abs(log2FoldChange)))
annotUP = annot %>% dplyr::filter(log2FoldChange > 0)
annotUP = annotUP[1:200,]
annotUP = annotUP[order(annotUP$padj),]
annotDOWN = annot %>% dplyr::filter(log2FoldChange < 0)
annotDOWN = annotDOWN[1:200,]
annotDOWN = annotDOWN[order(annotDOWN$padj),]
#Remove LCRNAs and alternative trascripts
annot = c("POU3F3", "DLX6-AS1", "PCDH19", "SLIT2", "VEPH1", "NKX2-1", "TRPS1", "ANKH", "CNR1", "DLX1", "STMN2", "ISLR2", "NOG", "HSPA4L", "ABAT", "ANO1", "TRIM69", "LOX", "NOS2", "SYT4", "ST18", "DLX6", "CA12", "EDNRB", "RUNX1T1", "OTX2-AS1", "AMER2", "LHX9", "LMCD1", "SFXN3", "FGF19", "MAFB", "PRRX1", "CNTN1", "GRIK3", "GAS7", "CHST15", "CPLX2", "UNC13C", "COL24A1", "GRIK2", "BCL11A", "PTCHD1", "C1orf61", "MMRN1", "TGFB3", "CCNA1", "SFTA3", "GAD1", "EMX2", "NHLH1", "DIO3", "DIO2", "CCDC69", "PCSK2", "SHH", "PMEPA1", "FAM135B", "NFIB", "SEMA3C", "OLIG2", "SAMD5", "ZNF536", "HAPLN1", "EMX2OS", "NCR3LG1", "BFSP1", "NSG2", "TRPC3", "FAM181B", "DMTN", "INSM2", "FAM107A", "INSM1", "MYORG", "COL8A1", "AMBN", "HSPB8", "BMP4", "ZIC3", "ISL1", "VWA5B1", "PRR16", "GASK1B", "HEY1", "C21orf62", "MAMLD1", "ONECUT3", "SLC17A6", "CNTNAP4", "SVOP", "IER3", "PTPRB", "LRRTM3", "PDYN", "MKRN3", "NEUROD1", "ERBB4", "HCRT", "SLIT1",
"HS3ST3B1", "RAX", "TPBG", "KCNJ13", "VRTN", "HESX1", "ERBB3", "UGT3A2", "MARVELD2", "POU3F1", "KCNJ3", "SH3D19", "S1PR3", "DOCK9", "BMP3", "NID2", "LAMP5", "LMO2", "ARHGEF5", "ROBO1", "PAPLN", "SLC7A11", "IGFN1", "STOML3", "ZNF788P", "KCNE3", "DMKN", "VLDLR", "NBAT1", "F13A1", "PTN", "NR6A1", "FOXA3", "IRS4", "MYO5B", "TRAF5", "SLC7A7", "UNC5C", "FZD7", "CLMP", "SHISAL2B", "PCDH18", "PAX8", "MAP7", "STOM", "BMP2", "FAT2", "APELA", "TSHZ2", "SMAGP", "HLA-DOA", "ROR2", "DIRAS2", "SNTB1", "PSMB8", "ROR1", "PCYT1B", "PAMR1", "OPRK1", "EMX1", "NRP2", "NRXN1", "FERMT1", "COL14A1", "ULBP1", "TMEM108", "VAMP8", "MACC1", "FUT1", "SEMA3G", "SFRP2", "HRK", "RHOH", "ADM2", "RBMS1", "CNMD", "ATP8B4", "PCDH15", "RASGEF1B", "ARFGEF3", "B4GALNT3", "IGSF11", "INHBE", "IFIT1", "QPCT", "SPTSSB", "ADAM12", "APOL6", "AURKC", "ADM", "PRKCB", "PRPH2", "PROKR1", "CPNE8", "VWDE", "CD74", "LPAR3", "OPRD1", "KCNH5", "HS3ST5"
)
#Manuelt tatt ut
keyvals.colour <- ifelse(
D7D13$padj >= 0.01 | D7D13$padj == "NA", 'grey30',
ifelse(D7D13$padj < 0.01, 'red2',
'grey30'))
keyvals.colour[is.na(keyvals.colour)] <- 'grey30'
names(keyvals.colour)[keyvals.colour == 'red2'] <- 'Significant'
names(keyvals.colour)[keyvals.colour == 'black'] <- '?'
names(keyvals.colour)[keyvals.colour == 'grey30'] <- 'Not significant'
pdf("CtrlDay7vsDay13.EnhVolA.pdf", width = 12, height = 12)
EnhancedVolcano(D7D13,
lab = D7D13$external_gene_name,
selectLab = annot,
x = 'log2FoldChange',
y = 'pvalue',
labSize = 2.0,
FCcutoff = 0.5,
cutoffLineType = "blank",
colCustom = keyvals.colour,
title = "Day 7 vs day 13",
caption = "")
dev.off()
## Day 7 vs day 20-------------------------------
sum(D7D20$padj < 0.01, na.rm=TRUE)
D7D20 = merge(D7D20, myannot, by = "Id")
D7D20 = D7D20[order(D7D20$pvalue),]
#write.csv(D7D20, file="D7D20_results.csv")
annot = D7D20%>% dplyr::filter(padj<10^-10) %>% arrange(desc(abs(log2FoldChange)))
annotUP = annot %>% dplyr::filter(log2FoldChange > 0)
annotUP = annotUP[1:200,]
annotUP = annotUP[order(annotUP$padj),]
annotDOWN = annot %>% dplyr::filter(log2FoldChange < 0)
annotDOWN = annotDOWN[1:200,]
annotDOWN = annotDOWN[order(annotDOWN$padj),]
#Remove LCRNAs and alternative trascripts
annot = c("STMN2", "CNR1", "POU3F3", "DLX6-AS1", "DLX1", "NFIB", "SYT4", "NSG2", "LHX9", "AMER2", "NKX2-1", "ISLR2", "BCL11A", "ZNF536", "ANO1", "RUNX1T1", "CPLX2", "ST18", "SFXN3", "OTX2-AS1", "C1orf61", "SAMD5", "PRRX1", "CNTNAP4", "INSM1", "PCSK2", "HPCAL4", "SVOP", "CHRNB4", "LRRTM2", "GAD1", "ONECUT3", "PTPRB", "INSM2", "MKRN3", "SPARCL1", "ZIC4", "CDH12", "INSRR", "SFTA3", "NTRK1", "DMTN", "CCSER1", "MAMLD1", "CALB1", "LRRTM3", "ISL1", "C21orf62", "PMEPA1", "KLHL1", "UNC13C", "FGF19", "ONECUT1", "IL13RA2", "SYT5", "SLC17A6", "CDK5R2", "GRIA2", "PTPRH", "NKX2-1-AS1", "FAM181B", "GIPR", "CNGB1", "OLIG2", "FAM107A", "DDC", "NEUROD1", "IFI44L", "AMER3", "SLC18A1", "SMPD3", "FLI1", "DCT", "PAX5", "DLX2", "ELAVL3", "TRIM67", "ARX", "MLC1", "MYT1L", "ASCL1", "RALYL", "TFAP2B", "PDK4", "CYSLTR2", "CNGA3", "NEUROG1", "HDC", "NKILA", "SIAH3", "VWC2L", "BRINP2", "ACSL6", "USP43", "SMIM18", "HEPACAM2", "TRH", "OLIG1", "ACTL6B", "ZCCHC12",
"SFRP1", "CDH11", "TPBG", "RAX", "FZD7", "PRSS23", "HS3ST3B1", "HESX1", "NID1", "CPVL", "ERBB3", "NR6A1", "KCNJ13", "ROR2", "IGFBP3", "SLC7A2", "RBMS1", "LRP4", "LCTL", "STOM", "SHISAL2B", "MARVELD2", "PMEL", "PAX8", "DMKN", "APELA", "UGT3A2", "PRTG", "CGNL1", "S1PR3", "DIAPH2", "LAMP5", "SALL1", "SFRP2", "JPH1", "STOML3", "HPSE2", "EGF", "STC2", "IFIT1", "PAMR1", "SIPA1L2", "ARHGEF5", "BMP3", "MAP7", "EDNRA", "TGFBR3", "NR2F2", "ATF3", "FAS", "EYA1", "ROR1", "COL14A1", "DDIT4L", "VAMP8", "PCOLCE2", "IGFN1", "RASSF3", "ST8SIA6", "NBAT1", "SLC7A11", "B4GALNT3", "KCNE3", "MACC1", "L1TD1", "SUSD5", "FOSB", "GLI2", "VRTN", "FOXA3", "AOX1", "ZNF385B", "NID2", "ANOS1", "GAS1", "MTHFD2", "MICB", "DAAM2", "ADM2", "ULBP1", "TRABD2A", "FAT2", "FLVCR2", "PLCG2", "SNTB1", "ATP8B4", "C6orf141", "SH2D4A", "CD74", "EDARADD", "RASL12", "NGFR", "FERMT1", "SMAGP", "MYH2", "MAMDC2", "PROKR1", "OPRD1", "KRT19", "TCERG1L"
)
keyvals.colour <- ifelse(
D7D20$padj >= 0.01 | D7D20$padj == "NA", 'grey30',
ifelse(D7D20$padj < 0.01, 'red2',
'grey30'))
keyvals.colour[is.na(keyvals.colour)] <- 'grey30'
names(keyvals.colour)[keyvals.colour == 'red2'] <- 'Significant'
names(keyvals.colour)[keyvals.colour == 'black'] <- '?'
names(keyvals.colour)[keyvals.colour == 'grey30'] <- 'Not significant'
pdf("CtrlDay7vsDay20.EnhVolA.pdf", width = 12, height = 12)
EnhancedVolcano(D7D20,
lab = D7D20$external_gene_name,
selectLab = annot,
x = 'log2FoldChange',
y = 'pvalue',
FCcutoff = 0.5,
labSize = 2.0,
cutoffLineType = "blank",
colCustom = keyvals.colour,
title = "Day 7 vs day 20",
caption = "")
dev.off()
#Day13 vs day 20 -----------------------------------------
sum(D13D20$padj < 0.01, na.rm=TRUE)
D13D20 = merge(D13D20, myannot, by = "Id")
D13D20 = D13D20[order(D13D20$pvalue),]
#write.csv(D13D20, file="D13D20_results.csv")
annot = D13D20%>% dplyr::filter(padj<10^-10) %>% arrange(desc(abs(log2FoldChange)))
annotUP = annot %>% dplyr::filter(log2FoldChange > 0)
annotUP = annotUP[1:200,]
annotUP = annotUP[order(annotUP$padj),]
annotDOWN = annot %>% dplyr::filter(log2FoldChange < 0)
annotDOWN = annotDOWN[1:200,]
annotDOWN = annotDOWN[order(annotDOWN$padj),]
#Remove LCRNAs and alternative trascripts
annot = c("DNER", "BCL11A", "KLHL1", "LRRTM2", "STMN2", "KCNJ3", "NSG2", "NMNAT2", "EPB41L4A", "ST8SIA3", "CNGB1", "FGF14", "ISL1", "RELN", "INA", "NFIA", "INSRR", "IL13RA2", "C21orf62", "ONECUT3", "MKRN3", "NFIB", "TMEM196", "GRIA2", "DDC", "REEP1", "TMEFF2", "CDK5R2", "HPCAL4", "XKR7", "CNTNAP4", "NRXN1", "CNR1", "SCN2A", "XKR4", "PIPOX", "SHISA6", "BRINP1", "DIRAS2", "AP3B2", "MYT1L", "NTRK1", "MASP1", "CNGA3", "RUNDC3B", "KCNJ9", "DOK5", "FSTL5", "SLC18A1", "PTPRN", "POU2F2", "LHX9", "BRINP3", "STMN4", "CAMK2A", "CHGB", "SMIM18", "SYP", "ABLIM3", "VWC2L", "ZNF536", "BICDL1", "NOS1AP", "TMEM63C", "C22orf42", "CDH12", "SCN3A", "PROK2", "PCDH10", "JHY", "GLB1L3", "SLC17A6", "SVOP", "SCN3B", "HEPACAM2", "GPR22", "TFAP2B", "DISP2", "KCTD16", "SYN1", "SLC4A10", "OPCML", "AJAP1", "KCNK9", "PTPRH", "TNR", "RUNDC3A", "CHRNB4", "DRD2", "CALB1", "PLPPR4", "SPARCL1", "MLC1", "GOLGA7B", "ONECUT2", "CACNA1B", "GRID2", "PSD2", "ARHGAP36", "NWD2",
"SFRP1", "CAPN6", "CDH11", "HPSE2", "CPVL", "DSP", "LRP4", "CMTM7", "SLC7A2", "DIAPH2", "PVT1", "NID1", "IGFBP3", "DDIT4L", "RGS5", "NXPH2", "SIPA1L2", "ACTC1", "STXBP6", "EPHA7", "FIRRE", "SORCS1", "FZD7", "LAMB1", "ADAMTS19", "CGNL1", "FBN2", "GLI2", "LCTL", "CCDC169", "EYA1", "PDPN", "PRSS23", "GJA1", "AMBN", "SORL1", "FAM71F1", "LTBP1", "SALL4", "EGF", "TRIM71", "SCRG1", "JPH1", "COL25A1", "TEK", "LYN", "SLC38A4", "DAAM2", "KCNJ13", "NR2F1", "PMEL", "TNFRSF10D", "TCF7L2", "MAMDC2", "DIO3", "DOCK8", "PRTG", "SMCO4", "TPM1", "RASSF3", "SHISAL2B", "CTDSPL", "P3H2", "CCN1", "PCOLCE2", "ENPP2", "LAMP5", "ABCA5"
)
#Manuelt tatt ut
keyvals.colour <- ifelse(
D13D20$padj >= 0.01 | D13D20$padj == "NA", 'grey30',
ifelse(D13D20$padj < 0.01, 'red2',
'grey30'))
keyvals.colour[is.na(keyvals.colour)] <- 'grey30'
names(keyvals.colour)[keyvals.colour == 'red2'] <- 'Significant'
names(keyvals.colour)[keyvals.colour == 'black'] <- '?'
names(keyvals.colour)[keyvals.colour == 'grey30'] <- 'Not significant'
pdf("CtrlDay13vsDay20.EnhVolA.pdf", width = 12, height = 12)
EnhancedVolcano(D13D20,
lab = D13D20$external_gene_name,
selectLab = annot,
x = 'log2FoldChange',
y = 'pvalue',
labSize = 2.0,
FCcutoff = 0.5,
cutoffLineType = "blank",
colCustom = keyvals.colour,
title = "Day 13 vs day 20",
caption = "")
dev.off()
```
#Create ranked lists for GSEA, RNAseq
```{r Ranked lists for GSEA}
### D0 vs d7
gsea = data.frame(D0D7)
gsea$fcsign <- sign(gsea$log2FoldChange) #Sign for up/downregulated genes
gsea$pvalue[gsea$pvalue == 0] = 2.225074e-308 #When p-value is 0, tranform to lowest possible p-value in R to avoid inf after log-transformation
gsea$logP=-log10(gsea$pvalue) #p-value magnitude (low p: high logp)
gsea$metric = gsea$logP/gsea$fcsign # logP with sign
gsea <- gsea[order(gsea$metric, decreasing = T),] #Order
gsea <- gsea[!is.infinite(gsea$metric),] #remove infinite values
gsea <- gsea[!is.na(gsea$metric),] #Remove NAs
gsea$ENSID = gsea$Id
gsea = gsea[,c("ENSID","metric")]
rownames(gsea) = NULL
write.table(gsea, sep="\t",file="GSEA-RNA-D0D7.rnk", quote=F, row.names=F, col.names = F)
### D0 vs d13
gsea = data.frame(D0D13)
gsea$fcsign <- sign(gsea$log2FoldChange) #Sign for up/downregulated genes
gsea$pvalue[gsea$pvalue == 0] = 2.225074e-308 #When p-value is 0, tranform to lowest possible p-value in R to avoid inf after log-transformation
gsea$logP=-log10(gsea$pvalue) #p-value magnitude (low p: high logp)
gsea$metric = gsea$logP/gsea$fcsign # logP with sign
gsea <- gsea[order(gsea$metric, decreasing = T),]
gsea <- gsea[!is.infinite(gsea$metric),]#remove infinite values
gsea <- gsea[!is.na(gsea$metric),]#Remove NAs
gsea$ENSID = gsea$Id
gsea = gsea[,c("ENSID","metric")]
rownames(gsea) = NULL
write.table(gsea, sep="\t",file="GSEA-RNA-D0D13.rnk", quote=F, row.names=F, col.names = F)
### D0 vs d20
gsea = data.frame(D0D20)
gsea$fcsign <- sign(gsea$log2FoldChange) #Sign for up/downregulated genes
gsea$pvalue[gsea$pvalue == 0] = 2.225074e-308 #When p-value is 0, tranform to lowest possible p-value in R to avoid inf after log-transformation
gsea$logP=-log10(gsea$pvalue) #p-value magnitude (low p: high logp)
gsea$metric = gsea$logP/gsea$fcsign # logP with sign
gsea <- gsea[order(gsea$metric, decreasing = T),]
gsea <- gsea[!is.infinite(gsea$metric),]#remove infinite values
gsea <- gsea[!is.na(gsea$metric),]#Remove NAs
gsea$ENSID = gsea$Id
gsea = gsea[,c("ENSID","metric")]
rownames(gsea) = NULL
write.table(gsea, sep="\t",file="GSEA-RNA-D0D20.rnk", quote=F, row.names=F, col.names = F)
### D7 vs d13
gsea = data.frame(D7D13)
gsea$fcsign <- sign(gsea$log2FoldChange) #Sign for up/downregulated genes
gsea$pvalue[gsea$pvalue == 0] = 2.225074e-308 #When p-value is 0, tranform to lowest possible p-value in R to avoid inf after log-transformation
gsea$logP=-log10(gsea$pvalue) #p-value magnitude (low p: high logp)
gsea$metric = gsea$logP/gsea$fcsign # logP with sign
gsea <- gsea[order(gsea$metric, decreasing = T),]
gsea <- gsea[!is.infinite(gsea$metric),]#remove infinite values
gsea$ENSID = gsea$Id
gsea <- gsea[!is.na(gsea$metric),]#Remove NAs
gsea = gsea[,c("ENSID","metric")]
rownames(gsea) = NULL
write.table(gsea, sep="\t",file="GSEA-RNA-D7D13.rnk", quote=F, row.names=F, col.names = F)
### D7 vs d20
gsea = data.frame(D7D20)
gsea$fcsign <- sign(gsea$log2FoldChange) #Sign for up/downregulated genes
gsea$pvalue[gsea$pvalue == 0] = 2.225074e-308 #When p-value is 0, tranform to lowest possible p-value in R to avoid inf after log-transformation
gsea$logP=-log10(gsea$pvalue) #p-value magnitude (low p: high logp)
gsea$metric = gsea$logP/gsea$fcsign # logP with sign
gsea <- gsea[order(gsea$metric, decreasing = T),]
gsea <- gsea[!is.infinite(gsea$metric),]#remove infinite values
gsea$ENSID = gsea$Id
gsea <- gsea[!is.na(gsea$metric),]#Remove NAs
gsea = gsea[,c("ENSID","metric")]
rownames(gsea) = NULL
write.table(gsea, sep="\t",file="GSEA-RNA-D7D20.rnk", quote=F, row.names=F, col.names = F)
### D13 vs d20
gsea = data.frame(D13D20)
gsea$fcsign <- sign(gsea$log2FoldChange) #Sign for up/downregulated genes
gsea$pvalue[gsea$pvalue == 0] = 2.225074e-308 #When p-value is 0, tranform to lowest possible p-value in R to avoid inf after log-transformation
gsea$logP=-log10(gsea$pvalue) #p-value magnitude (low p: high logp)
gsea$metric = gsea$logP/gsea$fcsign # logP with sign
gsea <- gsea[order(gsea$metric, decreasing = T),]
gsea <- gsea[!is.infinite(gsea$metric),]#remove infinite values
gsea$ENSID = gsea$Id
gsea <- gsea[!is.na(gsea$metric),]#Remove NAs
gsea = gsea[,c("ENSID","metric")]
rownames(gsea) = NULL
write.table(gsea, sep="\t",file="GSEA-RNA-D13D20.rnk", quote=F, row.names=F, col.names = F)
```
##GSEA plots
```{r GSEA plots}
library(colorspace)
#Day 0 vs day 7---------------------------------------------
up = read.table(file = "GSEA/D0D7.GseaPreranked.1629887770850/gsea_report_for_na_pos_1629887770850.tsv", sep = '\t', header = TRUE)
down = read.table(file = "GSEA/D0D7.GseaPreranked.1629887770850/gsea_report_for_na_neg_1629887770850.tsv", sep = '\t', header = TRUE)
GSEAd0d7 = rbind(up,down)
GSEAd0d7 = GSEAd0d7 %>% dplyr::filter(FDR.q.val< 0.05) %>% arrange(desc(abs(NES)))
GSEAd0d7$NES = as.numeric(GSEAd0d7$NES)
write.csv(GSEAd0d7, file="GSEAd0d7.csv")
pdf("D0D7_GSEAPre.pdf",width = 11, height = 10)
ggplot(GSEAd0d7[1:30,], aes(x = NES , y = reorder(NAME,NES), fill = SIZE))+
geom_bar(stat = "identity") +
geom_text(aes(label = sprintf("%0.3f", round(FDR.q.val, digits = 3))), size = 3,vjust = 0.5)+
scale_fill_continuous_sequential(palette = "Purple-Blue",guide = guide_colorbar(reverse = TRUE), limits = c(0,450), breaks = c(20, 100, 200, 300, 400))+
labs(title = "Day 0 vs day 7",
subtitle = "Top 30 GO terms", x = "Normalized enrichment score", y = "")+
theme_minimal()
dev.off()
#Day 0 vs day 13----------------------------------------------
up = read.table(file = "GSEA/D0D13.GseaPreranked.1629888030417/gsea_report_for_na_pos_1629888030417.tsv", sep = '\t', header = TRUE)
down = read.table(file = "GSEA/D0D13.GseaPreranked.1629888030417/gsea_report_for_na_neg_1629888030417.tsv", sep = '\t', header = TRUE)
GSEAd0d13 = rbind(up,down)
GSEAd0d13 = GSEAd0d13 %>% dplyr::filter(FDR.q.val< 0.05) %>% arrange(desc(abs(NES)))
GSEAd0d13$NES = as.numeric(GSEAd0d13$NES)
GSEAd0d13$FDR.q.val = as.numeric(GSEAd0d13$FDR.q.val)
write.csv(GSEAd0d13, file="GSEAd0d13.csv")
pdf("D0D13_GSEAPre.pdf",width = 12, height = 10)
ggplot(GSEAd0d13[1:30,], aes(x = NES , y = reorder(NAME,NES), fill = SIZE))+
geom_bar(stat = "identity") +
geom_text(aes(label = sprintf("%0.4f", round(FDR.q.val, digits = 4))), size = 3,vjust = 0.5)+
scale_fill_continuous_sequential(palette = "Purple-Blue",
guide = guide_colorbar(reverse = TRUE), limits = c(0,450), breaks = c(20, 100, 200, 300, 400))+
labs(title = "Day 0 vs day 13",
subtitle = "Top 30 GO terms", x = "Normalized enrichment score", y = "")+
theme_minimal()
dev.off()
#Day 0 vs day 20----------------------------------------------
up = read.table(file = "GSEA/D0D20.GseaPreranked.1629903691253/gsea_report_for_na_pos_1629903691253.tsv", sep = '\t', header = TRUE)
down = read.table(file = "GSEA/D0D20.GseaPreranked.1629903691253/gsea_report_for_na_neg_1629903691253.tsv", sep = '\t', header = TRUE)
GSEAd0d20 = rbind(up,down)
GSEAd0d20 = GSEAd0d20 %>% dplyr::filter(FDR.q.val< 0.05) %>% arrange(desc(abs(NES)))
GSEAd0d20$NES = as.numeric(GSEAd0d20$NES)
write.csv(GSEAd0d20, file="GSEAd0d20.csv")
pdf("D0D20_GSEAPre.pdf",width = 12, height = 10)
ggplot(GSEAd0d20[1:30,], aes(x = NES , y = reorder(NAME,NES), fill = SIZE))+
geom_bar(stat = "identity") +
geom_text(aes(label = sprintf("%0.4f", round(FDR.q.val, digits = 4))), size = 3,vjust = 0.5)+
scale_fill_continuous_sequential(palette = "Purple-Blue",
guide = guide_colorbar(reverse = TRUE), limits = c(0,600), breaks = c(20, 100, 200, 300, 400, 500))+
labs(title = "Day 0 vs day 20",
subtitle = "Top 30 GO terms", x = "Normalized enrichment score", y = "")+
theme_minimal()
dev.off()
#Day 7 vs day 13----------------------------------------------
up = read.table(file = "GSEA/D7D13.GseaPreranked.1629892096039/gsea_report_for_na_pos_1629892096039.tsv", sep = '\t', header = TRUE)
down = read.table(file = "GSEA/D7D13.GseaPreranked.1629892096039/gsea_report_for_na_neg_1629892096039.tsv", sep = '\t', header = TRUE)
GSEAd7d13 = rbind(up,down)
GSEAd7d13 = GSEAd7d13 %>% dplyr::filter(FDR.q.val< 0.05) %>% arrange(desc(abs(NES)))
GSEAd7d13$NES = as.numeric(GSEAd7d13$NES)
#Ingen signifikante
GSEAd7d13 = rbind(up,down)
GSEAd7d13 = GSEAd7d13 %>% arrange(FDR.q.val)
GSEAd7d13 = GSEAd7d13[1:30,]
GSEAd7d13$NES = as.numeric(GSEAd7d13$NES)
write.csv(GSEAd7d13, file="GSEAd7d13.csv")
pdf("D7D13_GSEAPre.pdf",width = 12, height = 10)
ggplot(GSEAd7d13, aes(x = NES , y = reorder(NAME,NES), fill = SIZE))+
geom_bar(stat = "identity") +
geom_text(aes(label = sprintf("%0.2f", round(FDR.q.val, digits = 2))), size = 3,vjust = 0.5)+
scale_fill_continuous_sequential(palette = "Purple-Blue",
guide = guide_colorbar(reverse = TRUE), limits = c(0,350), breaks = c(15, 100, 200, 300, 400))+
labs(title = "Day 7 vs day 13",
subtitle = "Top 30 GO terms", x = "Normalized enrichment score", y = "")+
theme_minimal()
dev.off()
#Day 7 vs day 20----------------------------------------------
up = read.table(file = "GSEA/D7D20.GseaPreranked.1629892079343/gsea_report_for_na_pos_1629892079343.tsv", sep = '\t', header = TRUE)
down = read.table(file = "GSEA/D7D20.GseaPreranked.1629892079343/gsea_report_for_na_neg_1629892079343.tsv", sep = '\t', header = TRUE)
GSEAd7d20 = rbind(up,down)
GSEAd7d20 = GSEAd7d20 %>% dplyr::filter(FDR.q.val< 0.05) %>% arrange(desc(abs(NES)))
GSEAd7d20$NES = as.numeric(GSEAd7d20$NES)
write.csv(GSEAd7d20, file="GSEAd7d20.csv")
pdf("D7D20_GSEAPre.pdf",width = 12, height = 10)
ggplot(GSEAd7d20[1:30,], aes(x = NES , y = reorder(NAME,NES), fill = SIZE))+
geom_bar(stat = "identity") +
geom_text(aes(label = sprintf("%0.4f", round(FDR.q.val, digits = 4))), size = 3,vjust = 0.5)+
scale_fill_continuous_sequential(palette = "Purple-Blue",
guide = guide_colorbar(reverse = TRUE), limits = c(0,650), breaks = c(20, 100, 200, 300, 400, 500, 600))+
labs(title = "Day 7 vs day 20",
subtitle = "Top 30 GO terms", x = "Normalized enrichment score", y = "")+
theme_minimal()
dev.off()
#Day 13 vs day 20----------------------------------------------
up = read.table(file = "GSEA/D13D20.GseaPreranked.1629903714096/gsea_report_for_na_pos_1629903714096.tsv", sep = '\t', header = TRUE)
down = read.table(file = "GSEA/D13D20.GseaPreranked.1629903714096/gsea_report_for_na_neg_1629903714096.tsv", sep = '\t', header = TRUE)
GSEAd13d20 = rbind(up,down)
GSEAd13d20 = GSEAd13d20 %>% dplyr::filter(FDR.q.val< 0.05) %>% arrange(desc(abs(NES)))
GSEAd13d20$NES = as.numeric(GSEAd13d20$NES)
write.csv(GSEAd13d20, file="GSEAd13d20.csv")
pdf("D13D20_GSEAPre.pdf",width = 12, height = 10)
ggplot(GSEAd13d20[1:30,], aes(x = NES , y = reorder(NAME,NES), fill = SIZE))+
geom_bar(stat = "identity") +
geom_text(aes(label = sprintf("%0.4f", round(FDR.q.val, digits = 4))), size = 3,vjust = 0.5)+
scale_fill_continuous_sequential(palette = "Purple-Blue",
guide = guide_colorbar(reverse = TRUE), limits = c(0, 820), breaks = c(20, 200, 400, 600, 800))+
labs(title = "Day 13 vs day 20",
subtitle = "Top 30 GO terms", x = "Normalized enrichment score", y = "")+
theme_minimal()
dev.off()
```
# Annotation
```{r get annotation for downstream analysis}
# get the EPIC annotation data
annoEPIC = getAnnotation(IlluminaHumanMethylationEPICanno.ilm10b5.hg38)
#head(annoEPIC)
annoEPIC = as.data.frame(annoEPIC)
annoEPIC$cgid=row.names(annoEPIC)
annoEPICsub <- annoEPIC[match(rownames(mVals),annoEPIC$Name),
c(1:4,12:19,22,24, 31,37,49:ncol(annoEPIC))]
```
#Find unique gene annotation for cpgs
```{r}
#Finn unike annoterte cpg-er
all.genes = annoEPICsub[c("cgid", "UCSC_RefGene_Name")] # subset annotated beta matrix
s = strsplit(all.genes$UCSC_RefGene_Name, split = ";")
uniqueGenes = data.frame(cgid = rep(all.genes$cgid, sapply(s, length)), external_gene_name = unlist(s))
uniqueGenes = uniqueGenes[!duplicated(uniqueGenes[c(names(uniqueGenes))]),]
```
#Find number of overlapping DM genes with DE genes
```{r overlapping DM genes with DE genes}
colnames(myannot) = c("external_gene_name", "Id")
#D7 ctrl vs day 0 control
#RNA DEG
sigD0D7 = merge(sigD0D7, myannot, by = "Id") #Annotate significant CpGs with external gene names
sigD0D7 = sigD0D7[order(sigD0D7$pvalue),]
#DM CpGs
sig_b_7 = r_b_7 %>% dplyr::select(cgid,logFC) #Subset only fc and cgid
sig_b_7 = merge(sig_b_7, uniqueGenes, by = "cgid") #annotate with external gene names
length(unique(sig_b_7$cgid)) #Number of unique cpgs annotated to genes
length(intersect(sigD0D7$external_gene_name, sig_b_7$external_gene_name)) #number of overlapping DMG with DEG
#-------------------------------------------------------------
#D13 ctrl vs day 0 control
#RNA DEG
sigD0D13 = merge(sigD0D13, myannot, by = "Id") #Annotate with external gene names
sigD0D13 = sigD0D13[order(sigD0D13$pvalue),]
#DM CpGs
sig_b_13 = r_b_13 %>% dplyr::select(cgid,logFC) #Subset only fc and cgid
sig_b_13 = merge(sig_b_13, uniqueGenes, by = "cgid") #annotate with external gene names
length(unique(sig_b_13$cgid)) #Number of unique cpgs annotated to genes
length(intersect(sigD0D13$external_gene_name, sig_b_13$external_gene_name)) #number of overlapping DMG with DEG
#--------------------------------------------------------------
#D20 ctrl vs day 0 control
#RNA DEG
sigD0D20 = merge(sigD0D20, myannot, by = "Id") #Annotate with external gene names
sigD0D20 = sigD0D20[order(sigD0D20$pvalue),]
#DM CpGs
sig_b_20 = r_b_20 %>% dplyr::select(cgid,logFC) #Subset only fc and cgid
sig_b_20 = merge(sig_b_20, uniqueGenes, by = "cgid") #annotate with external gene names
length(unique(sig_b_20$cgid)) #Number of unique cpgs annotated to genes
length(intersect(sigD0D20$external_gene_name, sig_b_20$external_gene_name)) #number of overlapping DMG with DEG
#--------------------------------------------------------------
#D13 ctrl vs day 7 control
#RNA DEG
sigD7D13 = merge(sigD7D13, myannot, by = "Id") #Annotate with external gene names
sigD7D13 = sigD7D13[order(sigD7D13$pvalue),]
#DM CpGs
sig_7_13 = r_7_13 %>% dplyr::select(cgid,logFC) #Subset only fc and cgid
sig_7_13 = merge(sig_7_13, uniqueGenes, by = "cgid") #annotate with external gene names
length(unique(sig_7_13$cgid)) #Number of unique cpgs annotated to genes
length(intersect(sigD7D13$external_gene_name, sig_7_13$external_gene_name)) #number of overlapping DMG with DEG
#-----------------------------------------------------------------
#D20 ctrl vs day 7 control
#RNA DEG
sigD7D20 = merge(sigD7D20, myannot, by = "Id") #Annotate with external gene names
sigD7D20 = sigD7D20[order(sigD7D20$pvalue),]
#DM CpGs
sig_7_20 = r_7_20 %>% dplyr::select(cgid,logFC) #Subset only fc and cgid
sig_7_20 = merge(sig_7_20, uniqueGenes, by = "cgid") #annotate with external gene names
length(unique(sig_7_20$cgid)) #Number of unique cpgs annotated to genes
length(intersect(sigD7D20$external_gene_name, sig_7_20$external_gene_name)) #number of overlapping DMG with DEG
#------------------------------------------------------------------
#D20 ctrl vs day 13 control
#RNA DEG
sigD13D20 = merge(sigD13D20, myannot, by = "Id") #Annotate with external gene names
sigD13D20 = sigD13D20[order(sigD13D20$pvalue),]
#DM CpGs
sig_13_20 = r_13_20 %>% dplyr::select(cgid,logFC) #Subset only fc and cgid
sig_13_20 = merge(sig_13_20, uniqueGenes, by = "cgid") #annotate with external gene names
length(unique(sig_13_20$cgid)) #Number of unique cpgs annotated to genes
length(intersect(sigD13D20$external_gene_name, sig_13_20$external_gene_name)) #number of overlapping DMG with DEG
```
#Overlapping genes Day 7 - 13- 20
```{r}
length(intersect(sigD7D13$external_gene_name, sigD7D20$external_gene_name))
length(intersect(sigD7D13$external_gene_name, sigD13D20$external_gene_name))
length(intersect(sigD7D20$external_gene_name, sigD13D20$external_gene_name))
a = intersect(sigD7D13$external_gene_name, sigD7D20$external_gene_name)
b = intersect(sigD7D13$external_gene_name, sigD13D20$external_gene_name)
c = intersect(sigD7D20$external_gene_name, sigD13D20$external_gene_name)
write.csv(a, "OL_D7D13_D7D20.csv", sep = ";")
write.csv(b, "OL_D7D13_D13D20.csv", sep = ";")
write.csv(c, "OL_D7D20_D13D20.csv", sep = ";")
d = intersect(intersect(sigD7D13$external_gene_name, sigD7D20$external_gene_name), sigD13D20$external_gene_name)
write.csv(d, "OL_D7D13_D7D20_D13D20.csv")
e = setdiff(sigD7D13$external_gene_name, sigD7D20$external_gene_name)
write.csv(f, "difference_D7D13_D7D20.csv")
f = setdiff(sigD13D20$external_gene_name, sigD7D20$external_gene_name)
write.csv(f, "difference_D7D20_D13D20.csv")
```
##HEATMAP
```{r HEATMAP}
#Find top differentially expressed genes
D0D7genes = sigD0D7[1:30,]
D0D7genes = subset(D0D7genes, select = c("Id", "log2FoldChange", "padj"))
D0D7genes = D0D7genes %>% mutate(comparison = "Day0vs7")
D0D13genes = sigD0D13[1:30,]
D0D13genes = subset(D0D13genes, select = c("Id", "log2FoldChange", "padj"))
D0D13genes = D0D13genes %>% mutate(comparison = "Day0vs13")
D0D20genes = sigD0D20[1:30,]
D0D20genes = subset(D0D20genes, select = c("Id", "log2FoldChange", "padj"))
D0D20genes = D0D20genes %>% mutate(comparison = "Day0vs20")
D7D13genes = sigD7D13[1:20,]
D7D13genes = subset(D7D13genes, select = c("Id", "log2FoldChange", "padj"))
D7D13genes = D7D13genes %>% mutate(comparison = "Day7vs13")
D7D20genes = sigD7D20[1:20,]
D7D20genes = subset(D7D20genes, select = c("Id", "log2FoldChange", "padj"))
D7D20genes = D7D20genes %>% mutate(comparison = "Day7vs20")
D13D20genes = sigD13D20[1:20,]
D13D20genes = subset(D13D20genes, select = c("Id", "log2FoldChange", "padj"))
D13D20genes = D13D20genes %>% mutate(comparison = "Day13vs20")
topGenesCtrl = rbind(D0D7genes,D0D13genes, D0D20genes,D7D13genes, D7D20genes,D13D20genes )
topGenesCtrl = topGenesCtrl[!duplicated(topGenesCtrl$Id),]
colnames(topGenesCtrl) = c("ensembl_gene_id", "log2FoldChange", "padj", "comparison")
colnames(myannot) = c("external_gene_name","ensembl_gene_id" )
topGenesCtrl = merge(topGenesCtrl, myannot, by = "ensembl_gene_id")
topGenesCtrl = topGenesCtrl[order(topGenesCtrl$padj),]
topGenesCtrl_name = topGenesCtrl$external_gene_name
#Subset top genes in normcounts dataframe
normcounts.top = normcounts.df.annot %>% dplyr::filter(external_gene_name %in% topGenesCtrl_name)
rownames(normcounts.top) = normcounts.top$external_gene_name
normcounts.top$ensembl_gene_id = NULL
normcounts.top$external_gene_name = NULL
normcounts.top = log2(normcounts.top)
normcounts.top[normcounts.top == -Inf] = 0
annotation_col = data.frame(
Day = factor(rep(c("0", "7", "13", "20"), c(5,6,4,4))))
annColor = list(Day = c("0" = "#FF9888","7" = "#B60A1C", "13" = "#C3CE3D", "20" = "#C0D6E4"))
col_dend = dendsort(hclust(dist(t(normcounts.top))), isReverse = FALSE)
p2 = pheatmap(normcounts.top,show_row_dend = FALSE, annotation_col = annotation_col, annotation_colors = annColor, name = "log2(normalized expression)", cluster_cols = col_dend)
#, col = colorRampPalette(c("navy", "white", "firebrick3")) (10)
pdf("pheatmap_topgenes.pdf", height = 11, width = 8)
p2
dev.off()
#Day 0 vs day 7 ---------------------------------------------------
D0D7top50 = sigD0D7[1:51,] #One does not have external gene name annotation
D0D7top50 = D0D7top50[!D0D7top50$external_gene_name == "",] #Remove
normcountsD0D7 = normcounts.df.annot %>% dplyr::filter(normcounts.df.annot$external_gene_name %in% D0D7top50$external_gene_name) %>% dplyr::select(contains("D0") | contains("D7") | ensembl_gene_id |external_gene_name) %>% dplyr::select(!ensembl_gene_id) #Select top 50 genes normalized counts
rownames(normcountsD0D7) = normcountsD0D7$external_gene_name #External gene name as rownames
normcountsD0D7 = normcountsD0D7 %>% dplyr::select(!external_gene_name) %>% log2(.) #Convert to log(normcounts)
normcountsD0D7[normcountsD0D7 == -Inf] = 0 #Convert infinite values to 0
annotation_col = data.frame(Day = factor(rep(c("0", "7"), c(5,6)))) #Annotation data frame
annColor = list(Day = c("0" = "#FF9888","7" = "#B60A1C")) #Color data frame
D0D7top50 = D0D7top50[order(match(D0D7top50$external_gene_name, rownames(normcountsD0D7))), , drop = FALSE] #Order normcounts same as top50
identical(D0D7top50$external_gene_name, rownames(normcountsD0D7)) #Check that they are same order
p1 = rowAnnotation(FoldChange = anno_barplot(D0D7top50$log2FoldChange)) #Fold change plot
p2 = pheatmap(normcountsD0D7,show_row_dend = FALSE, annotation_col = annotation_col, annotation_colors = annColor, name = "log2(normalized expression)") #heatmap
pdf("heatmat_Day0vs7_pheatmap.pdf", height = 9, width = 8)
p1 + p2
dev.off()
#Day 0 vs day 13 ----------------------------------------------------------------------------------
D0D13top50 = sigD0D13[1:52,]
D0D13top50 = D0D13top50[!D0D13top50$external_gene_name == "",] #Remove
normcountsD0D13 = normcounts.df.annot %>% dplyr::filter(normcounts.df.annot$external_gene_name %in% D0D13top50$external_gene_name) %>% dplyr::select(contains("D0") | contains("D13") | ensembl_gene_id |external_gene_name) %>% dplyr::select(!ensembl_gene_id)
rownames(normcountsD0D13) = normcountsD0D13$external_gene_name
normcountsD0D13 = normcountsD0D13 %>% dplyr::select(!external_gene_name) %>% log2(.)
normcountsD0D13[normcountsD0D13 == -Inf] = 0
col_dend = dendsort(hclust(dist(t(normcountsD0D13))), isReverse = TRUE)
annotation_col = data.frame(Day = factor(rep(c("0", "13"), c(5,4))))
annColor = list(Day = c("0" = "#FF9888","13" = "#C3CE3D"))
D0D13top50 = D0D13top50[order(match(D0D13top50$external_gene_name, rownames(normcountsD0D13))), , drop = FALSE]
identical(D0D13top50$external_gene_name, rownames(normcountsD0D13)) #Sjekk at det er samme rekkefølge
p1 = rowAnnotation(FoldChange = anno_barplot(D0D13top50$log2FoldChange))
p2 = pheatmap(normcountsD0D13,show_row_dend = FALSE, annotation_col = annotation_col, annotation_colors = annColor, name = "log2(normalized expression)", cluster_cols = col_dend)
pdf("heatmat_Day0vs13_pheatmap.pdf", height = 9, width = 8)
p1 + p2
dev.off()
#Day 0 vs day 20----------------------------------------------------------------------------------
D0D20top50 = sigD0D20[1:51,]
D0D20top50 = D0D20top50[!D0D20top50$external_gene_name == "",] #Remove
normcountsD0D20 = normcounts.df.annot %>% dplyr::filter(normcounts.df.annot$external_gene_name %in% D0D20top50$external_gene_name) %>% dplyr::select(contains("D0") | contains("D20") | ensembl_gene_id |external_gene_name) %>% dplyr::select(!ensembl_gene_id)
rownames(normcountsD0D20) = normcountsD0D20$external_gene_name
normcountsD0D20 = normcountsD0D20 %>% dplyr::select(!external_gene_name) %>% log2(.)
normcountsD0D20[normcountsD0D20 == -Inf] = 0
col_dend = dendsort(hclust(dist(t(normcountsD0D20))), isReverse = TRUE)
annotation_col = data.frame(Day = factor(rep(c("0", "20"), c(5,4))))
annColor = list(Day = c("0" = "#FF9888","20" = "#C0D6E4"))
D0D20top50 = D0D20top50[order(match(D0D20top50$external_gene_name, rownames(normcountsD0D20))), , drop = FALSE]
identical(D0D20top50$external_gene_name, rownames(normcountsD0D20)) #Sjekk at det er samme rekkefølge
p1 = rowAnnotation(FoldChange = anno_barplot(D0D20top50$log2FoldChange))
p2 = pheatmap(normcountsD0D20,show_row_dend = FALSE, annotation_col = annotation_col, annotation_colors = annColor, name = "log2(normalized expression)", cluster_cols = col_dend)
pdf("heatmat_Day0vs20_pheatmap.pdf", height = 9, width = 8)
p1 + p2
dev.off()
#Day 7 vs day 13 ----------------------------------------------------------------------------------
D7D13top50 = sigD7D13[1:51,]
D7D13top50 = D7D13top50[!D7D13top50$external_gene_name == "",] #Remove
normcountsD7D13 = normcounts.df.annot %>% dplyr::filter(normcounts.df.annot$external_gene_name %in% D7D13top50$external_gene_name) %>% dplyr::select(contains("D7") | contains("D13") | ensembl_gene_id |external_gene_name) %>% dplyr::select(!ensembl_gene_id)
rownames(normcountsD7D13) = normcountsD7D13$external_gene_name
normcountsD7D13 = normcountsD7D13 %>% dplyr::select(!external_gene_name) %>% log2(.)
normcountsD7D13[normcountsD7D13 == -Inf] = 0
col_dend = dendsort(hclust(dist(t(normcountsD7D13))), isReverse = TRUE)
annotation_col = data.frame(Day = factor(rep(c("7", "13"), c(6,4))))
annColor = list(Day = c("7" = "#B60A1C","13" = "#C3CE3D"))
D7D13top50 = D7D13top50[order(match(D7D13top50$external_gene_name, rownames(normcountsD7D13))), , drop = FALSE]
identical(D7D13top50$external_gene_name, rownames(normcountsD7D13)) #Sjekk at det er samme rekkefølge