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The master working directory on UChicago RCC
cd /project2/xuanyao/marie/E-GEUV-1
# Download the fastq dataset from the Geuvadis project
# sample information https://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/
# fastq files ftp://ftp.sra.ebi.ac.uk/vol1/fastq/
cat getfastq.csv | awk 'NR==21,NR==90' > getfastqtest2.csv
csvfile=getfastqtest2.csv
for line in `cat ${csvfile} | grep -v ^#`
do
url=`echo ${line} | cut -d ',' -f 2`
file=`echo ${line} | cut -d ',' -f 4`
wget ${url} -O fastq2/${file}.fastq.gz
doneDownload the genotype dataset from the 1000 Genomes project phase 3
mkdir genotype
wget ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/ALL.chr{1..22}.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gzKallisto - make index files
module load kallisto
wget ftp://ftp.ensembl.org/pub/grch37/current/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh37.cdna.all.fa.gz
gunzip Homo_sapiens.GRCh37.cdna.all.fa.gz
kallisto index -i human.GRCh37.cdna.all.idx Homo_sapiens.GRCh37.cdna.all.fa
wget https://github.com/pachterlab/kallisto-transcriptome-indices/releases/download/ensembl-96/homo_sapiens.tar.gz
gunzip Homo_sapiens
homo_sapiens/transcriptome.idxGene expression quantification for all samples with Kallisto
module load kallisto
csvfile=sample.filelist.csv
for line in `cat ${csvfile} | grep -v ^#`
do
file=`echo ${line} | cut -d ',' -f 5`
kallisto quant -i human.GRCh37.cdna.all.idx -o kallisto/${file}.kallistoOut -n 100 -t 32 fastq/${file}.1.fastq fastq/${file}.2.fastq
done
# summarize TPM (gene expression) data from each individual
samples <-read.csv("1000g.EUR.sample.csv",header=TRUE)
files <- file.path(dir, "kallisto", samples$kallisto, "abundance.tsv")
names(files) <- paste0(samples$X)
all(file.exists(files))
txi <- tximport(files, type = "kallisto", tx2gene = tx2gene, txOut = FALSE, ignoreAfterBar = TRUE)
write.table(txi$abundance,sep="\t", file = "EUR.TPM.tsv")# add gene annotations to the TPM file
library('biomaRt')
mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl", host="grch37.ensembl.org"))
df <- read.csv(file.choose(), header = T, sep = ",")
gene <- df$gene
G_list <- getBM(filters= "ensembl_gene_id", attributes=c('ensembl_gene_id', 'hgnc_symbol','description','chromosome_name','start_position','end_position'),mart= mart,values=gene)
G_list2<-merge(df,G_list,by.x="gene",by.y="ensembl_gene_id",all.x=T)
write.csv(G_list2, file = "British.gene.TPM.csv")# keep the individuals who are reported in both the 1000 Genome Project and Geuvadis
module load vcftools
for i in `seq 1 22`
do
vcftools --gzvcf /genotype/phase3/ALL.chr$i.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz --keep 1000g.ind.sample.txt --recode --out /genotype/phase3/chr$i.1000gphase3.455
done
# exclude rare variants
vcftools --vcf /genotype/phase3/chr$i.1000gphase3.455.recode.vcf --maf 0.01 --max-maf 0.99 --recode --out /genotype/phase3/chr$i.1000gphase3.455.0.01
# keep only bi-allelic variants
vcftools --vcf /genotype/phase3/chr$i.1000gphase3.455.0.01.recode.vcf --min-alleles 2 --max-alleles 2 --recode --out /genotype/phase3/chr$i.1000gphase3.455.0.01.biallelic# Convert vcf file to plink format
plink --vcf chr22.1000gphase3.455.0.01.recode.vcf --make-bed --out chr22.1000gphase3.455.0.01.recode.bed
# Run pca
plink --bfile chr22.1000gphase3.455.0.01.recode.bed --pca --out
# Plot pca
library(ggplot2)
ggplot(data=pca, aes(V3,V4)) + geom_point()
# surrogate variable analysis
library(sva)
mm <- model.matrix(~ population, colData(ddsTxi))
mm0 <- model.matrix(~ 1, colData(ddsTxi))
norm.cts <- norm.cts[rowSums(norm.cts) > 10,]
fit <- svaseq(norm.cts, mod=mm, mod0=mm0)
# 50 SVs are found. I use 7 SVs
sva7 = sva(norm.cts , mm, mm0, n.sv=7)
write.csv(sva7$sv[,1:7], file = "sva7.csv")library("DESeq2")
colData <-read.csv("1000g.sample.csv",header = T,row.names=1)
class(colData$date)
colData$date1<-factor(colData$date)
ddsTxi <- DESeqDataSetFromTximport(txi,
colData = colData,
design = ~ sex+population+performer)
colData <-read.csv("1000g.EUR.YRI.csv",header = T,row.names=1)
ddsEurYri <- DESeqDataSetFromTximport(txi,
colData = colData,
design = ~ sex+population+V1+V2+V3+V4+V5+V6+V7)
keep <- rowsume(counds(ddsEurYri)) >=455
ddsEurYri <- ddsEurYri[keep,]
ddsEY<-DESeq(ddsEurYri)
deg <- results(ddsEY, contrast=c("population","EUR","Yoruba"))
write.csv(deg, file = "E-GEUV_EUR_Yoruba.csv")preparation
# prepare input bed.gz and index files
module load bedtools
bedtools sort -i British.gene.TPM.bed -header > British.gene.TPM.sorted.bed
bgzip British.gene.TPM.sorted.bed && tabix -p bed British.gene.TPM.sorted.bed.gz
# prepare input vcf.gz and index files
module load htslib
module load tabix
for i in `seq 1 22`
do
bgzip genotype/phase3/chr$i.1000gphase3.Yoruba.0.01.biallelic.recode.vcf && tabix -p vcf genotype/phase3/chr$i.1000gphase3.Yoruba.0.01.biallelic.recode.vcf.gz
done
for i in `seq 1 22`
do
bgzip genotype/phase3/chr$i.1000gphase3.EUR.0.01.biallelic.recode.vcf && tabix -p vcf genotype/phase3/chr$i.1000gphase3.EUR.0.01.biallelic.recode.vcf.gz
done
run fastQTL
## https://github.com/francois-a/fastqtl
for i in `seq 1 22`
do
./bin/fastQTL.static fastQTL --vcf /project2/xuanyao/marie/E-GEUV-1/genotype/phase3/chr$i.1000gphase3.Yoruba.0.01.biallelic.recode.vcf.gz --bed GEUV/Yoruba.gene.TPM.sorted.bed.gz --region $i:1-249250621 --threshold 0.01 --out GEUV/Yoruba.chr$i.nominals.0.01.txt --cov GEUV/Yoruba.cov.txt --normal
done
cat Yoruba.chr*.nominals.0.01.txt > Yoruba.auto.nominals.0.01.txt
setwd("/project2/xuanyao/marie/E-GEUV-1")
library(ggplot2)
library("ggrepel")
library("plyr")
library(gplots)
library(reshape2)
## remove multi-allelic SNPs reported as bi-allelic SNPs which confuse the result
EUR1<-subset(EUR2,
EUR2$gene.SNP!="ENSG00000188659.rs542232278"&
EUR2$gene.SNP!="ENSG00000255769.rs145926341"&
EUR2$gene.SNP!="ENSG00000255769.rs371891811"&
EUR2$gene.SNP!="ENSG00000259328.rs145926341"&
EUR2$gene.SNP!="ENSG00000259323.rs145926341"&
EUR2$gene.SNP!="ENSG00000259472.rs145926341"&
EUR2$gene.SNP!="ENSG00000103942.rs1610794"&
EUR2$gene.SNP!="ENSG00000103342.rs140839133"&
EUR2$gene.SNP!="ENSG00000159202.rs77094622"&
EUR2$gene.SNP!="ENSG00000189050.rs112549034"&
EUR2$gene.SNP!="ENSG00000108592.rs138776605"&
EUR2$gene.SNP!="ENSG00000256771.rs10650867"&
EUR2$gene.SNP!="ENSG00000134330.rs139337028"&
EUR2$gene.SNP!="ENSG00000125991.rs139252705"&
EUR2$gene.SNP!="ENSG00000214078.rs142898689"&
EUR2$gene.SNP!="ENSG00000087586.rs5842156"&
EUR2$gene.SNP!="ENSG00000171522.rs139036988"&
EUR2$gene.SNP!="ENSG00000196284.rs112143344"&
EUR2$gene.SNP!="ENSG00000135316.rs71553453"&
EUR2$gene.SNP!="ENSG00000232559.rs377632592"&
EUR2$gene.SNP!="ENSG00000131558.rs141161799"&
EUR2$gene.SNP!="ENSG00000162441.rs36126617"&
EUR2$gene.SNP!="ENSG00000162441.rs151178549"&
EUR2$gene.SNP!="ENSG00000142794.rs35506192"&
EUR2$gene.SNP!="ENSG00000142794.rs145038894"&
EUR2$gene.SNP!="ENSG00000116128.rs10657777"&
EUR2$gene.SNP!="ENSG00000117280.rs149256505"&
EUR2$gene.SNP!="ENSG00000107719.rs138584752"&
EUR2$gene.SNP!="ENSG00000198561.rs142865693"&
EUR2$gene.SNP!="ENSG00000087365.rs5792377"&
EUR2$gene.SNP!="ENSG00000162341.rs111786372"&
EUR2$gene.SNP!="ENSG00000110092.rs59333593"&
EUR2$gene.SNP!="ENSG00000111215.rs61604574"&
EUR2$gene.SNP!="ENSG00000165502.rs141634854"&
EUR2$gene.SNP!="ENSG00000104093.rs138911097")
YRI1<-subset(YRI2, YRI2$gene.SNP!="ENSG00000143106.rs545041240"&
YRI2$gene.SNP!="ENSG00000078403.rs528760884"&
YRI2$gene.SNP!="ENSG00000167996.rs150035626"&
YRI2$gene.SNP!="ENSG00000111252.rs367797687"&
YRI2$gene.SNP!="ENSG00000172458.rs112879834"&
YRI2$gene.SNP!="ENSG00000183044.rs367630500"&
YRI2$gene.SNP!="ENSG00000154874.rs138555657"&
YRI2$gene.SNP!="ENSG00000011132.rs71166969"&
YRI2$gene.SNP!="ENSG00000125991.rs142898689"&
YRI2$gene.SNP!="ENSG00000198832.rs35065681"&
YRI2$gene.SNP!="ENSG00000128268.rs142897975"&
YRI2$gene.SNP!="ENSG00000161013.rs71591436"&
YRI2$gene.SNP!="ENSG00000170727.rs60257564")Manhattan plot example
x<-read.csv("YRI.EUR.DEG.for.manhattan.csv", header = TRUE)
x<- x[order(x$Chromosome),]
nCHR <- length(unique(x$Chromosome))
x$BPcum <- NA
s <- 0
nbp <- c()
for (i in unique(x$Chromosome)){
nbp[i] <- max(x[x$Chromosome == i,]$start)
x[x$Chromosome == i,"BPcum"] <- x[x$Chromosome == i,"start"] + s
s <- s + nbp[i]
}
axis.set <- x %>%
group_by(Chromosome) %>%
summarize(center = (max(BPcum) + min(BPcum)) / 2)
x1 <- subset (x, -log10(padj)>40 )
manhplot <- ggplot(x, aes(x = BPcum, y = sign(log2FoldChange)*(-log10(padj)),
color = as.factor(Chromosome))) +
geom_point(alpha = 0.85) +
scale_x_continuous(label = axis.set$Chromosome, breaks = axis.set$center) +
scale_color_manual(values = rep(c("gray", "black"), nCHR)) +
scale_size_continuous(range = c(0.5,3)) +
theme_minimal() +theme_bw(base_size = 12)+
theme(
legend.position = "none",
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.x = element_text(angle = 90, size = 8, vjust = 0.5)
)+geom_label_repel(data=x1,aes(label = gene.name), color = 'blue',
size = 3) # GTEx comparison
# GTEx.Cells_EBV-transformed_lymphocytes.v8.egenes.txt
# GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.6
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_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 rprojroot_1.3-2 digest_0.6.25 later_1.1.0.1
[5] R6_2.4.1 backports_1.1.8 git2r_0.27.1 magrittr_1.5
[9] evaluate_0.14 stringi_1.4.6 rlang_0.4.7 fs_1.4.1
[13] promises_1.1.1 whisker_0.4 rmarkdown_2.3 tools_4.0.0
[17] stringr_1.4.0 glue_1.4.1 httpuv_1.5.4 xfun_0.14
[21] yaml_2.2.1 compiler_4.0.0 htmltools_0.4.0 knitr_1.28