轉錄組分析流程

原創 sunchengquan 2019-01-01 20:32

文章目錄

分析流程概述

參考文獻：hppRNA—a Snakemake-based handy parameter-free pipeline for RNA-Seq analysis of numerous samples

六種主要流程示意圖：
在這裏插入圖片描述

下載測試數據

wget ftp://ftp.ccb.jhu.edu/pub/RNAseq_protocol/chrX_data.tar.gz

如果文章中的數據不是fastq格式，而是給GSExxxx，這是需要下載SRA數據

需要安裝sratoolkit，SRR_Acc_List.txt文件儲存SRR號碼

cat SRR_Acc_List.txt | while read id; do (prefetch  ${id} &);done


# 批量轉換sra到fq格式
ls /public/project/RNA/airway/sra/*  | while read id; do ( nohup fastq-dump --gzip --split-3 -O ./ ${id} & ); done

下載的數據：

[sunchengquan 15:45:09 /data/Data_base/test_tmp/RNA_seq_practice/chrX_data/samples]
$ ll
總用量 1.8G
-rwxr-xr-x 1 sunchengquan sunchengquan  88M 1月  15 2016 ERR188044_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  88M 1月  15 2016 ERR188044_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  84M 1月  15 2016 ERR188104_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  85M 1月  15 2016 ERR188104_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan 108M 1月  15 2016 ERR188234_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan 109M 1月  15 2016 ERR188234_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  59M 1月  15 2016 ERR188245_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  60M 1月  15 2016 ERR188245_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  65M 1月  15 2016 ERR188257_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  66M 1月  15 2016 ERR188257_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  39M 1月  15 2016 ERR188273_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  39M 1月  15 2016 ERR188273_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  88M 1月  15 2016 ERR188337_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  88M 1月  15 2016 ERR188337_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  63M 1月  15 2016 ERR188383_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  63M 1月  15 2016 ERR188383_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  86M 1月  15 2016 ERR188401_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  87M 1月  15 2016 ERR188401_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  56M 1月  15 2016 ERR188428_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  56M 1月  15 2016 ERR188428_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  70M 1月  15 2016 ERR188454_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  70M 1月  15 2016 ERR188454_chrX_2.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  75M 1月  15 2016 ERR204916_chrX_1.fastq.gz
-rwxr-xr-x 1 sunchengquan sunchengquan  75M 1月  15 2016 ERR204916_chrX_2.fastq.gz

數據質量控制

reads質量評估軟件：fastqc生成質控報告，multiqc將各個樣本的質控報告整合爲一個。

reads質量控制軟件：prinseq，cutadapt，trimmomatic,trim_galore

查看qc.sh


#!/usr/bin/env bash

set -e

settings(){
        samples=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/samples
        if test -w $samples;then
                mkdir -p {$samples/qc,$samples/cleandata/qc}   
        else
                echo "沒有寫入權限"
        fi

}


thread(){
        tmp_fifofile="/tmp/$$.fifo" #腳本運行的當前進程ID號作爲文件名 
    mkfifo "$tmp_fifofile" 
    exec 6<>"$tmp_fifofile"  #將fd6指向fifo類型
    rm $tmp_fifofile
    thread_num=$1 # 此處定義線程數
    for((i=0;i<$thread_num;i++));do 
        echo 
    done >&6 # 事實上就是在fd6中放置了$thread個回車符
    $2 6 $3
    exec 6>&- # 關閉df6
}


qc(){
        source activate RNA
        printf "[%s %s %s %s %s %s]::數據質量評估\n" $(echo `date`)
        start=$(date +%s.%N)
        list=$(find $2 -name *q\.gz)
        file_num=`ls -l $2/qc|wc -l`
        if [ $file_num -lt 2 ];then
                for i in $list;do
                        read -u$1
                        {
                        name=`awk -v each=$i 'BEGIN{split(each,arr,"/");l=length(arr);print arr[l]}' `
                        fastqc  $i -o $2/qc  &>> $2/qc/qc.log
                        printf "[%s %s %s %s %s %s]::%s質量評估完成\n" $(echo `date`) $name
                        echo >&$1 
                        } &
                done && wait

                multiqc -d $2/qc -o $2/qc 
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l) 
                printf "[%s %s %s %s %s %s]::數據質量評估耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
}

trim_qc(){
        printf "[%s %s %s %s %s %s]::數據質量控制\n" $(echo `date`)
        source activate RNA
        start=$(date +%s.%N)
        dir=$samples/cleandata
        find $samples -name *1?f*q?gz|sort >$dir/1
        find $samples -name *2?f*q?gz|sort >$dir/2
        paste -d ":" $dir/1 $dir/2  > $dir/config && rm $dir/1 $dir/2
        file_num=`ls -l $dir|wc -l`
        
        if [ $file_num -lt 3 ];then
                for id in `cat $dir/config`;do
                        read -u$1
                        fq1=$(echo $id|cut -d":" -f1)
                        fq2=$(echo $id |cut -d":" -f2)
                        base_name=$(basename $fq1)
                        name=`awk -v each=$base_name 'BEGIN{split(each,arr,"_");print arr[1]}' ` 
                        {
                        trim_galore -q 25 --phred33 --length 25 --stringency 3 --paired -o $dir $fq1 $fq2 &> $dir/trim.log
                        printf "[%s %s %s %s %s %s]::%s質量控制完成\n" $(echo `date`) $name
                        echo >&$1
                        } &
                done && wait 
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::數據質量控制耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
}


settings
thread 6 qc $samples
thread 3 trim_qc 
thread 6 qc $samples/cleandata

Tophat –> Cufflink –> Cuffdiff

參考文獻：Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks

流程圖：
在這裏插入圖片描述

手動安裝相關軟件

我們已經使用bioconda安裝相關的軟件，現在手動安裝一下，本流程所需要的軟件

下載並安裝比對軟件bowtie2
cd ~/local/app
curl -OL http://downloads.sourceforge.net/project/bowtie-bio/bowtie2/2.2.4/bowtie2-2.2.4-linux-x86_64.zip
unzip bowtie2-2.2.4-linux-x86_64.zip

把比對軟件以及相關程序鏈接到bin文件夾
ln -s ~/local/app/bowtie2-2.2.4/bowtie2 ~/bin/
ln -s ~/local/app/bowtie2-2.2.4/bowtie2-align* ~/bin/
ln -s ~/local/app/bowtie2-2.2.4/bowtie2-build ~/bin/

安裝tophat2
cd ~/local/app/
curl -OL http://ccb.jhu.edu/software/tophat/downloads/tophat-2.1.1.Linux_x86_64.tar.gz
tar zxvf tophat-2.0.13.Linux_x86_64.tar.gz
cd ~/bin/
vi tophat
#!/usr/bin/env bash
python2 ~/local/app/tophat-2.1.1.Linux_x86_64/tophat $@
chmod 755 tophat
保存退出

#註釋文件
cd /data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genes
curl -L ftp://ftp.ensembl.org/pub/release-77/gtf/homo_sapiens/Homo_sapiens.GRCh38.77.gtf.gz > hg38.gtf.gz
gunzip *.gz
cat hg38.gtf | awk ' $1 =="X" { print $0 }' > chr_X.gtf


安裝cufflinks
cd ~/local/app
curl -OL http://cole-trapnell-lab.github.io/cufflinks/assets/downloads/cufflinks-2.1.1.Linux_x86_64.tar.gz
tar zxvf cufflinks-2.1.1.Linux_x86_64.tar.gz

ln -fs ~/local/app/cufflinks-2.1.1.Linux_x86_64/cufflinks ~/bin
ln -fs ~/local/app/cufflinks-2.1.1.Linux_x86_64/cuffdiff ~/bin
ln -fs ~/local/app/cufflinks-2.1.1.Linux_x86_64/gtf_to_sam ~/bin
ln -fs ~/local/app/cufflinks-2.1.1.Linux_x86_64/cuffcompare ~/bin
cd ~/bin
vi cuffmerge
#!/usr/bin/env bash
python2 ~/local/app/cufflinks-2.1.1.Linux_x86_64/cuffmerge $@
chmod 755 cuffmerge

流程代碼

#!/usr/bin/env bash

set -ue

settings(){
        samples=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/samples
        index=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genome/index
        output=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/tophat+cuff
        if test -w $(dirname $output) &&  test -w $(dirname index);then
                mkdir -p {$index/bowtie,$output/1_tophat,$output/2_cufflinks,$output/3_cuffdiff}
        fi
        cuffdiff=$output/3_cuffdiff
        indexes=$index/bowtie/chrX
        genome=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genome/chrX.fa
        gene=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genes/chrX.gtf
}


thread(){
    tmp_fifofile="/tmp/$$.fifo" 
    mkfifo "$tmp_fifofile" 
    exec 6<>"$tmp_fifofile"  
    rm $tmp_fifofile
    thread_num=$1 
    for((i=0;i<$thread_num;i++));do 
        echo 
    done >&6 
    $2 6 
    exec 6>&- 
}


index(){
        printf "[%s %s %s %s %s %s]::建立索引bowtie2-build\n" $(echo `date`)
        start=$(date +%s.%N)
        file_num=`ls -l $index/bowtie|wc -l`    
        source activate RNA
        base_name=$(basename $genome)
    name=`awk -v each=$base_name 'BEGIN{split(each,arr,".");print arr[1]}' ` 
        if [ $file_num -lt 2 ];then
                bowtie2-build -f $genome $index/bowtie/$name &> $index/bowtie/index.log
                ln -s $genome $index/bowtie/$basename
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::建立索引耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
}


mapping(){
        printf "[%s %s %s %s %s %s]::與參考基因組比對tophat\n" $(echo `date`)
        start=$(date +%s.%N)
        dir=$output/1_tophat
        find $samples/cleandata -name *1?f*q.gz|sort > $dir/1
        find $samples/cleandata -name *2?f*q.gz|sort > $dir/2
        paste -d ":" $dir/1 $dir/2  > $dir/config && rm $dir/1 $dir/2
        file_num=`ls -l $dir|wc -l`
        source activate RNA
        if [ $file_num -lt 3 ];then
                for id in $(cat $dir/config);do
                        fq1=$(echo $id|cut -d":" -f1)
                        fq2=$(echo $id |cut -d":" -f2)
                        name=`awk -v each=$(basename $fq1) 'BEGIN{split(each,arr,"_");print arr[1]}' ` 
                        read -u$1
                        {
                        tophat -p 8 -G $gene -o $dir/$name $indexes $fq1 $fq2 &>> $dir/mapping.log
                        printf "[%s %s %s %s %s %s]::%s比對完成\n" $(echo `date`) $name
                        echo >&$1
                        } &
                done && wait
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::比對耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
}


assemble(){
        printf "[%s %s %s %s %s %s]::轉錄本組裝和定量cufflinks\n" $(echo `date`)
        start=$(date +%s.%N)
        dir=$output/2_cufflinks
        file_num=`ls -l $dir|wc -l`
        source activate RNA
        if [ $file_num -lt 3 ];then
                for id in $(cat $output/1_tophat/config);do
                        fq1=$(echo $id|cut -d":" -f1)
                        name=`awk -v each=$(basename $fq1) 'BEGIN{split(each,arr,"_");print arr[1]}' ` 
                        read -u$1
                        {
                        cufflinks -p 8 -g $gene -o $dir/$name $output/1_tophat/$name/accepted_hits.bam &> $dir/$name.log
                        printf "[%s %s %s %s %s %s]::%s轉錄本組裝完成\n" $(echo `date`) $name
                        echo >&$1
                        } &
                done && wait
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::轉錄本組裝耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA

}


merge(){
        printf "[%s %s %s %s %s %s]::轉錄本合併cuffmerge\n" $(echo `date`)
        start=$(date +%s.%N)
        dir=$output/2_cufflinks
        find $dir -name *transcripts?gtf|sort > $dir/assemblies.txt
        source activate RNA
        if [ ! -d $dir/merged_asm ];then
                cuffmerge -p 8 -o $dir/merged_asm -g $gene -s $genome $dir/assemblies.txt &> $dir/cuffmerge.log
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::轉錄本合併耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
    source deactivate RNA

}


diff(){
        printf "[%s %s %s %s %s %s]::差異分析cuffdiff\n" $(echo `date`)
        start=$(date +%s.%N)
        dir=$output/3_cuffdiff
        S1=$output/1_tophat/ERR188245/accepted_hits.bam;S2=$output/1_tophat/ERR188428/accepted_hits.bam;S3=$output/1_tophat/ERR188337/accepted_hits.bam
        S4=$output/1_tophat/ERR204916/accepted_hits.bam;S5=$output/1_tophat/ERR188234/accepted_hits.bam;S6=$output/1_tophat/ERR188273/accepted_hits.bam
        S7=$output/1_tophat/ERR188401/accepted_hits.bam;S8=$output/1_tophat/ERR188257/accepted_hits.bam;S9=$output/1_tophat/ERR188383/accepted_hits.bam
        S10=$output/1_tophat/ERR188454/accepted_hits.bam;S11=$output/1_tophat/ERR188104/accepted_hits.bam;S12=$output/1_tophat/ERR188044/accepted_hits.bam
        
        file_num=`ls -l $dir|wc -l`
        source activate RNA
        if [ $file_num -lt 3 ];then
                cuffdiff -p 8 -b $genome -o $dir -L Female,Male -u $output/2_cufflinks/merged_asm/merged.gtf $S1,$S2,$S3,$S4,$S5,$S6 $S7,$S8,$S9,$S10,$S11,$S12 &> $dir/cuffdiff.log
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::差異分析cuffdiff耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
}

expression_matrix(){
        dir=$output/3_cuffdiff
        expr=$dir/gene_exp.diff
        ##篩選出下調的基因（log2_fold_change < -2 & pvalue < 0.001）
        awk '{if(($10<-2)&&($11<0.001))print $3"\t"$8"\t"$9"\t"$10}' $dir/gene_exp.diff | grep -v 'inf' > $dir/down.txt
        ## 篩選出上調的基因（log2_fold_change > 2 & pvalue < 0.001
        awk '{if(($10>2)&&($11<0.001))print $3"\t"$8"\t"$9"\t"$10}' $dir/gene_exp.diff | grep -v 'inf' > $dir/up.txt
}


settings
index
thread 4 mapping
thread 4 assemble
merge
diff

HISAT2 ->StringTie -> Ballgown

參考文獻:Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown
流程示意圖
在這裏插入圖片描述

流程代碼

#!/usr/bin/env bash
set -ue

settings(){
        samples=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/samples
        index=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genome/index
        output=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/hisat+stringtie
        if test -w $(dirname $output) &&  test -w $(dirname index);then
                mkdir -p {$index/hisat2,$output/1_hisat,$output/2_stringtie,$output/3_ballgown,$output/4_DE}
        fi
        hisat=$output/1_hisat
        stringtie=$output/2_stringtie
        ballgown=$output/3_ballgown
        genome=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genome/chrX.fa
        gene=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genes/chrX.gtf
}

thread(){
    tmp_fifofile="/tmp/$$.fifo" 
    mkfifo "$tmp_fifofile" 
    exec 6<>"$tmp_fifofile"  
    rm $tmp_fifofile
    thread_num=$1 
    for((i=0;i<$thread_num;i++));do 
        echo 
    done >&6 
    $2 6 
    exec 6>&- 
}

index(){
        printf "[%s %s %s %s %s %s]::建立索引hisat2-build\n" $(echo `date`)
        start=$(date +%s.%N)
        file_num=`ls -l $index/hisat2|wc -l`    
        source activate RNA
        base_name=$(basename $genome)
                name=`awk -v each=$base_name 'BEGIN{split(each,arr,".");print arr[1]}' ` 
        if [ $file_num -lt 2 ];then
                                hisat2_extract_exons.py $gene >$index/hisat2/$name'.exon'
                                hisat2_extract_splice_sites.py $gene >$index/hisat2/$name'.ss'
                                hisat2-build --ss $index/hisat2/$name'.ss' --exon $index/hisat2/$name'.exon' $genome $index/hisat2/$name 1>$index/hisat2/index.log 2>&1
                                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::建立索引耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
        
}

mapping(){
        printf "[%s %s %s %s %s %s]::與參考基因組比對hisat2\n" $(echo `date`)
    start=$(date +%s.%N)
    dir=$output/1_hisat
    find $samples/cleandata -name *1?f*q.gz|sort > $dir/1
    find $samples/cleandata -name *2?f*q.gz|sort > $dir/2
    paste -d ":" $dir/1 $dir/2  > $dir/config && rm $dir/1 $dir/2
    file_num=`ls -l $dir|wc -l`
        index_prefix=`awk -v each=$(basename $genome) 'BEGIN{split(each,arr,".");print arr[1]}' `
    source activate RNA
    if [ $file_num -lt 3 ];then
        for id in $(cat $dir/config);do
            fq1=$(echo $id|cut -d":" -f1)
            fq2=$(echo $id |cut -d":" -f2)
            name=`awk -v each=$(basename $fq1) 'BEGIN{split(each,arr,"_");print arr[1]}' ` 
            read -u$1
            {
                        hisat2 -p 8 --dta -x $index/hisat2/${index_prefix} -1 $fq1 -2 $fq2 -S $dir/${name}.sam &> $dir/${name}.hisat.log
                        samtools sort -@ 8 -o $dir/${name}.bam $dir/${name}.sam &> /dev/null && rm $dir/${name}.sam 
            printf "[%s %s %s %s %s %s]::%s比對完成\n" $(echo `date`) $name
            echo >&$1
            } &
        done && wait
        dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::比對耗時%.2f分鐘\n" $(echo `date`) $dur
    fi
    source deactivate RNA

}

assemble(){
        printf "[%s %s %s %s %s %s]::轉錄本組裝stringtie\n" $(echo `date`)
        start=$(date +%s.%N)
        dir=$output/2_stringtie
        file_num=`ls -l $dir|wc -l`
        source activate RNA
        if [ $file_num -lt 3 ];then
                for id in $output/1_hisat/*.bam;do
                        base_name=$(basename $id)
                        i=${base_name%.bam*}
                        read -u$1
                        {
                        stringtie -p 8 -G $gene -o $dir/${i}.gtf -l $i $output/1_hisat/${i}.bam &>> $dir/assemble.log
                        printf "[%s %s %s %s %s %s]::%s轉錄本組裝完成\n" $(echo `date`) $i
                        echo >&$1
                        } &
                done && wait
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::轉錄本組裝耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
}


merge(){
        printf "[%s %s %s %s %s %s]::轉錄本合併stringtie --merge\n" $(echo `date`)
        start=$(date +%s.%N)
        dir=$output/2_stringtie
        find $dir -name *?gtf|grep -v '.*merge.gtf'|sort > $dir/mergelist.txt
        source activate RNA
        if [ ! -f $dir/stringtie_merge.gtf ];then
                stringtie --merge -p 8 -G $gene -o $dir/stringtie_merge.gtf $dir/mergelist.txt
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::轉錄本合併耗時%.2f分鐘\n" $(echo `date`) $dur
        fi      
        source deactivate RNA
}

count(){
        printf "[%s %s %s %s %s %s]::轉錄本（基因）的定量：stringtie\n" $(echo `date`)  
        start=$(date +%s.%N)
        dir=$output/3_ballgown
        file_num=`ls -l $dir|wc -l`
        source activate RNA
        if [ $file_num -lt 3 ];then
                for id in $(cat $output/2_stringtie/mergelist.txt);do
                        base_name=$(basename $id)       
                        name=${base_name%.gtf}
                        read -u$1
                        {
                        stringtie -B -p 8 -G $output/2_stringtie/stringtie_merge.gtf -o $dir/$name/$base_name $output/1_hisat/${name}.bam &>> $dir/count.log
                        printf "[%s %s %s %s %s %s]::%s轉錄本定量完成\n" $(echo `date`) $name
                        echo >&$1
                        } &
                done && wait
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::轉錄本合併耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
}


settings
index
thread 4 mapping
thread 6 assemble
merge
thread 6 count

差異表達分析ballgown

設置工作目錄

就設置爲當前目錄下，新建一個DE目錄，根據需求調整

!pwd
!mkdir DE

/root/python_code_scq/python_bio_file
mkdir: 無法創建目錄"DE": 文件已存在

setwd("/root/python_code_scq/python_bio_file/DE")
suppressMessages(library(ballgown))
suppressMessages(library(genefilter))
suppressMessages(library(dplyr))

讀取樣本的表型數據

pheno_data=read.csv("/root/python_code_scq/python_bio_file/geuvadis_phenodata.csv")
pheno_data
idssexpopulation
ERR188044male YRI
ERR188104male YRI
ERR188234female YRI
ERR188245female GBR
ERR188257male GBR
ERR188273female YRI
ERR188337female GBR
ERR188383male GBR
ERR188401male GBR
ERR188428female GBR
ERR188454male YRI
ERR204916female YRI

讀取定量分析的表達數據

bg_chrX = ballgown(dataDir = "../3_ballgown",samplePattern = "ERR", pData = pheno_data )

Sat Dec 29 10:56:05 2018
Sat Dec 29 10:56:05 2018: Reading linking tables
Sat Dec 29 10:56:06 2018: Reading intron data files
Sat Dec 29 10:56:07 2018: Merging intron data
Sat Dec 29 10:56:07 2018: Reading exon data files
Sat Dec 29 10:56:09 2018: Merging exon data
Sat Dec 29 10:56:10 2018: Reading transcript data files
Sat Dec 29 10:56:10 2018: Merging transcript data
Wrapping up the results
Sat Dec 29 10:56:10 2018

bg_chrX

ballgown instance with 3354 transcripts and 12 samples

過濾掉表達量低的基因

bg_chrX_filt=subset(bg_chrX,"rowVars(texpr(bg_chrX))>1",genomesubset=TRUE)

對轉錄本進行差異分析

results_transcripts=stattest(bg_chrX_filt,feature="transcript",covariate="sex",adjustvars=c("population"),getFC=TRUE,meas="FPKM")

對基因進行差異分析

results_genes=stattest(bg_chrX_filt,feature="gene",covariate="sex",adjustvars=c("population"),getFC=TRUE,meas="FPKM")

給轉錄本添加基因名和基因ID

results_transcripts=data.frame(geneNames=ballgown::geneNames(bg_chrX_filt),geneIDs=geneIDs(bg_chrX_filt),results_transcripts)

根據p-value值對分析結果進行排序(從小到大)

results_transcripts=arrange(results_transcripts,pval)
results_genes=arrange(results_genes,pval)
write.csv(results_transcripts, "chrX_transcript_results.csv",row.names=FALSE)
write.csv(results_genes, "chrX_gene_results.csv",row.names=FALSE)

提取顯著差異表達的基因和轉錄本（q-value<0.05）

results_transcripts_0.05=subset(results_transcripts,results_transcripts$qval<0.05)
results_genes_0.05=subset(results_genes,results_genes$qval<0.05)
write.csv(results_transcripts_0.05,file="chrX_transcript_0.05.csv",row.names=FALSE)
write.csv(results_genes_0.05,file="chrX_genes_0.05.csv",row.names=FALSE)

數據可視化之顏色設定

tropical= c('darkorange', 'dodgerblue','hotpink', 'limegreen', 'yellow')
palette(tropical)
#當然rainbow()函數也可以完成這個任務
#palette(rainbow(5))

以FPKM爲參考值作圖，以性別作爲區分條件

options(repr.plot.width = 9, repr.plot.height = 6)
fpkm = texpr(bg_chrX,meas="FPKM")
#方便作圖將其log轉換，+1是爲了避免出現log2(0)的情況
fpkm = log2(fpkm+1)
# tiff(filename ="FPKM.tiff", compression = "lzw")
boxplot(fpkm,col=as.numeric(pheno_data$sex),las=2,ylab='log2(FPKM+1)')
# dev.off()

在這裏插入圖片描述

suppressMessages(library(reshape))
suppressMessages(library(ggplot2))
fpkm_L <- melt(fpkm)
tail(fpkm_L)
X1X2value
402433349 FPKM.ERR2049160.00000000
402443350 FPKM.ERR2049163.18196839
402453351 FPKM.ERR2049160.04649665
402463352 FPKM.ERR2049160.00000000
402473353 FPKM.ERR2049160.00000000
402483354 FPKM.ERR2049160.00000000 
colnames(fpkm_L)=c('n','sample','value')
head(fpkm_L)
nsamplevalue
1 FPKM.ERR1880442.218154
2 FPKM.ERR1880440.000000
3 FPKM.ERR1880445.333466
4 FPKM.ERR1880440.000000
5 FPKM.ERR1880440.000000
6 FPKM.ERR1880448.949343 
group_list=as.character(pheno_data$sex)
group_list
  1. 'male'
  2. 'male'
  3. 'female'
  4. 'female'
  5. 'male'
  6. 'female'
  7. 'female'
  8. 'male'
  9. 'male'
  10. 'female'
  11. 'male'
  12. 'female'
fpkm_L$group=rep(group_list,each=nrow(fpkm))
tail(fpkm_L)
nsamplevaluegroup
402433349 FPKM.ERR2049160.00000000 female
402443350 FPKM.ERR2049163.18196839 female
402453351 FPKM.ERR2049160.04649665 female
402463352 FPKM.ERR2049160.00000000 female
402473353 FPKM.ERR2049160.00000000 female
402483354 FPKM.ERR2049160.00000000 female 
options(repr.plot.width = 9, repr.plot.height = 5)
p=ggplot(fpkm_L,aes(x=sample,y=value,fill=group))+
geom_boxplot() + 
theme(text=element_text(face='bold'),axis.text.x=element_text(angle=30,hjust=1,size = 6))
p

在這裏插入圖片描述

#?graphics::boxplot

Subread -> featureCounts -> DESeq2

流程代碼

#!/usr/bin/env bash

set -ue

settings(){
        samples=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/samples
        index=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genome/index
    output=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/subread+featurecounts
    if test -w $(dirname $output) &&  test -w $(dirname index);then
        mkdir -p {$index/subread,$output/1_subjunc,$output/2_featurecounts}   
    else
        echo "沒有寫入權限"
    fi
        genome=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genome/chrX.fa
    gene=/data/Data_base/test_tmp/RNA_seq_practice/chrX_data/genes/chr_X.gtf
        
}

thread(){
    tmp_fifofile="/tmp/$$.fifo" 
    mkfifo "$tmp_fifofile" 
    exec 6<>"$tmp_fifofile"  
    rm $tmp_fifofile
    thread_num=$1 
    for((i=0;i<$thread_num;i++));do 
        echo 
    done >&6 
    $2 6 
    exec 6>&- 
}

index(){
    printf "[%s %s %s %s %s %s]::建立索引subread-buildindex\n" $(echo `date`)
    start=$(date +%s.%N)
    file_num=`ls -l $index/subread|wc -l`   
    source activate RNA
        base_name=$(basename $genome)
        name=`awk -v each=$base_name 'BEGIN{split(each,arr,".");print arr[1]}' ` 
    if [ $file_num -lt 2 ];then
        subread-buildindex -o $index/subread/$name $genome &> $index/subread/index.log
    fi
    dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
    printf "[%s %s %s %s %s %s]::建立索引耗時%.2f分鐘\n" $(echo `date`) $dur
    source deactivate RNA
}



mapping(){
        printf "[%s %s %s %s %s %s]::與參考基因組比對subjunc\n" $(echo `date`)
        start=$(date +%s.%N)
        dir=$output/1_subjunc
        find $samples/cleandata -name *1?f*q.gz|sort > $dir/1
        find $samples/cleandata -name *2?f*q.gz|sort > $dir/2
        paste -d ":" $dir/1 $dir/2  > $dir/config && rm $dir/1 $dir/2
        file_num=`ls -l $dir|wc -l`
        index_prefix=`awk -v each=$(basename $genome) 'BEGIN{split(each,arr,".");print arr[1]}' `
        source activate RNA
        if [ $file_num -lt 3 ];then
                for id in $(cat $dir/config);do
                        fq1=$(echo $id|cut -d":" -f1)
                        fq2=$(echo $id |cut -d":" -f2)
                        name=`awk -v each=$(basename $fq1) 'BEGIN{split(each,arr,"_");print arr[1]}' ` 
                        read -u$1
                        {
                        subjunc -T 5 -i $index/subread/$index_prefix -r $fq1 -R $fq2 -o $dir/${name}.subjunc.bam &> $dir/${name}.log
                        printf "[%s %s %s %s %s %s]::%s比對完成\n" $(echo `date`) $name
                        echo >&$1       
                        } &
                done && wait
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::比對耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA

}


count(){
        printf "[%s %s %s %s %s %s]:: 轉錄本定量featureCounts\n" $(echo `date`)         
        start=$(date +%s.%N)
        dir=$output/2_featurecounts
        file_num=`ls -l $dir|wc -l`
        source activate RNA
        if [ $file_num -lt 3 ];then
                featureCounts -T 5 -p -t exon -g gene_id  -a $gene -o $dir/all.id.txt  $output/1_subjunc/*.bam  1>$dir/counts.log 2>&1 
                # -g 默認gene_id, 可以選擇gene_name，後面可以用R將ID轉換成gene symbol
                # 這樣得到的all.id.txt文件就是表達矩陣，但是，這個featureCounts有非常多的參數可以調整
                dur=$(echo "($(date +%s.%N) - $start)/60" | bc -l)
                printf "[%s %s %s %s %s %s]::轉錄本定量耗時%.2f分鐘\n" $(echo `date`) $dur
        fi
        source deactivate RNA
}

settings
index
thread 3 mapping
count

DESeq2差異分析

差異分析需要的包：

  • limma
  • edgeR
  • gplots
  • org.Mm.eg.db
  • RColorBrewer
  • Glimma
  • DESeq2

DESeq2差異分析的三個主要數據：

  • 表達矩陣（原始的count數據）
  • 分組矩陣（實驗設計的分組情況）
  • 差異比較矩陣（指定差異比較對象）

DESeq2差異分析的三個主要步驟：

  • 構建DESeqDataSet（dds對象）
  • DESeq()函數進行差異分析
  • results()函數提取差異分析結果

構建DESeqDataSet對象有兩種方式，一種是通過DESeqDataSet()函數將SummarizedExperiment->DESeqDataSet,如果我們只有表達矩陣和樣本表型數據，如何構建DESeqDataSet數據集,可以通過另一種方式，使用DESeqDataSetFromMatrix()函數將表達矩陣和樣本表型數據轉化爲DESeqDataSet數據對象。

colData裏面的信息，就相當於我們自己準備的表型數據，建議是csv，tsv格式（comma-separated value；tab- separated value）文件，可以把樣本表型的數據賦值給colData對象

suppressMessages(library(edgeR))
suppressMessages(library(limma))
suppressMessages(library(Glimma))
suppressMessages(library(gplots))
suppressMessages(library(DESeq2))
suppressMessages(library(org.Hs.eg.db))
suppressMessages(library(RColorBrewer))

讀取數據,提取表達矩陣

#讀取count table數據
data <- read.table("all.id.txt",stringsAsFactors = F, header = T)
meta = data[,1:6]
exprSet = data[,7:ncol(data)]
rownames(exprSet) <- data[,1]
colnames(exprSet)
  1. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188044.subjunc.bam'
  2. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188104.subjunc.bam'
  3. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188234.subjunc.bam'
  4. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188245.subjunc.bam'
  5. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188257.subjunc.bam'
  6. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188273.subjunc.bam'
  7. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188337.subjunc.bam'
  8. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188383.subjunc.bam'
  9. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188401.subjunc.bam'
  10. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188428.subjunc.bam'
  11. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR188454.subjunc.bam'
  12. 'X.data.Data_base.test_tmp.RNA_seq_practice.chrX_data.subread.featurecounts.1_subjunc.ERR204916.subjunc.bam'
class(exprSet)

‘data.frame’

修改列名

# suppressMessages(library(dplyr))
# name <- lapply(strsplit(as.character(colnames(exprSet)),'[.]'),function(x){x[length(x)-2]}) %>% unlist
name <- unlist(lapply(strsplit(as.character(colnames(exprSet)),'[.]'),function(x){x[length(x)-2]}))
colnames(exprSet) <- name 
colnames(exprSet)
  1. 'ERR188044'
  2. 'ERR188104'
  3. 'ERR188234'
  4. 'ERR188245'
  5. 'ERR188257'
  6. 'ERR188273'
  7. 'ERR188337'
  8. 'ERR188383'
  9. 'ERR188401'
  10. 'ERR188428'
  11. 'ERR188454'
  12. 'ERR204916'
head(exprSet,6)
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000228572 1 0 0 0 0 0 1 0 0 0 1 0
ENSG000001823784128264631381631100720593251 8661944897 15172587
ENSG000001786051235 854140610351437 343179212461967606 12481469
ENSG00000226179 13 13 9 4 1 2 6 3 8 4 7 10
ENSG00000167393 243 173 236 282 171 76 279 60 327111 96 258
ENSG00000275287 0 1 0 0 0 0 0 0 0 0 0 0
 
exprSet[rownames(exprSet)=="ENSG00000205755",]
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG000002057559 105 3 4 2 8 4 6 3 2 5

查看數據維度，即幾行幾列

dim(exprSet)
  1. 2450
  2. 12

表型數據讀取,樣本分組信息

# Read the sample information into R
coldata <- read.csv("geuvadis_phenodata.csv")
coldata
idssexpopulation
ERR188044male YRI
ERR188104male YRI
ERR188234female YRI
ERR188245female GBR
ERR188257male GBR
ERR188273female YRI
ERR188337female GBR
ERR188383male GBR
ERR188401male GBR
ERR188428female GBR
ERR188454male YRI
ERR204916female YRI 
coldata$group <- factor(paste0(coldata$sex, coldata$population))

coldata
idssexpopulationgroup
ERR188044male YRI maleYRI
ERR188104male YRI maleYRI
ERR188234female YRI femaleYRI
ERR188245female GBR femaleGBR
ERR188257male GBR maleGBR
ERR188273female YRI femaleYRI
ERR188337female GBR femaleGBR
ERR188383male GBR maleGBR
ERR188401male GBR maleGBR
ERR188428female GBR femaleGBR
ERR188454male YRI maleYRI
ERR204916female YRI femaleYRI
 
suppressMessages(library(pheatmap))
suppressMessages(library(corrplot))

可視化樣本間的相似性

options(repr.plot.width = 5, repr.plot.height = 5)
# png('cor.png')
corrplot(cor(log2(exprSet+1)))
# dev.off()

在這裏插入圖片描述

m=cor(log2(exprSet+1))
#cor矩陣用scale歸一化
pheatmap(scale(cor(log2(exprSet+1))))

構建DESeqDataSet（dds）對象

#將讀取數據由data.frame轉換成matrix類型
countdata <- as.matrix(exprSet)
#將countdata轉換成DESeq2的數據格式（構建dds）
dds <- DESeqDataSetFromMatrix(countData = countdata,colData = coldata, design = ~ group )
dds

class: DESeqDataSet 
dim: 2450 12 
metadata(1): version
assays(1): counts
rownames(2450): ENSG00000228572 ENSG00000182378 ... ENSG00000276543
  ENSG00000227159
rowData names(0):
colnames(12): ERR188044 ERR188104 ... ERR188454 ERR204916
colData names(4): ids sex population group

#將所有樣本中count值均爲0的基因所在行去除
dds <- dds[rowSums(counts(dds) > 0) != 0 ,] #dds[rowSums(assay(dds) > 0) != 0 , ]
dds

class: DESeqDataSet 
dim: 1412 12 
metadata(1): version
assays(1): counts
rownames(1412): ENSG00000228572 ENSG00000182378 ... ENSG00000182484
  ENSG00000227159
rowData names(0):
colnames(12): ERR188044 ERR188104 ... ERR188454 ERR204916
colData names(4): ids sex population group

# DESeq分析，大小因子的估計，離差估計，負二項分佈的擬合以及計算相應的統計量
dds <- DESeq(dds, parallel = T) #parallel = T程序並行，提高速度

estimating size factors
estimating dispersions
gene-wise dispersion estimates: 1 workers
mean-dispersion relationship
final dispersion estimates, fitting model and testing: 1 workers

plotDispEsts(dds, main="Dispersion plot")

在這裏插入圖片描述

resultsNames(dds)
  1. 'Intercept'
  2. 'group_femaleYRI_vs_femaleGBR'
  3. 'group_maleGBR_vs_femaleGBR'
  4. 'group_maleYRI_vs_femaleGBR'

使用rlog-transformed歸一化數據

rld <- rlog(dds)
countdata_rlog <- assay(rld) 
head(assay(rld))
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000228572-1.716339-1.743507-1.743821-1.737338-1.739732-1.731162-1.716953-1.740015-1.744537-1.736857-1.713780-1.740743
ENSG0000018237811.46140810.98992611.13860410.96376810.32728511.67554811.16820710.16994810.61162710.41876510.64481511.174434
ENSG0000017860510.007361 9.64054010.09599610.26933310.421237 9.64724210.33147510.25630310.378303 9.78266910.20476410.367591
ENSG00000226179 2.753014 2.747507 2.620197 2.538756 2.352026 2.503809 2.517179 2.450266 2.573627 2.546540 2.612662 2.723157
ENSG00000167393 7.540045 7.216756 7.476484 8.109476 7.458899 7.303154 7.627266 6.576983 7.734151 7.262772 6.894270 7.775687
ENSG00000275287-2.216337-2.207251-2.216517-2.214314-2.215128-2.212215-2.216546-2.215224-2.216760-2.214150-2.215468-2.215471

使用rlog-transformed 與 正常 區別

options(repr.plot.width = 9, repr.plot.height = 9)
par(cex = 0.7)
n.sample=ncol(countdata)
if(n.sample>40) par(cex = 0.5)
cols <- rainbow(n.sample*1.2)
par(mfrow=c(2,2))
boxplot(countdata, col = cols,main="expression value",las=2)
boxplot(countdata_rlog, col = cols,main="expression value",las=2)
hist(countdata)
hist(countdata_rlog)

在這裏插入圖片描述

熱圖呈現樣品間的距離

mycols <- brewer.pal(8, "Dark2")[1:length(unique(coldata$group))]
sampleDists <- as.matrix(dist(t(countdata_rlog)))
heatmap.2(as.matrix(sampleDists), key=F, trace="none",
            col=colorpanel(100, "black", "white"),
            ColSideColors=mycols[coldata$group], RowSideColors=mycols[coldata$group],
margin=c(10, 10), main="Sample Distance Matrix")

在這裏插入圖片描述

差異分析及結果提取

#提取性別間差異分析的結果
sex_Y_result <- results(dds, contrast = c("group","maleYRI","femaleYRI"), parallel = T)
#?results 查看用法
sex_G_result <- results(dds, contrast = c("group","maleGBR","femaleGBR"), parallel = T)
sex_G_result

log2 fold change (MLE): group maleGBR vs femaleGBR 
Wald test p-value: group maleGBR vs femaleGBR 
DataFrame with 1412 rows and 6 columns
                         baseMean     log2FoldChange             lfcSE
                        <numeric>          <numeric>         <numeric>
ENSG00000228572 0.212064658186015  -1.18134449838418  4.40721523394822
ENSG00000182378   2080.8358537599 -0.759383223494156 0.369671551206202
ENSG00000178605  1146.43865301777  0.288751709902595 0.319524288527945
ENSG00000226179  6.08058463520939 -0.493246072149153 0.820198203867892
ENSG00000167393  183.376192726023 -0.562850597829534 0.484234566315877
...                           ...                ...               ...
ENSG00000124334  60.2368766837945  0.489304618353469 0.581968571149684
ENSG00000270726 0.209832834505338  -0.22070381980218  4.40735632395141
ENSG00000185203  1.03705472979752   1.36980197787972  2.33437609518548
ENSG00000182484  493.281659491792  0.264865107532117 0.359069049586416
ENSG00000227159  3.18504610277688 -0.645544611222065  1.25294052192237
                               stat             pvalue              padj
                          <numeric>          <numeric>         <numeric>
ENSG00000228572  -0.268047834216135  0.788662499091892 0.992519295917494
ENSG00000182378   -2.05421061214032 0.0399553117393148 0.992519295917494
ENSG00000178605   0.903692521256771  0.366158465847581 0.992519295917494
ENSG00000226179  -0.601374240790949  0.547590751585147 0.992519295917494
ENSG00000167393   -1.16235113513638  0.245092863468186 0.992519295917494
...                             ...                ...               ...
ENSG00000124334   0.840774987877512  0.400474001859852 0.992519295917494
ENSG00000270726 -0.0500762369955846  0.960061636105264 0.992519295917494
ENSG00000185203    0.58679575270877  0.557340889682515 0.992519295917494
ENSG00000182484   0.737643937390858  0.460730848276013 0.992519295917494
ENSG00000227159  -0.515223667785613  0.606396732014109 0.992519295917494

結果說明

  • 第一列基因名
  • baseMean 經過矯正的reads count均值
  • log2FoldChange 對差異倍數取以2爲底的對數
  • lfcSE 差異倍數取對數後的標準差
  • stat Wald統計量，由log2FoldChange除以標準差所得
  • pvalue 原始的p值
  • padj 校正後的p值
summary(sex_G_result)

out of 1412 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 2, 0.14%
LFC < 0 (down)     : 13, 0.92%
outliers [1]       : 1, 0.071%
low counts [2]     : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

#在羣體間差異分析的基因分析結果
population_F_result <- results(dds, contrast = c("group","femaleYRI","femaleGBR"), parallel = T)
population_M_result <- results(dds, contrast = c("group","maleYRI","maleGBR"), parallel = T)

#提取性別間差異表達的基因，並存入文件
sex_Y_result_0.01 <- sex_Y_result[which(sex_Y_result$pvalue < 0.01),]
write.csv(sex_Y_result_0.01, file="DE/sex_Y_result_0.01_DESeq2.csv")

#提取種羣間差異表達的基因，並存入文件
population_F_result_0.01 <- subset(population_F_result,pvalue < 0.01)
write.csv(population_F_result_0.01, file="DE/population_F_result_0.01_DESeq2.csv")

流程參考文獻RNA-Seq workflow: gene-level exploratory analysis and differential expression

edgeR差異分析

edgeR差異分析的三個主要數據：

  • 表達矩陣（原始的count數據）
  • 分組矩陣（實驗設計的分組情況）
  • 差異比較矩陣（指定差異比較對象）

edgeR差異分析的五個主要步驟：

  • DGElist()函數構造DGEList對象，轉化counts 成DGEList對象
  • model.matrix()函數構建設計矩陣
  • estimateDisp()函數進行差異分析
  • glmQLFit()函數構建DGEGLM對象
  • glmQLFTest()函數進行QL F-test

讀取數據,提取表達矩陣

上面已經做過了

表型數據讀取,樣本分組信息

上面已經做過了

#再轉換爲數據框
class(countdata)
head(countdata,3)
countdata <- as.data.frame(countdata)
dim(countdata)
class(countdata)

‘matrix’

ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000228572 1 0 0 0 0 0 1 0 0 0 1 0
ENSG000001823784128264631381631100720593251 8661944897 15172587
ENSG000001786051235 854140610351437 343179212461967606 12481469
  1. 2450
  2. 12

‘data.frame’

coldata
idssexpopulationgroup
ERR188044male YRI maleYRI
ERR188104male YRI maleYRI
ERR188234female YRI femaleYRI
ERR188245female GBR femaleGBR
ERR188257male GBR maleGBR
ERR188273female YRI femaleYRI
ERR188337female GBR femaleGBR
ERR188383male GBR maleGBR
ERR188401male GBR maleGBR
ERR188428female GBR femaleGBR
ERR188454male YRI maleYRI
ERR204916female YRI femaleYRI

過濾表達量低的基因

在這個數據集中，我們選擇保留至少在兩個樣本中的CPM（counts-per-million）值大於0.5的基因(根據實際情況作調整)

使用edgeR 包中的cpm 函數

myCPM <- cpm(countdata)
# Have a look at the output
head(myCPM)
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000228572 0.8414109 0.0000000 0.000000 0.00000 0.000000 0.000000 0.9116842 0.000000 0.0000 0.00000 1.058151 0.00000
ENSG000001823783473.34410342263.37430962255.726656 2125.22542 1185.821950 3899.517816 2963.8854523 976.066093 1629.1449 1167.61147 1605.214567 2623.31454
ENSG000001786051039.1424340 730.50705231010.692058 1348.62557 1692.180876 649.603988 1633.73815151404.362993 1648.4198 788.82113 1320.572036 1489.62082
ENSG00000226179 10.9383414 11.1201308 6.469579 5.21208 1.177579 3.787778 5.4701054 3.381291 6.7043 5.20674 7.407055 10.14037
ENSG00000167393 204.4628433 147.9832787 169.646747 367.45161 201.365992 143.935577 254.3599019 67.625826 274.0383 144.48704 101.582464 261.62163
ENSG00000275287 0.0000000 0.8553947 0.000000 0.00000 0.000000 0.000000 0.0000000 0.000000 0.0000 0.00000 0.000000 0.00000 
# Which values in myCPM are greater than 0.5?
thresh <- myCPM > 0.5
# This produces a logical matrix with TRUEs and FALSEs
head(thresh)
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000228572 TRUEFALSEFALSEFALSEFALSEFALSE TRUEFALSEFALSEFALSE TRUEFALSE
ENSG00000182378 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
ENSG00000178605 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
ENSG00000226179 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
ENSG00000167393 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
ENSG00000275287FALSE TRUEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSEFALSE
 
# Summary of how many TRUEs there are in each row
# There are 672  genes that have TRUEs in all 12 samples.
table(rowSums(thresh))

   0    1    2    3    4    5    6    7    8    9   10   11   12 
1038  208  106   79   58   45   40   44   28   34   40   58  672 

# we would like to keep genes that have at least 2 TRUES in each row of thresh
keep <- rowSums(thresh) >= 2
# Subset the rows of countdata to keep the more highly expressed genes
counts.keep <- countdata[keep,]
summary(keep)

   Mode   FALSE    TRUE 
logical    1246    1204 

dim(counts.keep)
  1. 1204
  2. 12

ID轉換，基因註釋

# rm(list=ls())
ls('package:org.Hs.eg.db')
  1. 'org.Hs.eg'
  2. 'org.Hs.eg_dbconn'
  3. 'org.Hs.eg_dbfile'
  4. 'org.Hs.eg_dbInfo'
  5. 'org.Hs.eg_dbschema'
  6. 'org.Hs.eg.db'
  7. 'org.Hs.egACCNUM'
  8. 'org.Hs.egACCNUM2EG'
  9. 'org.Hs.egALIAS2EG'
  10. 'org.Hs.egCHR'
  11. 'org.Hs.egCHRLENGTHS'
  12. 'org.Hs.egCHRLOC'
  13. 'org.Hs.egCHRLOCEND'
  14. 'org.Hs.egENSEMBL'
  15. 'org.Hs.egENSEMBL2EG'
  16. 'org.Hs.egENSEMBLPROT'
  17. 'org.Hs.egENSEMBLPROT2EG'
  18. 'org.Hs.egENSEMBLTRANS'
  19. 'org.Hs.egENSEMBLTRANS2EG'
  20. 'org.Hs.egENZYME'
  21. 'org.Hs.egENZYME2EG'
  22. 'org.Hs.egGENENAME'
  23. 'org.Hs.egGO'
  24. 'org.Hs.egGO2ALLEGS'
  25. 'org.Hs.egGO2EG'
  26. 'org.Hs.egMAP'
  27. 'org.Hs.egMAP2EG'
  28. 'org.Hs.egMAPCOUNTS'
  29. 'org.Hs.egOMIM'
  30. 'org.Hs.egOMIM2EG'
  31. 'org.Hs.egORGANISM'
  32. 'org.Hs.egPATH'
  33. 'org.Hs.egPATH2EG'
  34. 'org.Hs.egPFAM'
  35. 'org.Hs.egPMID'
  36. 'org.Hs.egPMID2EG'
  37. 'org.Hs.egPROSITE'
  38. 'org.Hs.egREFSEQ'
  39. 'org.Hs.egREFSEQ2EG'
  40. 'org.Hs.egSYMBOL'
  41. 'org.Hs.egSYMBOL2EG'
  42. 'org.Hs.egUCSCKG'
  43. 'org.Hs.egUNIGENE'
  44. 'org.Hs.egUNIGENE2EG'
  45. 'org.Hs.egUNIPROT'
entrezID2ensemblID <- toTable(org.Hs.egENSEMBL)
head(entrezID2ensemblID)
gene_idensembl_id
1 ENSG00000121410
2 ENSG00000175899
3 ENSG00000256069
9 ENSG00000171428
10 ENSG00000156006
12 ENSG00000196136
 
entrezID2symbol <- toTable(org.Hs.egSYMBOL)
head(entrezID2symbol)
gene_idsymbol
1 A1BG
2 A2M
3 A2MP1
9 NAT1
10 NAT2
11 NATP 
columns(org.Hs.eg.db)
  1. 'ACCNUM'
  2. 'ALIAS'
  3. 'ENSEMBL'
  4. 'ENSEMBLPROT'
  5. 'ENSEMBLTRANS'
  6. 'ENTREZID'
  7. 'ENZYME'
  8. 'EVIDENCE'
  9. 'EVIDENCEALL'
  10. 'GENENAME'
  11. 'GO'
  12. 'GOALL'
  13. 'IPI'
  14. 'MAP'
  15. 'OMIM'
  16. 'ONTOLOGY'
  17. 'ONTOLOGYALL'
  18. 'PATH'
  19. 'PFAM'
  20. 'PMID'
  21. 'PROSITE'
  22. 'REFSEQ'
  23. 'SYMBOL'
  24. 'UCSCKG'
  25. 'UNIGENE'
  26. 'UNIPROT'
help('select')

ann <- select(org.Hs.eg.db,keys = rownames(counts.keep), keytype="ENSEMBL",columns = c("ENSEMBL","SYMBOL","GENENAME"))

'select()' returned 1:many mapping between keys and columns

head(ann)
dim(ann)
ENSEMBLSYMBOLGENENAME
ENSG00000228572 NA NA
ENSG00000182378 PLCXD1 phosphatidylinositol specific phospholipase C X domain containing 1
ENSG00000178605 GTPBP6 GTP binding protein 6 (putative)
ENSG00000226179 NA NA
ENSG00000167393 PPP2R3B protein phosphatase 2 regulatory subunit B''beta
ENSG00000237531 NA NA
  1. 1213
  2. 3
symbol <-  ann[match(rownames(counts.keep),ann$ENSEMBL),2]
rownames(counts.keep) <- paste(rownames(counts.keep) ,':', symbol)
head(counts.keep)
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000228572 : NA 1 0 0 0 0 0 1 0 0 0 1 0
ENSG00000182378 : PLCXD14128264631381631100720593251 8661944897 15172587
ENSG00000178605 : GTPBP61235 854140610351437 343179212461967606 12481469
ENSG00000226179 : NA 13 13 9 4 1 2 6 3 8 4 7 10
ENSG00000167393 : PPP2R3B 243 173 236 282 171 76 279 60 327111 96 258
ENSG00000237531 : NA 0 1 4 0 0 0 4 0 0 6 2 0
 
counts.keep <- counts.keep[grep(".*: NA$",rownames(counts.keep),invert = T), ]
head(counts.keep)
dim(counts.keep)
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000182378 : PLCXD14128264631381631100720593251 8661944897 15172587
ENSG00000178605 : GTPBP61235 854140610351437 343179212461967606 12481469
ENSG00000167393 : PPP2R3B 243 173 236 282 171 76 279 60 327111 96 258
ENSG00000205755 : CRLF2 9 10 5 3 4 2 8 4 6 3 2 5
ENSG00000198223 : CSF2RA 7 2 3 1 1 2 1 6 2 0 0 7
ENSG00000185291 : IL3RA 651 664 195 202 455 216 283 413 693221 593 620
  1. 697
  2. 12

構建DGEList對象（edgeR的數據分析格式）

edgeR_data <- DGEList(counts = counts.keep, group = coldata$group)
# See what slots are stored in edgeR_data
names(edgeR_data)
head(edgeR_data$counts)
  1. 'counts'
  2. 'samples'
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000182378 : PLCXD14128264631381631100720593251 8661944897 15172587
ENSG00000178605 : GTPBP61235 854140610351437 343179212461967606 12481469
ENSG00000167393 : PPP2R3B 243 173 236 282 171 76 279 60 327111 96 258
ENSG00000205755 : CRLF2 9 10 5 3 4 2 8 4 6 3 2 5
ENSG00000198223 : CSF2RA 7 2 3 1 1 2 1 6 2 0 0 7
ENSG00000185291 : IL3RA 651 664 195 202 455 216 283 413 693221 593 620
 
# Library size information is stored in the samples slot
edgeR_data$samples
grouplib.sizenorm.factors
ERR188044maleYRI 1062212 1
ERR188104maleYRI 1037630 1
ERR188234femaleYRI1236643 1
ERR188245femaleGBR 688237 1
ERR188257maleGBR 769281 1
ERR188273femaleYRI 476704 1
ERR188337femaleGBR1009898 1
ERR188383maleGBR 786937 1
ERR188401maleGBR 1087875 1
ERR188428femaleGBR 697609 1
ERR188454maleYRI 836018 1
ERR204916femaleYRI 876941 1

可以自己手動計算一下，看一致否

apply(counts.keep,2,sum)
ERR188044
1062212
ERR188104
1037630
ERR188234
1236643
ERR188245
688237
ERR188257
769281
ERR188273
476704
ERR188337
1009898
ERR188383
786937
ERR188401
1087875
ERR188428
697609
ERR188454
836018
ERR204916
876941

質控

文庫大小及其分佈圖

edgeR_data$samples$lib.size
  1. 1062212
  2. 1037630
  3. 1236643
  4. 688237
  5. 769281
  6. 476704
  7. 1009898
  8. 786937
  9. 1087875
  10. 697609
  11. 836018
  12. 876941
# The names argument tells the barplot to use the sample names on the x-axis
# The las argument rotates the axis names
options(repr.plot.width = 6, repr.plot.height = 4)
barplot(edgeR_data$samples$lib.size,names=colnames(edgeR_data),las=2)
title("Barplot of library sizes")

在這裏插入圖片描述

# Get log2 counts per million
logcounts <- cpm(edgeR_data,log=TRUE)
# Check distributions of samples using boxplots
boxplot(logcounts, xlab="", ylab="Log2 counts per million",las=2)
# Let's add a blue horizontal line that corresponds to the median logCPM
abline(h=median(logcounts),col="blue")
title("Boxplots of logCPMs (unnormalised)")

在這裏插入圖片描述

Multidimensional scaling plots

A principle components analysis is an example of an unsupervised analysis, where we don’t need to specify the groups.

plotMDS(edgeR_data)

在這裏插入圖片描述

按組添加顏色

# We specify the option to let us plot two plots side-by-sde
par(mfrow=c(1,2))
options(repr.plot.width = 9, repr.plot.height = 5)
# Let's set up colour schemes for sex
levels(coldata$sex)
## Let's choose purple for basal and orange for luminal
col.sex <- c("purple","orange")[coldata$sex]
data.frame(coldata$sex,col.sex)
# Redo the MDS with cell type colouring
plotMDS(edgeR_data,col=col.sex)
# Let's add a legend to the plot so we know which colours correspond to which sex
legend("topleft",fill=c("purple","orange"),legend=levels(coldata$sex))
# Add a title
title("sex")

# Similarly for population
levels(coldata$population)
col.population <- c("blue","red")[coldata$population]
plotMDS(edgeR_data,col=col.population)
legend("topleft",fill=c("blue","red"),legend=levels(coldata$population),cex=0.8)
title("population")
  1. 'female'
  2. 'male'
coldata.sexcol.sex
male orange
male orange
femalepurple
femalepurple
male orange
femalepurple
femalepurple
male orange
male orange
femalepurple
male orange
femalepurple
  1. 'GBR'
  2. 'YRI'

在這裏插入圖片描述

整合成一張圖

cols <- c("#1B9E77", "#D95F02")
col.population <- c("#1B9E77", "#D95F02")[coldata$population]
char.sex <- c(1,4)[coldata$sex]
char.sex
  1. 4
  2. 4
  3. 1
  4. 1
  5. 4
  6. 1
  7. 1
  8. 4
  9. 4
  10. 1
  11. 4
  12. 1
options(repr.plot.width = 6, repr.plot.height = 5)
plotMDS(edgeR_data,dim=c(1,2),col=col.population,pch=char.sex,cex=2)
legend("top",legend=levels(coldata$population),col=cols,pch=16)
legend("right",legend=levels(coldata$sex),pch=c(1,4))

在這裏插入圖片描述

標準化，歸一化

Normalisation for composition bias

# Apply normalisation to DGEList object
edgeR_data <- calcNormFactors(edgeR_data)

This will update the normalisation factors in the DGEList object (their default values are 1). Take a look at the normalisation factors for these samples.

edgeR_data$samples
grouplib.sizenorm.factors
ERR188044maleYRI 1062212 1.0091479
ERR188104maleYRI 1037630 1.0063310
ERR188234femaleYRI1236643 0.8810990
ERR188245femaleGBR 688237 0.9781766
ERR188257maleGBR 769281 1.0374696
ERR188273femaleYRI 476704 0.9219804
ERR188337femaleGBR1009898 1.0665011
ERR188383maleGBR 786937 1.0556198
ERR188401maleGBR 1087875 1.0580396
ERR188428femaleGBR 697609 0.9437640
ERR188454maleYRI 836018 1.0681350
ERR204916femaleYRI 876941 0.9947293

ERR188234樣本的normalisation factors最小，ERR188337樣本的normalisation factors最大，
If we plot mean difference plots using the plotMD function for these samples, we should be able to see the composition bias problem. We will use the logcounts, which have been normalised for library size, but not for composition bias.

options(repr.plot.width = 9, repr.plot.height = 5)
par(mfrow=c(1,2))
plotMD(logcounts,column = 3)
abline(h=0,col="grey")
plotMD(logcounts,column = 7)
abline(h=0,col="grey")

在這裏插入圖片描述

The mean-difference plots show average expression (mean: x-axis) against log-fold-changes (difference: y-axis). Because our DGEList object contains the normalisation factors, if we redo these plots using edgeR_data, we should see the composition bias problem has been solved.

par(mfrow=c(1,2))
plotMD(edgeR_data,column = 3)
abline(h=0,col="grey")
plotMD(edgeR_data,column = 7)
abline(h=0,col="grey")

在這裏插入圖片描述

熱圖層次聚類

# We estimate the variance for each row in the logcounts matrix
var_genes <- apply(logcounts, 1, var)
head(var_genes)
ENSG00000182378 : PLCXD1
0.411283212250483
ENSG00000178605 : GTPBP6
0.233040789399784
ENSG00000167393 : PPP2R3B
0.440930844051291
ENSG00000205755 : CRLF2
0.267770197328411
ENSG00000198223 : CSF2RA
2.65972252131491
ENSG00000185291 : IL3RA
0.468356036950437
# Get the gene names for the top 50 most variable genes
select_var <- names(sort(var_genes, decreasing=TRUE))[1:50]
head(select_var)
  1. 'ENSG00000229807 : XIST'
  2. 'ENSG00000198759 : EGFL6'
  3. 'ENSG00000146938 : NLGN4X'
  4. 'ENSG00000269096 : CT45A3'
  5. 'ENSG00000175556 : LONRF3'
  6. 'ENSG00000003096 : KLHL13'
# Subset logcounts matrix
highly_variable_lcpm <- logcounts[select_var,]
dim(highly_variable_lcpm)
  1. 50
  2. 12
options(repr.plot.width = 9, repr.plot.height = 6)

my_heatmap <- function(countdata,col_label,row_lable,group){
    mypalette <- brewer.pal(11,"RdYlBu")
    morecols <- colorRampPalette(mypalette)
    col.sex <- c("purple","orange")[group]
    # Plot the heatmap
    heatmap.2(countdata,labCol=col_label,srtCol=15, labRow=row_lable,
              col=rev(morecols(50)),trace="none", main="Top 50 most variable genes across samples",
              ColSideColors=col.sex,colCol=col.sex,scale="row")
}


col_label <- paste(coldata$sex ,'.', coldata$population)
row_lable <- unlist(lapply(strsplit(rownames(highly_variable_lcpm),":"),function(x) x[2]))
group <- coldata$sex
my_heatmap(highly_variable_lcpm, col_label, row_lable, group)

在這裏插入圖片描述

建立設計矩陣

design <- model.matrix(~0 + group , data = coldata)
design
groupfemaleGBRgroupfemaleYRIgroupmaleGBRgroupmaleYRI
10001
20001
30100
41000
50010
60100
71000
80010
90010
101000
110001
120100
 
#修改設計矩陣的列名,行名
colnames(design)
  1. 'groupfemaleGBR'
  2. 'groupfemaleYRI'
  3. 'groupmaleGBR'
  4. 'groupmaleYRI'
levels(coldata$group)
  1. 'femaleGBR'
  2. 'femaleYRI'
  3. 'maleGBR'
  4. 'maleYRI'
colnames(design) <- levels(coldata$group)
colnames(design)
rownames(design) <- coldata$id
rownames(design)
design
  1. 'femaleGBR'
  2. 'femaleYRI'
  3. 'maleGBR'
  4. 'maleYRI'
  1. 'ERR188044'
  2. 'ERR188104'
  3. 'ERR188234'
  4. 'ERR188245'
  5. 'ERR188257'
  6. 'ERR188273'
  7. 'ERR188337'
  8. 'ERR188383'
  9. 'ERR188401'
  10. 'ERR188428'
  11. 'ERR188454'
  12. 'ERR204916'
femaleGBRfemaleYRImaleGBRmaleYRI
ERR1880440001
ERR1881040001
ERR1882340100
ERR1882451000
ERR1882570010
ERR1882730100
ERR1883371000
ERR1883830010
ERR1884010010
ERR1884281000
ERR1884540001
ERR2049160100

估計離散度

edgeR_data <- estimateDisp(edgeR_data, design)
names(edgeR_data)
  1. 'counts'
  2. 'samples'
  3. 'design'
  4. 'common.dispersion'
  5. 'trended.dispersion'
  6. 'tagwise.dispersion'
  7. 'AveLogCPM'
  8. 'trend.method'
  9. 'prior.df'
  10. 'prior.n'
  11. 'span'

構建DGEGLM對象

fit <- glmQLFit(edgeR_data, design)

建立對照

?makeContrasts

my.contrasts <- makeContrasts(YRI.f.vs.m = femaleYRI - maleYRI,  GBR.f.vs.m = femaleGBR - maleGBR, f.YRI.vs.GBR = femaleYRI - femaleGBR, m.YRI.vs.GBR = maleYRI - maleGBR, levels = design)

差異分析（用quasi-likelihood(QL) F-test）

qlf_YRI.f.vs.m <- glmQLFTest(fit, contrast = my.contrasts[, "YRI.f.vs.m"])
qlf_GBR.f.vs.m <- glmQLFTest(fit, contrast = my.contrasts[, "GBR.f.vs.m"])
qlf_f.YRI.vs.GBR <- glmQLFTest(fit, contrast = my.contrasts[, "f.YRI.vs.GBR"])
qlf_m.YRI.vs.GBR <- glmQLFTest(fit, contrast = my.contrasts[, "m.YRI.vs.GBR"])
names(qlf_m.YRI.vs.GBR)
  1. 'coefficients'
  2. 'fitted.values'
  3. 'deviance'
  4. 'method'
  5. 'unshrunk.coefficients'
  6. 'df.residual'
  7. 'design'
  8. 'offset'
  9. 'dispersion'
  10. 'prior.count'
  11. 'AveLogCPM'
  12. 'df.residual.zeros'
  13. 'df.prior'
  14. 'var.post'
  15. 'var.prior'
  16. 'samples'
  17. 'table'
  18. 'comparison'
  19. 'df.test'
  20. 'df.total'

對每個差異分析分析結果的pvalue進行校正

YRI.f.vs.m_padjust <- p.adjust(qlf_YRI.f.vs.m$table$PValue, method = "BH")
GBR.f.vs.m_padjust <- p.adjust(qlf_GBR.f.vs.m$table$PValue, method = "BH")
f.YRI.vs.GBR_padjust <- p.adjust(qlf_f.YRI.vs.GBR$table$PValue, method = "BH")
m.YRI.vs.GBR_padjust <- p.adjust(qlf_m.YRI.vs.GBR$table$PValue, method = "BH")

提取差異表達的基因

YRI.f.vs.m.sig <- qlf_YRI.f.vs.m$table[which(YRI.f.vs.m_padjust < 0.05),]
write.csv(YRI.f.vs.m.sig, file = "DE/YRI.f.vs.m_0.05_edgeR.csv")
GBR.f.vs.m.sig <- qlf_GBR.f.vs.m$table[which(qlf_GBR.f.vs.m$table$PValue < 0.05),]
write.csv(GBR.f.vs.m.sig, file = "DE/GBR.f.vs.m_0.05_edgeR.csv")
write.csv(qlf_GBR.f.vs.m$table, file = "DE/GBR.f.vs.m_edgeR.csv")

結果可視化

火山圖

write.csv(counts.keep, file = "DE/counts.keep.csv")
DE <- read.csv('DE/GBR.f.vs.m_edgeR.csv',row.names = 1)

tail(DE)
logFClogCPMFPValue
ENSG00000224533 : TMLHE-AS1-0.056469327 2.052160 4.307370e-030.9486785
ENSG00000185973 : TMLHE 0.221289532 8.365665 1.286092e+000.2774115
ENSG00000168939 : SPRY3-0.002682364 4.420135 3.410488e-050.9954298
ENSG00000124333 : VAMP7-0.13738283810.762397 9.979491e-010.3361840
ENSG00000124334 : IL9R-0.488450923 6.182421 7.899469e-010.3903941
ENSG00000185203 : WASIR1-1.144990880 1.771968 5.055304e-010.4897413 
DEG <- data.frame(DE$PValue,DE$logFC)
colnames(DEG) <- c('p','logFC')
rownames(DEG) <- rownames(DE)
tail(DEG)
plogFC
ENSG00000224533 : TMLHE-AS10.9486785 -0.056469327
ENSG00000185973 : TMLHE0.2774115 0.221289532
ENSG00000168939 : SPRY30.9954298 -0.002682364
ENSG00000124333 : VAMP70.3361840 -0.137382838
ENSG00000124334 : IL9R0.3903941 -0.488450923
ENSG00000185203 : WASIR10.4897413 -1.144990880

示例數據量少，篩選出的差異基因也少，畫圖的效果不太好

options(repr.plot.width = 8, repr.plot.height = 6)

my_volcano <- function(DEG, t_p, t_FC = 0, t_marker){
    ## example: print(my_volcano(a[,c(5,1)], 0.01, 0.6, 0))
    library(ggplot2)
    DEG = na.omit(DEG)
    colnames(DEG) = c('p','logFC')
    DEG$gene <- unlist(lapply(strsplit(rownames(DEG),":"),function(x) x[2]))
    DEG[DEG$p < 1e-10, 'p'] = 1e-10
    
    if (t_FC == 0){
        t_FC <- with(DEG, mean(abs(logFC)) + 2 * sd(abs(logFC)))
    }
    DEG$change = as.factor(ifelse(DEG$p < t_p & abs(DEG$logFC) > t_FC,ifelse(DEG$logFC > t_FC, "UP", "DOWN"),"NOT"))
    this_title <- paste0("Cutoff for logFC is ", round(t_FC, 3),
                         "\nThe number of up gene is ",
                         nrow(DEG[DEG$change == "UP", ]),
                         "\nThe number of down gene is ",
                         nrow(DEG[DEG$change == "DOWN", ])
                        ) 
    p = ggplot(data = DEG, aes(x = logFC, y = -log10(p), color = change)) +
    geom_point() +
    scale_color_manual(values = c('blue', 'black', 'red')) +
    geom_hline(yintercept = -log10(t_p), lty = 4, lwd = 0.6, alpha = 0.8) +
    geom_vline(xintercept = c(t_FC, -t_FC), lty = 4, lwd = 0.6, alpha = 0.8) +
    theme_bw() +
    ylim(0,5) +
    xlim(-10,10) +
    theme(panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
         axis.line = element_line(colour = "black")) + ggtitle(this_title) +
    labs(x = "log2(fold change)", y = "-log10(p-value)") +
    theme(plot.title = element_text(hjust = 0.5))
    
    if (t_marker != 0) {
        DE <- subset(DEG, abs(logFC) > t_marker)
        p = p + geom_text(data = DE , aes(label = gene), col = "green",alpha = 0.5)
        }
    
    return(p)
}


print(my_volcano(DEG, 0.05, 1, 4.2))

Warning message:
“Removed 1 rows containing missing values (geom_point).”Warning message:
“Removed 1 rows containing missing values (geom_text).”

在這裏插入圖片描述

熱圖

suppressMessages(library(pheatmap))
suppressMessages(library(gplots))
suppressMessages(library(ComplexHeatmap))

DE_0.05 <- read.csv('DE/GBR.f.vs.m_0.05_edgeR.csv',row.names = 1)
coldata <- read.csv("geuvadis_phenodata.csv")
counts.keep <- read.csv("DE/counts.keep.csv",row.names = 1)

choose_gene=head(rownames(DE_0.05),50) 
choose_matrix=counts.keep[choose_gene,]
choose_matrix=t(scale(t(choose_matrix)))

head(choose_matrix)
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
ENSG00000182378 : PLCXD1 1.9215927 0.4896378 0.9650236 -0.4910871 -1.0940155 -0.07754007 1.0742078 -1.2302541 -0.1886567 -1.2003010 -0.60123750 0.4326301
ENSG00000185291 : IL3RA 1.0684102 1.1323672 -1.1750052 -1.1405668 0.1041351 -1.07169002-0.7420654 -0.1024952 1.2750405 -1.0470912 0.78306348 0.9158973
ENSG00000196433 : ASMT 2.1365053 0.6224464 0.5215092 -0.6897379 -0.1850516 -0.89161243-0.8916124 1.5308817 -0.3869261 -0.7906752 -0.68973792-0.2859889
ENSG00000183943 : PRKX-0.2822280 -0.1640627 1.8727853 -0.8229841 -1.3016534 -1.46788583 1.3420432 -0.2101271 -0.1300151 0.2505170 -0.05190591 0.9655167
ENSG00000130021 : PUDP-0.7621578 0.2543147 2.1551890 -0.4343396 -0.9649807 -0.57820227 0.3675181 -0.8682861 -0.2739680 0.1057352 -0.67961368 1.6787912
ENSG00000101846 : STS-0.3061919 1.1965013 1.4125252 -1.2271362 -0.7609792 -0.79508827 1.5792806 -0.7041308 -0.9807931 0.1467005 -0.36872515 0.8080371 
rownames(choose_matrix) <- unlist(lapply(strsplit(rownames(choose_matrix),":"),function(x) x[2]))
head(choose_matrix)
ERR188044ERR188104ERR188234ERR188245ERR188257ERR188273ERR188337ERR188383ERR188401ERR188428ERR188454ERR204916
PLCXD1 1.9215927 0.4896378 0.9650236 -0.4910871 -1.0940155 -0.07754007 1.0742078 -1.2302541 -0.1886567 -1.2003010 -0.60123750 0.4326301
IL3RA 1.0684102 1.1323672 -1.1750052 -1.1405668 0.1041351 -1.07169002-0.7420654 -0.1024952 1.2750405 -1.0470912 0.78306348 0.9158973
ASMT 2.1365053 0.6224464 0.5215092 -0.6897379 -0.1850516 -0.89161243-0.8916124 1.5308817 -0.3869261 -0.7906752 -0.68973792-0.2859889
PRKX-0.2822280 -0.1640627 1.8727853 -0.8229841 -1.3016534 -1.46788583 1.3420432 -0.2101271 -0.1300151 0.2505170 -0.05190591 0.9655167
PUDP-0.7621578 0.2543147 2.1551890 -0.4343396 -0.9649807 -0.57820227 0.3675181 -0.8682861 -0.2739680 0.1057352 -0.67961368 1.6787912
STS-0.3061919 1.1965013 1.4125252 -1.2271362 -0.7609792 -0.79508827 1.5792806 -0.7041308 -0.9807931 0.1467005 -0.36872515 0.8080371 
options(repr.plot.width = 8, repr.plot.height = 6)

morecols <- colorRampPalette(c("green","black","red"))
col.sex <- c("purple","orange")[coldata$sex]
label_col = paste(coldata$sex ,'.', coldata$population)
annotation_col = data.frame(population = coldata$population,sex = coldata$sex)
rownames(annotation_col) = coldata$ids

ann_colors = list(population = c(GBR = "#7570B3", YRI = "#E7298A"),
                  sex = c(male = "#1B9E77", female = "#D95F02")
                 )

pheatmap(choose_matrix, color = morecols(100), labels_col = label_col, annotation_col = annotation_col,
         annotation_colors = ann_colors , fontsize_row = 6, scale="row",
         annotation_legend = T, border_color=NA,fontsize = 8, main = "genes  GBR.f.vs.m")


# pheatmap(choose_matrix, color = morecols(100), labels_col = label_col, annotation_col = annotation_col,
#          annotation_colors = ann_colors , fontsize_row = 6, scale="row",
#          annotation_legend = T, fontsize = 8, main = "genes  GBR.f.vs.m",filename = 'DEG_top50_heatmap.pdf')

在這裏插入圖片描述

pheatmap參數用法 ：使用pheatmap包繪製熱圖

my_heatmap <- function(countdata,col_label,row_lable,group){
    morecols <- colorRampPalette(c("red","black","green"))
    col.sex <- c("purple","orange")[group]
    # Plot the heatmap
    heatmap.2(countdata,labCol=col_label,srtCol=15, 
              col=rev(morecols(50)),trace="none", main="genes  GBR.f.vs.m",
              ColSideColors=col.sex,colCol=col.sex,scale="row")
}


col_label <- paste(coldata$sex ,'.', coldata$population)
group <- coldata$sex
my_heatmap(choose_matrix, col_label, row_lable, group)

在這裏插入圖片描述

heatmap.2詳細文檔

library("GetoptLong")

expr <- choose_matrix
colnames(expr) <- paste(coldata$sex ,'.', coldata$population)                     

annotation_col = data.frame(population = coldata$population,sex = coldata$sex)
rownames(annotation_col) = coldata$ids
ann_colors = list(population = c(GBR = "#7570B3", YRI = "#E7298A"),
                  sex = c(male = "#1B9E77", female = "#D95F02")
                 )
ann <- HeatmapAnnotation(annotation_col, col = ann_colors)
                       
morecols <- colorRampPalette(c("green","black","red"))                        
Heatmap(expr,heatmap_legend_param = list(title= "legend", title_position = "topcenter", 
  legend_height=unit(2,"cm"), legend_direction="vertical"), col = morecols(100), column_title = "genes  GBR.f.vs.m",
  top_annotation = ann,row_names_gp = gpar(fontsize = 6, fontface = "bold") )


#註釋名稱可以使用下面的R代碼添加
for(an in colnames(annotation_col)) { 
seekViewport(qq("annotation_@{an}")) 
grid.text(an, unit(1, "npc") + unit(2, "mm"), 0.5, default.units = "npc", just = "left")}

在這裏插入圖片描述

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