Last updated: 2019-02-14

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/ApproachForGeneAssignment.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/IndPeakUsageDiff.Rmd
    Modified:   analysis/QConApaQTLs.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explainpQTLs.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/mispriming_approach.Rmd
    Modified:   analysis/overlapMolQTL.Rmd
    Modified:   analysis/overlapMolQTL.opposite.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/peakQCPPlots.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/pipeline_55Ind.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile
    Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
Expand here to see past versions:
    File Version Author Date Message
    Rmd 41a2537 Briana Mittleman 2019-02-14 add map stat plots
    html b8cfd6f Briana Mittleman 2019-02-07 Build site.
    Rmd 3fea644 Briana Mittleman 2019-02-07 add accountmapbias 

library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

We are worried there amy be false positives in the QTL analysis if the QTL is in the read and the snp leads to a mapping bias for the data. I can account for this using WASP.

I have an example script from Yang:

/project2/yangili1/yangili/TCGA_pipe/script_process.sh

STAR2.6  --genomeDir /project2/yangili1/RNAseq_pipeline/index/GRCh37/STAR_hg19 --readFilesIn $inFile\_1.fastq $inFile\_2.fastq --outSAMstrandField intronMotif --outFileNamePrefix $outFile. --outSAMtype BAM Unsorted --varVCFfile $vcfFile --waspOutputMode SAMtag --outSAMattributes vA vG

First I need to find my star indexed genome:

*/project2/gilad/briana/genome_anotation_data/star_genome

Next I need my VCF file:

  • /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf.gz

runStarwWASP.sh

#!/bin/bash


#SBATCH --job-name=runStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=runStarwWASP.out
#SBATCH --error=runStarwWASP.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


in=$1 
out=$2
STAR --runThreadN 4 --genomeDir /project2/gilad/briana/genome_anotation_data/star_genome --readFilesIn $1  --outSAMstrandField intronMotif --outFileNamePrefix /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP/$2.combined.STARwWASP.bam --outSAMtype BAM Unsorted --varVCFfile /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf   --waspOutputMode SAMtag --outSAMattributes vA vG

test_runStartwWASP.sh

#!/bin/bash


#SBATCH --job-name=test_runStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=test_runStarwWASP.out
#SBATCH --error=test_runStarwWASP.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


i=/project2/gilad/briana/threeprimeseq/data/fastq/YL-SP-19239-T-combined.fastq  
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/combined.fastq//")
sbatch runStarwWASP.sh $i $describer

Wraper:
wrap_runStarwWASP.sh

#!/bin/bash


#SBATCH --job-name=wrap_runStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=wrap_runStarwWASP.out
#SBATCH --error=wrap_runStarwWASP.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  

for i in $(ls /project2/gilad/briana/threeprimeseq/data/fastq/*);do
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/combined.fastq//")
sbatch runStarwWASP.sh $i $describer
done

Quota reached at 19193N for jobs- create a wrap2
wrap_runStarwWASP2.sh

#!/bin/bash


#SBATCH --job-name=wrap_runStarwWASP2
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=wrap_runStarwWASP2.out
#SBATCH --error=wrap_runStarwWASP2.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 


for i in $(ls /project2/gilad/briana/threeprimeseq/data/fastq/YL-SP-192*); do
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/combined.fastq//")
sbatch runStarwWASP.sh $i $describer
done

Sort and index these files.

SortIndexStarwWASP.sh

#!/bin/bash


#SBATCH --job-name=SortIndexStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=SortIndexStarwWASP.out
#SBATCH --error=SortIndexStarwWASP.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

describer=$1

samtools sort /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP/${describer}combined.STARwWASP.bamAligned.out.bam > /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/${describer}combined.STARwWASP.bamAligned.sort.bam
samtools index /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/${describer}combined.STARwWASP.bamAligned.sort.bam

wrap_SortIndexStarwWASP.sh

#!/bin/bash


#SBATCH --job-name=wrap_SortIndexStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=wrap_SortIndexStarwWASP.out
#SBATCH --error=wrap_SortIndexStarwWASP.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 



for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP/*STARwWASP.bamAligned.out.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP\///' | sed -e "s/combined.STARwWASP.bamAligned.out.bam//")
sbatch SortIndexStarwWASP.sh $describer
done

Now I want to filter out reads with mapping problems at place we see a variant. I want to keep reads with the vW:i:1 tag. ( I will resort and index these files after this step)

I can use pysam to do this. Then I can move the final sorted duplicate files.

filterBamBasedonWasp.py

def main(Bamin, out):
    okRead={}
    #pysam to read in bam allignments
    bamfile = pysam.AlignmentFile(Bamin, "rb")
    finalBam =  pysam.AlignmentFile(out, "wb", template=bamfile)
    n=0
    k=0
    #read name is the first col in each bam file
    for read in bamfile.fetch():
        #last piece is always the right piece  
        #vw=read.split(\t)[-1]
        if read.has_tag('vW'):
            x= read.get_tag('vW')
            print(x)
            if x == 1:
                k+=1
                finalBam.write(read)
            else:
                n+=1
                continue
        else:
          finalBam.write(read)
  
    print("with wv" + n)
    print("pass filter" + k)
    bamfile.close()
    finalBam.close()
    
    
    
if __name__ == "__main__":
    import sys, pysam
    describer = sys.argv[1]
    inBam="/project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/" + describer + "combined.STARwWASP.bamAligned.sort.bam"
    outBam="/project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered/" + describer + "combined.STARwWASP.bamAligned.filtered.out.bam"
    main(inBam, outBam)

Run this on all individuals:

run_filterBamBasedonWasp.sh

#!/bin/bash


#SBATCH --job-name=run_filterBamBasedonWasp
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_filterBamBasedonWasp.out
#SBATCH --error=run_filterBamBasedonWasp.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/*.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_sort\///' | sed -e "s/combined.STARwWASP.bamAligned.sort.bam//")
python filterBamBasedonWasp.py $describer
done

Sort and index these:

SortIndexStarwWASP_filtered.sh

#!/bin/bash


#SBATCH --job-name=SortIndexStarwWASP_filtered
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=SortIndexStarwWASP_filtered.out
#SBATCH --error=SortIndexStarwWASP_filtered.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

describer=$1

samtools sort /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered/${describer}combined.STARwWASP.bamAligned.filtered.out.bam > /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/${describer}combined.STARwWASP.bamAligned.filtered.sort.bam
samtools index /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/${describer}combined.STARwWASP.bamAligned.filtered.sort.bam

wrap_SortIndexStarwWASP_filtered.sh

#!/bin/bash


#SBATCH --job-name=wrap_SortIndexStarwWASP_filtered
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=wrap_SortIndexStarwWASP_filtered.out
#SBATCH --error=wrap_SortIndexStarwWASP_filtered.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 



for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered/*STARwWASP.bamAligned.filtered.out.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_filtered\///' | sed -e "s/combined.STARwWASP.bamAligned.filtered.out.bam//")
sbatch SortIndexStarwWASP_filtered.sh $describer
done

Now I need to make these into a bed format. I also will move the old files and but these in the sort/ bed/ dirs. This way I can use the same pipeline from the Pipeline for 55 indivduals analysis.

At this point I will move the old bam and bed files to different directories

  • /project2/gilad/briana/threeprimeseq/data/sort_oldmapp/

  • /project2/gilad/briana/threeprimeseq/data/bed_sort_oldMap

  • /project2/gilad/briana/threeprimeseq/data/bed_oldMap

Run bam to bed:

bam2BedandSort.waspmap.sh

#!/bin/bash


#SBATCH --job-name=bam2BedandSort.waspmap
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=bam2BedandSort.waspmap.out
#SBATCH --error=bam2BedandSort.waspmap.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 



for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/*.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_filtered_sort\///' | sed -e "s/.STARwWASP.bamAligned.filtered.sort.bam//")
bedtools bamtobed -i $i > /project2/gilad/briana/threeprimeseq/data/bed/YL-SP-$describer.combined.bed
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/bed/YL-SP-$describer.combined.bed > /project2/gilad/briana/threeprimeseq/data/bed_sort/YL-SP-$describer.combined.sort.bed
done

Move duplicate files and rename:

problem: these are called combined.combined (fix this)


for i in $(ls /project2/gilad/briana/threeprimeseq/data/bed_10up)
do
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/.combined.sort10up.bed//")
mv $i /project2/gilad/briana/threeprimeseq/data/bed_10up/YL-SP-$describer-sort10up.bed
done

Also move the bam files to the sort dir from /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/

for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/*.bam)
do 
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_filtered_sort\///' | sed -e "s/.STARwWASP.bamAligned.filtered.sort.bam//")
mv $i /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-$describer-sort.bam
done

Index all of these files:

reIndex.sh

#!/bin/bash
#SBATCH --job-name=reIndex
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=reIndex.out
#SBATCH --error=reIndex.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

for i in $(ls /project2/gilad/briana/threeprimeseq/data/sort/)
samtools index /project2/gilad/briana/threeprimeseq/data/sort/$i 
done

  • Get 10 basepairs upstream: wrap_Upstream10Bases.sh

  • Find sequence for these regions: Nuc10BasesUp.sh

Fixed names (ok now)

for i in $(ls /project2/gilad/briana/threeprimeseq/data/bed_sort/)
do
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/.combined.sort.bed//")  
cp $i  /project2/gilad/briana/threeprimeseq/data/bed_sort/YL-SP-$describer-sort.bed
done
  • find which are bad run_filterMissprimingInNuc10.sh
  • filter bed file run_filterSortBedbyCleanedBed.sh
  • sort clean bed file sort_filterSortBedbyCleanedBed.sh

  • filter bam files wrap_filterBamforMP.pysam2.sh

  • sort and index clean bam SortIndexBam_noMP.sh
  • merge clean bam files mergeBamFiles_noMP.sh and mergeBamFiles_byfrac_noMP.sh

  • sort and index merged SortIndexMergedBam_noMP.sh and SortIndex_mergeBamFiles_byfrac_noMP.sh

  • create BW mergedBam2Bedgraph.sh
  • make it a coverage file run_bgtocov_noMP.sh
  • call peaks run_callPeaksYL_noMP.sh
  • filter peaks
    • cat /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/*.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/APApeaks_merged_allchrom_noMP.bed
    • make SAF file bed2saf_noMP.py
    • run feature counts peak_fc_noMP.sh
    • filter peaks run_filter_peaks_noMP.sh

  • name peaks

170480 = wc -l /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.bed



seq 1 170480 > peak.num.txt

sort -k1,1 -k2,2n Filtered_APApeaks_merged_allchrom_noMP.bed > Filtered_APApeaks_merged_allchrom_noMP.sort.bed

paste /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.bed peak.num.txt | column -s $'\t' -t > temp
awk '{print $1 "\t" $2 "\t" $3 "\t" $7  "\t"  $4 "\t"  $5 "\t" $6}' temp >   /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.bed

#cut the chr  

sed 's/^chr//' /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.bed

  • Gene assignments mapnoMPPeaks2GenomeLoc.sh

  • make SAF processGenLocPeakAnno2SAF.py
  • feature counts GeneLocAnno_fc_TN_noMP.sh
  • fix header fix_head_fc_geneLoc_tot_noMP.py
  • fix header fix_head_fc_geneLoc_nuc_noMP.py
  • create_fileid_geneLocAnno_total.py (remove top line)
  • create_fileid_geneLocAnno_nuclear.py (remove top line)
  • make phenotype run_makePhen_sep_GeneLocAnno_noMP.sh
  • counts to numeric convertCount2Numeric_noMP_GeneLocAnno.py
  • run_filter_5percUsagePeaks.sh

  • filterPheno_bothFraction_GeneLocAnno_5perc.py

In /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/

#zip file 
gzip filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc
gzip filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc


module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz

python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz




#source activate three-prime-env
 sh filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz_prepare.sh
sh filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz_prepare.sh


#keep only 2 PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.2PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.2PCs

  • makeSampleList_newGeneAnno.py

  • APAqtl_nominal_GeneLocAnno_noMP_5percUsage.sh

  • APAqtl_perm_GeneLocAnno_noMP_5percUsage.sh

  • run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs.sh

REDO!!! QC reruns: * filternamePeaks5percCov_GeneLocAnno_withAnno.py

peakswAnno=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov_withAnno.SAF", header=T) %>% separate(GeneID, into=c("Peak", "chrom", "start", "end", "strand", "gene", "loc"),sep=":") %>% select(Peak, loc) %>% group_by(loc) %>% summarise(Num=n())
locationOfPeaks=ggplot(peakswAnno, aes(x=loc, y=Num)) + geom_bar(stat="identity", fill="blue") + labs(x="Gene Location", y="Number of Peaks", title="Location distribution for all PAS with 5% Usage")
locationOfPeaks

ggsave(locationOfPeaks, file="../output/plots/PeakLocationByAnnotation.png")
Saving 7 x 5 in image
  • GetDistTXNend2Peak.py
distTXN2Peak=read.table("../data/DistTXN2Peak_genelocAnno/distPeak2EndTXN_newMAp.txt", col.names = c("Peak", "name2", "Distance", "Gene_Strand"),stringsAsFactors = F)
txnanno=read.table("../data/RefSeq_annotations/Transcript2GeneName.dms", header=T,stringsAsFactors = F) %>% mutate(length=abs(txEnd-txStart)) %>% semi_join(distTXN2Peak, by="name2")
distTXN2Peak =distTXN2Peak %>% mutate(AbsDist=abs(Distance))
mean(txnanno$length)
[1] 60808.79
distTXN2PeakPlot=ggplot(distTXN2Peak, aes(x=AbsDist + 1)) + geom_density() + scale_x_log10() + labs(x="Absolute Distance between end of Transcription and center of Peak", title="Distribution of transcription to peak absolute distance") +  geom_vline(xintercept=mean(txnanno$length), col="red") + annotate("text", x=1000000, y=.4, label="Average transcript length \n for genes in peaks", col='red')

distTXN2PeakPlot

Look at number of reads lost due to WASP filter

getWASPfiltStats.py

def main(Bamin,out,desc):
    okRead={}
    #pysam to read in bam allignments
    outF=open(out, "w")
    bamfile = pysam.AlignmentFile(Bamin, "rb")
    n=0
    k=0
    #read name is the first col in each bam file
    for read in bamfile.fetch():
        #last piece is always the right piece  
        #vw=read.split(\t)[-1]
        if read.has_tag('vW'):
            x= read.get_tag('vW')
            #print(x)
            if x == 1:
                k+=1
                #finalBam.write(read)
            else:
                n+=1
                continue
        else:
          continue
          #finalBam.write(read)
    outF.write("%s\t%d\n"%(desc, n))
    bamfile.close()
    outF.clos()
    
    
if __name__ == "__main__":
    import sys, pysam
    describer = sys.argv[1]
    describer2=describer[:-1]
    inBam="/project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/" + describer + "combined.STARwWASP.bamAligned.sort.bam"
    outFile="/project2/gilad/briana/threeprimeseq/data/WASP_filt_stat/WASPFilt" + describer2 + ".txt"
    main(inBam,outFile, describer2)

run_getWASPfiltStats.sh

#!/bin/bash

#SBATCH --job-name=run_getWASPfiltStats
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_getWASPfiltStats.out
#SBATCH --error=run_getWASPfiltStats.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/*.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_sort\///' | sed -e "s/combined.STARwWASP.bamAligned.sort.bam//")
python getWASPfiltStats.py $describer
done

Cat all of the files together and move the duplicates to replicate folder

waspStat=read.table("../data/WASP_STAT/WASP_Filt_AllLineStats.txt",stringsAsFactors = F, col.names = c("Sample", "FilteredReads")) %>% separate(Sample, into=c("Line", "Fraction"), sep="-")

Plot

ggplot(waspStat, aes(x=Line, fill=Fraction, y=FilteredReads, by=Fraction)) + geom_bar(stat="identity", position="dodge")

make boxplot

ggplot(waspStat, aes(x=Fraction, y=log10(FilteredReads), fill=Fraction)) + geom_boxplot()

Plto barplots by fractions with error bar

waspStat_sem= waspStat %>% group_by(Fraction) %>% summarise(mean=mean(FilteredReads), sd=sd(FilteredReads))

ggplot(waspStat_sem, aes(x=Fraction, y=mean, fill=Fraction)) + geom_bar(stat='identity')  + geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,) + labs(title="Reads filtered out due to WASP filter", y='Reads') +scale_fill_manual(values=c("deepskyblue3","darkviolet"))

Map stat plots:

mapStats_wasp=read.table("../data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt", stringsAsFactors = F, header = T)

Plot mappeded reads no MP by fractions:

mapStats_wasp_noMP=mapStats_wasp %>% group_by(fraction) %>% summarise(mean=mean(Mapped_noMP), sd=sd(Mapped_noMP))
mapreads_plot=ggplot(mapStats_wasp_noMP, aes(x=fraction, y=mean, fill=fraction)) + geom_bar(stat='identity')+  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2)+ scale_fill_manual(values=c("deepskyblue3","darkviolet")) +  labs(title="Number of reads\n mapping and passing missprime filter", y="Number of sequence reads")
mapStats_wasp_propnoMP=mapStats_wasp %>% group_by(fraction) %>% summarise(mean=mean(prop_MappedwithoutMP), sd=sd(prop_MappedwithoutMP))
propmap_plot=ggplot(mapStats_wasp_propnoMP, aes(x=fraction, y=mean, fill=fraction)) + geom_bar(stat='identity')+  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2)+ scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Proportion of reads\n mapping and passing missprime filter", y="Proportion of sequence reads")
mapStats_wasp_reads=mapStats_wasp %>% group_by(fraction) %>% summarise(mean=mean(reads), sd=sd(reads))
seqread_plot=ggplot(mapStats_wasp_reads, aes(x=fraction, y=mean, fill=fraction)) + geom_bar(stat='identity')+  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2)+ scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Sequenced Reads", y="Number of sequence reads")

All plots:

library(cowplot)
allmapstatplots=plot_grid(seqread_plot,mapreads_plot,propmap_plot,ncol = 3)
allmapstatplots

ggsave(allmapstatplots, file="../output/plots/MapStatBarPlots.png",width=15)
Saving 15 x 5 in image

Boxplot:

seqread_plotbar=ggplot(mapStats_wasp, aes(x=fraction, y=log10(reads), fill=fraction)) + geom_boxplot()+scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Sequenced Reads", y="log10(Number of sequence reads)") 
seqread_plotbar

mapreads_plotbar=ggplot(mapStats_wasp, aes(x=fraction, y=log10(Mapped_noMP), fill=fraction)) + geom_boxplot()+scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Mapped Reads\n filtered for misspriming", y="log10(Mapped Reads)") 
mapreads_plotbar

maprop_plotbar=ggplot(mapStats_wasp, aes(x=fraction, y=prop_MappedwithoutMP, fill=fraction)) + geom_boxplot()+scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Proportion Mapped Reads\n and filtered for misspriming", y="Proportion mapped post misspriming") 
maprop_plotbar

allmapstatboxplots=plot_grid(seqread_plotbar,mapreads_plotbar,maprop_plotbar,ncol = 3)
allmapstatboxplots

ggsave(allmapstatboxplots, file="../output/plots/MapStatBoxPlots.png",width=15)
Saving 15 x 5 in image

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] bindrcpp_0.2.2  cowplot_0.9.3   forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.7.6     purrr_0.2.5     readr_1.1.1     tidyr_0.8.1    
 [9] tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] rlang_0.2.2       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.7.0    
[13] modelr_0.1.2      readxl_1.1.0      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] workflowr_1.1.1   cellranger_1.1.0  rvest_0.3.2      
[22] R.methodsS3_1.7.1 evaluate_0.11     labeling_0.3     
[25] knitr_1.20        broom_0.5.0       Rcpp_0.12.19     
[28] scales_1.0.0      backports_1.1.2   jsonlite_1.5     
[31] hms_0.4.2         digest_0.6.17     stringi_1.2.4    
[34] grid_3.5.1        rprojroot_1.3-2   cli_1.0.1        
[37] tools_3.5.1       magrittr_1.5      lazyeval_0.2.1   
[40] crayon_1.3.4      whisker_0.3-2     pkgconfig_2.0.2  
[43] xml2_1.2.0        lubridate_1.7.4   assertthat_0.2.0 
[46] rmarkdown_1.10    httr_1.3.1        rstudioapi_0.8   
[49] R6_2.3.0          nlme_3.1-137      git2r_0.23.0     
[52] compiler_3.5.1

This reproducible R Markdown analysis was created with workflowr 1.1.1