Last updated: 2019-01-24

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    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
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    Rmd fd184be Briana Mittleman 2019-01-22 add code for leafcutter on processed 

library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
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✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ───────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
library(cowplot)

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

    ggsave

In this analysis https://brimittleman.github.io/threeprimeseq/PeakToGeneAssignment.html I ran an initial run of the leafcutter tool for differences between fractions. I will use the same pipeline here for the processed data.

This starts with running feature counts with all of the peaks. I will use peaks passing the filter in either the total or nucelar fraction. These peaks are in /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed_5percCov.bed and were created using the filternamePeaks5percCov.py script. I need to make this into an SAF file for FC.

This file has chr, start, end, peakNu, cov, strand, transcript:gene, distance. For the SAF file I want GeneID, Chr, start, end, strand. The GeneID is peak#:chr:start:end:strand:gene

bed2saf_bothFrac_Processed.py

from misc_helper import *

fout = open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed_5percCov.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed_5percCov.bed"):
    chrom, start, end, peakNum, cov, strand, trans, dist = ln.split()
    gene=trans.split(":")[1]
    ID = "peak%s:%s:%s:%s:%s:%s"%(peakNum,chrom,start, end,strand,gene)
    fout.write("%s\t%s\t%s\t%s\t%s\n"%(ID, chrom, start, end, strand))
fout.close()

bothFrac_processed_FC.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env


featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed_5percCov.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed.fc /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*sort.bam -s 2

Fix headers:

fix_head_fc_procBothFrac.py


#python 

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed.fc", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries = i_list[:6]
        print(libraries)
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

fc2leafphen_processed.py

inFile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed_fixed.fc", "r")
outFile= open("/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed_forLC.fc", "w")

for num, ln in enumerate(inFile):
        if num == 1:
            lines=ln.split()[6:]
            outFile.write(" ".join(lines)+'\n')
        if num > 1:
            ID=ln.split()[0]
            peak=ID.split(":")[0]
            chrom=ID.split(":")[1]
            start=ID.split(":")[2]
            start=int(start)
            end=ID.split(":")[3]
            end=int(end)
            strand=ID.split(":")[4]
            gene=ID.split(":")[5]
            new_ID="chr%s:%d:%d:%s"%(chrom, start, end, gene)
            pheno=ln.split()[6:]
            pheno.insert(0, new_ID)
            outFile.write(" ".join(pheno)+'\n')
            
outFile.close()

subset_diffisopheno_processed.py

def main(inFile, outFile, target):
    ifile=open(inFile, "r")
    ofile=open(outFile, "w")
    target=int(target)
    for num, ln in enumerate(ifile):
        if num == 0:
            ofile.write(ln)
        else:
            ID=ln.split()[0]
            chrom=ID.split(":")[0][3:]
            print(chrom)
            chrom=int(chrom)
            if chrom == target:
                ofile.write(ln)
            
if __name__ == "__main__":
    import sys

    target = sys.argv[1]
    inFile = "/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed_forLC.fc"
    outFile = "/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed_forLC_%s.txt"%(target)
    main(inFile, outFile, target)

Run this with: run_subset_diffisopheno_processed.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
python subset_diffisopheno_processed.py $i 
done

Make new sample list. I could use the old one but I want to have this pipeline work when I add individuals.

makeLCSampleList_processed.py

outfile=open("/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed/sample_groups.txt", "w")
infile=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed.fc", "r")

for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=[]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        for l in libraries:
            if l[-1] == "T":
                outfile.write("%s\tTotal\n"%(l))
            else:
                outfile.write("%s\tNuclear\n"%(l))
    else:
          next
                
outfile.close()

run_leafcutter_ds_bychrom_processed.sh

#!/bin/bash

#SBATCH --job-name=run_leafcutter_ds_bychrom_processed
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_leafcutter_ds_bychrom_processed.out
#SBATCH --error=run_leafcutter_ds_bychrom_processed.err
#SBATCH --partition=bigmem2
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load R

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
Rscript /project2/gilad/briana/davidaknowles-leafcutter-c3d9474/scripts/leafcutter_ds.R --num_threads 4  /project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_processed_forLC_${i}.txt /project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed/sample_groups.txt -o /project2/gilad/briana/threeprimeseq/data/diff_iso_processed/TN_diff_isoform_chr${i}.txt 
done

Cat all of the signficance files and bring them to my computer to look at here.

diffIso=read.table("../data/diff_iso_proc/TN_diff_isoform_allChrom_clusterSig.txt", header = T,col.names = c("status",   "loglr",    "df",   "p",    "cluster",  "p.adjust"),stringsAsFactors = F,sep="\t") %>% filter(status == "Success")


diffIso$p.adjust=as.numeric(as.character(diffIso$p.adjust))


qqplot(-log10(runif(nrow(diffIso))), -log10(diffIso$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between fractions")
abline(0,1)

This is based on successfull results for 9,532 genes with multiple PAS and enough coverage in enough individuals.

Next, I will look at the effect sizes.

awk '{if(NR>1)print}' /project2/gilad/briana/threeprimeseq/data/diff_iso_processed/TN_diff_isoform_chr*.txt_effect_sizes.txt > /project2/gilad/briana/threeprimeseq/data/diff_iso_processed/TN_diff_isoform_AllChrom.txt_effect_sizes.txt
effectsize=read.table("../data/diff_iso_proc/TN_diff_isoform_AllChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Nuclear', 'Total','deltapsi'))
effectsize$logef=as.numeric(as.character(effectsize$logef))
Warning: NAs introduced by coercion
plot(sort(effectsize$logef),main="Leafcutter effect Sizes", ylab="Effect size", xlab="Peak Index")

effectsize$deltapsi=as.numeric(as.character(effectsize$deltapsi))
Warning: NAs introduced by coercion
plot(sort(effectsize$deltapsi),main="Leafcutter delta PSI", ylab="Delta PSI", xlab="Peak Index")

I want to spot check some of these in IGV. First I will focus on the |PSI| >.4

filterHighPSI=effectsize %>% filter(abs(deltapsi)>.4) %>% arrange(deltapsi)
head(filterHighPSI)
                            intron     logef           Nuclear
1   chr2:108464199:108464261:RGPD4 -1.563069 0.786325014667375
2  chr11:71409928:71409980:FAM86C1 -1.750522 0.736879355169336
3   chr9:19443338:19443405:SLC24A2 -1.373452  0.87662816529528
4 chr7:151725930:151726019:GALNTL5 -1.229096 0.766827065986407
5     chr17:49248730:49248817:NME2 -1.185121 0.763505415306066
6   chr7:100970341:100970385:IFT22 -1.588218 0.668693582230009
              Total   deltapsi
1 0.139047439399753 -0.6472776
2 0.169115629694331 -0.5677637
3 0.313023025601665 -0.5636051
4 0.219649286101815 -0.5471778
5 0.231787036297737 -0.5317184
6 0.140656353549046 -0.5280372

Too look at this most effectively I will merge the total and nuclear clean bam files:

mergeBamFiles_byfrac_noMP.sh

#!/bin/bash

#SBATCH --job-name=mergeBamFiles_byfrac_noMP.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=mergeBamFiles_byfrac_noMP.out
#SBATCH --error=mergeBamFiles_byfrac_noMP.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


samtools merge  /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllTotalSamples.MergedBamFiles.noMP.bam /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*T*.bam


samtools merge  /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllNuclearSamples.MergedBamFiles.noMP.bam /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*N*.bam

SortIndex_mergeBamFiles_byfrac_noMP.sh

#!/bin/bash

#SBATCH --job-name=SortIndex_mergeBamFiles_byfrac_noMP.sh
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=SortIndex_mergeBamFiles_byfrac_noMP.out
#SBATCH --error=SortIndex_mergeBamFiles_byfrac_noMP.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


samtools sort  /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllTotalSamples.MergedBamFiles.noMP.bam > /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllTotalSamples.MergedBamFiles.noMP.sort.bam
samtools index /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllTotalSamples.MergedBamFiles.noMP.sort.bam

samtools sort  /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllNuclearSamples.MergedBamFiles.noMP.bam > /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllNuclearSamples.MergedBamFiles.noMP.sort.bam

samtools index /project2/gilad/briana/threeprimeseq/data/mergedBams_NoMP/AllNuclearSamples.MergedBamFiles.noMP.sort.bam

Take a look at some of the top hits in IGV:

slice(filterHighPSI,1:15)
                             intron     logef           Nuclear
1    chr2:108464199:108464261:RGPD4 -1.563069 0.786325014667375
2   chr11:71409928:71409980:FAM86C1 -1.750522 0.736879355169336
3    chr9:19443338:19443405:SLC24A2 -1.373452  0.87662816529528
4  chr7:151725930:151726019:GALNTL5 -1.229096 0.766827065986407
5      chr17:49248730:49248817:NME2 -1.185121 0.763505415306066
6    chr7:100970341:100970385:IFT22 -1.588218 0.668693582230009
7  chr3:142422173:142422217:PCOLCE2 -1.160742 0.747193256154251
8   chr12:121337613:121337664:HNF1A -1.439657 0.922672953632586
9    chr8:134586015:134586097:WISP1 -1.828958 0.861221198355898
10   chr15:76234769:76234851:FBXO22 -1.494914 0.702484460762617
11    chr13:65049589:65049665:PCDH9 -1.585817 0.677762517045467
12    chr20:30651036:30651094:CCM2L -1.657778 0.668557867828921
13     chr17:44777988:44778040:WNT3 -1.282798  0.61467112951709
14   chr4:122775166:122775266:TRPC3 -1.269060  0.61759501169366
15      chr3:60357325:60357413:FHIT -1.266671 0.610069267936541
               Total   deltapsi
1  0.139047439399753 -0.6472776
2  0.169115629694331 -0.5677637
3  0.313023025601665 -0.5636051
4  0.219649286101815 -0.5471778
5  0.231787036297737 -0.5317184
6  0.140656353549046 -0.5280372
7  0.224821665761922 -0.5223716
8   0.40129250694973 -0.5213804
9   0.34267701803463 -0.5185442
10 0.192803741815497 -0.5096807
11 0.171781437839461 -0.5059811
12 0.162584556335585 -0.5059733
13 0.109232403935245 -0.5054387
14 0.113169455110416 -0.5044256
15 0.110490691620035 -0.4995786

It would be good to know the mean usage scores for these as well:

names=c("chr", "start","end","gene","strand", "peak", "meanUsage")
tot_usage=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt", stringsAsFactors = F,col.names = names)
nuc_usage=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt",stringsAsFactors = F,col.names = names)
  • RGPD4:

Intronic Peak in the gene 64775-

Total mean usage: 0.1389744 Nuclear mean Usage: 0.7933333

The other peak in this gene is downstream of the annotated gene. 64776

Total mean Usage: 0.8610256 Nuclear mean Usage: 0.2066667

The nuclear has the truncated internal transcript.

  • FAM86C1

The most used peak in this gene in the nuclear fraction is peak20610 it is chr11:71409928:71409980. This peak is upstream of the gene. This means the peak is probably not real. However this does not look like a misprimed read.

  • SLC24A2

There are 2 peaks for this gene. They are downstream of the annoated in another gene but because they are on the opposite strand from the other gene, it makes sense for them to map to SLC24A2.

This means that without a strand specific protocol it would be difficult to see this effect.

Peak 118136 Total: 0.3012821 Nucelar: 0.8553846

Peak 118140 Total: 0.6987179 Nuclear: 0.1446154

The extended transcript is used in the nuclear.

  • GALNTL5

This gene is close to the GALNT11 gene. They are also going in the same direction. The peak here is in the first intron of the GALNT11 gene. The other peak is way upsteream and looks like it is near the end of the PRKAG2-ASI gene chr7:151,570,858-151,585,995.

  • NME2

Peak 50663

Total: 0.1615385 Nuclear:0.7335897

The other annotated peak is Peak 50662 Total:0.5564103 Nuclear:0.2407692

50663 is directly upstream of the UTR and 50662 is uspream in one of the introns.

  • IFT22

This gene has 3 peaks in the filtered peaks however the one called here is upstream of the gene. The other two make a lot more sense:

109623: Total:0.1310256 Nuclear:0.63794872

109622:
Total: 0.1866667 Nuclear: 0.08897436

109621: Total:0.6371795 Nuclear: 0.23717949

From these results it looks like there is still differencial ussage in this gene. Peak 109621 is also significant with a delta PSI of 0.45275927.

  • PCOLCE2

This peak is pretty far downstream of the gene but other genes in the region go the opposite way:

peak83037: Total:0.2151282 Nuclear: 0.7323077

peak83045: This peak is upstream of the UTR for the gene in one of the exons:
Total: 0.7848718 Nuclear: 0.2676923

These results look like the gene has significant run through in the nuclear fraciton.

  • HNF1A

The 2 called peaks for this gene are upstream of the gene. Probably not actually correct. It does not look like any peaks in the track would work for this gene.

  • WISP1

The peak is very far downstream but not in the wrong direciton for other peaks in the region. All of the peaks for this gene are pretty far downstream. I am not sure how best to know if these are real or not.

  • FBXO22

The peak here is downstream of the gene close to the UTR of the next gene but because it goes the other direction, I can be confident in the assignemnt.

peak41217: Total: 0.16333333 Nuclear: 0.4917949

The other peaks in this gene are peak41206 in both fractions and low usage of peak41207 in total.

peak41206: Total: 0.58538462 Nuclear: 0.1776923

Again this looks like a case of run on read in the nuclear

  • PCDH9

The peak is downsteam of the gene but it is in a region with many LINCs. This peak is 32303. THe other peaks for the gene are 32304 and 32305. Oeak 32305 is upstream meanin it is probably not real.

peak32303: Total:0.1761538 Nuclear: 0.6843590

Peak 32304: Total: 0.5946154 Nuclear: 0.2479487

This is another case of run on for the nuclear transcript.

  • CCM2L
    The peak is downstream in on if the first exons of the HCK gene. It is difficult to know which you should assign the peak to because they are in the same orientation. All three of the called peaks for this gene are downstream in the HCK gene.

  • WINT3:

The peak is downstream in the NSF gene but the NSF gene goes the other direction. Both peaks for this gene are downstream in the NSF gene.

Peak 50131
Total: 0.1087179 Nuclear: 0.5658974

Peak 50134
Total: 0.8912821 Nuclear: 0.4084615

Again here we see more run on for the nuclear fraction transcript.

  • TRPC3

The peak here is in an intron of the downstreem gene BBS7. It is difficult to know which gene to assign the peak because they go the same direction.

peak89306 total: 0.1207692 nuclear:0.6010256

peak89307
total:0.8535897 nuclear: 0.3989744

Again here, if these peaks truly go with this gene, the nuclear transcript is the run on transcript.

  • FHIT:

This peak is in an intron of the gene. This is also the only peak passing 5% in each fraction.

peak 79975:

Total:0.2602564
Nucelar:0.05923077

It is hard to draw further conclusions here.

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   reshape2_1.4.3  forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1
[13] workflowr_1.1.1

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] cellranger_1.1.0  rvest_0.3.2       R.methodsS3_1.7.1
[22] evaluate_0.11     knitr_1.20        broom_0.5.0      
[25] Rcpp_0.12.19      scales_1.0.0      backports_1.1.2  
[28] jsonlite_1.5      hms_0.4.2         digest_0.6.17    
[31] stringi_1.2.4     grid_3.5.1        rprojroot_1.3-2  
[34] cli_1.0.1         tools_3.5.1       magrittr_1.5     
[37] lazyeval_0.2.1    crayon_1.3.4      whisker_0.3-2    
[40] pkgconfig_2.0.2   xml2_1.2.0        lubridate_1.7.4  
[43] assertthat_0.2.0  rmarkdown_1.10    httr_1.3.1       
[46] rstudioapi_0.8    R6_2.3.0          nlme_3.1-137     
[49] git2r_0.23.0      compiler_3.5.1

This reproducible R Markdown analysis was created with workflowr 1.1.1