Leafcutter Differential Isoform Analysis

Last updated: 2018-09-07

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In my early analysis of the first 32 libraries I ran the leafcutter differential isoform tool. I am now going to rerun this with the peaks called from the 28 individuals. These peaks have been created with the Peak pipeline in https://brimittleman.github.io/threeprimeseq/peak.cov.pipeline.html. These are also the peaks used for the initial QTL analysis. https://brimittleman.github.io/threeprimeseq/apaQTLwLeafcutter.html. I can use the same phenotype and genotype files from this analysis.

To run the differential isoform analysis I need a file with the lines numbers and the fraction. This is similar to the sample.txt file from the QTL analysis.

I will work in the directory: /project2/gilad/briana/threeprimeseq/data/diff_iso/

make_samplegroups.py

outfile=open("/project2/gilad/briana/threeprimeseq/data/diff_iso/sample_groups.txt", "w")
infile=open("/project2/gilad/briana/threeprimeseq/data/diff_iso/filtered_APApeaks_merged_allchrom_refseqGenes_pheno.txt", "r")

for ln, i in enumerate(infile):
    if ln==0:
        header=i.split()
        lines=header[1:]
        for l in lines:
            if l[-1] == "T":
                outfile.write("%s\tTotal\n"%(l))
            else:

                outfile.write("%s\tNuclear\n"%(l))
                
outfile.close()
                

I need to create a phenotype file with all of the libraries (total/nuclear). I want the header to have the line then fraction like this:

• 18486_N
  
• 18486_T

To do this I need to run feature counts on all of the bam files, fix the header, then update the makePhenoRefSeqPeaks_opp_Total.py file to account for all libraries.

The fc code is in ref_gene_peakOppStrand_fc.sh. I wrote this script in the peakOverlap_oppstrand analysis. The results will be in filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.fc. I can update the fix_head_fc.py for the opposite strand results.

fix_head_oppstrand_fc.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        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()

Make a the file_id_mapping

makePhenoRefSeqPeaks_opp.py

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/file_id_mapping.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.ALL.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    inds_noL.append(each)  
fout.write(" ".join(peak + inds_noL) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=int(id_list[2])
        end=int(id_list[3])
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

run_makePhen_all.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks_opp.py 

I can now run the leafcutter_ds.R file.

run_leafcutter_ds.sh

Remove the chrom part of the header.

#!/bin/bash

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


module load R  


Rscript /project2/gilad/briana/threeprimeseq/data/diff_iso/leafcutter_ds.R /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.ALL.pheno_fixed_nochrom.txt /project2/gilad/briana/threeprimeseq/data/diff_iso/sample_groups.txt -o /project2/gilad/briana/threeprimeseq/data/diff_iso/TN_diff_isoform

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

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     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.18      digest_0.6.16    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.23.0      magrittr_1.5      evaluate_0.11    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.7.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20       

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