Leafcutter Differential Isoforma Analysis

Last updated: 2018-09-07

• ✔ R Markdown file: up-to-date

  Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

• ✔ Environment: empty

  Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

• ✔ Seed: set.seed(12345)

  The command set.seed(12345) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

• ✔ Session information: recorded

  Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

• ✔ Repository version: 8f67a99

  Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
  
  Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

  
  Ignored files:
      Ignored:    .DS_Store
      Ignored:    .Rhistory
      Ignored:    .Rproj.user/
      Ignored:    output/.DS_Store
  
  Untracked files:
      Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
      Untracked:  analysis/snake.config.notes.Rmd
      Untracked:  data/18486.genecov.txt
      Untracked:  data/APApeaksYL.total.inbrain.bed
      Untracked:  data/RNAkalisto/
      Untracked:  data/Totalpeaks_filtered_clean.bed
      Untracked:  data/YL-SP-18486-T-combined-genecov.txt
      Untracked:  data/YL-SP-18486-T_S9_R1_001-genecov.txt
      Untracked:  data/bedgraph_peaks/
      Untracked:  data/bin200.5.T.nuccov.bed
      Untracked:  data/bin200.Anuccov.bed
      Untracked:  data/bin200.nuccov.bed
      Untracked:  data/clean_peaks/
      Untracked:  data/comb_map_stats.csv
      Untracked:  data/comb_map_stats.xlsx
      Untracked:  data/combined_reads_mapped_three_prime_seq.csv
      Untracked:  data/gencov.test.csv
      Untracked:  data/gencov.test.txt
      Untracked:  data/gencov_zero.test.csv
      Untracked:  data/gencov_zero.test.txt
      Untracked:  data/gene_cov/
      Untracked:  data/joined
      Untracked:  data/leafcutter/
      Untracked:  data/merged_combined_YL-SP-threeprimeseq.bg
      Untracked:  data/nom_QTL/
      Untracked:  data/nom_QTL_opp/
      Untracked:  data/nuc6up/
      Untracked:  data/other_qtls/
      Untracked:  data/peakPerRefSeqGene/
      Untracked:  data/perm_QTL/
      Untracked:  data/perm_QTL_opp/
      Untracked:  data/reads_mapped_three_prime_seq.csv
      Untracked:  data/smash.cov.results.bed
      Untracked:  data/smash.cov.results.csv
      Untracked:  data/smash.cov.results.txt
      Untracked:  data/smash_testregion/
      Untracked:  data/ssFC200.cov.bed
      Untracked:  data/temp.file1
      Untracked:  data/temp.file2
      Untracked:  data/temp.gencov.test.txt
      Untracked:  data/temp.gencov_zero.test.txt
      Untracked:  output/picard/
      Untracked:  output/plots/
      Untracked:  output/qual.fig2.pdf
  
  Unstaged changes:
      Modified:   analysis/28ind.peak.explore.Rmd
      Modified:   analysis/cleanupdtseq.internalpriming.Rmd
      Modified:   analysis/dif.iso.usage.leafcutter.Rmd
      Modified:   analysis/explore.filters.Rmd
      Modified:   analysis/overlap_qtls.Rmd
      Modified:   analysis/peak.cov.pipeline.Rmd
      Modified:   analysis/peakOverlap_oppstrand.Rmd
      Modified:   analysis/pheno.leaf.comb.Rmd
      Modified:   analysis/test.max2.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.

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       

This reproducible R Markdown analysis was created with workflowr 1.1.1