Last updated: 2018-09-06
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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:
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=broadwl
#SBATCH --mem=12G
#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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