Last updated: 2019-02-15
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Knit directory: threeprimeseq/analysis/
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Unstaged changes:
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Modified: analysis/swarmPlots_QTLs.Rmd
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Modified: analysis/understandPeaks.Rmd
Modified: code/Snakefile
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File | Version | Author | Date | Message |
---|---|---|---|---|
html | b9cce4b | Briana Mittleman | 2018-12-05 | Build site. |
Rmd | e230640 | Briana Mittleman | 2018-12-05 | add code to save relevant figures |
html | a5b4cf6 | Briana Mittleman | 2018-10-29 | Build site. |
Rmd | afb0ce9 | Briana Mittleman | 2018-10-29 | change plot colors |
html | b78e12e | Briana Mittleman | 2018-10-24 | Build site. |
Rmd | 2d5ac08 | Briana Mittleman | 2018-10-24 | leafcutter effect size plots |
html | 73bc857 | Briana Mittleman | 2018-10-05 | Build site. |
Rmd | 0d45334 | Briana Mittleman | 2018-10-05 | new QTL assignment overlap |
html | 2a6cabd | Briana Mittleman | 2018-10-03 | Build site. |
Rmd | 338174b | Briana Mittleman | 2018-10-03 | qtl window around gene annoation |
html | f40b377 | Briana Mittleman | 2018-09-30 | Build site. |
Rmd | b79486f | Briana Mittleman | 2018-09-30 | diff iso code |
html | 51c8b9c | Briana Mittleman | 2018-09-29 | Build site. |
Rmd | 0f9bd65 | Briana Mittleman | 2018-09-29 | overlap total/nuc |
html | 607c719 | Briana Mittleman | 2018-09-29 | Build site. |
Rmd | f3779bc | Briana Mittleman | 2018-09-29 | evaluate number of qtls |
html | 1cd047d | Briana Mittleman | 2018-09-27 | Build site. |
Rmd | 43c3f5b | Briana Mittleman | 2018-09-27 | nom and perm qtl |
html | 27a43dc | Briana Mittleman | 2018-09-27 | Build site. |
Rmd | 22db068 | Briana Mittleman | 2018-09-27 | add filtering by peak score |
html | 1501499 | Briana Mittleman | 2018-09-26 | Build site. |
Rmd | dd2b07d | Briana Mittleman | 2018-09-26 | account for ties |
html | 149d033 | Briana Mittleman | 2018-09-26 | Build site. |
html | aaed5fd | Briana Mittleman | 2018-09-26 | Build site. |
Rmd | eda266e | Briana Mittleman | 2018-09-26 | test peak to gene transcript dist |
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ tibble 1.4.2 ✔ dplyr 0.7.6
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── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
library(reshape2)
Attaching package: 'reshape2'
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library(VennDiagram)
Loading required package: grid
Loading required package: futile.logger
I will use this analysis to investigate further the best way to assign the peaks to a gene. Right now I am using
#!/bin/bash
#SBATCH --job-name=intGenes_combfilterPeaksOppStrand
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=intGenes_combfilterPeaksOppStrand.out
#SBATCH --error=intGenes_combfilterPeaksOppStrand.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools intersect -wa -wb -sorted -S -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.mRNA.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand.bed
This results in peaks being mapped to multiple genes. I want to use a method where I look for the closest end of transcript to each peak then use that gene for the assignment. This would mean each peak is assigned to one gene.
Create a python script to process the NCBI file. I want protien coding transcript ends with the associated gene names. Original file: ncbiRefSeq.txt
EndOfProCodTrans.py
def main(inF, outF):
infile= open(inF, "r")
fout = open(outF,'w')
for line in infile:
linelist=line.split()
transcript=linelist[1]
transcript_id=transcript.split("_")[0]
if transcript_id=="NM":
chr=linelist[2][3:]
strand=linelist[3]
gene= linelist[12]
if strand == "+" :
end = int(linelist[7])
end2= end - 1
fout.write("%s\t%d\t%d\t%s:%s\t.\t%s\n"%(chr, end2, end, transcript,gene, strand))
if strand == "-":
end= int(linelist[4])
end2= end + 1
fout.write("%s\t%d\t%d\t%s:%s\t.\t%s\n"%(chr, end, end2, transcript,gene, strand))
if __name__ == "__main__":
inF = "/project2/gilad/briana/genome_anotation_data/ncbiRefSeq.txt"
outF= "/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes.txt"
main(inF, outF)
bedtools closest
-A peaks /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -B transcript file /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt -S (opposite strand) -D b (give distance wrt to gene strand)
#!/bin/bash
#SBATCH --job-name=TransClosest2End
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TransClosest2End.out
#SBATCH --error=TransClosest2End.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools closest -S -D b -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed
I will take a look at this file in R then I will process the file in python.
names=c("PeakChr", "PeakStart", "PeakEnd", "PeakName","PeakScore", "PeakStrand", "GeneChr", "GeneStart", "GeneEnd", "Transcript", "GeneScore", "GeneStrand", "Distance" )
peak2transDist=read.table("../data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed", col.names = names, stringsAsFactors = F, header=F)
ggplot(peak2transDist, aes(x=abs(Distance)))+ geom_density() + scale_x_log10()
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 4362 rows containing non-finite values (stat_density).
peak2transDist0=peak2transDist %>% filter(Distance==0)
nrow(peak2transDist0)
[1] 4362
peak2transDist200=peak2transDist %>% filter(abs(Distance)<200)
nrow(peak2transDist200)
[1] 23778
summary(peak2transDist$Distance)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-5523243 -57698 -12830 -23711 3373 5592124
try adding the no ties flag -t first.
peak2transDist_noties=read.table("../data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed", col.names = names, stringsAsFactors = F, header=F)
ggplot(peak2transDist_noties, aes(x=abs(Distance)))+ geom_density() + scale_x_log10()
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 2044 rows containing non-finite values (stat_density).
peak2transDist0_noT=peak2transDist_noties%>% filter(Distance==0)
nrow(peak2transDist0_noT)
[1] 2044
peak2transDist200_noT=peak2transDist_noties %>% filter(abs(Distance)<200)
nrow(peak2transDist200_noT)
[1] 10488
summary(peak2transDist$Distance)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-5523243 -57698 -12830 -23711 3373 5592124
ggplot(peak2transDist_noties, aes(x=abs(Distance)))+ geom_histogram(binwidth = .5) + scale_x_log10()
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 2044 rows containing non-finite values (stat_bin).
Looking at this visually suggests that we have way too many peaks. I want to compare the peak score which is related to the coverage to the abs(distace)
ggplot(peak2transDist_noties, aes(y=PeakScore, x=abs(Distance + 1))) + geom_point() + scale_x_log10() + scale_y_log10() + geom_density2d(na.rm = TRUE, size = 1, colour = 'red')
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Transformation introduced infinite values in continuous x-axis
Alternatively let me try to remove low peak score values.
allPeakplot=ggplot(peak2transDist_noties, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Distance all peaks to gene end") + annotate("text", label=nrow(peak2transDist_noties), x=10, y=.4)
peak2transDist_score500=peak2transDist_noties%>% filter(PeakScore>500)
score500plot=ggplot(peak2transDist_score500, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 500") + annotate("text", label=nrow(peak2transDist_score500), x=10, y=.4)
peak2transDist_score200=peak2transDist_noties%>% filter(PeakScore>200)
score200plot=ggplot(peak2transDist_score200, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 200") + annotate("text", label=nrow(peak2transDist_score200), x=10, y=.4)
peak2transDist_score100=peak2transDist_noties%>% filter(PeakScore>100)
score100plot=ggplot(peak2transDist_score100, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 100") + annotate("text", label=nrow(peak2transDist_score100), x=10, y=.4)
peak2transDist_score50=peak2transDist_noties%>% filter(PeakScore>50)
score50plot=ggplot(peak2transDist_score50, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 50")+ annotate("text", label=nrow(peak2transDist_score50), x=10, y=.4)
peak2transDist_score20=peak2transDist_noties%>% filter(PeakScore>20)
score20plot=ggplot(peak2transDist_score20, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 10")+ annotate("text", label=nrow(peak2transDist_score20), x=10, y=.4)
distance2peak_all=plot_grid(allPeakplot,score20plot,score50plot,score100plot,score200plot, score500plot)
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 662 rows containing non-finite values (stat_density).
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 431 rows containing non-finite values (stat_density).
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 327 rows containing non-finite values (stat_density).
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 234 rows containing non-finite values (stat_density).
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 150 rows containing non-finite values (stat_density).
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 78 rows containing non-finite values (stat_density).
ggsave("../output/plots/QC_plots/distance2peak_all.png", distance2peak_all, width=8, height=6)
I am gonig to use this assignment method to call QTLs. The bed file I will make the phenotypes from is
in the filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed file this is
awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $12 "\t" $10}' filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed > filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.bed
less /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SA | tr ":" "-" > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed
Make this an SAF file with the correct peak ID. bed2saf_peaks2trans.py
from misc_helper import *
fout = file("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed"):
chrom, start, end, name, score, strand, gene = ln.split()
name_i=int(name)
start_i=int(start)
end_i=int(end)
gene_only=gene.split("-")[1]
ID = "peak%d:%s:%d:%d:%s:%s"%(name_i, chrom, start_i, end_i, strand, gene_only)
fout.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
fout.close()
Run feature counts:
ref_gene_peakTranscript_fc_TN.sh
#!/bin/bash
#SBATCH --job-name=ref_gene_peakTranscript_fc_TN
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ref_gene_peakTranscript_fc_TN.out
#SBATCH --error=ref_gene_peakTranscript_fc_TN.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_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.fc /project2/gilad/briana/threeprimeseq/data/sort/*-T-*-sort.bam -s 2
featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.fc /project2/gilad/briana/threeprimeseq/data/sort/*-N-*-sort.bam -s 2
Fix the headers:
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_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()
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_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()
Create file IDS:
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line, i in enumerate(infile):
if line == 0:
i_list=i.split()
files= i_list[10:-2]
for each in files:
full = each.split("/")[7]
samp= full.split("-")[2:4]
lim="_"
samp_st=lim.join(samp)
outLine= full[:-1] + "\t" + samp_st
fout.write(outLine + "\n")
fout.close()
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line, i in enumerate(infile):
if line == 0:
i_list=i.split()
files= i_list[10:-2]
for each in files:
full = each.split("/")[7]
samp= full.split("-")[2:4]
lim="_"
samp_st=lim.join(samp)
outLine= full[:-1] + "\t" + samp_st
fout.write(outLine + "\n")
fout.close()
(remove top line)
awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript_head.txt > /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript.txt
awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript_head.txt > /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt
Make Phenotypes:
#PYTHON 3
dic_IND = {}
dic_BAM = {}
for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.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
#gene start and end dictionaries:
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt"):
chrom, start, end, geneID, score, strand = ln.split('\t')
gene= geneID.split(":")[1]
if "-" in gene:
gene=gene.split("-")[0]
if gene not in dic_geneS:
dic_geneS[gene]=int(start)
dic_geneE[gene]=int(end)
#list of genes
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_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.Transcript_sm_quant.Total_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_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
indsNA= "NA" + each[:-2]
inds_noL.append(indsNA)
fout.write(" ".join(peak + inds_noL) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_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=dic_geneS[id_list[5]]
end=dic_geneE[id_list[5]]
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()
#PYTHON 3
dic_IND = {}
dic_BAM = {}
for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript.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
#gene start and end dictionaries:
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt"):
chrom, start, end, geneID, score, strand = ln.split('\t')
gene= geneID.split(":")[1]
if "-" in gene:
gene=gene.split("-")[0]
if gene not in dic_geneS:
dic_geneS[gene]=int(start)
dic_geneE[gene]=int(end)
#list of genes
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_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.Transcript_sm_quant.Nuclear_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_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
indsNA= "NA" + each[:-2]
inds_noL.append(indsNA)
fout.write(" ".join(peak + inds_noL) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_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=dic_geneS[id_list[5]]
end=dic_geneE[id_list[5]]
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()
I can run these with the following bash script:
#!/bin/bash
#SBATCH --job-name=run_makepheno_sep_trans
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_makepheno_sep_trans.out
#SBATCH --error=run_makepheno_sep_trans.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
python makePhenoRefSeqPeaks_Transcript_Total.py
python makePhenoRefSeqPeaks_Transcript_Nuclear.py
I will do this in the /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/ directory.
module load samtools
#zip file
gzip filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt
module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz
#source activate three-prime-env
sh filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz_prepare.sh
#run for nuclear as well
gzip filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt
#unload anaconda, load python
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz
#load anaconda and env.
sh filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz_prepare.sh
#keep only 2 PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs
Make a sample list.
#make a sample list
fout = open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt",'w')
for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt", "r"):
bam, sample = ln.split()
line=sample[:-2]
fout.write("NA"+line + "\n")
fout.close()
** Manually ** Remove 18500, 19092 and 19193, 18497
#!/bin/bash
#SBATCH --job-name=APAqtl_nominal_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_transcript.out
#SBATCH --error=APAqtl_nominal_transcript.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
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
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1 --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done
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
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1 --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done
#!/bin/bash
#SBATCH --job-name=APAqtl_permuted_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_permuted_transcript.out
#SBATCH --error=APAqtl_permuted_transcript.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1 --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done
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
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1 --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done
APAqtlpermCorrectQQplot_trans.R
library(dplyr)
##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")
#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_transcript.png")
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps")
abline(0,1)
dev.off()
#write df with BH
write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", col.names = T, row.names = F, quote = F)
##nuclear results
nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")
#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_transcript.png")
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps")
abline(0,1)
dev.off()
# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", col.names = T, row.names = F, quote = F)
Write a script to run this:
run_APAqtlpermCorrectQQplot_trans.sh
#!/bin/bash
#SBATCH --job-name=run_APAqtlpermCorrectQQplot_trans
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_APAqtlpermCorrectQQplot_trans.out
#SBATCH --error=run_APAqtlpermCorrectQQplot_trans.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
Rscript APAqtlpermCorrectQQplot_trans.R
I may want to change this to not use the transcript ID but use the gene ID. I will look at these results then decide.
peak2transDist_noties_gene = peak2transDist_noties %>% separate(Transcript, c("OnlyTranscript", "Gene"), sep=":") %>% select(PeakName, Gene) %>% group_by(Gene) %>% tally() %>% mutate(onePeak=ifelse(n==1, 1, 0 )) %>% mutate(multPeaks=ifelse(n > 1, 1, 0 ))
sum(peak2transDist_noties_gene$onePeak==1)
[1] 1591
sum(peak2transDist_noties_gene$multPeaks==1)
[1] 13923
1591 Genes have 1 peak. 13923 genes have multiple, 3717 with 0
In total there are 19231 genes in the annotation.
Plot this:
PeakCategory=c("Zero", "One", "Multiple")
NumGenes=c(round((19231-sum(peak2transDist_noties_gene$onePeak==1)-sum(peak2transDist_noties_gene$multPeaks==1))/19231, digits = 3), round(sum(peak2transDist_noties_gene$onePeak==1)/19231,digits=3), round(sum(peak2transDist_noties_gene$multPeaks==1)/19231,digits = 3))
GenePeakNumTable=as.data.frame(cbind(PeakCategory,NumGenes))
GenePeakNumTable$NumGenes=as.numeric(as.character(GenePeakNumTable$NumGenes))
lab0=paste("Genes = ", 19231-sum(peak2transDist_noties_gene$onePeak==1)-sum(peak2transDist_noties_gene$multPeaks==1), sep=" ")
lab1=paste("Genes = ", sum(peak2transDist_noties_gene$onePeak==1), sep=" ")
labmult=paste("Genes = ", sum(peak2transDist_noties_gene$multPeaks==1), sep=" ")
GenePeakNumPlot=ggplot(GenePeakNumTable, aes(x="", y=NumGenes, by=PeakCategory, fill=PeakCategory)) + geom_bar(stat="identity",position = "stack") + labs(title="Characterize Protein Coding Genes \n by number of PAS", y="Proportion of genes", x="") + scale_fill_brewer(palette="Paired") + annotate("text", x="", y= .1, label=lab0) + annotate("text", x="", y= .24, label=lab1)+ annotate("text", x="", y= .6, label=labmult)
#ggsave(GenePeakNumPlot,filename = "../output/plots/PasPerProteinCodingGene.png")
Try this at transcript level:
peak2transDist_noties_transcript = peak2transDist_noties %>% separate(Transcript, c("OnlyTranscript", "Gene"), sep=":") %>% select(PeakName, OnlyTranscript) %>% group_by(OnlyTranscript) %>% tally() %>% mutate(onePeak=ifelse(n==1, 1, 0 )) %>% mutate(multPeaks=ifelse(n > 1, 1, 0 ))
sum(peak2transDist_noties_transcript$onePeak==1)
[1] 2065
sum(peak2transDist_noties_transcript$multPeaks==1)
[1] 15614
total transcripts: 45024
tot.perm= read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt",head=T, stringsAsFactors=F)
plot(tot.perm$ppval, tot.perm$bpval, xlab="Direct method", ylab="Beta approximation", main="Total Check plot")
abline(0, 1, col="red")
tot_qtl_10= tot.perm %>% filter(-log10(bh) > 1) %>% nrow()
tot_qtl_10
[1] 118
tot.perm %>% filter(-log10(bh) > 1) %>% summarise(n_distinct(sid))
n_distinct(sid)
1 112
nuc.perm= read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt",head=T, stringsAsFactors=F)
plot(nuc.perm$ppval, nuc.perm$bpval, xlab="Direct method", ylab="Beta approximation", main="Nuclear Check plot")
abline(0, 1, col="red")
nuc_qtl_10= nuc.perm %>% filter(-log10(bh) > 1) %>% nrow()
nuc_qtl_10
[1] 880
nuc.perm %>% filter(-log10(bh) > 1) %>% summarise(n_distinct(sid))
n_distinct(sid)
1 831
nQTL_tot=c()
FDR=seq(.05, .5, .01)
for (i in FDR){
x=tot.perm %>% filter(bh < i ) %>% nrow()
nQTL_tot=c(nQTL_tot, x)
}
FDR=seq(.05, .5, .01)
nQTL_nuc=c()
for (i in FDR){
x=nuc.perm %>% filter(bh < i ) %>% nrow()
nQTL_nuc=c(nQTL_nuc, x)
}
nQTL=as.data.frame(cbind(FDR, Total=nQTL_tot, Nuclear=nQTL_nuc))
nQTL_long=melt(nQTL, id.vars = "FDR")
sigQTLbyFDR=ggplot(nQTL_long, aes(x=FDR, y=value, by=variable, col=variable)) + geom_line(size=1.5) + labs(y="Number of Significant QTLs", title="APAqtls detected by FDR cuttoff", color="Fraction")+ scale_color_manual(values=c("#5D478B", "#87CEFF"))
ggsave(plot = sigQTLbyFDR,filename = "../output/plots/SigQTLbyFDR.png")
Saving 7 x 5 in image
I am going to perform this analysis on midway. I need condition QTLs on being other types of QTLs and plot the results. For this I use the nominal pvalues.
overlap_QTLplots_Trans.R
#!/bin/rscripts
#this script has no arguments, it will take the nuclear and total results then output qqplots of these results overlaped with the other molecular QTLs
library(dplyr)
library(scales)
#import other QTLs
QTL_names=c("gene", "snpID","distance", "pval", "slope")
fourSU30= read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_4su30.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)
fourSU60=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_4su60.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)
RNAseq=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)
guevardis=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseqGeuvadis.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)
ribo=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_ribo_phase2.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)
prot=read.table("/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot.fixed.nominal.out", header=F, stringsAsFactors = F, col.names = QTL_names)
#import nuc and tot results
res_names=c("peakID", "snpID", "dist", "res.pval", "slope")
nuc.nom=read.table("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt", header = F, col.names = res_names, stringsAsFactors = F)
tot.nom=read.table("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt", header = F, col.names = res_names, stringsAsFactors = F)
#subset total
fourSU30AndTot= fourSU30 %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
fourSU30_unif_T=runif(nrow(fourSU30AndTot))
fourSU60AndTot= fourSU60 %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
fourSU60_unif_T=runif(nrow(fourSU60AndTot))
RNAAndTot= RNAseq %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
RNAseq_unif_T=runif(nrow(RNAAndTot))
GuevAndTot= guevardis %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
guev_unif_T=runif(nrow(GuevAndTot))
riboAndTot= ribo %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
ribo_unif_T=runif(nrow(riboAndTot))
protAndTot= prot %>% inner_join(tot.nom, by="snpID") %>% select(snpID, res.pval)
prot_unif_T=runif(nrow(protAndTot))
#subset nuc
fourSU30AndNuc= fourSU30 %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
fourSU30_unif_N=runif(nrow(fourSU30AndNuc))
fourSU60AndNuc= fourSU60 %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
fourSU60_unif_N=runif(nrow(fourSU60AndNuc))
RNAAndNuc= RNAseq %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
RNAseq_unif_N=runif(nrow(RNAAndNuc))
GuevAndNuc= guevardis %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
guev_unif_N=runif(nrow(GuevAndNuc))
riboAndNuc= ribo %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
ribo_unif_N=runif(nrow(riboAndNuc))
protAndNuc= prot %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, res.pval)
prot_unif_N=runif(nrow(protAndNuc))
#plot res
##nuclear
png('/project2/gilad/briana/threeprimeseq/output/nuc.allQTLs.png')
qqplot(-log10(runif(nrow(nuc.nom))), -log10(nuc.nom$res.pval),ylab="-log10 Nuclear nominal pvalue", xlab="Uniform expectation", main="Nuclear Nominal pvalues for all snps")
points(sort(-log10(fourSU30_unif_N)), sort(-log10(fourSU30AndNuc$res.pval)), col= alpha("Red", 0.3))
points(sort(-log10(fourSU60_unif_N)), sort(-log10(fourSU60AndNuc$res.pval)), col=alpha("Orange",.3))
points(sort(-log10(RNAseq_unif_N)), sort(-log10(RNAAndNuc$res.pval)), col=alpha("Yellow",.3))
points(sort(-log10(guev_unif_N)), sort(-log10(GuevAndNuc$res.pval)), col=alpha("Green",.3))
points(sort(-log10(ribo_unif_N)), sort(-log10(riboAndNuc$res.pval)), col=alpha("Blue", .3))
points(sort(-log10(prot_unif_N)), sort(-log10(protAndNuc$res.pval)), col=alpha("Purple",.3))
abline(0,1)
legend("topleft", legend=c("All SNPs", "4su 30", "4su 60", "RNAseq", "Guevadis RNA", "Ribo", "Protein"), col=c("black", "red", "orange", "yellow", "green", "blue", "purple"), pch=19)
dev.off()
##total
png('/project2/gilad/briana/threeprimeseq/output/tot.allQTLs.png')
qqplot(-log10(runif(nrow(tot.nom))), -log10(tot.nom$res.pval),ylab="-log10 Total nominal pvalue", xlab="Uniform expectation", main="Total Nominal pvalues for all snps")
points(sort(-log10(fourSU30_unif_T)), sort(-log10(fourSU30AndTot$res.pval)), col= alpha("Red", 0.3))
points(sort(-log10(fourSU60_unif_T)), sort(-log10(fourSU60AndTot$res.pval)), col=alpha("Orange",.3))
points(sort(-log10(RNAseq_unif_T)), sort(-log10(RNAAndTot$res.pval)), col=alpha("Yellow",.3))
points(sort(-log10(guev_unif_T)), sort(-log10(GuevAndTot$res.pval)), col=alpha("Green",.3))
points(sort(-log10(ribo_unif_T)), sort(-log10(riboAndTot$res.pval)), col=alpha("Blue", .3))
points(sort(-log10(prot_unif_T)), sort(-log10(protAndTot$res.pval)), col=alpha("Purple",.3))
abline(0,1)
legend("topleft", legend=c("All SNPs", "4su 30", "4su 60", "RNAseq", "Guevadis RNA", "Ribo", "Protein"), col=c("black", "red", "orange", "yellow", "green", "blue", "purple"), pch=19)
dev.off()
Bash script to run this:
run_overlap_QTLplots_transcript.sh
#!/bin/bash
#SBATCH --job-name=run_overlapQTL_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_overlapQTL_transcript.out
#SBATCH --error=run_overlapQTL_transcript.err
#SBATCH --partition=bigmem2
#SBATCH --mem=64G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
Rscript overlap_QTLplots_Trans.R
tot.perm= tot.perm %>% mutate(sig=ifelse( -log10(bh) >= 1 , "Yes", "No"))
tot.perm$sig=as.factor(tot.perm$sig)
totQTLdist_plot= ggplot(tot.perm, aes(x=log10(abs(dist)), by=sig, fill=sig)) + geom_density(alpha=.5) + labs(title="Distance between snp and peak\n Total fraction")
nuc.perm= nuc.perm %>% mutate(sig=ifelse( -log10(bh) >= 1 , "Yes", "No"))
nuc.perm$sig=as.factor(nuc.perm$sig)
nucQTLdist_plot= ggplot(nuc.perm, aes(x=log10(abs(dist)), by=sig, fill=sig)) + geom_density(alpha=.5) + labs(title="Distance between snp and peak\n Nuclear fraction")
plot_grid(totQTLdist_plot, nucQTLdist_plot )
How many of the significant snps are the same.
tot.perm_sigOnly=tot.perm %>% filter(sig=="Yes")
nuc.perm_sigOnly=nuc.perm %>% filter(sig=="Yes")
I want to know how many overlap. I can use and innner join by the sid.
#nuc in total
nuc.perm_sigOnly_inT= nuc.perm_sigOnly %>% semi_join(tot.perm_sigOnly, by=c("sid", "pid"))
nrow(nuc.perm_sigOnly_inT)
[1] 22
nuc.perm_sigOnly_notT= nuc.perm_sigOnly %>% anti_join(tot.perm_sigOnly, by=c("sid", "pid"))
nrow(nuc.perm_sigOnly_notT)
[1] 858
#total in nuc
tot.perm_sigOnly_inT= tot.perm_sigOnly %>% semi_join(nuc.perm_sigOnly, by=c("sid", "pid"))
nrow(tot.perm_sigOnly_inT)
[1] 22
tot.perm_sigOnly_notT= tot.perm_sigOnly %>% anti_join(nuc.perm_sigOnly, by=c("sid", "pid"))
nrow(tot.perm_sigOnly_notT)
[1] 96
grid.newpage()
qtloverlap=draw.pairwise.venn(area1 = 3049, area2 = 677, cross.area = 148, category = c("Nuclear: QTLs", "Total: QTLs"), lty = rep("solid", 2), fill = c("light blue", "pink"), alpha = rep(0.5, 2), cat.pos = c(0, 0), cat.dist = rep(0.025, 2))
Overlap accouting for gene.
#nuc genes
nuc.perm_sigOnly_gene= nuc.perm_sigOnly %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(gene) %>% distinct(gene)
nrow(nuc.perm_sigOnly_gene)
[1] 715
#total genes
tot.perm_sigOnly_gene= tot.perm_sigOnly %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(gene) %>% distinct(gene)
nrow(tot.perm_sigOnly_gene)
[1] 106
nuc.perm_sigOnly_gene %>% semi_join(tot.perm_sigOnly_gene, by="gene") %>% nrow()
[1] 48
nuc.perm_sigOnly_gene %>% anti_join(tot.perm_sigOnly_gene, by="gene") %>% nrow()
[1] 667
tot.perm_sigOnly_gene %>% semi_join(nuc.perm_sigOnly_gene, by="gene") %>% nrow()
[1] 48
tot.perm_sigOnly_gene %>% anti_join(nuc.perm_sigOnly_gene, by="gene") %>% nrow()
[1] 58
grid.newpage()
png("../output/plots/geneswithAPAQTL.ven.png")
qtloverlap_gene=draw.pairwise.venn(area1 = 2272, area2 = 602, cross.area = 398, category = c("Genes with APAqtls\n Nuclear", "Genes with APAqtls\n Total"), lty = rep("solid", 2), fill = c("light blue", " purple"), alpha = rep(0.5, 2), cat.pos = c(0, 26), cat.dist = c(0.03, 0.03))
dev.off()
quartz_off_screen
2
Run on counts:
I need to run feature counts on all of the data so the total and nuclear files are in the same file
ref_gene_peakTranscript_fc.sh
#!/bin/bash
#SBATCH --job-name=ref_gene_peakTranscript_fc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ref_gene_peakTranscript_fc.out
#SBATCH --error=ref_gene_peakTranscript_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_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.fc /project2/gilad/briana/threeprimeseq/data/sort/*-sort.bam -s 2
fix_head_fc_trans.py
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_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_transcript.py
inFile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_fixed.fc", "r")
outFile= open("/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_transcript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_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_transcript.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_transcript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant_forLC.fc"
outFile = "/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_transcript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.ALL.pheno_fixed_%s.txt"%(target)
main(inFile, outFile, target)
Run this with: run_subset_diffisopheno_transcript.sh
#!/bin/bash
#SBATCH --job-name=run_subset_diffisopheno_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_subset_diffisopheno_transcript.out
#SBATCH --error=run_subset_diffisopheno_transcript.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_transcript.py $i
done
Make a samples list script.
MakeDifIsoSampleList_transcript.py
outfile=open("/project2/gilad/briana/threeprimeseq/data/diff_iso_transcript/sample_groups.txt", "w")
infile=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.fc", "r")
for line, i in enumerate(infile):
if line == 1:
i_list=i.split()
libraries=[]
for sample in i_list[6:]:
full = sample.split("/")[7]
samp= full.split("-")[2:4]
lim="_"
samp_st=lim.join(samp)
libraries.append(samp_st)
for l in libraries:
if l[-1] == "T":
outfile.write("%s\tTotal\n"%(l))
else:
outfile.write("%s\tNuclear\n"%(l))
else:
next
outfile.close()
run_leafcutter_ds_bychrom_tr.sh
#!/bin/bash
#SBATCH --job-name=run_leafcutter_ds_bychrom_tr
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_leafcutter_ds_bychrom_tr.out
#SBATCH --error=run_leafcutter_ds_bychrom_tr.err
#SBATCH --partition=bigmem2
#SBATCH --mem=50G
#SBATCH --mail-type=END
module load R
for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
do
Rscript /project2/gilad/briana/davidaknowles-leafcutter-c3d9474/scripts/leafcutter_ds.R --num_threads 4 /project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_transcript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.ALL.pheno_fixed_${i}.txt /project2/gilad/briana/threeprimeseq/data/diff_iso_transcript/sample_groups.txt -o /project2/gilad/briana/threeprimeseq/data/diff_iso_transcript/TN_diff_isoform_chr${i}.txt
done
Error in colSums(cluster_counts > 0): ‘x’ must be an array of at least two dimensions
Not enough valid samples NA NA NA chr7:TRPV6 NA
<=1 sample with coverage>min_coverage NA NA NA chr7:WBSCR17 NA
There are duplicates peak IDs in chr 6 and 19. This could be due to the same gene name on diff strands from diff versions of the gene. The problems on 6 come from HLA, the one overlap on 19 is DPP9. I am going to remove the dep lines with low coverage because they will probably drop out of the leafcutter analysis due to low numbers.
The errors in the significance files are due to clusters that do not satisfy requirements for leafcutter. Either there is only 1 peak in the gene, there are not enought samples with coverage or the min coverage is not satisfied. I can remove these peaks from the results.
Plot results:
diffIso=read.table("../data/diff_iso_trans/TN_diff_isoform_all_cluster_sig_Succ.txt", col.names = c("status", "loglr", "df", "p", "cluster", "p.adjust"))
qqplot(-log10(runif(nrow(diffIso))), -log10(diffIso$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between fractions")
abline(0,1)
A better way to look at this is effect sizes because we expect a large amount of signal here.
effectsize=read.table("../data/diff_iso_trans/TN_diff_isoform_ALL.txt_effect_sizes.fixed.txt", stringsAsFactors = F, col.names=c('intron', 'logef' ,'Nuclear', 'Total','deltapsi'))
Plot effect sizes:
effectsize$logef=as.numeric(effectsize$logef)
plot(sort(effectsize$logef),main="Leafcutter effect Sizes", ylab="Effect size", xlab="Peak Index")
Negative effect sizes are more in nuclear. There are 193842 negative effect sizes and 70873 positive.
I want to color this plot by top and bottom 5%.
quantile(effectsize$logef,na.rm=T,probs = seq(0, 1, .05))
0% 5% 10% 15% 20%
-20.08363095 -0.60972092 -0.45829291 -0.36423672 -0.29749657
25% 30% 35% 40% 45%
-0.25078432 -0.21370090 -0.18275473 -0.15592521 -0.13132430
50% 55% 60% 65% 70%
-0.11103719 -0.09127381 -0.07203322 -0.05144509 -0.02853051
75% 80% 85% 90% 95%
0.04114049 0.20084899 0.41743866 0.73096535 1.18251507
100%
7.45641015
5% is -.61, 95% is 1.18
effectsize$colorsF=ifelse(effectsize$logef >= 1.18, "darkviolet", ifelse(effectsize$logef <= -.61,"deepskyblue3", "black"))
plot(effectsize$logef, col = effectsize$colorsF ,main="Leafcutter effect Sizes", ylab="Effect size")
legend("bottomleft", legend=c("Top 5%: Total", "Bottom 5%: Nuclear"),
col=c( "darkviolet","deepskyblue3"), pch=19, cex=0.8)
I want to plot this by chr.
effectsize$colorsF=as.factor(effectsize$colorsF)
effectsize_chr=effectsize %>% tidyr::separate(intron, into=c("chrom", "start", "end", "gene"), sep=":")
effectsize_chr$chrom=as.factor(effectsize_chr$chrom)
ggplot(effectsize_chr, aes(x=chrom, y=logef, col=chrom)) + geom_jitter()+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] bindrcpp_0.2.2 VennDiagram_1.6.20 futile.logger_1.4.3
[4] reshape2_1.4.3 cowplot_0.9.3 workflowr_1.2.0
[7] forcats_0.3.0 stringr_1.4.0 dplyr_0.7.6
[10] purrr_0.2.5 readr_1.1.1 tidyr_0.8.1
[13] 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 pillar_1.3.0 glue_1.3.0
[10] withr_2.1.2 lambda.r_1.2.3 modelr_0.1.2
[13] readxl_1.1.0 bindr_0.1.1 plyr_1.8.4
[16] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0
[19] rvest_0.3.2 evaluate_0.13 labeling_0.3
[22] knitr_1.20 broom_0.5.0 Rcpp_0.12.19
[25] formatR_1.5 scales_1.0.0 backports_1.1.2
[28] jsonlite_1.6 fs_1.2.6 hms_0.4.2
[31] digest_0.6.17 stringi_1.2.4 rprojroot_1.3-2
[34] cli_1.0.1 tools_3.5.1 magrittr_1.5
[37] lazyeval_0.2.1 futile.options_1.0.1 crayon_1.3.4
[40] whisker_0.3-2 pkgconfig_2.0.2 MASS_7.3-50
[43] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[46] rmarkdown_1.11 httr_1.3.1 rstudioapi_0.9.0
[49] R6_2.3.0 nlme_3.1-137 git2r_0.24.0
[52] compiler_3.5.1