Last updated: 2018-11-07

workflowr checks: (Click a bullet for more information)
  • 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: e43bd07

    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:    data/.DS_Store
        Ignored:    output/.DS_Store
    
    Untracked files:
        Untracked:  KalistoAbundance18486.txt
        Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
        Untracked:  analysis/snake.config.notes.Rmd
        Untracked:  analysis/verifyBAM.Rmd
        Untracked:  data/18486.genecov.txt
        Untracked:  data/APApeaksYL.total.inbrain.bed
        Untracked:  data/ChromHmmOverlap/
        Untracked:  data/GM12878.chromHMM.bed
        Untracked:  data/GM12878.chromHMM.txt
        Untracked:  data/NuclearApaQTLs.txt
        Untracked:  data/PeaksUsed/
        Untracked:  data/RNAkalisto/
        Untracked:  data/TotalApaQTLs.txt
        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/apaExamp/
        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/comb_map_stats_39ind.csv
        Untracked:  data/combined_reads_mapped_three_prime_seq.csv
        Untracked:  data/diff_iso_trans/
        Untracked:  data/ensemble_to_genename.txt
        Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed
        Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed
        Untracked:  data/first50lines_closest.txt
        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/mol_overlap/
        Untracked:  data/mol_pheno/
        Untracked:  data/nom_QTL/
        Untracked:  data/nom_QTL_opp/
        Untracked:  data/nom_QTL_trans/
        Untracked:  data/nuc6up/
        Untracked:  data/other_qtls/
        Untracked:  data/peakPerRefSeqGene/
        Untracked:  data/perm_QTL/
        Untracked:  data/perm_QTL_opp/
        Untracked:  data/perm_QTL_trans/
        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/39indQC.Rmd
        Modified:   analysis/apaQTLoverlapGWAS.Rmd
        Modified:   analysis/cleanupdtseq.internalpriming.Rmd
        Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
        Modified:   analysis/dif.iso.usage.leafcutter.Rmd
        Modified:   analysis/diff_iso_pipeline.Rmd
        Modified:   analysis/explore.filters.Rmd
        Modified:   analysis/flash2mash.Rmd
        Modified:   analysis/overlapMolQTL.Rmd
        Modified:   analysis/overlap_qtls.Rmd
        Modified:   analysis/peakOverlap_oppstrand.Rmd
        Modified:   analysis/pheno.leaf.comb.Rmd
        Modified:   analysis/swarmPlots_QTLs.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.
Expand here to see past versions:
    File Version Author Date Message
    Rmd e43bd07 Briana Mittleman 2018-11-07 group chromhmm by number
    html b176cda Briana Mittleman 2018-11-06 Build site.
    Rmd 75467a1 Briana Mittleman 2018-11-06 load in permutation results
    html a5b4cf6 Briana Mittleman 2018-10-29 Build site.
    Rmd afb0ce9 Briana Mittleman 2018-10-29 change plot colors
    html 805dec6 Briana Mittleman 2018-10-26 Build site.
    Rmd 5cb6b0b Briana Mittleman 2018-10-26 permutation code
    html 96cfdcd Briana Mittleman 2018-10-24 Build site.
    Rmd 00b1020 Briana Mittleman 2018-10-24 naive enrichment
    html de860f0 Briana Mittleman 2018-10-24 Build site.
    Rmd 96a97f4 Briana Mittleman 2018-10-24 add nuclear characterization


This analysis is similar to the Characterize Total APAqtl analysis

I would like to study:

  • Distance metrics:
    • distance from snp to TSS of gene
    • Distance from snp to peak
  • Expression metrics:
    • expression of genes with significant QTLs vs other genes (by rna seq)
    • expression of genes with significant QTLs vs other genes (peak coverage)
  • Chrom HMM metrics:
    • look at the chrom HMM interval for the significant QTLs

Upload Libraries and Data:

Library

library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(reshape2)
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(VennDiagram)
Loading required package: grid
Loading required package: futile.logger
library(data.table)

Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':

    between, first, last
The following object is masked from 'package:purrr':

    transpose
The following objects are masked from 'package:reshape2':

    dcast, melt
library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract

Attaching package: 'ggpubr'
The following object is masked from 'package:VennDiagram':

    rotate
library(cowplot)

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

    get_legend
The following object is masked from 'package:ggplot2':

    ggsave

Permuted Results from APA:

I will add a column to this dataframe that will tell me if the association is significant at 10% FDR. This will help me plot based on significance later in the analysis. I am also going to seperate the PID into relevant pieces.

NuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", stringsAsFactors = F, header=T)  %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>%  separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak"))

NuclearAPA$sig=as.factor(NuclearAPA$sig)


print(names(NuclearAPA))
 [1] "chr"    "start"  "end"    "gene"   "strand" "peak"   "nvar"  
 [8] "shape1" "shape2" "dummy"  "sid"    "dist"   "npval"  "slope" 
[15] "ppval"  "bpval"  "bh"     "sig"   

Distance Metrics

Distance from snp to TSS

I ran the QTL analysis based on the starting position of the gene.

ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)  +  labs(title="Distance from snp to TSS", x="Base Pairs") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Expand here to see past versions of unnamed-chunk-3-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

Zoom in to 100,000.

ggplot(NuclearAPA, aes(x=dist, fill=sig, by=sig)) + geom_density(alpha=.5)+coord_cartesian(xlim = c(-150000, 150000)) + scale_fill_brewer(palette="Paired")

Expand here to see past versions of unnamed-chunk-4-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

Distance from snp to peak

To perform this analysis I need to recover the peak positions.

The peak file I used for the QTL analysis is: /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed

peaks=read.table("../data/PeaksUsed/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed", col.names = c("chr", "peakStart", "peakEnd", "PeakNum", "PeakScore", "Strand", "Gene")) %>% mutate(peak=paste("peak", PeakNum,sep="")) %>% mutate(PeakCenter=peakStart+ (peakEnd- peakStart))

I want to join the peak start to the NuclearAPA file but the peak column. I will then create a column that is snppos-peakcenter.

NuclearAPA_peakdist= NuclearAPA %>%  inner_join(peaks, by="peak") %>%  separate(sid, into=c("snpCHR", "snpLOC"), by=":")
NuclearAPA_peakdist$snpLOC= as.numeric(NuclearAPA_peakdist$snpLOC)

NuclearAPA_peakdist= NuclearAPA_peakdist %>%  mutate(DisttoPeak= snpLOC-PeakCenter)

Plot this by significance.

ggplot(NuclearAPA_peakdist, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density(alpha=.5)  +  labs(title="Distance from snp peak", x="log10 absolute value Distance to Peak") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Expand here to see past versions of unnamed-chunk-7-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

Look at the summarys based on significance:

NuclearAPA_peakdist_sig=NuclearAPA_peakdist %>% filter(sig==1)
NuclearAPA_peakdist_notsig=NuclearAPA_peakdist %>% filter(sig==0)


summary(NuclearAPA_peakdist_sig$DisttoPeak)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-1003786   -17579      -91    -8818     6588   891734 
summary(NuclearAPA_peakdist_notsig$DisttoPeak)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-70147526   -265059     -2067      7263    255169 125172864 
ggplot(NuclearAPA_peakdist, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Expand here to see past versions of unnamed-chunk-9-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

Look like there are some outliers that are really far. I will remove variants greater than 1*10^6th away

NuclearAPA_peakdist_filt=NuclearAPA_peakdist %>% filter(abs(DisttoPeak) <= 1*(10^6))

ggplot(NuclearAPA_peakdist_filt, aes(y=DisttoPeak,x=sig, fill=sig, by=sig)) + geom_boxplot()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand) + scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Expand here to see past versions of unnamed-chunk-10-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

ggplot(NuclearAPA_peakdist_filt, aes(x=DisttoPeak, fill=sig, by=sig)) + geom_density()  + scale_fill_discrete(guide = guide_legend(title = "Significant QTL")) + facet_grid(.~strand)+ scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Expand here to see past versions of unnamed-chunk-10-2.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

I am going to plot a violin plot for just the significant ones.

ggplot(NuclearAPA_peakdist_sig, aes(x=DisttoPeak)) + geom_density(fill="deepskyblue3")+ labs(title="Nuclear: Distance from QTL to PAS Peak", x="Distance from SNP to PAS")

Expand here to see past versions of unnamed-chunk-11-1.png:
Version Author Date
a5b4cf6 Briana Mittleman 2018-10-29
de860f0 Briana Mittleman 2018-10-24

Within 1000 bases of the peak center.

NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 1000) %>% nrow()
[1] 192
NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 10000) %>% nrow()
[1] 420
NuclearAPA_peakdist_sig %>% filter(abs(DisttoPeak) < 100000) %>% nrow()
[1] 726

192 QTLs are within 1000 bp, 420 are within 10000, and 726 are within 100,000bp

Expression metrics

Next I want to pull in the expression values and compare the expression of genes with a nuclear APA qtl in comparison to genes without one. I will also need to pull in the gene names file to add in the gene names from the ensg ID.

Remove the # from the file.

expression=read.table("../data/mol_pheno/fastqtl_qqnorm_RNAseq_phase2.fixed.noChr.txt", header = T,stringsAsFactors = F)
expression_mean=apply(expression[,5:73],1,mean,na.rm=TRUE)
expression_var=apply(expression[,5:73],1,var,na.rm=TRUE)
expression$exp.mean= expression_mean 
expression$exp.var=expression_var
expression= expression %>% separate(ID, into=c("Gene.stable.ID", "ver"), sep ="[.]")

Now I can pull in the names and join the dataframes.

geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", header=T,stringsAsFactors = F) 

expression=expression %>% inner_join(geneNames,by="Gene.stable.ID") 

expression=expression %>% select(Chr, start, end, Gene.name, exp.mean,exp.var) %>%  rename("gene"=Gene.name)

Now I can join this with the qtls.

NuclearAPA_wExp=NuclearAPA %>% inner_join(expression, by="gene") 
gene_wQTL_N= NuclearAPA_wExp %>% group_by(gene) %>% summarise(sig_gene=sum(as.numeric(as.character(sig)))) %>% ungroup() %>% inner_join(expression, by="gene") %>% mutate(sigGeneFactor=ifelse(sig_gene>=1, 1,0))

gene_wQTL_N$sigGeneFactor= as.factor(as.character(gene_wQTL_N$sigGeneFactor))
summary(gene_wQTL_N$sigGeneFactor)
   0    1 
4589  607 

There are 607 genes with a QTL

ggplot(gene_wQTL_N, aes(x=exp.mean, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) +labs(title="Mean in RNA expression by genes with significant QTL", x="Mean in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Expand here to see past versions of unnamed-chunk-17-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

I can do a similar analysis but test the variance in the gene expression.

ggplot(gene_wQTL_N, aes(x=exp.var, by=sigGeneFactor, fill=sigGeneFactor)) + geom_density(alpha=.3) + labs(title="Varriance in RNA expression by genes with significant QTL", x="Variance in normalized expression") + scale_fill_discrete(guide = guide_legend(title = "Significant QTL"))+ scale_fill_brewer(palette="Paired")
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Expand here to see past versions of unnamed-chunk-18-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

Peak coverage for QTLs

I can also look at peak coverage for peaks with QLTs and those without. I will first look at this on peak level then mvoe to gene level. The peak scores come from the coverage in the peaks.

The NuclearAPA_peakdist data frame has the information I need for this.

ggplot(NuclearAPA_peakdist, aes(x=PeakScore,fill=sig,by=sig)) + geom_density(alpha=.5)+ scale_x_log10() + labs(title="Peak score by significance") + scale_fill_brewer(palette="Paired")

Expand here to see past versions of unnamed-chunk-19-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

This is expected. It makes sense that we have more power to detect qtls in higher expressed peaks. This leads me to believe that filtering out low peaks may add power but will not mitigate the effect. ##Where are the SNPs

I created the significant SNP files in the Characterize Total APAqtl analysis analysis.

chromHmm=read.table("../data/ChromHmmOverlap/chromHMM_regions.txt", col.names = c("number", "name"), stringsAsFactors = F)

NuclearOverlapHMM=read.table("../data/ChromHmmOverlap/Nuc_overlapHMM.bed", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"))
NuclearOverlapHMM$number=as.integer(NuclearOverlapHMM$number)
NuclearOverlapHMM_names=NuclearOverlapHMM %>% left_join(chromHmm, by="number")
ggplot(NuclearOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Nuclear APAQtls" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))

Expand here to see past versions of unnamed-chunk-21-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

I do still need to get 880 random snps.

shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random880.txt

Run QTLNOMres2SigSNPbed.py with nuclear 880 and sort output

import pybedtools 

RANDnuc=pybedtools.BedTool('/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random880.sort.bed') 



hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")

#map hmm to snps  
NucRnad_overlapHMM=RANDnuc.map(hmm, c=4)


#save results  

NucRnad_overlapHMM.saveas("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/randomSnps/ApaQTL_nuclear_Random_overlapHMM.bed")
NuclearRandOverlapHMM=read.table("../data/ChromHmmOverlap/ApaQTL_nuclear_Random_overlapHMM.bed", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"))

NuclearRandOverlapHMM_names=NuclearRandOverlapHMM %>% left_join(chromHmm, by="number")
ggplot(NuclearRandOverlapHMM_names, aes(x=name)) + geom_bar() + labs(title="ChromHMM labels for Nuclear APAQtls (Random)" , y="Number of SNPs", x="Region")+theme(axis.text.x = element_text(angle = 90, hjust = 1))

Expand here to see past versions of unnamed-chunk-25-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

To put this on the same plot I can count the number in each then plot them next to eachother.

random_perChromHMM_nuc=NuclearRandOverlapHMM_names %>%  group_by(name) %>% summarise(Random=n())
sig_perChromHMM_nuc= NuclearOverlapHMM_names %>%  group_by(name) %>%  summarise(Nuclear_QTLs=n())

perChrommHMM_nuc=random_perChromHMM_nuc %>%  full_join(sig_perChromHMM_nuc, by="name", ) %>% replace_na(list(Random=0,Total_QTLs=0))  

perChrommHMM_nuc_melt=melt(perChrommHMM_nuc, id.vars="name")
names(perChrommHMM_nuc_melt)=c("Region","Set", "N_Snps" )
chromenrichNuclearplot=ggplot(perChrommHMM_nuc_melt, aes(x=Region, y=N_Snps, by=Set, fill=Set)) + geom_bar(position="dodge", stat="identity") +theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Enrichment of Nuclear QTLs by chromatin region", y="Number of Snps", x="Chromatin Region") + scale_fill_brewer(palette="Paired")
chromenrichNuclearplot

Expand here to see past versions of unnamed-chunk-27-1.png:
Version Author Date
de860f0 Briana Mittleman 2018-10-24

ggsave("../output/plots/ChromHmmEnrich_Nuclear.png", chromenrichNuclearplot)
Saving 7 x 5 in image

Chompare enrichment between fractions

I want to make a plot with the enrichment by fraction. I am first going to get an enrichemnt score for each bin naively by looking at the QTL/random in each category.

perChrommHMM_nuc$Random= as.integer(perChrommHMM_nuc$Random)
perChrommHMM_nuc_enr=perChrommHMM_nuc %>%  mutate(Nuclear=Nuclear_QTLs-Random)

perChrommHMM_tot_enr=read.table("../data/ChromHmmOverlap/perChrommHMM_Total_enr.txt",stringsAsFactors = F,header = T)
allenrich=perChrommHMM_tot_enr %>% inner_join(perChrommHMM_nuc_enr, by="name") %>% select(name, Total, Nuclear)

allenrich_melt=melt(allenrich, id.vars="name")

plot it

chromenrichBoth=ggplot(allenrich_melt, aes(x=name, by=variable, y=value, fill=variable)) + geom_bar(stat="identity", position = "dodge") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="QTL-Random for each bin by fraction", y="Num QTL SNPs - Num Random SNPs") + scale_fill_manual(values=c("darkviolet", "deepskyblue3"))


ggsave("../output/plots/ChromHmmEnrich_BothFrac.png", chromenrichBoth)
Saving 7 x 5 in image

Permutations

I want to permute the background snps so i can get a better expectation. To do this I need to chose random lines from the nominal file, change the lines to snp format, overlap with HMM, count how many are in each category, and append the list to a dataframe that is category by permuation. I will do all of this in python.



def main(inFile, outFile, nperm,nsamp):
  nom_res= pd.read_csv(inFile, sep="\t", encoding="utf-8",header=None)
  out=open(outFile, "w")
  categories=list(range(1,16))
  out.write(" ".join(categories)+'\n')
  
  def make_rand_snp(x):
    #x is from the random snps pulled from the nom_res, return the snp df
    chrom_list=list()
    start_list=list()
    end_list=list()
    name_list=list()
    pval_list=list()
    strand_list=list()
    for ln in x:
          pid, sid, dist, pval, slope = ln.split()
          chrom, pos= sid.split(":")
          name=sid
          start= int(pos)-1
          end=int(pos)
          strand=pid.split(":")[3].split("_")[1]
          pval=float(pval)
          chrom_list.append(chrom)
          start_list.append(start)
          end_list.append(end)
          name_list.append(name)
          pval_list.append(pval)
          strand_list.append(strand)
          # add info to the lists 
    #zip lists
    zip_list=list(zip(chrom_list,start_list,end_list,name_list,pval_list, strand_list))
    snp_df=pd.DataFrame(data=zip_list, columns=["Chrom", "Start", "End", "Name", "Pval", "Strand"])
    return snp_df
  
  for i in range(1, nperm+1):
    sample=nom_res.sample(nsamp)
    sample_snp=make_rand_snp(sample)
    sample_snp_sort=sample_snp.sort_values(by=['Chrom', 'Start'])
    hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")
    sample_snp_bed=pybedtools.from_dataframe(sample_snp_sort)
    samp_overHMM=sample_snp_bed(hmm, c=4)
    samp_overHMM_df=pybedtools.to_dataframe(samp_overHMM,names=["chrom", "start", "end", "sid", "significance", "strand", "number"])
    samp_overHMM_df.groupby('number').count()
    #need to see how this comes out and how I can make it into a list, after i have the list for each I can zip them together (list_i)

if __name__ == "__main__":
    import sys
    import pybedtools
    import pandas as pd
    fraction = sys.argv[1]
    nperm= sys.argv[2]
    nperm=int(nperm)
    nsamp=sys.argv[3]
    nsamp=int(nsamp)
    inFile = "/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_%s_NomRes.txt"%(fraction)
    outFile = "dataframe with res"%()
    main(inFile, outFile, nperm, nsamp)
    
    
    

Maybe it is better to make this a bash script that has a pipeline of different scripts. This way I wont have to worry about files/dataframes and all of that.

DO this for total first (118 snps)

total_random118_chromHmm.sh

#!/bin/bash

#SBATCH --job-name=total_random118_chromHmm_f
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=total_random118_chromHmm_f.out
#SBATCH --error=total_random118_chromHmm_f.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#test with 2 permutations then make it 1000  
#choose random res
for i in {1..1000};
do
shuf -n 118 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/randomRes_Total_118_${i}.txt
done

#make random 
for i in {1..1000};
do
python randomRes2SNPbed.py Total 118 ${i}
done 


#cat res together   
cat /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed/* > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed_all/randomRes_Total_118_ALLperm.bed


#sort full file 
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed_all/randomRes_Total_118_ALLperm.bed > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed_all/randomRes_Total_118_ALLperm.sort.bed


#hmm overlap
python overlap_chromHMM.py  Total 118 1000

#Next I would pull this into R to do the group by and average!

pull_random_lines.py

def main(inFile, outFile ,nsamp):
  nom_res= pd.read_csv(inFile, sep="\t", encoding="utf-8",header=None)
  out=open(outFile, "w")
  sample=nom_res.sample(nsamp)
  sample.to_csv(out, sep="\t", encoding='utf-8', index=False, header=F)
  out.close()
    
if __name__ == "__main__":
    import sys
    import pandas as pd
    fraction = sys.argv[1]
    nsamp=sys.argv[2]
    nsamp=int(nsamp)
    iter=sys.argv[3]
    inFile = "/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_%s_NomRes.txt"%(fraction)
    outFile = "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/randomRes_%s_%d_%s.txt"%(fraction,fraction, nsamp, iter)
    main(inFile, outFile, nsamp)

randomRes2SNPbed.py

def main(inFile, outFile):
    fout=open(outFile, "w")
    fin=open(inFile, "r")
    for ln in fin:
          pid, sid, dist, pval, slope = ln.split()
          chrom, pos= sid.split(":")
          name=sid
          start= int(pos)-1
          end=int(pos)
          strand=pid.split(":")[3].split("_")[1]
          pval=float(pval)
          fout.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chrom, start, end, name, pval, strand))
    fout.close()

if __name__ == "__main__":
    import sys
    fraction=sys.argv[1]
    nsamp=sys.argv[2]
    nsamp=int(nsamp)
    iter=sys.argv[3]
    inFile = "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/randomRes_%s_%d_%s.txt"%(fraction,fraction, nsamp, iter)
    outFile= "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/snp_bed/randomRes_%s_%d_%s.bed"%(fraction,fraction, nsamp, iter)
    main(inFile,outFile) 

overlap_chromHMM.py




def main(inFile, outFile):
  rand=pybedtools.BedTool(inFile) 
  hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")
  #map hmm to snps
  Rand_overlapHMM=rand.map(hmm, c=4)
  #save results
  Rand_overlapHMM.saveas(outFile)


if __name__ == "__main__":
    import sys
    import pandas as pd
    import pybedtools
    fraction=sys.argv[1]
    nsamp=sys.argv[2]
    niter=sys.argv[3]
    inFile = "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/snp_bed_all/randomRes_%s_%s_ALLperm.sort.bed"%(fraction,fraction, nsamp)
    outFile= "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/chromHMM_overlap/randomres_overlapChromHMM_%s_%s_%s.txt"%(fraction,fraction,nsamp, niter)
    main(inFile,outFile)

*Nuclear 880

nuclear_random880_chromHmm.sh

#!/bin/bash

#SBATCH --job-name=nuc_random880_chromHmm
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=nuc_random880_chromHmm.out
#SBATCH --error=nuc_random880_chromHmm.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#test with 2 permutations then make it 1000  
#choose random res
for i in {1..1000};
do
shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/randomRes_Nuclear_880_${i}.txt
done

#make random 
for i in {1..1000};
do
python randomRes2SNPbed.py Nuclear 880 ${i} 
done 


#cat res together   
cat /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed/* > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.bed


#sort full file 
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.bed > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.sort.bed


#hmm overlap
python overlap_chromHMM.py  Nuclear 880 1000

#Next I would pull this into R to do the group by and average!

Perm didnt finish: do this with less (824)

nuclear_random880_chromHmm.sm.sh

#!/bin/bash

#SBATCH --job-name=nuc_random880_chromHmm_sm
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=nuc_random880_chromHmm_sm.out
#SBATCH --error=nuc_random880_chromHmm_sm.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env
#make random 
for i in {1..824};
do
python randomRes2SNPbed.py Nuclear 880 ${i} 
done 


#cat res together   
cat /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed/* > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.bed


#sort full file 
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.bed > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_all/randomRes_Nuclear_880_ALLperm.sort.bed


#hmm overlap
python overlap_chromHMM.py  Nuclear 880 824

I need a way to make this more efficient to run 1000 permutations. Here I will look at the results from the 824 permutations.

nuclear_perm824= read.table("../data/ChromHmmOverlap/randomres_overlapChromHMM_Nuclear_880_824.txt", col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"),stringsAsFactors = F, na.strings = "NA")
#924 snps are not annoated 

nuclear_perm824$number=as.integer(as.factor(nuclear_perm824$number))

nuclear_perm824_names=nuclear_perm824 %>% left_join(chromHmm, by="number")

random_perChromHMM_nuc_PERM=nuclear_perm824_names %>%  group_by(name) %>% summarise(Random=n()) %>% mutate(Random_perm=Random/824) %>%  replace_na(list(name="No_annoation")) 

perChrommHMM_nuc_withPerm=random_perChromHMM_nuc_PERM %>%  full_join(sig_perChromHMM_nuc, by="name" ) %>% replace_na(list(Random=0,Nuclear_QTLs=0)) %>%  select(name,Random_perm, Nuclear_QTLs)

 

perChrommHMM_nuc_withPerm_melt=melt(perChrommHMM_nuc_withPerm, id.vars="name")
names(perChrommHMM_nuc_withPerm_melt)=c("Region","Set", "N_Snps" )




ggplot(perChrommHMM_nuc_withPerm_melt, aes(x=Region, y=N_Snps, by=Set, fill=Set)) + geom_bar(position="dodge", stat="identity") +theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Enrichment of Nuclear QTLs by chromatin region", y="Number of Snps", x="Chromatin Region") + scale_fill_brewer(palette="Paired")

Expand here to see past versions of unnamed-chunk-38-1.png:
Version Author Date
b176cda Briana Mittleman 2018-11-06

ENrichment is the actual/random:

perChrommHMM_nuc_withPerm_enrich = perChrommHMM_nuc_withPerm %>% mutate(Nuclear_Enrichment=(Nuclear_QTLs-Random_perm)/Random_perm, chiSq=(Nuclear_QTLs-Random_perm)^2/Random_perm)

ggplot(perChrommHMM_nuc_withPerm_enrich, aes(x=name, y=Nuclear_Enrichment)) + geom_bar(stat="identity",fill="deepskyblue3")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="ChromHMM Enrichment of Nuclear ApaQTLs \n over 824 Random Permuations", x="Region")

Expand here to see past versions of unnamed-chunk-39-1.png:
Version Author Date
b176cda Briana Mittleman 2018-11-06

ggplot(perChrommHMM_nuc_withPerm_enrich, aes(x=name, y=chiSq)) + geom_bar(stat="identity",fill="deepskyblue3")+ theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="ChromHMM ChiSq of Nuclear ApaQTLs \n over 824 Random Permuations", x="Region") 

Expand here to see past versions of unnamed-chunk-39-2.png:
Version Author Date
b176cda Briana Mittleman 2018-11-06

To parallelize this I will run the permutations in 4 bash scripts:

nuc_random880_chromHmm_set1.sh

#!/bin/bash

#SBATCH --job-name=nuc_random880_chromHmm_set1
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=nuc_random880_chromHmm_set1.out
#SBATCH --error=nuc_random880_chromHmm_set1.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env
#make random 
for i in {1..250};
do
shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/randomRes_Nuclear_880_${i}.txt
done

nuc_random880_chromHmm_set2.sh

#!/bin/bash

#SBATCH --job-name=nuc_random880_chromHmm_set2
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=nuc_random880_chromHmm_set2.out
#SBATCH --error=nuc_random880_chromHmm_set2.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env
#make random 
for i in {251..500};
do
shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/randomRes_Nuclear_880_${i}.txt
done

nuc_random880_chromHmm_set3.sh

#!/bin/bash

#SBATCH --job-name=nuc_random880_chromHmm_set3
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=nuc_random880_chromHmm_set3.out
#SBATCH --error=nuc_random880_chromHmm_set3.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env
#make random 
for i in {501..750};
do
shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/randomRes_Nuclear_880_${i}.txt
done

nuc_random880_chromHmm_set4.sh

#!/bin/bash

#SBATCH --job-name=nuc_random880_chromHmm_set4
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=nuc_random880_chromHmm_set4.out
#SBATCH --error=nuc_random880_chromHmm_set4.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env
#make random 
for i in {751..1000};
do
shuf -n 880 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/randomRes_Nuclear_880_${i}.txt
done

Same for total:

total_random118_chromHmm_set1.sh

#!/bin/bash

#SBATCH --job-name=total_random118_chromHmm_set1
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=total_random118_chromHmm_set1.out
#SBATCH --error=total_random118_chromHmm_set1.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#test with 2 permutations then make it 1000  
#choose random res
for i in {1..250};
do
shuf -n 118 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/randomRes_Total_118_${i}.txt
done

total_random118_chromHmm_set2.sh

#!/bin/bash

#SBATCH --job-name=total_random118_chromHmm_set2
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=total_random118_chromHmm_set2.out
#SBATCH --error=total_random118_chromHmm_set2.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#test with 2 permutations then make it 1000  
#choose random res
for i in {251..500};
do
shuf -n 118 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/randomRes_Total_118_${i}.txt
done

total_random118_chromHmm_set3.sh

#!/bin/bash

#SBATCH --job-name=total_random118_chromHmm_set3
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=total_random118_chromHmm_set3.out
#SBATCH --error=total_random118_chromHmm_set3.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#test with 2 permutations then make it 1000  
#choose random res
for i in {501..750};
do
shuf -n 118 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/randomRes_Total_118_${i}.txt
done

total_random118_chromHmm_set4.sh

#!/bin/bash

#SBATCH --job-name=total_random118_chromHmm_set4
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=total_random118_chromHmm_set4.out
#SBATCH --error=total_random118_chromHmm_set4.err
#SBATCH --partition=bigmem2
#SBATCH --mem=200G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#test with 2 permutations then make it 1000  
#choose random res
for i in {751..1000};
do
shuf -n 118 /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/randomRes_Total_118_${i}.txt
done

I want to turn each of these into snp files:

randomLines2Snp.sh

#!/bin/bash

#SBATCH --job-name=randomLines2Snp
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=randomLines2Snp.out
#SBATCH --error=randomLines2Snp.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


#make random 
for i in {1..1000};
do
python randomRes2SNPbed.py Nuclear 880 ${i} 
done 

#make random 
for i in {1..1000};
do
python randomRes2SNPbed.py Total 118 ${i}
done 

Next step is the overlap. I want this to run on each seperatly.

sortRandomSnps.sh

#!/bin/bash

#SBATCH --job-name=sortRandomSnps
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=sortRandomSnps.out
#SBATCH --error=sortRandomSnps.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


for i in $(ls /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed/);
do
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed/$i > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/snp_bed_sort/$i.sort.bed
done

for i in $(ls /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed/);
do
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed/$i > /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/snp_bed_sort/$i.sort.bed
done

Rewrite overlap with ChromHMM script to do it on each file seperatly.

overlap_chromHMM_sepfiles.py

def main(inFile, outFile):
  rand=pybedtools.BedTool(inFile) 
  hmm=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/GM12878.chromHMM.sort.bed")
  #map hmm to snps
  Rand_overlapHMM=rand.map(hmm, c=4)
  #save results
  Rand_overlapHMM.saveas(outFile)


if __name__ == "__main__":
    import sys
    import pandas as pd
    import pybedtools
    fraction=sys.argv[1]
    nsamp=sys.argv[2]
    niter=sys.argv[3]
    #which itteration we are on 
    inFile ="/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/snp_bed_sort/randomRes_%s_%s_%s.bed.sort.bed"%(fraction,fraction, nsamp, iter)
    outFile= "/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/%s/chromHMM_overlap/randomres_overlapChromHMM_%s_%s_%s.txt"%(fraction,fraction,nsamp, niter)
    main(inFile,outFile)

overlap_chromHMM_sepfiles.sh

#!/bin/bash

#SBATCH --job-name=overlap_chromHMM_sepfiles
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=overlap_chromHMM_sepfiles.out
#SBATCH --error=overlap_chromHMM_sepfiles.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

for i in {1..1000};
do
python overlap_chromHMM_sepfiles.py  Nuclear 880 $i
done

for i in {1..1000};
do
python overlap_chromHMM_sepfiles.py  Total 118 $i
done

I will next make an R script that will take in each file and perform the groupby command to get the number of snps in each group.

groupRandomByChromHMM.R

#!/bin/rscripts

# usage: groupRandomByChromHMM.R -f infile -o outfile 

#this file will take any of the itterations and output a file with chrom hmm number, name, numberof snps

library(optparse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(readr)

option_list = list(
  make_option(c("-f", "--file"), action="store", default=NA, type='character',
              help="input coverage file"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file")
)

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


#interrupt execution if no file is  supplied
if (is.null(opt$file)){
  print_help(opt_parser)
  stop("Need input file", call.=FALSE)
}
if (is.null(opt$output)){
  print_help(opt_parser)
  stop("Need output file", call.=FALSE)
}

randomSNPS=read.table(opt$file, col.names=c("chrom", "start", "end", "sid", "significance", "strand", "number"),stringsAsFactors = F, na.strings = "NA")
hmm_names=read.table("/project2/gilad/briana/genome_anotation_data/chromHMM_regions.txt", col.names = c("number", "name"),stringsAsFactors=F)
randomSNPS$number=as.integer(as.factor(randomSNPS$number))
randomSNPS_names= randomSNPS  %>% left_join(hmm_names, by="number")
#split the name of the file to get the iteration number
fileSplit=strsplit(opt$file, "/")[[1]][10]
iter.txt=strsplit(fileSplit, "_")[[1]][5]
iter=substr(iter.txt, 1, nchar(iter.txt)-4) 

randomSNPS_names_grouped=randomSNPS_names %>%  group_by(number) %>% summarise(!!iter:=n()) %>%  replace_na(list(name="No_annotation")) %>%  dplyr::select(number, !!iter) 
hmm_names$number=as.character(hmm_names$number)
final=hmm_names %>% left_join(randomSNPS_names_grouped,by="number")

write.table(final,opt$output,quote=FALSE, col.names = T, row.names = F)

groupRandomChromHMM.sh

#!/bin/bash

#SBATCH --job-name=groupRandomChromHMM
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=groupRandomChromHMM.out
#SBATCH --error=groupRandomChromHMM.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

for i in {1..1000};
do
Rscript groupRandomByChromHMM.R -f /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/chromHMM_overlap/randomres_overlapChromHMM_Nuclear_880_${i}.txt -o /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/chromHMM_overlap_group/randomres_overlapChromHMM_Nuclear_880_${i}_grouped.txt
done

for i in {1..1000};
do
Rscript groupRandomByChromHMM.R -f /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/chromHMM_overlap/randomres_overlapChromHMM_Total_118_${i}.txt -o /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/chromHMM_overlap_group/randomres_overlapChromHMM_Total_118_${i}_grouped.txt
done

Once I have the results I will paste the third column of each file together

cut -d$' ' -f 1,2 randomres_overlapChromHMM_Nuclear_880_1_grouped.txt > Nuc_chromOverlap.txt

for i in {1..1000};
do
paste -d" " Nuc_chromOverlap.txt <(cut -d" " -f 3 randomres_overlapChromHMM_Nuclear_880_${i}_grouped.txt) > tmp
mv tmp Nuc_chromOverlap.txt
done


cut -d$' ' -f 1,2 randomres_overlapChromHMM_Total_118_99_grouped.txt> Tot_chromOverlap.txt

for i in {1..1000};
do
paste -d" " Tot_chromOverlap.txt <(cut -d" " -f 3 randomres_overlapChromHMM_Total_118_${i}_grouped.txt) > tmp
mv tmp Tot_chromOverlap.txt
done

There will be NAs in this file. I will turn them into 0s when I bring it in R.

Pull files onto computer:

/project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Nuclear/chromHMM_overlap_group/Nuc_chromOverlap.txt /project2/gilad/briana/threeprimeseq/data/random_QTLsnps/Total/chromHMM_overlap_group/Tot_chromOverlap.txt

regions=c('Txn_Elongation','Weak_Txn','Repressed','Heterochrom/lo','Repetitive/CNV1','Repetitive/CNV2','Active_Promoter','Weak_Promoter','Poised_Promoter','Strong_Enhancer1','Strong_Enhancer2','Weak_Enhancer1','Weak_Enhancer2','Insulator','Txn_Transition')


permutationResTotal=read.table("../data/ChromHmmOverlap/Tot_chromOverlap.txt", header=T)
permutationResTotal[is.na(permutationResTotal)] <- 0
permutationResTotal= as_data_frame(permutationResTotal)
permutationResTotal=permutationResTotal[,3:ncol(permutationResTotal)]
permutationResTotal_T=permutationResTotal %>% t()
colnames(permutationResTotal_T)=regions

permutationResNuclear=read.table("../data/ChromHmmOverlap/Nuc_chromOverlap.txt",header = T)
permutationResNuclear[is.na(permutationResNuclear)] <- 0
permutationResNuclear= as_data_frame(permutationResNuclear)
permutationResNuclear=permutationResNuclear[,3:ncol(permutationResNuclear)]
permutationResNuclear_T=permutationResNuclear %>% t()
colnames(permutationResNuclear_T)=regions
#nuclear
summary(permutationResNuclear_T)
 Txn_Elongation      Weak_Txn        Repressed      Heterochrom/lo 
 Min.   :  1.00   Min.   :  4.00   Min.   :  3.00   Min.   :  0.0  
 1st Qu.:  8.00   1st Qu.: 11.00   1st Qu.: 12.00   1st Qu.: 11.0  
 Median : 13.00   Median : 14.00   Median : 17.00   Median : 26.0  
 Mean   : 35.23   Mean   : 50.08   Mean   : 47.18   Mean   :149.6  
 3rd Qu.: 72.00   3rd Qu.:130.00   3rd Qu.: 34.00   3rd Qu.:486.2  
 Max.   :177.00   Max.   :185.00   Max.   :544.00   Max.   :552.0  
 Repetitive/CNV1 Repetitive/CNV2 Active_Promoter  Weak_Promoter  
 Min.   : 0.00   Min.   : 0.00   Min.   : 1.000   Min.   : 2.00  
 1st Qu.: 2.00   1st Qu.: 2.00   1st Qu.: 1.000   1st Qu.: 9.00  
 Median :12.00   Median :12.00   Median : 2.000   Median :11.00  
 Mean   :13.79   Mean   :13.79   Mean   : 4.963   Mean   :10.89  
 3rd Qu.:19.00   3rd Qu.:19.00   3rd Qu.: 9.000   3rd Qu.:13.00  
 Max.   :55.00   Max.   :55.00   Max.   :22.000   Max.   :23.00  
 Poised_Promoter  Strong_Enhancer1 Strong_Enhancer2 Weak_Enhancer1 
 Min.   :  1.00   Min.   : 14      Min.   : 14      Min.   : 23.0  
 1st Qu.:  4.00   1st Qu.: 28      1st Qu.: 28      1st Qu.: 46.0  
 Median : 73.00   Median :177      Median :177      Median :502.0  
 Mean   : 54.39   Mean   :134      Mean   :134      Mean   :360.2  
 3rd Qu.: 81.00   3rd Qu.:190      3rd Qu.:190      3rd Qu.:517.0  
 Max.   :100.00   Max.   :227      Max.   :227      Max.   :560.0  
 Weak_Enhancer2    Insulator      Txn_Transition 
 Min.   : 23.0   Min.   : 1.000   Min.   : 1.00  
 1st Qu.: 46.0   1st Qu.: 2.000   1st Qu.: 3.00  
 Median :502.0   Median : 6.000   Median : 9.00  
 Mean   :360.2   Mean   : 6.419   Mean   :11.57  
 3rd Qu.:517.0   3rd Qu.: 9.000   3rd Qu.:12.00  
 Max.   :560.0   Max.   :20.000   Max.   :99.00  
#total
summary(permutationResTotal_T)
 Txn_Elongation     Weak_Txn       Repressed      Heterochrom/lo  
 Min.   : 0.00   Min.   : 0.00   Min.   : 0.000   Min.   : 0.000  
 1st Qu.: 2.00   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000  
 Median : 6.00   Median : 2.00   Median : 0.000   Median : 0.000  
 Mean   :21.75   Mean   :20.57   Mean   : 9.382   Mean   : 1.524  
 3rd Qu.:26.25   3rd Qu.:60.00   3rd Qu.: 1.000   3rd Qu.: 0.000  
 Max.   :82.00   Max.   :83.00   Max.   :81.000   Max.   :79.000  
 Repetitive/CNV1 Repetitive/CNV2 Active_Promoter Weak_Promoter   
 Min.   :0.000   Min.   :0.000   Min.   :1.000   Min.   : 1.000  
 1st Qu.:0.000   1st Qu.:0.000   1st Qu.:1.000   1st Qu.: 1.000  
 Median :0.000   Median :0.000   Median :1.000   Median : 2.000  
 Mean   :0.024   Mean   :0.024   Mean   :1.802   Mean   : 2.106  
 3rd Qu.:0.000   3rd Qu.:0.000   3rd Qu.:2.000   3rd Qu.: 2.000  
 Max.   :5.000   Max.   :5.000   Max.   :6.000   Max.   :21.000  
 Poised_Promoter  Strong_Enhancer1 Strong_Enhancer2 Weak_Enhancer1 
 Min.   : 1.000   Min.   : 2.00    Min.   : 2.00    Min.   : 2.00  
 1st Qu.: 1.000   1st Qu.: 4.00    1st Qu.: 4.00    1st Qu.: 5.00  
 Median : 2.000   Median : 5.00    Median : 5.00    Median : 9.00  
 Mean   : 3.585   Mean   :10.24    Mean   :10.24    Mean   :18.13  
 3rd Qu.: 3.000   3rd Qu.: 8.00    3rd Qu.: 8.00    3rd Qu.:25.25  
 Max.   :29.000   Max.   :92.00    Max.   :92.00    Max.   :85.00  
 Weak_Enhancer2    Insulator     Txn_Transition 
 Min.   : 2.00   Min.   : 0.00   Min.   : 0.00  
 1st Qu.: 5.00   1st Qu.: 2.00   1st Qu.: 4.00  
 Median : 9.00   Median : 8.00   Median : 9.00  
 Mean   :18.13   Mean   :11.43   Mean   :17.45  
 3rd Qu.:25.25   3rd Qu.:16.00   3rd Qu.:21.00  
 Max.   :85.00   Max.   :83.00   Max.   :85.00  

Session information

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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] bindrcpp_0.2.2      cowplot_0.9.3       ggpubr_0.1.8       
 [4] magrittr_1.5        data.table_1.11.8   VennDiagram_1.6.20 
 [7] futile.logger_1.4.3 forcats_0.3.0       stringr_1.3.1      
[10] dplyr_0.7.6         purrr_0.2.5         readr_1.1.1        
[13] tidyr_0.8.1         tibble_1.4.2        ggplot2_3.0.0      
[16] tidyverse_1.2.1     reshape2_1.4.3      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] RColorBrewer_1.1-2   lambda.r_1.2.3       modelr_0.1.2        
[16] readxl_1.1.0         bindr_0.1.1          plyr_1.8.4          
[19] munsell_0.5.0        gtable_0.2.0         cellranger_1.1.0    
[22] rvest_0.3.2          R.methodsS3_1.7.1    evaluate_0.11       
[25] labeling_0.3         knitr_1.20           broom_0.5.0         
[28] Rcpp_0.12.19         formatR_1.5          backports_1.1.2     
[31] scales_1.0.0         jsonlite_1.5         hms_0.4.2           
[34] digest_0.6.17        stringi_1.2.4        rprojroot_1.3-2     
[37] cli_1.0.1            tools_3.5.1          lazyeval_0.2.1      
[40] futile.options_1.0.1 crayon_1.3.4         whisker_0.3-2       
[43] pkgconfig_2.0.2      xml2_1.2.0           lubridate_1.7.4     
[46] assertthat_0.2.0     rmarkdown_1.10       httr_1.3.1          
[49] rstudioapi_0.8       R6_2.3.0             nlme_3.1-137        
[52] git2r_0.23.0         compiler_3.5.1      



This reproducible R Markdown analysis was created with workflowr 1.1.1