Peak coverage along Gene examples

Last updated: 2018-11-13

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/39indQC.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

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Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	f8d8c94	Briana Mittleman	2018-11-13	add plots for peak coverage
html	24821f2	Briana Mittleman	2018-11-12	Build site.
Rmd	7d1bd9a	Briana Mittleman	2018-11-12	add code for looking at sig gene peaks

The quantified peak files are:

/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc
/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc

I want to grep specific genes and look at the read distribution for peaks along a gene. In these files the peakIDs stil have the peak locations. Before I ran the QTL analysis I changed the final coverage (ex /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz) to have the TSS as the ID.

Librarys

library(workflowr)

This is workflowr version 1.1.1
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library(reshape2)
library(tidyverse)

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✔ readr   1.1.1     ✔ forcats 0.3.0

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library(VennDiagram)

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library(data.table)


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library(ggpubr)

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library(cowplot)


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nuc_names=c('Geneid',   'Chr',  'Start',    'End',  'Strand',   'Length',   '18486_N'   ,'18497_N', '18500_N'   ,'18505_N', '18508_N'   ,'18511_N', '18519_N',  '18520_N',  '18853_N'   ,'18858_N', '18861_N',  '18870_N'   ,'18909_N'  ,'18912_N'  ,'18916_N', '19092_N'   ,'19093_N', '19119_N',  '19128_N'   ,'19130_N', '19131_N'   ,'19137_N', '19140_N',  '19141_N'   ,'19144_N', '19152_N'   ,'19153_N', '19160_N'   ,'19171_N', '19193_N'   ,'19200_N', '19207_N',  '19209_N',  '19210_N',  '19223_N'   ,'19225_N', '19238_N'   ,'19239_N', '19257_N')


tot_names=c('Geneid',   'Chr'   ,'Start',   'End',  'Strand',   'Length',   '18486_T',  '18497_T'   ,'18500_T','18505_T',   '18508_T'   ,'18511_T', '18519_T',  '18520_T',  '18853_T',  '18858_T',  '18861_T',  '18870_T',  '18909_T'   ,'18912_T', '18916_T',  '19092_T'   ,'19093_T', '19119_T',  '19128_T',  '19130_T',  '19131_T'   ,'19137_T', '19140_T'   ,'19141_T', '19144_T',  '19152_T'   ,'19153_T', '19160_T'   ,'19171_T', '19193_T',  '19200_T',  '19207_T'   ,'19209_T'  ,'19210_T', '19223_T',  '19225_T',  '19238_T',  '19239_T',  '19257_T')

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")) %>% filter(sig==1)
totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_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")) %>% filter(sig==1)

examples to look at Nuclear: IRF5, HSF1, NOL9,DCAF16,

Total: NBEAL2, SACM1L, COX7A2L


#nuclear
grep IRF5 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt

grep HSF1 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt


grep NOL9 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt

grep DCAF16 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt

grep PPP4C /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt



#total
grep NBEAL2 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/NBEAL2_TotalCov_peaks.txt

grep SACM1L /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt



grep TESK1 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/TESK1_TotalCov_peaks.txt  


grep DGCR14 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt

Copy these to my computer so I can work with them here. I am going to want to make a function that makes the histogram reproducibly for anyfile. I will need to know how many bins to include in the histogram. First I will make the graph for one example then I will make it more general.

Files are in /Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/example_gene_peakQuant

Start wit a small file.

pos=c(3,4,7:39)
PPP4c=read.table("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt", stringsAsFactors = F, col.names = nuc_names) %>% select(pos)

PPP4c$peaks=seq(0, (nrow(PPP4c)-1))

PPP4c_melt=melt(PPP4c, id.vars=c('peaks','Start','End'))

Plot:

ggplot(PPP4c_melt, aes(x=peaks, y=value, by=variable, fill=variable)) + geom_histogram(stat="identity")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Try with actual location as the center of the peak.

pos=c(3,4,7:39)
PPP4c_2=read.table("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt", stringsAsFactors = F, col.names = tot_names) %>% select(pos)
PPP4c_2$peaks=seq(0, (nrow(PPP4c_2)-1))
PPP4c_2= PPP4c_2 %>% mutate(PeakCenter=(Start+ (End-Start)/2))
PPP4c2_melt=melt(PPP4c_2, id.vars=c('peaks','PeakCenter', "Start", "End"))
colnames(PPP4c2_melt)= c('peaks','PeakCenter', "Start", "End", "Individual", "ReadCount")

Plot:

ggplot(PPP4c2_melt, aes(x=PeakCenter, y=ReadCount, by=Individual, fill=Individual)) + geom_histogram(stat="identity") + labs(title="Peak Coverage and Location PP4c")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Generalize this for more genes:

makePeakLocplot=function(file, geneName,fraction){
  pos=c(3,4,7:39)
  if (fraction=="Total"){
  gene=read.table(file, stringsAsFactors = F, col.names = tot_names) %>% select(pos)
  }
  else{
    gene=read.table(file, stringsAsFactors = F, col.names = nuc_names) %>% select(pos)
  }
  
  gene$peaks=seq(0, (nrow(gene)-1))
  gene= gene %>% mutate(PeakCenter=(Start+ (End-Start)/2))
  gene_melt=melt(gene, id.vars=c('peaks','PeakCenter', "Start", "End"))
  colnames(gene_melt)= c('peaks','PeakCenter', "Start", "End", "Individual", "ReadCount")
  finalplot=ggplot(gene_melt, aes(x=PeakCenter, y=ReadCount, by=Individual, fill=Individual)) + geom_histogram(stat="identity", show.legend = FALSE) + labs(title=paste("Peak Coverage and Location", geneName, sep = " ")) 
  return(finalplot)
}

Try for another gene:

makePeakLocplot("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt",'PPP4c',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

makePeakLocplot("../data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt",'DCAF16',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

makePeakLocplot("../data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt",'DGCR14',"Total")

Warning: Ignoring unknown parameters: binwidth, bins, pad

makePeakLocplot("../data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt",'IRF5',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

makePeakLocplot("../data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt",'HSF1',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

makePeakLocplot("../data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt",'NOL9',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

makePeakLocplot("../data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt",'SACM1L',"Total")

Warning: Ignoring unknown parameters: binwidth, bins, pad

Make a function to do this by peak number (ignoring direction)

makePeakNumplot=function(file, geneName,fraction){
  pos=c(7:39)
  if (fraction=="Total"){
  gene=read.table(file, stringsAsFactors = F, col.names = tot_names) %>% select(pos)
  }
  else{
    gene=read.table(file, stringsAsFactors = F, col.names = nuc_names) %>% select(pos)
  }
  
  gene$peaks=seq(0, (nrow(gene)-1))
  gene_melt=melt(gene, id.vars=c('peaks'))
  colnames(gene_melt)= c('peaks',"Individual", "ReadCount")
  finalplot=ggplot(gene_melt, aes(x=peaks, y=ReadCount, by=Individual, fill=Individual)) + geom_histogram(stat="identity", show.legend = FALSE) + labs(title=paste("Peak Coverage", geneName, sep = " ")) 
  return(finalplot)
}

I can plot them next to eachother using cowplot

ppp4c_loc=makePeakLocplot("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt",'PPP4c',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

ppp4c_num=makePeakNumplot("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt",'PPP4c',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

plot_grid(ppp4c_loc,ppp4c_num)

dcaf16_loc=makePeakLocplot("../data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt",'DCAF16',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

dcaf16_num=makePeakNumplot("../data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt",'DCAF16',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

plot_grid(dcaf16_loc,dcaf16_num)

dgcr14_loc=makePeakLocplot("../data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt",'DGCR14',"Total")

Warning: Ignoring unknown parameters: binwidth, bins, pad

dgcr14_num=makePeakNumplot("../data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt",'DGCR14',"Total")

Warning: Ignoring unknown parameters: binwidth, bins, pad

plot_grid(dgcr14_loc,dgcr14_num)

irf5_loc=makePeakLocplot("../data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt",'IRF5',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

irf5_num=makePeakNumplot("../data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt",'IRF5',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

plot_grid(irf5_loc,irf5_num)

HSF1_loc=makePeakLocplot("../data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt",'HSF1',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

HSF1_num=makePeakNumplot("../data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt",'HSF1',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

plot_grid(HSF1_loc,HSF1_num)

NOL9_loc=makePeakLocplot("../data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt",'NOL9',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

NOL9_num=makePeakNumplot("../data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt",'NOL9',"Nuclear")

Warning: Ignoring unknown parameters: binwidth, bins, pad

plot_grid(NOL9_loc,NOL9_num)

SACM1L_loc=makePeakLocplot("../data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt",'SACM1L',"Total")

Warning: Ignoring unknown parameters: binwidth, bins, pad

SACM1L_num=makePeakNumplot("../data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt",'SACM1L',"Total")

Warning: Ignoring unknown parameters: binwidth, bins, pad

plot_grid(SACM1L_loc,SACM1L_num)

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] lambda.r_1.2.3       modelr_0.1.2         readxl_1.1.0        
[16] bindr_0.1.1          plyr_1.8.4           munsell_0.5.0       
[19] gtable_0.2.0         cellranger_1.1.0     rvest_0.3.2         
[22] R.methodsS3_1.7.1    evaluate_0.11        labeling_0.3        
[25] knitr_1.20           broom_0.5.0          Rcpp_0.12.19        
[28] formatR_1.5          backports_1.1.2      scales_1.0.0        
[31] jsonlite_1.5         hms_0.4.2            digest_0.6.17       
[34] stringi_1.2.4        rprojroot_1.3-2      cli_1.0.1           
[37] tools_3.5.1          lazyeval_0.2.1       futile.options_1.0.1
[40] crayon_1.3.4         whisker_0.3-2        pkgconfig_2.0.2     
[43] xml2_1.2.0           lubridate_1.7.4      assertthat_0.2.0    
[46] rmarkdown_1.10       httr_1.3.1           rstudioapi_0.8      
[49] R6_2.3.0             nlme_3.1-137         git2r_0.23.0        
[52] compiler_3.5.1

This reproducible R Markdown analysis was created with workflowr 1.1.1

Peak coverage along Gene examples

Briana Mittleman

11/12/2018

Session information