Last updated: 2019-01-24

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    File Version Author Date Message
    Rmd 9df8d7e Briana Mittleman 2019-01-24 add linear model code with res for stephens lab

There are a few things about the data I need to understand before I can run ash. First I need to find the genes that overlap with protein and RNA. Then I need to pick those with 1 dominant peak.

set.seed(1)
library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
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(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

Upload data:

I want the filtered peak counts. I need to filter the counts file for the total fraction based on the filtered peaks.

Total counts: /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.fc

okPeaks: /project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt

filterTotalCounts_noMP_5percCov.py

totalokPeaks5perc_file="/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt"
countFile=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.fc","r")

outFile=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_5percCov_fixed.fc", "w")

allPeakOk={}
for ln in open(totalokPeaks5perc_file,"r"):
    peakname=ln.split()[5]
    peaknum=peakname[4:]
    if peaknum not in allPeakOk.keys():
        allPeakOk[peaknum]=""
    
for i,ln in enumerate(countFile):
    if i==1:
      outFile.write(ln)
    if i>1:
      ID=ln.split()[0]
      peak=ID.split(":")[0]
      peaknum=peak[4:]
      if peaknum in allPeakOk.keys():
          outFile.write(ln)
outFile.close()
total_Cov=read.table("../data/PeakCounts_noMP_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_5percCov_fixed.fc", stringsAsFactors = F,header = T) %>%  separate(Geneid, into=c("peak", "chr", "start", "end", "strand", "Name"), sep=":")
total_genes=total_Cov %>% select(Name) %>% arrange(Name) %>% unique()

Gene names:

geneNames=read.table("../data/ensemble_to_genename.txt",sep="\t", header=T, stringsAsFactors = F, col.names=c("ID", "Name", "Source"))
prot=read.table("../data/mol_pheno/fastqtl_qqnorm_prot.fixed.noChr.txt",header=T,stringsAsFactors = F) %>% inner_join(geneNames, by="ID")

Keep the protein genes in APA:

prot_inAPA=prot %>% semi_join(total_genes, by="Name")

This shows we have 4209 genes with data for both. Now I can back filter the total peaks for the genes in prot_inAPA

total_Cov_wProt= total_Cov %>% semi_join(prot_inAPA,by="Name")

Need to give Stephens lab: unadjusted R-squared, and n and p for each protein, where p is the number of apa s that you are using in the regression and n is the number of samples?

To do this I need to get the overlapping individuals:

protInd=colnames(prot)[5:(dim(prot)[2]-2)]

ApaInd=c()
for (i in colnames(total_Cov)[12:ncol(total_Cov)]){
  num=substr(i,2,6)
  name=paste("NA", num, sep="")
  ApaInd=c(ApaInd, name)
}


IndBoth=intersect(protInd,ApaInd)
length(IndBoth)
[1] 29

I have 29 individuals in common for these.

I need to make a matrix for each gene. It will have a row for each commmon individual. A column for the protein, and a column for each assocaited peaks. After I have this I will be able to get the R2 value.

First create a function.

get_R2=function(gene, Cov=total_Cov, prot=prot_inAPA, apaName=ApaInd){
  gene_un= enexpr(gene)
  #deal with APA
  genePeaks=total_Cov %>% filter(Name==!!gene_un)
  n=nrow(genePeaks)
  drop_col=c('chr','Chr', 'start','end','strand','Name', 'Start','End','Strand','Length')
  genePeaks_sm= genePeaks %>% select(-one_of(drop_col))
  colnames(genePeaks_sm)=c("peak", ApaInd) 
  genePeakM=genePeaks_sm %>% column_to_rownames(var="peak") %>% t()
  genePeakDF=as.data.frame(genePeakM) %>% rownames_to_column(var="Ind")
  #deal with prot
  drop_col_prot= c("Chr", "start", "end", "ID", "Name", "Source")
  geneProt=prot %>% filter(Name==!!gene_un) %>% select(-one_of(drop_col_prot)) %>% t()
  colnames(geneProt)="prot"
  #print(dim(geneProt))
  geneProt_df=as.data.frame(geneProt) %>% rownames_to_column(var="Ind") %>% drop_na(prot)
  #print(geneProt_df)
  both=geneProt_df %>% inner_join(genePeakDF,by="Ind")
  num=seq(1,n)
  base="summary(lm(both$prot~"
  for (i in 3:dim(both)[2]){
    base=paste(base, "+both[,",i,"]",sep="")
  }
  code=paste(base, "))$r.squared", sep="")
  r2=eval(parse(text=code))
  final=c(gene, r2,nrow(both),n)
  return(final)
}

Run this on all genes:

final_matrix=matrix(c("gene","r2","n","p"),1,4)
for (i in prot_inAPA$Name){
 final_matrix= rbind(final_matrix,get_R2(i))
}

Make this a dataframe:

final_df=as.data.frame(final_matrix)

colnames(final_df)=as.character(unlist(final_df[1,]))

final_df <- final_df[-1 ,]

save(final_df,file="../data/protAndAPAlmRes.Rda")

Session information

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  cowplot_0.9.3   reshape2_1.4.3  forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1
[13] 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] modelr_0.1.2      readxl_1.1.0      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] cellranger_1.1.0  rvest_0.3.2       R.methodsS3_1.7.1
[22] evaluate_0.11     knitr_1.20        broom_0.5.0      
[25] Rcpp_0.12.19      scales_1.0.0      backports_1.1.2  
[28] jsonlite_1.5      hms_0.4.2         digest_0.6.17    
[31] stringi_1.2.4     grid_3.5.1        rprojroot_1.3-2  
[34] cli_1.0.1         tools_3.5.1       magrittr_1.5     
[37] lazyeval_0.2.1    crayon_1.3.4      whisker_0.3-2    
[40] pkgconfig_2.0.2   xml2_1.2.0        lubridate_1.7.4  
[43] assertthat_0.2.0  rmarkdown_1.10    httr_1.3.1       
[46] rstudioapi_0.8    R6_2.3.0          nlme_3.1-137     
[49] git2r_0.23.0      compiler_3.5.1

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