Premature Termination QTL

Last updated: 2019-07-02

Checks: 7 0

Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/signalsiteanalysis.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	fe7b5dc	brimittleman	2019-07-02	add eQTL overlap
html	dad7bd8	brimittleman	2019-07-02	Build site.
Rmd	fe41a93	brimittleman	2019-07-02	add prop of tested genes
html	2a63cde	brimittleman	2019-07-01	Build site.
Rmd	8d36f9b	brimittleman	2019-07-01	add res
html	5ba28ec	brimittleman	2019-07-01	Build site.
Rmd	6db6003	brimittleman	2019-07-01	add qtl code
html	a4a34bf	brimittleman	2019-07-01	Build site.
Rmd	75b84f4	brimittleman	2019-07-01	add code premature term

library(reshape2)
library(workflowr)

This is workflowr version 1.4.0
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──

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── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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Many papers have started to talk about premature termination. Premature terminated isoforms may be truncated protein or may be degraded. I am going to create a measure for this and test for genetic variation associated with it in my data. The measure will be sum of the reads in intronic PAS and the sum of the UTR reads. I will use leafcutter to put the ratios onto a normal distribution. I will then test for QTLs these ratios.

mkdir ../data/PreTerm_pheno

Prepare phenotype

Total

gene start and end

genes=read.table("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/FullTranscriptByName.bed", col.names = c("chr", "Gene_start", "Gene_end", "gene", "score", "strand"),stringsAsFactors = F) %>% select(chr,Gene_start, Gene_end, gene)

totalPAS=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz",stringsAsFactors = F,header = T) 


totalPASPheno=totalPAS %>% melt(id.vars="chrom", variable.name="Ind", value.name = "ratio") %>% separate(ratio, into=c("count", "geneCount"), sep="/") %>% separate(chrom, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3" | loc=="intron") %>% inner_join(genes,by=c("chr", "gene"))%>% mutate(gene=paste(chr,Gene_start, Gene_end, gene,sep=":")) %>% group_by(Ind,gene,loc) %>% summarise(SumCount=sum(as.integer(count))) %>% ungroup() %>% group_by(Ind,gene) %>% mutate(nType=n()) %>% filter(nType==2) %>% spread(loc, SumCount) %>% mutate(total=intron+utr3,PreTermInt=paste(intron,total, sep="/"),PreTermUTR=paste(utr3,total, sep="/")) %>% select(-nType, -intron,-utr3,-total)


totalPASPheno_melt= totalPASPheno %>% melt(id.vars=c("Ind", "gene"), variable.name="Type", value.name = "Value") %>% mutate(chrom=paste(gene, Type, sep="_")) %>% spread(Ind, Value) %>% select(-gene, -Type)


#write.table(totalPASPheno_melt,"../data/PreTerm_pheno/Total_preterminationPheno.txt",quote=F, row.names=F,col.names=T, sep=" ")

#python2
gzip ../data/PreTerm_pheno/Total_preterminationPheno.txt
python prepare_phenotype_table.py ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz

#activate env  

sh ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz_prepare.sh

#top 2 pcs
head -n 3  ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz.PCs > ../data/PreTerm_pheno/Total_preterminationPheno.txt.gz.2PCs

Nuclear

nuclearPAS=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz",stringsAsFactors = F,header = T) 


nuclearPASPheno=nuclearPAS %>% melt(id.vars="chrom", variable.name="Ind", value.name = "ratio") %>% separate(ratio, into=c("count", "geneCount"), sep="/") %>% separate(chrom, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc","strand", "PAS"), sep="_") %>% filter(loc=="utr3" | loc=="intron") %>% inner_join(genes,by=c("chr", "gene"))%>% mutate(gene=paste(chr,Gene_start, Gene_end, gene,sep=":")) %>% group_by(Ind,gene,loc) %>% summarise(SumCount=sum(as.integer(count))) %>% ungroup() %>% group_by(Ind,gene) %>% mutate(nType=n()) %>% filter(nType==2) %>% spread(loc, SumCount) %>% mutate(total=intron+utr3,PreTermInt=paste(intron,total, sep="/"),PreTermUTR=paste(utr3,total, sep="/")) %>% select(-nType, -intron,-utr3,-total)


nuclearPASPheno_melt= nuclearPASPheno %>% melt(id.vars=c("Ind", "gene"), variable.name="Type", value.name = "Value") %>% mutate(chrom=paste(gene, Type, sep="_")) %>% spread(Ind, Value) %>% select(-gene, -Type)


#write.table(nuclearPASPheno_melt,"../data/PreTerm_pheno/Nuclear_preterminationPheno.txt",quote=F, row.names=F,col.names=T, sep=" ")

#python2
gzip ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt
python prepare_phenotype_table.py ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz
#env
sh ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz_prepare.sh

#top 2 pcs
head -n 3  ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz.PCs > ../data/PreTerm_pheno/Nuclear_preterminationPheno.txt.gz.2PCs

Call QTLs

Sample list from previous work

mkdir ../data/PrematureQTLNominal
mkdir ../data/PrematureQTLPermuted

sbatch PrematureQTLNominal.sh
sbatch PrematureQTLPermuted.sh

May want to only test one number per gene but do this for now because I want to take advantage of the leafcutter normalization software.

cat ../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_chr* > ../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChr.txt

cat ../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_chr* > ../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChr.txt

Tot

totRes=read.table("../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChr.txt", stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

totRes$bh=p.adjust(totRes$bpval, method="fdr")

totRes_sig=totRes %>% filter(-log10(bh)>1) 


totRes_sig_genes=totRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()

write.table(totRes, file = "../data/PrematureQTLPermuted/Total_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", col.names = T, row.names = F, quote = F)

nrow(totRes_sig_genes)

[1] 40

Proportion of genes tested:

tottested_genes=totRes %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
nrow(totRes_sig_genes)/nrow(tottested_genes)

[1] 0.01162453

qqplot:

qqplot(-log10(runif(nrow(totRes))), -log10(totRes$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total premature termination")
abline(0,1)

Version	Author	Date
dad7bd8	brimittleman	2019-07-02

Nuclear:

nucRes=read.table("../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChr.txt", stringsAsFactors = F,col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

nucRes$bh=p.adjust(nucRes$bpval, method="fdr")

nucRes_sig=nucRes %>% filter(-log10(bh)>1)


nucRes_sig_genes=nucRes_sig %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()


write.table(nucRes, file = "../data/PrematureQTLPermuted/Nuclear_preterminationPheno.txt.gz.qqnorm_AllChrBH.txt", col.names = T, row.names = F, quote = F)

nrow(nucRes_sig_genes)

[1] 106

Proportion of genes tested:

nuctested_genes=nucRes %>% separate(pid, into=c("chr","start","end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "Frac"),sep="_") %>% select(gene) %>% unique()
nrow(nucRes_sig_genes)/nrow(nuctested_genes)

[1] 0.02061856

qqplot:

qqplot(-log10(runif(nrow(nucRes))), -log10(nucRes$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear premature termination")
abline(0,1)

Version	Author	Date
dad7bd8	brimittleman	2019-07-02

More likely in nuclear:

prop.test(x=c(nrow(nucRes_sig_genes),nrow(totRes_sig_genes)), n=c(nrow(nuctested_genes),nrow(tottested_genes)),alternative = "greater")


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nucRes_sig_genes), nrow(totRes_sig_genes)) out of c(nrow(nuctested_genes), nrow(tottested_genes))
X-squared = 9.4405, df = 1, p-value = 0.001061
alternative hypothesis: greater
95 percent confidence interval:
 0.004317405 1.000000000
sample estimates:
    prop 1     prop 2 
0.02061856 0.01162453

overlap with eGenes

I next want to look at the proportion of eGenes.

explainedEgenes=read.table("../data/Li_eQTLs/explainedEgenes.txt", col.names = c("gene"), stringsAsFactors = F)
unexplainedEgenes=read.table("../data/Li_eQTLs/UnexplainedEgenes.txt", col.names = c("gene"), stringsAsFactors = F)


allEgenes=bind_rows(explainedEgenes, unexplainedEgenes)

I want to test the proportion of overlap.

TotPre_uneGene=totRes_sig_genes %>% inner_join(unexplainedEgenes,by="gene")
NucPre_uneGene=nucRes_sig_genes %>% inner_join(unexplainedEgenes,by="gene")

TotPre_exeGene=totRes_sig_genes %>% inner_join(explainedEgenes,by="gene")
NucPre_exeGene=nucRes_sig_genes %>% inner_join(explainedEgenes,by="gene")

TotPre_alleGene=totRes_sig_genes %>% inner_join(allEgenes,by="gene")
NucPre_alleGene=nucRes_sig_genes %>% inner_join(allEgenes,by="gene")

Proportion of eGenes explaiend by this:

#total

nrow(TotPre_uneGene)/nrow(unexplainedEgenes)

[1] 0.007894737

nrow(TotPre_exeGene)/nrow(explainedEgenes)

[1] 0.006578947

nrow(TotPre_alleGene)/nrow(allEgenes)

[1] 0.007127193

#nuclear

nrow(NucPre_uneGene)/nrow(unexplainedEgenes)

[1] 0.01447368

nrow(NucPre_exeGene)/nrow(explainedEgenes)

[1] 0.01221805

nrow(NucPre_alleGene)/nrow(allEgenes)

[1] 0.01315789

prop.test(x=c(nrow(NucPre_uneGene),nrow(TotPre_uneGene)), n=c(nrow(unexplainedEgenes),nrow(unexplainedEgenes)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(NucPre_uneGene), nrow(TotPre_uneGene)) out of c(nrow(unexplainedEgenes), nrow(unexplainedEgenes))
X-squared = 0.95182, df = 1, p-value = 0.3293
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.005305117  0.018463011
sample estimates:
     prop 1      prop 2 
0.014473684 0.007894737

prop.test(x=c(nrow(NucPre_exeGene),nrow(TotPre_exeGene)), n=c(nrow(explainedEgenes),nrow(explainedEgenes)))


    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(NucPre_exeGene), nrow(TotPre_exeGene)) out of c(nrow(explainedEgenes), nrow(explainedEgenes))
X-squared = 1.2619, df = 1, p-value = 0.2613
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.003496441  0.014774636
sample estimates:
     prop 1      prop 2 
0.012218045 0.006578947

Conclusion:

Total- 13 overlaps with all eGenes, 7 ex, 6 unexplained Nuclear- 24 overlaps with all eGenes, 13 ex, 11 unexpained

All eGenes=1824 Unexplained=760 Explained=1064

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.4.0 reshape2_1.4.3 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 compiler_3.5.1   pillar_1.3.1    
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4