Remove non-database intronic

Last updated: 2020-02-13

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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File	Version	Author	Date	Message
Rmd	cc5a842	brimittleman	2020-02-13	add non database robustness

I want to make sure my analysis are robust to misprimming if some still exists in the intronic PAS. To do this I will remove the intronic PAS that are not the PAS database.

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(tidyverse)

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

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ──────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

PAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, col.names=c("chr","start","end","name","score", "strand")) %>% separate(name, into=c("pasNum","geneloc"),sep=":") %>% separate(geneloc,into=c("gene",'loc'), sep="_")
 
dist=read.table("../data/AnnotatedPAS/DistanceMyPAS2Anno.bed", col.names = c("chr", "start","end","myPAS", "score","strand","chr2", "start2", "end2", "anno", "score2", "strand2", "distance"),stringsAsFactors = F)
PAS_withmatch=dist %>% filter(abs(distance)<=10) %>% select(myPAS,anno) %>% unique() %>% separate(myPAS, into=c("pasNum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% select(pasNum)


IntronicNoAnno= PAS %>% filter(loc=="intron") %>% anti_join(PAS_withmatch)

Joining, by = "pasNum"

PAS_filter= PAS %>% anti_join(IntronicNoAnno,by = c("chr", "start", "end", "pasNum", "gene", "loc", "score", "strand")) %>% mutate(PAS=paste("peak",pasNum, sep=""))

eQTL and intronic correlation

eQTLeffect=read.table("../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.AllNomRes.GeneName_snploc.txt", stringsAsFactors = F, col.names = c("gene","snp","dist", "pval", "eQTL_es")) %>% select(gene, snp, eQTL_es)

nomnames=c("peakID", 'snp','dist', 'pval', 'slope')
nuclearunex_all=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% filter(PASnum %in% PAS_filter$PAS)

nuclearex_all=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>%  separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_")%>% filter(PASnum %in% PAS_filter$PAS)

alleQTLS_nuc=bind_rows(nuclearex_all, nuclearunex_all) %>% filter(loc=="intron") %>% inner_join(eQTLeffect, by=c("gene","snp"))

cor.test(alleQTLS_nuc$slope ,alleQTLS_nuc$eQTL_es, alternative="less")


    Pearson's product-moment correlation

data:  alleQTLS_nuc$slope and alleQTLS_nuc$eQTL_es
t = -6.3702, df = 347, p-value = 3e-10
alternative hypothesis: true correlation is less than 0
95 percent confidence interval:
 -1.0000000 -0.2422876
sample estimates:
       cor 
-0.3235712

summary(lm(alleQTLS_nuc$slope ~alleQTLS_nuc$eQTL_es))


Call:
lm(formula = alleQTLS_nuc$slope ~ alleQTLS_nuc$eQTL_es)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.14821 -0.19797 -0.00462  0.16101  1.53138 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          -0.002081   0.017678  -0.118    0.906    
alleQTLS_nuc$eQTL_es -0.179811   0.028227  -6.370    6e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3297 on 347 degrees of freedom
Multiple R-squared:  0.1047,    Adjusted R-squared:  0.1021 
F-statistic: 40.58 on 1 and 347 DF,  p-value: 6e-10

ggplot(alleQTLS_nuc,aes(x=eQTL_es, y=slope)) + geom_point() + geom_smooth(method = "lm")+ geom_text(y=-1, x=-1.5, label="Correlation= -0.32, P= 3x10^-10") + labs(title="Nuclear apa effect sizes vs eQTL eqtl effect sizes removed non annotated", y="Nuclear apaQTL effect size",x="eQTL effect size")

Proportion explained:

I need the total apa results as well.

totalapaUnexplained=read.table("../data/overlapeQTL_try2/apaTotal_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames)%>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>% mutate(nPeaks=n(), adjPval=pval* nPeaks)%>%  dplyr::slice(which.min(adjPval)) %>% filter(PASnum %in% PAS_filter$PAS)


totalapaexplained=read.table("../data/overlapeQTL_try2/apaTotal_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval)) %>% filter(PASnum %in% PAS_filter$PAS)

nuclearapaUnexplained=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval))%>% filter(PASnum %in% PAS_filter$PAS)

nuclearapaexplained=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))%>% filter(PASnum %in% PAS_filter$PAS)

prop_overlap=function(status, fraction, cutoff){
  if (fraction=="Total"){
    if (status=="Explained"){
      file=totalapaexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=round(nrow(sig)/nrow(file),digits=2)
    }else {
      file=totalapaUnexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=round(nrow(sig)/nrow(file),digits=2)
    }
  } else{
    if (status=="Explained"){
      file=nuclearapaexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=round(nrow(sig)/nrow(file),digits=2)
     }else {
      file=nuclearapaUnexplained
      sig=file %>% filter(adjPval<=cutoff)
      proportion=round(nrow(sig)/nrow(file),digits=2)
     }
  }
  return(proportion)
}

cutoffs=c(0.001,0.01,0.02,0.03,0.04,0.05,0.1,0.2,0.3,0.4,0.5)

TotalExplained_Proportions=c()
for(i in cutoffs){
  TotalExplained_Proportions=c( TotalExplained_Proportions, prop_overlap("Explained", "Total", i))
}
TotalExplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=TotalExplained_Proportions, Status=rep("Explained", 11), Fraction=rep("Total", 11)))

TotalUnexplained_Proportions=c()
for(i in cutoffs){
  TotalUnexplained_Proportions=c(TotalUnexplained_Proportions, prop_overlap("Unexplained", "Total", i))
}
TotalUnexplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=TotalUnexplained_Proportions, Status=rep("Unexplained", 11), Fraction=rep("Total", 11)))

NuclearExplained_Proportions=c()
for(i in cutoffs){
  NuclearExplained_Proportions=c( NuclearExplained_Proportions, prop_overlap("Explained", "Nuclear", i))
}
NuclearExplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=NuclearExplained_Proportions, Status=rep("Explained", 11), Fraction=rep("Nuclear", 11)))


NuclearUnexplained_Proportions=c()
for(i in cutoffs){
  NuclearUnexplained_Proportions=c( NuclearUnexplained_Proportions, prop_overlap("Unexplained", "Nuclear", i))
}
NuclearUnexplained_ProportionsDF=as.data.frame(cbind(cutoffs,Prop=NuclearUnexplained_Proportions, Status=rep("Unexplained", 11), Fraction=rep("Nuclear", 11)))



AllPropDF=bind_rows(TotalExplained_ProportionsDF,TotalUnexplained_ProportionsDF,NuclearExplained_ProportionsDF,NuclearUnexplained_ProportionsDF)

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

AllPropDF$Prop=as.numeric(AllPropDF$Prop)

ggplot(AllPropDF, aes(x=cutoffs, y=Prop, fill=Status)) + geom_bar(position = "dodge", stat="identity") + facet_grid(~Fraction) + labs(title="Proportion of eQTLs explained by apaQTLs remove Intronic PAS not in database", y="Proportion", "P-Value cut off") + scale_fill_manual(values=c("orange", "blue"))

Prot independent

I will test if any of the expression independent are in these PAS

Expind=read.table("../data/ExpressionIndependentapaQTLs.txt",header= T) 

Expind %>% select(SNP) %>% unique() %>% nrow()

[1] 25

Expind_filt=Expind %>% filter(PAS_ID %in% PAS_filter$PAS)


Expind_filt %>% select(SNP) %>% unique() %>% nrow()

[1] 20

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_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 reshape2_1.4.3   haven_1.1.2      lattice_0.20-38 
 [5] colorspace_1.3-2 generics_0.0.2   htmltools_0.3.6  yaml_2.2.0      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      magrittr_1.5     lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1

Remove non-database intronic

Briana Mittleman

2/13/2020

eQTL and intronic correlation

Proportion explained:

Prot independent