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

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	78d7349	brimittleman	2020-02-13	add signal site distribution
html	77d21e0	brimittleman	2020-02-13	Build site.
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()

library(RColorBrewer)

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) %>% mutate(PAS=paste("peak",pasNum, sep=""))

Joining, by = "pasNum"

UTRnoAnno=PAS %>% filter(loc=="utr3") %>% anti_join(PAS_withmatch) %>% mutate(PAS=paste("peak",pasNum, sep=""))

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")

Version	Author	Date
77d21e0	brimittleman	2020-02-13

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"))

Version	Author	Date
77d21e0	brimittleman	2020-02-13

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

Motif enrichement

I will look at the signal site enrichment for the Intronic not in the database sites.

Loc_AATAAA= read.table("../data/SignalSiteFiles/Loc_AATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAAA")
Loc_AAAAAG= read.table("../data/SignalSiteFiles/Loc_AAAAAG_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAG")
Loc_AATACA= read.table("../data/SignalSiteFiles/Loc_AATACA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATACA")
Loc_AATAGA= read.table("../data/SignalSiteFiles/Loc_AATAGA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATAGA")
Loc_AATATA= read.table("../data/SignalSiteFiles/Loc_AATATA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AATATA")
Loc_ACTAAA= read.table("../data/SignalSiteFiles/Loc_ACTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ACTAAA")
Loc_AGTAAA= read.table("../data/SignalSiteFiles/Loc_AGTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AGTAAA")
Loc_ATTAAA= read.table("../data/SignalSiteFiles/Loc_ATTAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="ATTAAA")
Loc_CATAAA= read.table("../data/SignalSiteFiles/Loc_CATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="CATAAA")
Loc_GATAAA= read.table("../data/SignalSiteFiles/Loc_GATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="GATAAA")
Loc_TATAAA= read.table("../data/SignalSiteFiles/Loc_TATAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="TATAAA")
Loc_AAAAAA= read.table("../data/SignalSiteFiles/Loc_AAAAAA_Distance2end.txt", header=F, col.names =c( "PAS", "Distance2PAS")) %>% mutate(Site="AAAAAA")

AllsiteDF=as.data.frame(rbind(Loc_AATAAA,Loc_AAAAAG,Loc_AATACA,Loc_AATAGA,Loc_AATATA,Loc_ACTAAA,Loc_AGTAAA,Loc_ATTAAA, Loc_GATAAA,Loc_TATAAA,Loc_CATAAA, Loc_AAAAAA))

colourCount = length(unique(AllsiteDF$Site))

getPalette = colorRampPalette(brewer.pal(8, "Set1"))


AllsiteDF_1per=read.table(file="../data/SignalSiteFiles/AllSignalSite_1perPAS.txt", col.names = colnames(AllsiteDF)) %>% mutate(NegCount=-1*as.integer(as.character(Distance2PAS))) %>% separate(PAS, into=c("peak", "geneloc"), sep=":") %>% mutate(PAS=paste("peak",peak ,sep="")) %>% semi_join(IntronicNoAnno, by="PAS")

nrow(IntronicNoAnno)

[1] 9605

Of the 9605, 2364 have a signal site. (2362/9605) 25%

This is similar to the percentage overall in the data.

ggplot(AllsiteDF_1per, aes(group=Site, x=NegCount, fill=Site)) + geom_histogram(position="stack",bins=50 ) + labs(x="Distance from PAS", y="N annotated Sites", title="Location of intronic unannotated signal sites")  +  scale_fill_manual(values = getPalette(colourCount))

There are 3000 unannotated 3’ UTR sites.

AllsiteDF_1perUTR=read.table(file="../data/SignalSiteFiles/AllSignalSite_1perPAS.txt", col.names = colnames(AllsiteDF)) %>% mutate(NegCount=-1*as.integer(as.character(Distance2PAS))) %>% separate(PAS, into=c("peak", "geneloc"), sep=":") %>% mutate(PAS=paste("peak",peak ,sep="")) %>% semi_join(UTRnoAnno, by="PAS")


ggplot(AllsiteDF_1perUTR, aes(group=Site, x=NegCount, fill=Site)) + geom_histogram(position="stack",bins=50 ) + labs(x="Distance from PAS", y="N annotated Sites", title="Location of 3' UTR unannotated signal sites")  +  scale_fill_manual(values = getPalette(colourCount))

1792 have signal sites. (1792/3044) 60%

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] RColorBrewer_1.1-2 forcats_0.3.0      stringr_1.3.1     
 [4] dplyr_0.8.0.1      purrr_0.3.2        readr_1.3.1       
 [7] tidyr_0.8.3        tibble_2.1.1       ggplot2_3.1.1     
[10] 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

Motif enrichement