Last updated: 2019-08-08

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Knit directory: apaQTL/analysis/

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Rmd b71d1be brimittleman 2019-08-08 add prop test
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Rmd 63e21ea brimittleman 2019-06-18 add credible set analysis 
library(tidyverse)
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library(workflowr)
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library(cowplot)

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    ggsave

Full credible

I want to get the most credible PAS set I can. To do this I can use analysis I have previously done. These are those that have a signal site.

  1. Signal site analyis

First I can look at the differences between the sites with a signal site and those without.

signalPAS=read.table("../data/PAS/PASwSignalSite.txt", header  =T, stringsAsFactors = F) 
allPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", header = F, stringsAsFactors = F,col.names = c("chr","start", "end", "peakID", "score", "strand")) %>% separate(peakID, into=c("peak", "loc"), sep="_") %>% separate(peak, into=c("peaknum", "gene"), sep=":") %>% mutate(PAS=paste("peak", peaknum, sep=""))

Peaks with signal site in a vector:

credsites=as.vector(signalPAS$PAS)

allPAS= allPAS %>% mutate(SS=ifelse(PAS %in% credsites, "Yes", "No"))
allPAS$SS= as.factor(allPAS$SS)

Plot these by location:

ggplot(allPAS, aes(x=loc, by=SS, fill=SS)) + geom_bar(stat="count")

Version Author Date
8bed611 brimittleman 2019-06-19

Proportion of each:

allPAS_loc= allPAS %>% group_by(loc,SS) %>% summarise(nSS=n()) %>% ungroup() %>% group_by(loc) %>% mutate(nLoc=sum(nSS)) %>% ungroup() %>% mutate(prop=nSS/nLoc)

withSSPlot=ggplot(allPAS_loc, aes(x=loc, y=prop, fill=SS)) + geom_bar(stat="identity") +scale_fill_brewer(palette="Dark2") +labs(title="Proportion of PAS with signal site", x="Location", y="Propotion")  + labs(fill = "Presence of Signal Site",x="") +scale_x_discrete(labels = c('Coding','5KB downstream','Intronic',"3' UTR", "5' UTR")) +theme(axis.text.x = element_text(angle = 45, hjust = 1,size=14),axis.text.y = element_text(size=14),legend.position = "top" )  
withSSPlot

Version Author Date
96d85de brimittleman 2019-07-07
6b0fbce brimittleman 2019-06-21
8bed611 brimittleman 2019-06-19 
prop.test(x=c(15098, 3207), n=c(20318,14433))

    2-sample test for equality of proportions with continuity
    correction

data:  c(15098, 3207) out of c(20318, 14433)
X-squared = 9182.5, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.5117660 0.5300056
sample estimates:
   prop 1    prop 2 
0.7430849 0.2221991

Look at usage of those with a signal site to those without. In each fraction

Total:

TotalPASUsage=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Total.5perc.fc",stringsAsFactors = F,col.names = c("chr","start","end", "gene", "loc", "Strand", "PAS", "TotalUsage")) %>% select(PAS, TotalUsage)

allPAS_totUsage=allPAS %>% inner_join(TotalPASUsage, by="PAS")
ggplot(allPAS_totUsage, aes(x=loc, y=TotalUsage, fill=SS)) + geom_boxplot() + labs(title="Mean Usage in total fraction\n by presence of signal site")

Nuclear

NuclearPASUsage=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.fc",stringsAsFactors = F,col.names = c("chr","start","end", "gene", "loc", "Strand", "PAS", "NuclearUsage")) %>% select(PAS, NuclearUsage)

allPAS_nucUsage=allPAS %>% inner_join(NuclearPASUsage, by="PAS")
ggplot(allPAS_nucUsage, aes(x=loc, y=NuclearUsage, fill=SS)) + geom_boxplot()

Intronic credible set

For these I will add the criteria that there are more RNA seq reads upstream. For these I am looking at those in the total fraction.

  1. Signal site analyis

  2. RNA evidence upstream

signalPASIntronic=read.table("../data/PAS/PASwSignalSite.txt", header  =T, stringsAsFactors = F) %>% filter(loc=="intron")
RNAupstream=read.table(file="../data/intronRNAratio/TotalPAS_MoreUpstreamRNAreads.txt", header = T, stringsAsFactors = F) 
allPAS_intron=allPAS %>% filter(loc=="intron")

Make vectors to add the information

RNAupstreamvec=as.vector(RNAupstream$PAS)
signalPASIntronicVec=as.vector(signalPASIntronic$PAS)

PAS_signalandRNA=allPAS_intron %>% mutate(SS=ifelse(PAS %in% signalPASIntronicVec, "Yes", "No"), RNA= ifelse(PAS %in% RNAupstreamvec, "Yes" , "No"), BothEv=ifelse(SS=="Yes"& RNA=="Yes", "Yes", "No"))

Where are these with respect to the gene body:

length=read.table("../../genome_anotation_data/refseq.ProteinCoding.bed",col.names = c("chrom", "start", "end", "gene", "score", "strand") ,stringsAsFactors = F) %>% mutate(length=abs(end-start)) %>%  mutate(TSS= ifelse(strand=="+", start, end)) %>% select(gene, length,TSS, strand) %>% select(-strand)


#filter those outside genes (problem do to multiple transcripts)
PAS_signalandRNA_Len=PAS_signalandRNA %>% inner_join(length, by="gene") %>% mutate(distance=ifelse(strand=="+", end- TSS, TSS-end), perlength=distance/length) %>% filter(perlength<1, perlength>0)

Plot these:

ssintronlength=ggplot(PAS_signalandRNA_Len, aes(fill=SS, x=perlength)) + geom_density(alpha=.5) + labs(title="Distribution of intronic PAS along genes\n by presence of signal site", x="Percent gene length")

rnaintronlength=ggplot(PAS_signalandRNA_Len, aes(fill=RNA, x=perlength)) + geom_density(alpha=.5)+labs(title="Distribution of intronic PAS along genes\n by presence of more RNA upstream", x="Percent gene length")

bothintronlength=ggplot(PAS_signalandRNA_Len, aes(fill=BothEv, x=perlength)) + geom_density(alpha=.5)+ labs(title="Distribution of intronic PAS along genes\n by Both lines of evidence", x="Percent gene length")
plot_grid(ssintronlength,rnaintronlength,bothintronlength)

Version Author Date
e22e31c brimittleman 2019-06-20

Which intron are these in.

nucIntronIDPAS=read.table("../data/intron_analysis/NuclearIntronPASwithWhichintron.txt",header = T, stringsAsFactors = F) %>% separate(PeakID, into=c("PAS", "gene2","loc"), sep=":") %>% mutate(SS=ifelse(PAS %in% signalPASIntronicVec, "yes","no")) %>% mutate(IntronCat=ifelse(nintron<=6, "first (<6)", ifelse(nintron>6 &nintron<=11, "second (6-11)", ifelse(nintron>11 &nintron<=18, "third (11-18)", "fourth (>18)"))))

ggplot(nucIntronIDPAS, aes(x=Intronid, fill=SS)) + geom_bar(alpha=.5,position = "dodge") + xlim(0,10) + facet_grid(~IntronCat)
Warning: Removed 2108 rows containing non-finite values (stat_count).
Warning: Removed 3 rows containing missing values (geom_bar).

Version Author Date
e22e31c brimittleman 2019-06-20 
totIntronIDPAS=read.table("../data/intron_analysis/TotalIntronPASwithWhichintron.txt",header = T, stringsAsFactors = F) %>% separate(PeakID, into=c("PAS", "gene2","loc"), sep=":") %>% mutate(SS=ifelse(PAS %in% signalPASIntronicVec, "yes","no")) %>% mutate(IntronCat=ifelse(nintron<=6, "first (<6)", ifelse(nintron>6 &nintron<=11, "second (6-11)", ifelse(nintron>11 &nintron<=18, "third (11-18)", "fourth (>18)"))))

ggplot(totIntronIDPAS, aes(x=Intronid, fill=SS)) + geom_bar(alpha=.5,position = "dodge") + xlim(0,10) + facet_grid(~IntronCat)
Warning: Removed 1327 rows containing non-finite values (stat_count).
Warning: Removed 3 rows containing missing values (geom_bar).

No signal ###QTL

Are any of these total QTLs?

totQTL=read.table("../data/apaQTLs/Total_apaQTLs_5fdr.txt", header = T, stringsAsFactors = F) %>% dplyr::rename("PAS"=Peak)

Filter join the PAS set with the QTLs

highcredwQTL=PAS_signalandRNA %>%  semi_join(totQTL, by= "PAS")

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] cowplot_0.9.4   workflowr_1.4.0 forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] plyr_1.8.4         compiler_3.5.1     pillar_1.3.1      
 [7] git2r_0.25.2       highr_0.7          tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] reshape2_1.4.3     modelr_0.1.2       magrittr_1.5      
[40] whisker_0.3-2      backports_1.1.2    scales_1.0.0      
[43] htmltools_0.3.6    rvest_0.3.2        assertthat_0.2.0  
[46] colorspace_1.3-2   labeling_0.3       stringi_1.2.4     
[49] lazyeval_0.2.1     munsell_0.5.0      broom_0.5.1       
[52] crayon_1.3.4