Most credible set PAS

Last updated: 2019-06-20

Checks: 6 0

Knit directory: apaQTL/analysis/

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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	b2def08	brimittleman	2019-06-20	first intron in sites with ss
html	8bed611	brimittleman	2019-06-19	Build site.
Rmd	090a2c2	brimittleman	2019-06-19	compare with ss to those without
html	529a38a	brimittleman	2019-06-18	Build site.
Rmd	63e21ea	brimittleman	2019-06-18	add credible set analysis

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
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library(workflowr)

This is workflowr version 1.3.0
Run ?workflowr for help getting started

library(cowplot)


Attaching package: 'cowplot'

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

    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.

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)

ggplot(allPAS_loc, aes(x=loc, y=prop, fill=SS)) + geom_bar(stat="identity") + labs(title="Proportion of PAS with signal site", x="Location", y="Propotion")

Version	Author	Date
8bed611	brimittleman	2019-06-19

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")

Version	Author	Date
8bed611	brimittleman	2019-06-19

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()

Version	Author	Date
8bed611	brimittleman	2019-06-19

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.

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
8bed611	brimittleman	2019-06-19

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).

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_1.3.0 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       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [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   reshape2_1.4.3   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4

Most credible set PAS

Briana Mittleman

6/18/2019

Full credible

Total:

Nuclear

Intronic credible set