Most credible set PAS
Briana Mittleman
6/18/2019
Last updated: 2019-07-31
Checks: 6 1
Knit directory: apaQTL/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 9c38a22 | brimittleman | 2019-07-31 | Build site. |
| Rmd | 589abea | brimittleman | 2019-07-31 | paper figs |
| html | 96d85de | brimittleman | 2019-07-07 | Build site. |
| html | 6b0fbce | brimittleman | 2019-06-21 | Build site. |
| Rmd | d5ec3ba | brimittleman | 2019-06-21 | change colors |
| html | e22e31c | brimittleman | 2019-06-20 | Build site. |
| 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
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Run ?workflowr for help getting startedlibrary(cowplot)
Attaching package: 'cowplot'The following object is masked from 'package:ggplot2':
ggsaveFull 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.
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
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)
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).
| Version | Author | Date |
|---|---|---|
| e22e31c | brimittleman | 2019-06-20 |
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