Last updated: 2019-09-09

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/compareAnnotatedpas.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/signalsiteanalysis.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Deleted:    docs/figure/HighCrediblePAS.Rmd/figure1bsubset-1.pdf
    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
html 385031b brimittleman 2019-08-08 Build site.
Rmd b71d1be brimittleman 2019-08-08 add prop test
html 86dbd94 brimittleman 2019-07-31 Build site.
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)
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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")

Version Author Date
385031b brimittleman 2019-08-08

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
385031b brimittleman 2019-08-08

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
385031b brimittleman 2019-08-08
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
385031b brimittleman 2019-08-08
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
385031b brimittleman 2019-08-08

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