Last updated: 2019-09-04

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rerunQTL_changePC.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
    Modified:   docs/figure/signalsiteanalysis.Rmd/figure1bMain-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
Rmd 41d1961 brimittleman 2019-09-04 wflow_publish(c(“analysis/signalsiteanalysis.Rmd”, “analysis/corrbetweenind.Rmd”,
html 16e4212 brimittleman 2019-07-17 Build site.
Rmd 64bcc48 brimittleman 2019-07-17 fix plots meeting 7.15
html d48ec93 brimittleman 2019-07-16 Build site.
html fb1fde6 brimittleman 2019-07-16 Build site.
Rmd 2572d13 brimittleman 2019-07-16 add compare annotated and additional coverage 
library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
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✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths

I will se the annotated PAS from the Tian lab database (http://exon.umdnj.edu/polya_db/v3/misc/download.php)

mkdir ../data/AnnotatedPAS/
  
#file =human.PAS.txt

I want to make this into a file I can overlap with my PAS. In order to know what resolution I should use for calling a PAS the same, I will look for the closest annotated PAS to each of my sites. To do this I will need to create a bed file with these.

python annotatedPAS2bed.py
sort -k1,1 -k2,2n ../data/AnnotatedPAS/human.PAS.bed > ../data/AnnotatedPAS/human.PAS.sort.bed
sort -k1,1 -k2,2n ../data/PAS/APAPAS_GeneLocAnno.5perc.bed > ../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed
sbatch closestannotated.sh
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)

Plot the distance.

ggplot(dist,aes(x=distance))+ geom_histogram(bins=300) + xlim(-25, 25) + labs(y="Number of PAS", x="Distance between PAS and closest annotated")
Warning: Removed 17921 rows containing non-finite values (stat_bin).

Version Author Date
fb1fde6 brimittleman 2019-07-16

Looks like about 10 basepairs is ok resolution. I need to make sure these map 1 to 1 when you filter these.

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="_")

ggplot(PAS_withmatch,aes(x=loc)) + geom_histogram(stat="count")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
fb1fde6 brimittleman 2019-07-16

I want to look at those I find that they do not.

allMyPAS=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.sort.bed",stringsAsFactors = F, col.names = c("chr","start","end", "PASID", "score","strand"))  %>% separate(PASID, into=c("pasNum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"), sep="_") %>% mutate(withAnno=ifelse(pasNum %in% PAS_withmatch$pasNum, "Yes","No"))

PASnoMatch=allMyPAS %>% anti_join(PAS_withmatch,by="pasNum")
ggplot(allMyPAS,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "PAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
fb1fde6 brimittleman 2019-07-16

Look at total and nuclear seperatly.

python NuclearPAS_5per.bed.py


python TotalPAS_5perc.bed.py
sort -k1,1 -k2,2n ../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.bed > ../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed

sort -k1,1 -k2,2n ../data/PAS/APApeak_Peaks_GeneLocAnno.Total.5perc.bed > ../data/PAS/APApeak_Peaks_GeneLocAnno.Total.5perc.sort.bed

Run the distance script with these.

sbatch closestannotated_byfrac.sh

Total

Totaldist=read.table("../data/AnnotatedPAS/Total_DistanceMyPAS2Anno.bed", col.names = c("chr", "start","end","myPAS", "score","strand","chr2", "start2", "end2", "anno", "score2", "strand2", "distance"),stringsAsFactors = F)

ggplot(Totaldist,aes(x=distance))+ geom_histogram(bins=300) + xlim(-25, 25)
Warning: Removed 11926 rows containing non-finite values (stat_bin).

Version Author Date
d48ec93 brimittleman 2019-07-16 
Totaldist_withAnno=Totaldist %>% filter(abs(distance)<=10) %>% select(myPAS,anno) %>% unique() %>% separate(myPAS, into=c("pasNum", "geneID", "loc"), sep=":")

allTotalPAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Total.5perc.sort.bed",stringsAsFactors = F, col.names = c("chr","start","end", "PASID", "score","strand"))  %>% separate(PASID, into=c("pasNum", "geneID", "loc"), sep=":") %>% mutate(withAnno=ifelse(pasNum %in% Totaldist_withAnno$pasNum, "Yes","No"))
ggplot(allTotalPAS,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "TotalPAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
d48ec93 brimittleman 2019-07-16

Nuclear

Nucleardist=read.table("../data/AnnotatedPAS/Nuclear_DistanceMyPAS2Anno.bed", col.names = c("chr", "start","end","myPAS", "score","strand","chr2", "start2", "end2", "anno", "score2", "strand2", "distance"),stringsAsFactors = F)

ggplot(Nucleardist,aes(x=distance))+ geom_histogram(bins=300) + xlim(-25, 25)
Warning: Removed 17008 rows containing non-finite values (stat_bin).

Version Author Date
d48ec93 brimittleman 2019-07-16 
Nucleardist_withAnno=Nucleardist %>% filter(abs(distance)<=10) %>% select(myPAS,anno) %>% unique() %>% separate(myPAS, into=c("pasNum", "geneID", "loc"), sep=":")

allNuclearPAS=read.table("../data/PAS/APApeak_Peaks_GeneLocAnno.Nuclear.5perc.sort.bed",stringsAsFactors = F, col.names = c("chr","start","end", "PASID", "score","strand"))  %>% separate(PASID, into=c("pasNum", "geneID", "loc"), sep=":") %>% mutate(withAnno=ifelse(pasNum %in% Nucleardist_withAnno$pasNum, "Yes","No"))
ggplot(allNuclearPAS,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear PAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
d48ec93 brimittleman 2019-07-16

Nuclear specific:

NuclearSpec=allNuclearPAS %>% anti_join(allTotalPAS,by = "pasNum")

ggplot(NuclearSpec,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear  Specific PAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
d48ec93 brimittleman 2019-07-16

Total Specific:

TotalSpec=allTotalPAS %>% anti_join(allNuclearPAS,by = "pasNum")

ggplot(TotalSpec,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Total  Specific PAS by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
d48ec93 brimittleman 2019-07-16

By usage

NuclearMeanUsage=read.table("../data/PAS/NuclearPASMeanUsage.txt",header = T, stringsAsFactors = F) %>% separate(ID, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PAS"),sep="_")
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [12630,
12631, 12632].
TotalMeanUsage=read.table("../data/PAS/TotalPASMeanUsage.txt",header = T, stringsAsFactors = F) %>% separate(ID, into=c("chr", "start", "end", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PAS"),sep="_")
Warning: Expected 4 pieces. Additional pieces discarded in 3 rows [12630,
12631, 12632].
NuclearMeanUsage_25= NuclearMeanUsage %>% filter(meanUsage >.25)


allNuclearPAS_25= allNuclearPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(NuclearMeanUsage_25, by="PAS")

ggplot(allNuclearPAS_25,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear PAS 25% by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2")
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
16e4212 brimittleman 2019-07-17 
NuclearMeanUsage_50= NuclearMeanUsage %>% filter(meanUsage >.5)


allNuclearPAS_50= allNuclearPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(NuclearMeanUsage_50, by="PAS")

ggplot(allNuclearPAS_50,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear PAS 50% by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2") 
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
16e4212 brimittleman 2019-07-17 
NuclearMeanUsage_75= NuclearMeanUsage %>% filter(meanUsage >.75)


allNuclearPAS_75= allNuclearPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(NuclearMeanUsage_75, by="PAS")

ggplot(allNuclearPAS_75,aes(x=loc,fill=withAnno)) + geom_histogram(stat="count") + labs(title = "Nuclear PAS 75% by annotated PAS within 10bp") +scale_fill_brewer(palette = "Dark2") 
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
16e4212 brimittleman 2019-07-17

Proportion of previosly identified:

allNuclearPAS %>% group_by(withAnno) %>% summarise(All=n()) %>% ungroup() %>% mutate(Prop=All/sum(All))
# A tibble: 2 x 3
  withAnno   All  Prop
  <chr>    <int> <dbl>
1 No       17013 0.434
2 Yes      22151 0.566
allNuclearPAS_25 %>% group_by(withAnno) %>% summarise(TwentyFive=n()) %>% ungroup() %>% mutate(Prop=TwentyFive/sum(TwentyFive))
# A tibble: 2 x 3
  withAnno TwentyFive  Prop
  <chr>         <int> <dbl>
1 No            12950 0.580
2 Yes            9377 0.420
allNuclearPAS_50 %>% group_by(withAnno) %>% summarise(Fifty=n()) %>% ungroup() %>% mutate(Prop=Fifty/sum(Fifty))
# A tibble: 2 x 3
  withAnno Fifty  Prop
  <chr>    <int> <dbl>
1 No       15024 0.522
2 Yes      13785 0.478
allNuclearPAS_75 %>% group_by(withAnno) %>% summarise(Seventyfive=n()) %>% ungroup() %>% mutate(Prop=Seventyfive/sum(Seventyfive))
# A tibble: 2 x 3
  withAnno Seventyfive  Prop
  <chr>          <int> <dbl>
1 No             16100 0.484
2 Yes            17182 0.516

Do this for usage 1-100%

withAnno_nuc=function(fraction){
  NuclearMeanUsage_prop= NuclearMeanUsage %>% filter(meanUsage >= fraction)
  allNuclearPAS_prop= allNuclearPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(NuclearMeanUsage_prop, by="PAS") %>% group_by(withAnno) %>% summarise(All=n()) %>% ungroup() %>% mutate(Prop=All/sum(All)) %>% filter(withAnno=="Yes")
  #print(paste(fraction,allNuclearPAS_prop$Prop))

return(allNuclearPAS_prop$Prop)
}

propYes=c()

cutoffs=seq(from=.1, to=1, by=.05)
for (val in cutoffs){
  newVal=withAnno_nuc(val)
  propYes=c(propYes,newVal )
}
nucCuttoff=cbind(cutoff=cutoffs,Nuclear=propYes)


withAnno_tot=function(fraction){
  TotalMeanUsage_prop=TotalMeanUsage %>% filter(meanUsage >= fraction)
  allTotalPAS_prop= allTotalPAS %>% mutate(PAS=paste("peak", pasNum,sep="")) %>% anti_join(TotalMeanUsage_prop, by="PAS") %>% group_by(withAnno) %>% summarise(All=n()) %>% ungroup() %>% mutate(Prop=All/sum(All)) %>% filter(withAnno=="Yes")
  #print(paste(fraction,allNuclearPAS_prop$Prop))
return(allTotalPAS_prop$Prop)
}

propYesTot=c()

cutoffs=seq(from=.1, to=1, by=.05)
for (val in cutoffs){
  newVal=withAnno_tot(val)
  propYesTot=c(propYesTot,newVal )
}
AllCuttoff=as.data.frame(cbind(cutoff=cutoffs,Nuclear=propYes, Total=propYesTot))
AllCuttoff_melt=melt(AllCuttoff,id.vars="cutoff", variable.name = "Fraction", value.name = "PropwithAnno")


ggplot(AllCuttoff_melt, aes(x=cutoff, col=Fraction, y= PropwithAnno)) + geom_line(size=2) + scale_color_brewer(palette = "Dark2") + labs(title="Cumulative proportion of PAS in annoation \n by usage filter", y="Proportion in Annotation", x="Usage Filter")

Version Author Date
16e4212 brimittleman 2019-07-17 

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] reshape2_1.4.3  forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1 workflowr_1.4.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   haven_1.1.2        lattice_0.20-38   
 [4] colorspace_1.3-2   generics_0.0.2     htmltools_0.3.6   
 [7] yaml_2.2.0         utf8_1.1.4         rlang_0.4.0       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.2       readxl_1.1.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.12     
[22] labeling_0.3       knitr_1.20         fansi_0.4.0       
[25] highr_0.7          broom_0.5.1        Rcpp_1.0.0        
[28] scales_1.0.0       backports_1.1.2    jsonlite_1.6      
[31] fs_1.3.1           hms_0.4.2          digest_0.6.18     
[34] stringi_1.2.4      grid_3.5.1         rprojroot_1.3-2   
[37] cli_1.1.0          tools_3.5.1        magrittr_1.5      
[40] lazyeval_0.2.1     crayon_1.3.4       whisker_0.3-2     
[43] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[46] assertthat_0.2.0   rmarkdown_1.10     httr_1.3.1        
[49] rstudioapi_0.10    R6_2.3.0           nlme_3.1-137      
[52] git2r_0.25.2       compiler_3.5.1