Last updated: 2019-06-07

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/DiffIsoAnalysis.Rmd
    Modified:   analysis/PASusageQC.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/choosePCs.Rmd
    Modified:   analysis/corrbetweenind.Rmd
    Modified:   analysis/exvunexpeQTL.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucintronicanalysis.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rerunQTL_changePC.Rmd
    Modified:   analysis/rna_netseq_h3k12ac.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/config.yaml
    Deleted:    code/test.txt

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Rmd 3e5cc8e brimittleman 2019-06-07 code up to FC

I am interested in finding examples of intronic PAS that show RNAseq signatures upstream of the PAS but not downstream. To do this I will create a ratio of reads upstream/reads downstream standardized by the length of the region (up/downstream).

To do this I can use the work I did previously. Here I assigned each intronic PAS to an intron. I will do this analysis with the total fraction because I will be looking at steady state RNA seq.

library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
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
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
mkdir ../data/intronRNAratio
totIntronicPeaks=read.table("../data/peaks_5perc/APApeak_Peaks_GeneLocAnno.Total.5perc.fc", stringsAsFactors = F, header = F,col.names = c("chr", "start", "end", "gene", "loc", "strand", "peak", "avgUsage")) %>% filter(loc=="intron") 

pas2intronTot=read.table("../data/intron_analysis/TotalIntronPeaksontoIntrons.bed",col.names = c("intronCHR", "intronStart", "intronEnd", "gene", "score", "strand", "peakCHR", "peakStart", "peakEnd", "PeakID", "meanUsage", "peakStrand"))  %>% mutate(PASloc=ifelse(strand=="+", peakEnd, peakStart)) %>% dplyr::select(intronCHR,intronStart, intronEnd, gene, strand, PeakID, PASloc ,meanUsage)

write.table(pas2intronTot, "../data/intronRNAratio/TotalIntronicPAS2Intron.txt", quote = F, row.names = F, col.names = F, sep="\t")

Make upstream and downstream PAS saf files using python.

 python getIntronUpstreamPAS.py
 python getIntronDownstreamPAS.py

These make Bed and SAF files. I will use the SAF files for feature counts with all of the RNA seq. These files are in /project2/yangili1/LCL/RNAseqGeuvadisBams/*.final.bam

sbatch FC_intornUpandDownsteamPAS.sh

Downstream Results:

downstream=read.table("../data/intronRNAratio/DownstreamIntron.fc", header = T,stringsAsFactors = F)
downstreamMean=rowSums(downstream[,7:ncol(downstream)])

downstreanMeanDF=as.data.frame(cbind(downstream[,1:6], downstreamMean)) %>% mutate(DownstreamMean_st=downstreamMean/Length) %>% select(Geneid,DownstreamMean_st )

Upstream Results:

upstream=read.table("../data/intronRNAratio/UpstreamIntron.fc", header = T,stringsAsFactors = F)
upstreamMean=rowSums(upstream[,7:ncol(upstream)])
upstreamMeanDF=as.data.frame(cbind(upstream[,1:6], upstreamMean)) %>% mutate(UpstreamMean_st=upstreamMean/Length) %>% select(Geneid,UpstreamMean_st )

Join Results:

I will use upstream - downstream instead of a ratio to deal with 0s

UpandDown=upstreamMeanDF %>% inner_join(downstreanMeanDF, by="Geneid") %>% mutate(ratio=UpstreamMean_st-DownstreamMean_st) %>% arrange(desc(ratio)) %>% separate(Geneid, sep=":", into=c("PAS", "gene", "loc", "PASloc", "Usage"))

summary(UpandDown$ratio)
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max. 
-26617.062     -0.103      0.000     -2.511      0.236   5380.027

I want to know how many are positive:

MoreUp=UpandDown %>% filter(ratio>0) 
summary(MoreUp$ratio)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
   0.000    0.064    0.346    7.631    2.044 5380.027 
nrow(MoreUp)
[1] 13903

13903 examples where there are more reads upstream in the intron than downstream.

To look for examples:

head(MoreUp)
         PAS   gene    loc    PASloc             Usage UpstreamMean_st
1 peak113671  RPS18 intron  33240934 0.110925925925926        5404.273
2  peak56811  RPL27 intron  41152263 0.796296296296296        3739.972
3  peak78512 RPL37A intron 217365085 0.407407407407407        3284.307
4  peak70756 RPS27A intron  55460229 0.537037037037037        3233.041
5 peak113669  RPS18 intron  33240357 0.537222222222222        2942.865
6  peak20394  RPLP2 intron    810791 0.648148148148148        1683.759
  DownstreamMean_st    ratio
1         24.246212 5380.027
2          7.555006 3732.417
3         15.007150 3269.300
4         15.651852 3217.389
5        369.229167 2573.636
6         25.031017 1658.728

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

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   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4