Last updated: 2019-04-23

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   README.md
    Modified:   analysis/._fastq2bam.Rmd
    Modified:   analysis/PASusageQC.Rmd
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    Modified:   analysis/_site.yml
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    Modified:   code/UsageDifferenceHeatmap.R
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
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    Modified:   code/bed2saf.py
    Modified:   code/callPeaksYL.py
    Modified:   code/chooseAnno2SAF.py
    Modified:   code/cluster.json
    Modified:   code/clusterPAS.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/convertNumeric.py
    Modified:   code/filter5perc.R
    Modified:   code/filter5percPheno.py
    Modified:   code/filterpeaks.py
    Modified:   code/fixFChead.py
    Modified:   code/make5percPeakbed.py
    Modified:   code/makeFileID.py
    Modified:   code/makePheno.py
    Modified:   code/makeSampleList.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/namePeaks.py
    Modified:   code/peak2PAS.py
    Modified:   code/peakFC.sh
    Modified:   code/pheno2countonly.R
    Modified:   code/quantassign2parsedpeak.py
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
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    Modified:   code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Modified:   output/README.md

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.


There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.


In this analysis I will create discriptive plots for the PAS identified in the 54 LCLs.

library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ purrr   0.3.1  
✔ tibble  2.0.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.4.0  
✔ readr   1.3.1       ✔ forcats 0.4.0  
Warning: package 'tibble' was built under R version 3.5.2
Warning: package 'tidyr' was built under R version 3.5.2
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── Conflicts ─────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(reshape2)

Attaching package: 'reshape2'
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library(cowplot)
Warning: package 'cowplot' was built under R version 3.5.2

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

    ggsave

Peaks per gene:

I want to plot how many genes have 0, 1, 2 and more than 2 PAS in the set. I need to join my PAS with the annotation to find out how many genes have 0 PAS.

pas=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("peaknum", "geneAnno"), sep=":") %>% separate(geneAnno, into=c("Gene", "Loc"),sep="_")

pasbygene= pas %>% group_by(Gene) %>% summarise(PAS=n())

annotation=read.table("../../genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed", col.names = c("chr", "start", "end", "anno", "score", "strand")) %>% separate(anno, into=c("Loc", "Gene"),sep=":") %>% group_by(Gene) %>% summarise(annos=n()) %>% select(Gene)

PASallgene=annotation %>% full_join(pasbygene, by="Gene") %>% replace_na(list(PAS=0)) 

#group with 0,1,2,more than 2
PASallgene_grouped=PASallgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))

Plot this:

Genes=c(sum(PASallgene_grouped$Zero),sum(PASallgene_grouped$One),sum(PASallgene_grouped$Multiple))
PAS=c("Zero", "One", "Multiple")
AllPAS=c(sum(PASallgene_grouped$Zero),sum(PASallgene_grouped$One),sum(PASallgene_grouped$Multiple))
GenebyPAS=as.data.frame(cbind(PAS,AllPAS))
GenebyPAS$AllPAS=as.numeric(as.character(GenebyPAS$AllPAS))

ggplot(GenebyPAS, aes(x="",y=AllPAS, fill=PAS)) + geom_bar(stat="identity")

Subset and get stats for UTR

pasUTR=pas %>% filter(Loc=="utr3") %>% group_by(Gene) %>% summarise(PAS=n())

pasUTR_allgene=annotation %>% full_join(pasUTR, by="Gene") %>% replace_na(list(PAS=0)) 

PASUTRallgene_grouped=pasUTR_allgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))


GenesUTR=c(sum(PASUTRallgene_grouped$Zero),sum(PASUTRallgene_grouped$One),sum(PASUTRallgene_grouped$Multiple))
UTR=c(sum(PASUTRallgene_grouped$Zero),sum(PASUTRallgene_grouped$One),sum(PASUTRallgene_grouped$Multiple))
GenebyPASUTR=as.data.frame(cbind(PAS,UTR))
GenebyPASUTR$UTR=as.numeric(as.character(GenebyPASUTR$UTR))

ggplot(GenebyPASUTR, aes(x="",y=UTR, fill=PAS)) + geom_bar(stat="identity")

Subset and get stats for Intron

pasIntron=pas %>% filter(Loc=="intron" | Loc=='utr3') %>% group_by(Gene) %>% summarise(PAS=n())

pasIntron_allgene=annotation %>% full_join(pasIntron, by="Gene") %>% replace_na(list(PAS=0)) 

pasIntronallgene_grouped=pasIntron_allgene %>% mutate(Zero=ifelse(PAS==0,1, 0), One=ifelse(PAS==1,1,0), Multiple=ifelse(PAS>1,1,0))


UTRandIntron=c(sum(pasIntronallgene_grouped$Zero),sum(pasIntronallgene_grouped$One),sum(pasIntronallgene_grouped$Multiple))
GenebyPASIntron=as.data.frame(cbind(PAS,UTRandIntron))
GenebyPASIntron$UTRandIntron=as.numeric(as.character(GenebyPASIntron$UTRandIntron))

ggplot(GenebyPASIntron, aes(x="",y=UTRandIntron, fill=PAS)) + geom_bar(stat="identity")

Make these side by side:

GenebyPASUTR_melt=melt(GenebyPASUTR, id.vars = "PAS", value.name = "Genes", variable.name = "Set")

GenebyPAS_melt=melt(GenebyPAS, id.vars = "PAS", value.name = "Genes", variable.name = "Set")

GenebyPASIntron_melt=melt(GenebyPASIntron, id.vars = "PAS", value.name = "Genes", variable.name = "Set")

GenebyPAStoplot=rbind(GenebyPAS_melt,GenebyPASUTR_melt,GenebyPASIntron_melt)

geneswithAPA=ggplot(GenebyPAStoplot, aes(x=Set,y=Genes, fill=PAS, by=Set)) + geom_bar(stat="identity")+ scale_fill_brewer(palette="YlGnBu") + labs(title="Genes with APA poential")

geneswithAPA

ggsave(geneswithAPA, file="../output/GeneswithAPApotential.png")
Saving 7 x 5 in image
GenebyPAStoplot
       PAS          Set Genes
1     Zero       AllPAS 11659
2      One       AllPAS  4111
3 Multiple       AllPAS 11345
4     Zero          UTR 14503
5      One          UTR  6996
6 Multiple          UTR  5616
7     Zero UTRandIntron 13025
8      One UTRandIntron  4328
9 Multiple UTRandIntron  9762

Location of PAS

PAS_loc=pas %>% group_by(Loc) %>% summarise(nPAS=n())

PASLocPlot=ggplot(PAS_loc, aes(x=Loc, y=nPAS, fill=Loc)) + geom_bar(stat="identity")+ scale_fill_brewer(palette = "YlGnBu") + labs(x="Gene location", y="Number of identified PAS", title="Location distribution for identified PAS") + theme(legend.position = "none")


ggsave(PASLocPlot, file="../output/PASlocation.png")
Saving 7 x 5 in image


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] cowplot_0.9.4   reshape2_1.4.3  forcats_0.4.0   stringr_1.4.0  
 [5] dplyr_0.8.0.1   purrr_0.3.1     readr_1.3.1     tidyr_0.8.3    
 [9] tibble_2.0.1    ggplot2_3.1.0   tidyverse_1.2.1 workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5   xfun_0.5           haven_2.1.0       
 [4] lattice_0.20-38    colorspace_1.4-0   generics_0.0.2    
 [7] htmltools_0.3.6    yaml_2.2.0         rlang_0.3.1       
[10] pillar_1.3.1       glue_1.3.0         withr_2.1.2       
[13] RColorBrewer_1.1-2 modelr_0.1.4       readxl_1.3.0      
[16] plyr_1.8.4         munsell_0.5.0      gtable_0.2.0      
[19] cellranger_1.1.0   rvest_0.3.2        evaluate_0.13     
[22] labeling_0.3       knitr_1.21         broom_0.5.1       
[25] Rcpp_1.0.0         scales_1.0.0       backports_1.1.3   
[28] jsonlite_1.6       fs_1.2.6           hms_0.4.2         
[31] digest_0.6.18      stringi_1.3.1      grid_3.5.1        
[34] rprojroot_1.3-2    cli_1.0.1          tools_3.5.1       
[37] magrittr_1.5       lazyeval_0.2.1     crayon_1.3.4      
[40] pkgconfig_2.0.2    xml2_1.2.0         lubridate_1.7.4   
[43] assertthat_0.2.0   rmarkdown_1.11     httr_1.4.0        
[46] rstudioapi_0.9.0   R6_2.4.0           nlme_3.1-137      
[49] git2r_0.24.0       compiler_3.5.1