Last updated: 2018-08-28
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Modified: analysis/explore.filters.Rmd
Modified: analysis/peak.cov.pipeline.Rmd
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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. File | Version | Author | Date | Message |
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Rmd | fa818a1 | brimittleman | 2018-08-28 | first processing figure |
I will use this analysis to work on vizualising some of the processing steps of this analysis.
I want to create a figure similar to the one I created in https://brimittleman.github.io/comparative_threeprime/characterize.ortho.peaks.html. I will use the count distinct function from bedtools map. For this I am using the RefSeq mRNA annotations.
#!/bin/bash
#SBATCH --job-name=refseq_countdistinct
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=refseq_countdistinct.out
#SBATCH --error=refseq_countdistinct.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
bedtools map -c 4 -s -o count_distinct -a /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.mRNA.bed -b > /project2/gilad/briana/threeprimeseq/data/peakPerRefseqGene/filtered_APApeaks_perRefseqGene.txt
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0 ✔ purrr 0.2.5
✔ tibble 1.4.2 ✔ dplyr 0.7.6
✔ tidyr 0.8.1 ✔ stringr 1.3.1
✔ readr 1.1.1 ✔ forcats 0.3.0
── Conflicts ─────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
names=c("Chr", "Start", "End", "Name", "Score", "Strand", "numPeaks")
peakpergene=read.table("../data/peakPerRefSeqGene/filtered_APApeaks_perRefseqGene.txt", stringsAsFactors = F, header = F, col.names = names) %>% mutate(onePeak=ifelse(numPeaks==1, 1, 0 )) %>% mutate(multPeaks=ifelse(numPeaks > 1, 1, 0 ))
genes1peak=sum(peakpergene$onePeak)/nrow(peakpergene)
genesMultpeak=sum(peakpergene$multPeaks)/nrow(peakpergene)
genes0peak= 1- genes1peak - genesMultpeak
perPeak= c(round(genes0peak,digits = 3), round(genes1peak,digits = 3),round(genesMultpeak, digits = 3))
Category=c("Zero", "One", "Multiple")
perPeakdf=as.data.frame(cbind(Category,as.numeric(perPeak)))
Plot these proportions:
lab1=paste("Genes =", genes0peak*nrow(peakpergene), sep=" ")
lab2=paste("Genes =", sum(peakpergene$onePeak), sep=" ")
lab3=paste("Genes =", sum(peakpergene$multPeaks), sep=" ")
genepeakplot=ggplot(perPeakdf, aes(x="", y=perPeak, fill=Category)) + geom_bar(stat="identity")+ labs(title="Characterize genes by number of PAS", y="Proportion of Protein Coding gene", x="")+ scale_fill_brewer(palette="Paired") + coord_cartesian(ylim=c(0,1)) + annotate("text", x="", y= .35, label=lab1) + annotate("text", x="", y= .78, label=lab2) + annotate("text", x="", y= .92, label=lab3)
genepeakplot
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
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] bindrcpp_0.2.2 cowplot_0.9.3 reshape2_1.4.3 workflowr_1.1.1
[5] forcats_0.3.0 stringr_1.3.1 dplyr_0.7.6 purrr_0.2.5
[9] readr_1.1.1 tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[13] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35
[4] colorspace_1.3-2 htmltools_0.3.6 yaml_2.2.0
[7] rlang_0.2.2 R.oo_1.22.0 pillar_1.3.0
[10] glue_1.3.0 withr_2.1.2 R.utils_2.7.0
[13] RColorBrewer_1.1-2 modelr_0.1.2 readxl_1.1.0
[16] bindr_0.1.1 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[22] R.methodsS3_1.7.1 evaluate_0.11 labeling_0.3
[25] knitr_1.20 broom_0.5.0 Rcpp_0.12.18
[28] scales_1.0.0 backports_1.1.2 jsonlite_1.5
[31] hms_0.4.2 digest_0.6.16 stringi_1.2.4
[34] grid_3.5.1 rprojroot_1.3-2 cli_1.0.0
[37] tools_3.5.1 magrittr_1.5 lazyeval_0.2.1
[40] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[43] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[46] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.7
[49] R6_2.2.2 nlme_3.1-137 git2r_0.23.0
[52] compiler_3.5.1
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