Last updated: 2018-06-13
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Untracked files:
Untracked: data/18486.genecov.txt
Untracked: data/YL-SP-18486-T_S9_R1_001-genecov.txt
Untracked: data/bin200.5.T.nuccov.bed
Untracked: data/bin200.Anuccov.bed
Untracked: data/bin200.nuccov.bed
Untracked: data/gene_cov/
Untracked: data/leafcutter/
Untracked: data/reads_mapped_three_prime_seq.csv
Untracked: data/ssFC200.cov.bed
Untracked: output/picard/
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Untracked: output/qual.fig2.pdf
Unstaged changes:
Modified: analysis/dif.iso.usage.leafcutter.Rmd
Modified: analysis/explore.filters.Rmd
Modified: code/Snakefile
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. I am going to use this analysis to look for enrichment of my 3’ seq reads at annoated PAS sites. This is similar to the analysis I ran for the net-seq https://brimittleman.github.io/Net-seq/use_deeptools.html.
library(workflowr)
Loading required package: rmarkdown
This is workflowr version 1.0.1
Run ?workflowr for help getting started
library(ggplot2)
library(dplyr)
Warning: package 'dplyr' was built under R version 3.4.4
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
library(tidyr)
library(reshape2)
Warning: package 'reshape2' was built under R version 3.4.3
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
Step 1: Create bigwig coverage files with bamcoverage
Step 2: computeMatrix
I will need my normalized bigwig reads and the bed interval file (in my case PAS clusters)
ex: computeMatrix scale-regions -S
–skipZeros (option- not included in first try)
Step 3: Plot heatmap
required –matrixFile, -m (from the compute matrix), -out (file name to save image.png)
–sortRegions descending
–plotTitle, -T
#!/bin/bash
#SBATCH --job-name=deeptools_pas
#SBATCH --time=8:00:00
#SBATCH --partition=broadwl
#SBATCH --mem=40G
#SBATCH --tasks-per-node=4
#SBATCH --mail-type=END
#SBATCH --output=deeptool_pas_sbatch.out
#SBATCH --error=deeptools_pas_sbatch.err
module load Anaconda3
source activate three-prime-env
sample=$1
describer=$(echo ${sample} | sed -e 's/.*\YL-SP-//' | sed -e "s/-sort.bam$//")
bamCoverage -b $1 -o /project2/gilad/briana/threeprimeseq/output/deeptools/${describer}.bw
computeMatrix reference-point -S project2/gilad/briana/threeprimeseq/output/deeptools/${describer}.bw -R /project2/gilad/briana/apa_sites/rnaseq_LCL/clusters_fullAnno.bed -b 500 -a 500 -out /project2/gilad/briana/threeprimeseq/output/deeptools/${describer}.PAS.gz
plotHeatmap --sortRegions descend --refPointLabel "PAS" -m /project2/gilad/briana/threeprimeseq/output/deeptools/${describer}.PAS.gz -out /project2/gilad/briana/threeprimeseq/output/deeptools/${describer}.PAS.gz.png
I am running this on YL-SP-18486-N_S10_R1_001-sort.bam to try it first.
pic.enrich=read.csv("../output/picard/picard.all.enrichment.csv")
pic.enrich.melt=melt(pic.enrich, id.vars="normalized_position") %>% mutate(fraction=ifelse(grepl("T",variable), "total", "nuclear"))%>% mutate(line=substr(variable,3,7))
Warning: package 'bindrcpp' was built under R version 3.4.4
Plot this as line plot:
enrichment.by.line=ggplot(pic.enrich.melt, aes(x=normalized_position, y=value, col=fraction)) + geom_line() + facet_wrap(~line) + labs(y="Normalized Coverage", title="3' Seq enrichment at 3' end of genes", x="Normalized Position") +scale_color_manual(values=c("red", "blue"))
ggsave("../output/plots/enrich.by.line.png", enrichment.by.line)
Saving 7 x 5 in image
enrichment_byfrac=ggplot(pic.enrich.melt, aes(x=normalized_position, y=value, by=line, col=fraction)) + geom_line() + labs(y="Normalized Coverage", title="3' Seq enrichment at 3' end of genes", x="Normalized Position")+ scale_color_manual(values=c("red", "blue"))
ggsave("../output/plots/enrich.by.fraction.png", enrichment_byfrac)
Saving 7 x 5 in image
enrich.by.line.fraction=ggplot(pic.enrich.melt, aes(x=normalized_position, y=value, col=line)) + geom_line() + facet_wrap(~fraction) + labs(y="Normalized Coverage", title="3' Seq enrichment at 3' end of genes", x="Normalized Position")
ggsave("../output/plots/enrich.by.line.fraction.png",enrich.by.line.fraction)
Saving 7 x 5 in image
sessionInfo()
R version 3.4.2 (2017-09-28)
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/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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 reshape2_1.4.3 tidyr_0.7.2 dplyr_0.7.5
[5] ggplot2_2.2.1 workflowr_1.0.1 rmarkdown_1.8.5
loaded via a namespace (and not attached):
[1] Rcpp_0.12.17 compiler_3.4.2 pillar_1.1.0
[4] git2r_0.21.0 plyr_1.8.4 bindr_0.1.1
[7] R.methodsS3_1.7.1 R.utils_2.6.0 tools_3.4.2
[10] digest_0.6.15 evaluate_0.10.1 tibble_1.4.2
[13] gtable_0.2.0 pkgconfig_2.0.1 rlang_0.2.1
[16] yaml_2.1.19 stringr_1.3.1 knitr_1.18
[19] rprojroot_1.3-2 grid_3.4.2 tidyselect_0.2.4
[22] glue_1.2.0 R6_2.2.2 purrr_0.2.5
[25] magrittr_1.5 whisker_0.3-2 backports_1.1.2
[28] scales_0.5.0 htmltools_0.3.6 assertthat_0.2.0
[31] colorspace_1.3-2 labeling_0.3 stringi_1.2.2
[34] lazyeval_0.2.1 munsell_0.4.3 R.oo_1.22.0
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