Last updated: 2019-02-06
workflowr checks: (Click a bullet for more information) ✔ R Markdown file: up-to-date
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
✔ Environment: empty
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
✔ Seed:
set.seed(12345)
The command set.seed(12345)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
✔ Session information: recorded
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
✔ Repository version: 6bac9f9
wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data/.DS_Store
Ignored: data/perm_QTL_trans_noMP_5percov/
Ignored: output/.DS_Store
Untracked files:
Untracked: KalistoAbundance18486.txt
Untracked: analysis/4suDataIGV.Rmd
Untracked: analysis/DirectionapaQTL.Rmd
Untracked: analysis/EvaleQTLs.Rmd
Untracked: analysis/YL_QTL_test.Rmd
Untracked: analysis/ncbiRefSeq_sm.sort.mRNA.bed
Untracked: analysis/snake.config.notes.Rmd
Untracked: analysis/verifyBAM.Rmd
Untracked: analysis/verifybam_dubs.Rmd
Untracked: code/PeaksToCoverPerReads.py
Untracked: code/strober_pc_pve_heatmap_func.R
Untracked: data/18486.genecov.txt
Untracked: data/APApeaksYL.total.inbrain.bed
Untracked: data/ApaQTLs/
Untracked: data/ChromHmmOverlap/
Untracked: data/DistTXN2Peak_genelocAnno/
Untracked: data/GM12878.chromHMM.bed
Untracked: data/GM12878.chromHMM.txt
Untracked: data/LianoglouLCL/
Untracked: data/LocusZoom/
Untracked: data/NuclearApaQTLs.txt
Untracked: data/PeakCounts/
Untracked: data/PeakCounts_noMP_5perc/
Untracked: data/PeakCounts_noMP_genelocanno/
Untracked: data/PeakUsage/
Untracked: data/PeakUsage_noMP/
Untracked: data/PeakUsage_noMP_GeneLocAnno/
Untracked: data/PeaksUsed/
Untracked: data/PeaksUsed_noMP_5percCov/
Untracked: data/RNAkalisto/
Untracked: data/RefSeq_annotations/
Untracked: data/TotalApaQTLs.txt
Untracked: data/Totalpeaks_filtered_clean.bed
Untracked: data/UnderstandPeaksQC/
Untracked: data/YL-SP-18486-T-combined-genecov.txt
Untracked: data/YL-SP-18486-T_S9_R1_001-genecov.txt
Untracked: data/YL_QTL_test/
Untracked: data/apaExamp/
Untracked: data/apaQTL_examp_noMP/
Untracked: data/bedgraph_peaks/
Untracked: data/bin200.5.T.nuccov.bed
Untracked: data/bin200.Anuccov.bed
Untracked: data/bin200.nuccov.bed
Untracked: data/clean_peaks/
Untracked: data/comb_map_stats.csv
Untracked: data/comb_map_stats.xlsx
Untracked: data/comb_map_stats_39ind.csv
Untracked: data/combined_reads_mapped_three_prime_seq.csv
Untracked: data/diff_iso_GeneLocAnno/
Untracked: data/diff_iso_proc/
Untracked: data/diff_iso_trans/
Untracked: data/ensemble_to_genename.txt
Untracked: data/example_gene_peakQuant/
Untracked: data/explainProtVar/
Untracked: data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed
Untracked: data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed
Untracked: data/first50lines_closest.txt
Untracked: data/gencov.test.csv
Untracked: data/gencov.test.txt
Untracked: data/gencov_zero.test.csv
Untracked: data/gencov_zero.test.txt
Untracked: data/gene_cov/
Untracked: data/joined
Untracked: data/leafcutter/
Untracked: data/merged_combined_YL-SP-threeprimeseq.bg
Untracked: data/molPheno_noMP/
Untracked: data/mol_overlap/
Untracked: data/mol_pheno/
Untracked: data/nom_QTL/
Untracked: data/nom_QTL_opp/
Untracked: data/nom_QTL_trans/
Untracked: data/nuc6up/
Untracked: data/nuc_10up/
Untracked: data/other_qtls/
Untracked: data/pQTL_otherphen/
Untracked: data/peakPerRefSeqGene/
Untracked: data/perm_QTL/
Untracked: data/perm_QTL_GeneLocAnno_noMP_5percov/
Untracked: data/perm_QTL_GeneLocAnno_noMP_5percov_3UTR/
Untracked: data/perm_QTL_opp/
Untracked: data/perm_QTL_trans/
Untracked: data/perm_QTL_trans_filt/
Untracked: data/protAndAPAAndExplmRes.Rda
Untracked: data/protAndAPAlmRes.Rda
Untracked: data/protAndExpressionlmRes.Rda
Untracked: data/reads_mapped_three_prime_seq.csv
Untracked: data/smash.cov.results.bed
Untracked: data/smash.cov.results.csv
Untracked: data/smash.cov.results.txt
Untracked: data/smash_testregion/
Untracked: data/ssFC200.cov.bed
Untracked: data/temp.file1
Untracked: data/temp.file2
Untracked: data/temp.gencov.test.txt
Untracked: data/temp.gencov_zero.test.txt
Untracked: data/threePrimeSeqMetaData.csv
Untracked: data/threePrimeSeqMetaData55Ind.txt
Untracked: data/threePrimeSeqMetaData55Ind.xlsx
Untracked: output/picard/
Untracked: output/plots/
Untracked: output/qual.fig2.pdf
Unstaged changes:
Modified: analysis/28ind.peak.explore.Rmd
Modified: analysis/CompareLianoglouData.Rmd
Modified: analysis/apaQTLoverlapGWAS.Rmd
Modified: analysis/cleanupdtseq.internalpriming.Rmd
Modified: analysis/coloc_apaQTLs_protQTLs.Rmd
Modified: analysis/dif.iso.usage.leafcutter.Rmd
Modified: analysis/diff_iso_pipeline.Rmd
Modified: analysis/explainpQTLs.Rmd
Modified: analysis/explore.filters.Rmd
Modified: analysis/flash2mash.Rmd
Modified: analysis/mispriming_approach.Rmd
Modified: analysis/overlapMolQTL.Rmd
Modified: analysis/overlapMolQTL.opposite.Rmd
Modified: analysis/overlap_qtls.Rmd
Modified: analysis/peakOverlap_oppstrand.Rmd
Modified: analysis/peakQCPPlots.Rmd
Modified: analysis/pheno.leaf.comb.Rmd
Modified: analysis/swarmPlots_QTLs.Rmd
Modified: analysis/test.max2.Rmd
Modified: analysis/understandPeaks.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.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 6bac9f9 | Briana Mittleman | 2019-02-06 | add distance plots for QC on APAqtls |
I will use this to look at some metrics around the the QTLs from the pipeline for all 55 individuals. In this analysis I found 363 qtls in the total fraction and 623 in the nuclear.
library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
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()
totQTLs=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)%>% filter(-log10(bh)>=1)
write.table(totQTLs,"../data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", row.names = F, col.names = F, quote = F)
nucQTLs=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)%>% filter(-log10(bh)>=1)
write.table(nucQTLs,"../data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", row.names = F, col.names = F, quote = F)
I want to look at the distance between the QTL snp and the end of a peak. For a positive strand gene this is the end of the peak, for a - strand gene this is the start position of the peak. The peak strand here is opposite of the strand the gene is on.
I will make a python script that will take make the distance file for both the total and nucelar.
I copied these files to /project2/gilad/briana/threeprimeseq/data/ApaQTLs. I will put the QC files here as well.
getDistPeakEnd2QTL.py
#usage getDistPeakEnd2QTL.py "Total" or getDistPeakEnd2QTL.py "Nuclear"
def main(inFile, outFile):
iFile=open(inFile, "r")
oFile=open(outFile, "w")
oFile.write("PeakID\tPeakEnd\tGene\tGeneStrand\tSNP_chr\tSNP_loc\tEffectSize\tBH\tDistance\n")
for ln in iFile:
pid= ln.split()[0]
peakStrand=pid.split(":")[3].split("_")[1]
if peakStrand=="+":
strand = "-"
end = int(pid.split(":")[1])
else:
strand = "+"
end = int(pid.split(":")[2])
gene=pid.split(":")[3].split("_")[0]
peak=pid.split(":")[3].split("_")[2]
SNP_Chr=ln.split()[5].split(":")[0]
SNP_loc=int(ln.split()[5].split(":")[1])
effectSize=ln.split()[8]
BH=ln.split()[11]
Dist= end - SNP_loc
oFile.write("%s\t%d\t%s\t%s\t%s\t%d\t%s\t%s\t%d\n"%(peak, end, gene, strand, SNP_Chr, SNP_loc, effectSize, BH, Dist))
oFile.close()
if __name__ == "__main__":
import sys
fraction = sys.argv[1]
inFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%sapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt"%(fraction)
outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Distance2EndPeak.%s.apaQTLs.txt"%(fraction)
main(inFile, outFile)
Plot for total:
TotDist=read.table("../data/ApaQTLs/Distance2EndPeak.Total.apaQTLs.txt", header=T) %>% mutate(Fraction="Total") %>% select(Fraction, Distance)
NucDist=read.table("../data/ApaQTLs/Distance2EndPeak.Nuclear.apaQTLs.txt", header=T)%>% mutate(Fraction="Nuclear") %>% select(Fraction, Distance)
BothDist=data.frame(rbind(TotDist, NucDist))
ggplot(BothDist, aes(x=Distance, by=Fraction, fill=Fraction))+geom_histogram(bins=70, alpha=.5) + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + labs(title="Distance From apaQTL to End of Peak" )
I want to take all of the SNP locations see what region of the genome they are in. I can use the annotation in /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed. I can do this with bedtools intersect if I make a bedfile for the QTLs.
Goal file: chr, loc -1, loc, peak:QTLgene, BH, geneStrand
I can get all of this information most easily from the distance file I made.
QTLfile2Bed.py
#usage QTLfile2Bed.py "Total" or QTLfile2Bed.py "Nuclear"
def main(inFile, outFile):
iFile=open(inFile, "r")
oFile=open(outFile, "w")
for num, ln in enumerate(iFile):
if num > 0:
peakID, peakend, gene, strand, chr, loc, effect, bh, dist = ln.split()
start=int(loc) -1
end= int(loc)
name= peakID + ":" + gene
oFile.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chr, start, end, name, bh, strand))
oFile.close()
if __name__ == "__main__":
import sys
fraction = sys.argv[1]
inFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Distance2EndPeak.%s.apaQTLs.txt"%(fraction)
outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%s.apaQTLs.bed"%(fraction)
main(inFile, outFile)
I will need to sort the output
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.bed > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.bed > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed
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] bindrcpp_0.2.2 forcats_0.3.0 stringr_1.3.1 dplyr_0.7.6
[5] purrr_0.2.5 readr_1.1.1 tidyr_0.8.1 tibble_1.4.2
[9] ggplot2_3.0.0 tidyverse_1.2.1 workflowr_1.1.1
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] modelr_0.1.2 readxl_1.1.0 bindr_0.1.1
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] cellranger_1.1.0 rvest_0.3.2 R.methodsS3_1.7.1
[22] evaluate_0.11 labeling_0.3 knitr_1.20
[25] broom_0.5.0 Rcpp_0.12.19 scales_1.0.0
[28] backports_1.1.2 jsonlite_1.5 hms_0.4.2
[31] digest_0.6.17 stringi_1.2.4 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] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[43] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[46] httr_1.3.1 rstudioapi_0.8 R6_2.3.0
[49] nlme_3.1-137 git2r_0.23.0 compiler_3.5.1
This reproducible R Markdown analysis was created with workflowr 1.1.1