Last updated: 2018-10-29
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: 42d0e3d
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: output/.DS_Store
Untracked files:
Untracked: KalistoAbundance18486.txt
Untracked: analysis/genometrack_figs.Rmd
Untracked: analysis/ncbiRefSeq_sm.sort.mRNA.bed
Untracked: analysis/snake.config.notes.Rmd
Untracked: analysis/verifyBAM.Rmd
Untracked: data/18486.genecov.txt
Untracked: data/APApeaksYL.total.inbrain.bed
Untracked: data/ChromHmmOverlap/
Untracked: data/GM12878.chromHMM.bed
Untracked: data/GM12878.chromHMM.txt
Untracked: data/NuclearApaQTLs.txt
Untracked: data/PeaksUsed/
Untracked: data/RNAkalisto/
Untracked: data/TotalApaQTLs.txt
Untracked: data/Totalpeaks_filtered_clean.bed
Untracked: data/YL-SP-18486-T-combined-genecov.txt
Untracked: data/YL-SP-18486-T_S9_R1_001-genecov.txt
Untracked: data/apaExamp/
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_trans/
Untracked: data/ensemble_to_genename.txt
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/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/other_qtls/
Untracked: data/peakPerRefSeqGene/
Untracked: data/perm_QTL/
Untracked: data/perm_QTL_opp/
Untracked: data/perm_QTL_trans/
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: output/picard/
Untracked: output/plots/
Untracked: output/qual.fig2.pdf
Unstaged changes:
Modified: analysis/28ind.peak.explore.Rmd
Modified: analysis/39indQC.Rmd
Modified: analysis/characterizeNuclearApaQtls.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/explore.filters.Rmd
Modified: analysis/overlapMolQTL.Rmd
Modified: analysis/overlap_qtls.Rmd
Modified: analysis/peakOverlap_oppstrand.Rmd
Modified: analysis/pheno.leaf.comb.Rmd
Modified: analysis/test.max2.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 | 42d0e3d | Briana Mittleman | 2018-10-29 | add gwas overlap to index |
In this analysis I want to see if APAqtls show up in the GWAS catelog. I then want to see if they explain different signal then overlappnig the eQTLs.
I can use my significant snp bed file from /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/sigSnps to overlap with the GWAS catelog. First I can look at direct location then I will use an LD cutoff to colocalize.
The downloaded GWAS catalog from the UCSD table browser.
I will make this into a bed format to use with pybedtools.
-Chrom -start -end -name -score
fin=open("/project2/gilad/briana/genome_anotation_data/hg19GwasCatalog.txt", "r")
fout=open("/project2/gilad/briana/genome_anotation_data/hg19GwasCatalog.bed","w")
for num, ln in enumerate(fin):
if num > 0:
line=ln.split("\t")
id_list=[line[4],line[5], line[14]]
start=int(line[2])
end=int(line[3])
id=":".join(id_list)
chr=line[1][3:]
pval=line[16]
fout.write("%s\t%d\t%d\t%s\t%s\n"%(chr,start, end, id, pval)
fout.close()
Pybedtools to intersect my snps with catelog /project2/gilad/briana/threeprimeseq/data/GWAS_overlap
output dir:
import pybedtools
gwas=pybedtools.BedTool("/project2/gilad/briana/genome_anotation_data/hg19GwasCatalog.sort.bed")
nuc=pybedtools.BedTool("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/sigSnps/ApaQTLsignificantSnps_10percFDR_Nuclear.sort.bed")
tot=pybedtools.BedTool("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/sigSnps/ApaQTLsignificantSnps_10percFDR_Total.sort.bed")
nucOverGWAS=nuc.intersect(gwas, wa=True,wb=True)
totOverGWAS=tot.intersect(gwas,wa=True, wb=True)
#this only results in one overlap:
nucOverGWAS.saveas("/project2/gilad/briana/threeprimeseq/data/GWAS_overlap/nucFDR10overlapGWAS.txt")
Problem: I see this snp but it is assoicated with a different gene. I need to think about gene and snp overlap.
I can see if this snp is an eqtl.
16:30482494
eqtl=read.table(file = "../data/other_qtls/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.out")
eqtl_g= read.table("../data/other_qtls/fastqtl_qqnorm_RNAseqGeuvadis.fixed.perm.out")
This snp is not in either of these files. I will check for them in the nominal results.
grep 16:30482494 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out
grep 16:30482494 /project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseqGeuvadis.fixed.nominal.out
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
loaded via a namespace (and not attached):
[1] workflowr_1.1.1 Rcpp_0.12.19 digest_0.6.17
[4] rprojroot_1.3-2 R.methodsS3_1.7.1 backports_1.1.2
[7] git2r_0.23.0 magrittr_1.5 evaluate_0.11
[10] stringi_1.2.4 whisker_0.3-2 R.oo_1.22.0
[13] R.utils_2.7.0 rmarkdown_1.10 tools_3.5.1
[16] stringr_1.3.1 yaml_2.2.0 compiler_3.5.1
[19] htmltools_0.3.6 knitr_1.20
This reproducible R Markdown analysis was created with workflowr 1.1.1