Last updated: 2018-11-12
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: 7d1bd9a
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/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/example_gene_peakQuant/
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/apaQTLoverlapGWAS.Rmd
Modified: analysis/chromHmm_enrichment.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/flash2mash.Rmd
Modified: analysis/overlapMolQTL.Rmd
Modified: analysis/overlap_qtls.Rmd
Modified: analysis/peakOverlap_oppstrand.Rmd
Modified: analysis/pheno.leaf.comb.Rmd
Modified: analysis/swarmPlots_QTLs.Rmd
Modified: analysis/test.max2.Rmd
Modified: code/Snakefile
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7d1bd9a | Briana Mittleman | 2018-11-12 | add code for looking at sig gene peaks |
The quantified peak files are:
I want to grep specific genes and look at the read distribution for peaks along a gene. In these files the peakIDs stil have the peak locations. Before I ran the QTL analysis I changed the final coverage (ex /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz) to have the TSS as the ID.
Librarys
library(workflowr)This is workflowr version 1.1.1
Run ?workflowr for help getting startedlibrary(reshape2)
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(VennDiagram)Loading required package: gridLoading required package: futile.loggerlibrary(data.table)
Attaching package: 'data.table'The following objects are masked from 'package:dplyr':
between, first, lastThe following object is masked from 'package:purrr':
transposeThe following objects are masked from 'package:reshape2':
dcast, meltlibrary(ggpubr)Loading required package: magrittr
Attaching package: 'magrittr'The following object is masked from 'package:purrr':
set_namesThe following object is masked from 'package:tidyr':
extract
Attaching package: 'ggpubr'The following object is masked from 'package:VennDiagram':
rotatelibrary(cowplot)
Attaching package: 'cowplot'The following object is masked from 'package:ggpubr':
get_legendThe following object is masked from 'package:ggplot2':
ggsavenuc_names=c('Geneid', 'Chr', 'Start', 'End', 'Strand', 'Length', '18486_N' ,'18497_N', '18500_N' ,'18505_N', '18508_N' ,'18511_N', '18519_N', '18520_N', '18853_N' ,'18858_N', '18861_N', '18870_N' ,'18909_N' ,'18912_N' ,'18916_N', '19092_N' ,'19093_N', '19119_N', '19128_N' ,'19130_N', '19131_N' ,'19137_N', '19140_N', '19141_N' ,'19144_N', '19152_N' ,'19153_N', '19160_N' ,'19171_N', '19193_N' ,'19200_N', '19207_N', '19209_N', '19210_N', '19223_N' ,'19225_N', '19238_N' ,'19239_N', '19257_N')
tot_names=c('Geneid', 'Chr' ,'Start', 'End', 'Strand', 'Length', '18486_T', '18497_T' ,'18500_T','18505_T', '18508_T' ,'18511_T', '18519_T', '18520_T', '18853_T', '18858_T', '18861_T', '18870_T', '18909_T' ,'18912_T', '18916_T', '19092_T' ,'19093_T', '19119_T', '19128_T', '19130_T', '19131_T' ,'19137_T', '19140_T' ,'19141_T', '19144_T', '19152_T' ,'19153_T', '19160_T' ,'19171_T', '19193_T', '19200_T', '19207_T' ,'19209_T' ,'19210_T', '19223_T', '19225_T', '19238_T', '19239_T', '19257_T')NuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", stringsAsFactors = F, header=T) %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% filter(sig==1)
totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", stringsAsFactors = F, header=T) %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% filter(sig==1)examples to look at Nuclear: IRF5, HSF1, NOL9,DCAF16, PPP4C
Total: NBEAL2, SACM1L, COX7A2L
#nuclear
grep IRF5 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt
grep HSF1 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt
grep NOL9 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt
grep DCAF16 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt
grep PPP4C /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt
#total
grep NBEAL2 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/NBEAL2_TotalCov_peaks.txt
grep SACM1L /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt
grep TESK1 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/TESK1_TotalCov_peaks.txt
grep DGCR14 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt
Copy these to my computer so I can work with them here. I am going to want to make a function that makes the histogram reproducibly for anyfile. I will need to know how many bins to include in the histogram. First I will make the graph for one example then I will make it more general.
Files are in /Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/example_gene_peakQuant
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] bindrcpp_0.2.2 cowplot_0.9.3 ggpubr_0.1.8
[4] magrittr_1.5 data.table_1.11.8 VennDiagram_1.6.20
[7] futile.logger_1.4.3 forcats_0.3.0 stringr_1.3.1
[10] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[13] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[16] tidyverse_1.2.1 reshape2_1.4.3 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] lambda.r_1.2.3 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 knitr_1.20
[25] broom_0.5.0 Rcpp_0.12.19 formatR_1.5
[28] backports_1.1.2 scales_1.0.0 jsonlite_1.5
[31] hms_0.4.2 digest_0.6.17 stringi_1.2.4
[34] rprojroot_1.3-2 cli_1.0.1 tools_3.5.1
[37] lazyeval_0.2.1 futile.options_1.0.1 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