Last updated: 2019-02-15

Checks: 6 0

Knit directory: threeprimeseq/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: 606e562

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use 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/WASP_STAT/
    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/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/
    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:  data/threePrimeSeqMetaData55Ind_noDup.txt
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup.xlsx
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.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/NewPeakPostMP.Rmd
    Modified:   analysis/accountMapBias.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/pipeline_55Ind.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/test.smash.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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html cbd3edc Briana Mittleman 2019-01-20 Build site.
Rmd 57da244 Briana Mittleman 2019-01-20 updeated to remove mispriming
html 2b482ce Briana Mittleman 2019-01-18 Build site.
Rmd ee0c781 Briana Mittleman 2019-01-18 deeptools code
html 6e22653 Briana Mittleman 2019-01-17 Build site.
Rmd 7eaae54 Briana Mittleman 2019-01-17 select peaks to use in deeptools plot

I want to show RNA seq vs 3’ seq in peaks that are internal.

To do this I need to pull in the peaks (use the filtered ones from peakQCPlots) to get peaks that are used but are not the most distal for the gene. I need to split by strand when i do this becasue most distal is different in both cases.

This analysis is updated post cleaning for mispriming

Load Libraries

library(data.table)
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 1.4.0
✔ readr   1.1.1     ✔ forcats 0.3.0
Warning: package 'stringr' was built under R version 3.5.2
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()   masks data.table::between()
✖ dplyr::filter()    masks stats::filter()
✖ dplyr::first()     masks data.table::first()
✖ dplyr::lag()       masks stats::lag()
✖ dplyr::last()      masks data.table::last()
✖ purrr::transpose() masks data.table::transpose()
library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

Load peaks for total and nuclear post filter:

name=c("chr","start","end", "gene", "strand", "peak", "meanUsage")
total_PeakUsMean=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt", head = F,col.names = name)
nuclear_PeakUsMean=read.table("../data/PeakUsage_noMP/filtered_APApeaks_merged_allchrom_refseqGenes.TranscriptNoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt", head=F,col.names = name)

Seperate positive and negative:

  • Total
total_PeakUsMean_pos=total_PeakUsMean %>% filter(strand=="+")
total_PeakUsMean_neg=total_PeakUsMean %>% filter(strand=="-")
  • Nuclear
nuclear_PeakUsMean_pos=nuclear_PeakUsMean %>% filter(strand=="+")
nuclear_PeakUsMean_neg=nuclear_PeakUsMean %>% filter(strand=="-")

Group by gene and keep internal (remove genes with only 1)

  • For positive strand keep bottom one with top_n() peak # (most internal)
total_PeakUsMean_pos_internal=total_PeakUsMean_pos %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(1,peak)

nuclear_PeakUsMean_pos_internal=nuclear_PeakUsMean_pos %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(1,peak)

*For negative strand use top_n(-1)

total_PeakUsMean_neg_internal=total_PeakUsMean_neg %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(-1,peak)

nuclear_PeakUsMean_neg_internal=nuclear_PeakUsMean_neg %>% group_by(gene) %>% mutate(n=n()) %>% filter(n>1) %>% top_n(-1,peak)

Bind the total and nuclear rows back together:

Total

total_PeakUsMean_internal=as.data.frame(rbind(total_PeakUsMean_pos_internal,total_PeakUsMean_neg_internal)) %>% arrange(chr, start, end)

write.table(total_PeakUsMean_internal, file="../data/PeakUsage_noMP/InternalFilteredPeak.total.txt", row.names = F, col.names = F, quote = F)

Nuclear:

nuclear_PeakUsMean_internal=as.data.frame(rbind(nuclear_PeakUsMean_pos_internal,nuclear_PeakUsMean_neg_internal)) %>% arrange(chr, start, end)


write.table(nuclear_PeakUsMean_internal, file="../data/PeakUsage_noMP/InternalFilteredPeak.nuclear.txt", row.names = F, col.names = F, quote = F)

Use these peak numbers to filter the bed file that I use for deep tools. I can do this in python with a dictionary of the peaks to keep.

The file I want to subset is /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed

filterPeaksInner_noMP.py

#python  

peaksIn="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.refseqTrans.closest2end.sm.fixed_5percCov.bed"
innerPeaksTotal="/project2/gilad/briana/threeprimeseq/data/PeakUsage/InternalFilteredPeak.total.txt"
innerPeaksNuclear="/project2/gilad/briana/threeprimeseq/data/PeakUsage/InternalFilteredPeak.nuclear.txt"

OkPeakTot={}
for ln in open(innerPeaksTotal, "r"):
    peaknum=ln.split()[5]
    peaknumOnly=peaknum[4:]
    OkPeakTot[peaknumOnly] = ""
  
OkPeakNuc={}
for ln in open(innerPeaksNuclear, "r"):
   peaknum=ln.split()[5]
   peaknumOnly=peaknum[4:]
   OkPeakNuc[peaknumOnly]=""
   
TotOut=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_TotalInternal.bed", "w")

NucOut=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_NuclearInternal.bed", "w")


#read peaks and write out to each file 

for ln in open(peaksIn,"r"):
    peakName=ln.split()[3]
    peakNameStr=str(peakName)
    if peakName in OkPeakTot.keys():
        TotOut.write(ln)
    if peakName in OkPeakNuc.keys():
        NucOut.write(ln)

TotOut.close()
NucOut.close()

Now I can make the deep tools plots:

Three prime seq with inner total

TotalDTPlotmyPeaks_Internal.sh

#!/bin/bash

#SBATCH --job-name=TotalDTPlotmyPeaks_Internal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TotalDTPlotmyPeaks_Internal.out
#SBATCH --error=TotalDTPlotmyPeaks_Internal.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw  -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_TotalInternal.bed -b 1000 -a 1000  --outFileName /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Total_myPeaks_Internal.gz  

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Total_myPeaks_Internal.gz --refPointLabel "Internal Peaks" --plotTitle "Total Combined Reads Internal Peaks" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Total_myPeaks_internal.png

NuclearDTPlotmyPeaks_Internal.sh

#!/bin/bash

#SBATCH --job-name=NuclearDTPlotmyPeaks_Internal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=NucelarDTPlotmyPeaks_Internal.out
#SBATCH --error=NucelarDTPlotmyPeaks_Internal.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Nucelar_MergedBamCoverage.bw  -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_NuclearInternal.bed -b 1000 -a 1000  --outFileName /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Nuclear_myPeaks_Internal.gz  

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Nuclear_myPeaks_Internal.gz --refPointLabel "Internal Peaks" --plotTitle "Nuclear Combined Reads Internal Peaks" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/Nuclear_myPeaks_internal.png

RNA seq plot with the total internal peaks

RNAseqDTPlotmyPeaks_Internal.sh


#!/bin/bash

#SBATCH --job-name=RNAseqDTPlotmyPeaks_Internal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNAseqDTPlotmyPeaks_Internal.out
#SBATCH --error=RNAseqDTPlotmyPeaks_Internal.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw   -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_TotalInternal.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_Internal.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_Internal.gz --refPointLabel "Total Internal Peaks" --plotTitle "Combined RNAseq Reads at Total Internal"  --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_Internal.png

RNAseqDTPlotmyPeaks_NuclearInternal.sh


#!/bin/bash

#SBATCH --job-name=RNAseqDTPlotmyPeaks_NuclearInternal
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=RNAseqDTPlotmyPeaks_NuclearInternal.out
#SBATCH --error=RNAseqDTPlotmyPeaks_NuclearInternal.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/rnaseq_bw/RNAseqGeuvadis_STAR_6samp_MergedBams.sort.bw   -R /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb_noMP/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed_NuclearInternal.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_NuclearInternal.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_NuclearInternal.gz --refPointLabel "Nucelar Internal Peaks" --plotTitle "Combined RNAseq Reads at Nuclear Internal"  --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/rnaseq_deeptools/RNAseq_myPeaks_NuclearInternal.png


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    cowplot_0.9.3     workflowr_1.2.0  
 [4] forcats_0.3.0     stringr_1.4.0     dplyr_0.7.6      
 [7] purrr_0.2.5       readr_1.1.1       tidyr_0.8.1      
[10] tibble_1.4.2      ggplot2_3.0.0     tidyverse_1.2.1  
[13] data.table_1.11.8

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.0     git2r_0.24.0     bindr_0.1.1      tools_3.5.1     
 [9] digest_0.6.17    lubridate_1.7.4  jsonlite_1.6     evaluate_0.13   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-35  pkgconfig_2.0.2 
[17] rlang_0.2.2      cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.4 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.11   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.0      crayon_1.3.4