Last updated: 2019-04-08

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: 4568d1f

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:    analysis/figure/
    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/AdaptTonyExampleplots.Rmd
    Untracked:  analysis/DirectionapaQTL.Rmd
    Untracked:  analysis/EvaleQTLs.Rmd
    Untracked:  analysis/YL_QTL_test.Rmd
    Untracked:  analysis/groSeqAnalysis.Rmd
    Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
    Untracked:  analysis/peaksWithMisprimming.Rmd
    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/AllPeak_counts/
    Untracked:  data/ApaQTLs/
    Untracked:  data/ApaQTLs_otherPhen/
    Untracked:  data/CTCF/
    Untracked:  data/ChromHmmOverlap/
    Untracked:  data/DistTXN2Peak_genelocAnno/
    Untracked:  data/EmpiricalDists/
    Untracked:  data/ExampleQTLplot2/
    Untracked:  data/FeatureoverlapPeaks/
    Untracked:  data/GM12878.chromHMM.bed
    Untracked:  data/GM12878.chromHMM.txt
    Untracked:  data/GWAS_overlap/
    Untracked:  data/LianoglouLCL/
    Untracked:  data/LocusZoom/
    Untracked:  data/LocusZoom_Unexp/
    Untracked:  data/LocusZoom_proc/
    Untracked:  data/MatchedSnps/
    Untracked:  data/NucSpecQTL/
    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/PolyA_DB/
    Untracked:  data/QTL_overlap/
    Untracked:  data/RNAdecay/
    Untracked:  data/RNAkalisto/
    Untracked:  data/RefSeq_annotations/
    Untracked:  data/Replicates_usage/
    Untracked:  data/Signal_Loc/
    Untracked:  data/TNcompExamp/
    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/apaExamp_proc/
    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/eQTL_inAPA/
    Untracked:  data/eQTLs_Lietal/
    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_inAPA/
    Untracked:  data/pQTL_otherphen/
    Untracked:  data/pacbio_cov/
    Untracked:  data/peakPerRefSeqGene/
    Untracked:  data/peaks4DT/
    Untracked:  data/perm_QTL/
    Untracked:  data/perm_QTL_GeneLocAnno_noMP_5percov/
    Untracked:  data/perm_QTL_GeneLocAnno_noMP_5percov_3UTR/
    Untracked:  data/perm_QTL_diffWindow/
    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:  data/threePrimeSeqMetaData55Ind_redobatch4.xlsx
    Untracked:  data/~$threePrimeSeqMetaData55Ind_redobatch4.xlsx
    Untracked:  manuscript/
    Untracked:  output/APAqtlExamp/
    Untracked:  output/LZ/
    Untracked:  output/deeptools_plots/
    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/ExampleQTLPlot2.Rmd
    Modified:   analysis/HistoneModandPAS.Rmd
    Modified:   analysis/NewPeakPostMP.Rmd
    Modified:   analysis/NuclearSpecQTL.Rmd
    Modified:   analysis/PeakToXper.Rmd
    Modified:   analysis/RNAdecayAndAPA.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
    Modified:   analysis/characterize_apaQTLs.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diffIsoAnalysisNewMapping.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explainpQTLs.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/fixBWChromNames.Rmd
    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/initialPacBioQuant.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:   analysis/unexplainedeQTL_analysis.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
Rmd 4568d1f Briana Mittleman 2019-04-08 add boxplot and genometrack
html 0ff28d4 Briana Mittleman 2019-04-03 Build site.
Rmd a79791b Briana Mittleman 2019-04-03 start tot nuc example plots 
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.0       ✔ purrr   0.3.1  
✔ tibble  2.0.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.4.0  
✔ readr   1.3.1       ✔ forcats 0.4.0  
Warning: package 'tibble' was built under R version 3.5.2
Warning: package 'tidyr' was built under R version 3.5.2
Warning: package 'purrr' was built under R version 3.5.2
Warning: package 'dplyr' was built under R version 3.5.2
Warning: package 'stringr' was built under R version 3.5.2
Warning: package 'forcats' was built under R version 3.5.2
── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(reshape2)

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths

Heatmap in one gene

I want to make an example heatmap for the total vs nuclear difference similar to the ones I did for the qtls.

I will take a similar approach where I make one then create a script to make it for all examples

chr21:43762910:43762982:TFF2

Count data is in:

/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc

grep TFF2 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc > /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_TFF2.txt

I will need to divide by the mapped read counts for the library:

metadata=read.table("../data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt",header = T) %>% select(Sample_ID,Mapped_noMP )

metadata_melt=melt(metadata, id.vars = c("Sample_ID"), value.name = "MappedRead") %>% mutate(MappPM=MappedRead/10^6)

Header file:

TNhead=read.table("../data/TNcompExamp/TNCountheader.txt", header = T,stringsAsFactors = F)

read in data and melt it:

TN_TFF2=read.table("../data/TNcompExamp/TNcomp_TFF2.txt", col.names =colnames(TNhead),stringsAsFactors = F) %>% select(-Chr,-Geneid,-Strand, -Length)

TN_TFF2$Start=as.character(TN_TFF2$Start)
TN_TFF2$End=as.character(TN_TFF2$End)

TN_TFF2= TN_TFF2 %>% mutate(PeakLoc= paste(Start,End,sep="_")) %>% select(-Start, -End)

TN_TFF2_melt=melt(TN_TFF2, id.vars =c("PeakLoc"), variable.name = "ID", value.name = "PeakCount" ) %>% mutate(Sample_ID=substr(ID, 2, length(ID)))

Join:

TN_TFF2_withMeta=TN_TFF2_melt %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% mutate(Fraction=ifelse(grepl("T",Sample_ID), "Total","Nuclear")) %>%  mutate(NormCount=PeakCount/MappPM) %>% group_by(PeakLoc,Fraction) %>% summarise(meanCPM=mean(NormCount))
Warning: Column `Sample_ID` joining character vector and factor, coercing
into character vector
my_palette <- colorRampPalette(c("white", "khaki1", "orange", "red", "darkred", "black"))

ggplot(TN_TFF2_withMeta, aes(x=PeakLoc, y=Fraction)) + geom_tile(aes(fill = meanCPM))+ scale_fill_gradientn(colors =my_palette(100)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x="PAS", title="TFF2")

Version Author Date
0ff28d4 Briana Mittleman 2019-04-03

Super low expression of this gene. Better example when it is

Generalize:

TvNHeatmap.R

library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)


option_list = list(
  make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
              help="input pheno file"),
  make_option(c("-g", "--gene"), action="store", default=NA, type='character',
              help="gene"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file for plot")
)

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)

metadata=read.table("/project2/gilad/briana/threeprimeseq/data/TNcompExamp/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt",header=T, stringsAsFactors = F) %>% select(Sample_ID,Mapped_noMP )


metadata_melt=melt(metadata, id.vars = c("Sample_ID"), value.name = "MappedRead") %>% mutate(MappPM=MappedRead/10^6)


TNhead=read.table("/project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNCountheader.txt", header = T,stringsAsFactors = F)


TN=read.table(opt$pheno, col.names =colnames(TNhead),stringsAsFactors = F) %>% select(-Chr,-Geneid,-Strand, -Length)

TN$Start=as.character(TN$Start)
TN$End=as.character(TN$End)

TN= TN%>% mutate(PeakLoc= paste(Start,End,sep="-")) %>% select(-Start, -End)

TN_melt=melt(TN, id.vars =c("PeakLoc"), variable.name = "ID", value.name = "PeakCount" ) %>% mutate(Sample_ID=substr(ID, 2, length(ID))) 

TN_withMeta=TN_melt %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% mutate(Fraction=ifelse(grepl("T",Sample_ID), "Total","Nuclear")) %>%  mutate(NormCount=PeakCount/MappPM) %>% group_by(PeakLoc,Fraction) %>% summarise(meanCPM=mean(NormCount))

my_palette <- colorRampPalette(c("white", "khaki1", "orange", "red", "darkred", "black"))

outplot=ggplot(TN_withMeta, aes(x=PeakLoc, y=Fraction)) + geom_tile(aes(fill = meanCPM))+ scale_fill_gradientn(colors =my_palette(100)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x="PAS", title=opt$gene)



ggsave(plot=outplot, filename=opt$output, height=10, width=10)

Script to make phenotype file and run R script:

TvNMakeHeatmap.sh

#!/bin/bash

#SBATCH --job-name=TvNMakeHeatmap
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TvNMakeHeatmap.out
#SBATCH --error=TvNMakeHeatmap.err
#SBATCH --partition=broadwl
#SBATCH --mem=18G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

gene=$1


grep ${gene} /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc > /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt


Rscript TvNHeatmap.R -P /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}_heatmap.png

Examples to test

diffIso=read.table(file="../data/diff_iso_GeneLocAnno/SigPeaksHigherInNuc.txt", header = T) %>% arrange(deltapsi)
head(diffIso)
                              intron     logef   Nuclear     Total
1 chr1:151134497:151134579:TNFAIP8L2 -1.531127 0.7805416 0.1426529
2       chr21:43762910:43762982:TFF2 -1.292723 0.7517177 0.1857824
3      chr3:23306502:23306675:UBE2E2 -1.576854 0.6895186 0.1527728
4       chr14:67029307:67029417:GPHN -1.178720 0.7952505 0.2688294
5         chr6:84007319:84007404:ME1 -1.941535 0.6378959 0.1158490
6    chr7:73885912:73885994:GTF2IRD1 -1.094156 0.8030045 0.3136450
    deltapsi
1 -0.6378887
2 -0.5659353
3 -0.5367458
4 -0.5264211
5 -0.5220469
6 -0.4893595
sbatch TvNMakeHeatmap.sh GPHN

sbatch TvNMakeHeatmap.sh ME1


sbatch TvNMakeHeatmap.sh GTF2IRD1

sbatch TvNMakeHeatmap.sh DOCK9


sbatch TvNMakeHeatmap.sh UNQ6494

Track image with pygenome

I want to make normalized BW to plot on tracks. This will be easier to show for this. I can use bamCoverage in deeptools and normalize to cpm.

normbam2BW.sh

#!/bin/bash

#SBATCH --job-name=normbam2BW
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=normbam2BW.out
#SBATCH --error=normbam2BW.err
#SBATCH --partition=broadwl
#SBATCH --mem=18G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


i=$1

bamCoverage -b /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-$i-sort.bam -o /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-$i-norm.bw \
--normalizeUsingRPKM

Run on all file is /project2/gilad/briana/threeprimeseq/data/sort/

(need to move these)

run_normbam2BW.sh

#!/bin/bash

#SBATCH --job-name=run_normbam2BW
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_normbam2BW.out
#SBATCH --error=run_normbam2BW.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


for i in $(ls /project2/gilad/briana/threeprimeseq/data/sort/*.bam)
do 
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/-sort.bam//")
sbatch normbam2BW.sh $describer
done

Make a merged version.

/project2/gilad/briana/threeprimeseq/data/normalizedBW_merged

mergeNormBW.sh

#!/bin/bash

#SBATCH --job-name=mergeNormBW
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mergeNormBW.out
#SBATCH --error=mergeNormBW.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

ls -d -1 /project2/gilad/briana/threeprimeseq/data/normalizedBW/*T* | tail -n +2 > /project2/gilad/briana/threeprimeseq/data/list_bw/list_of_TotNorombigwig.txt

ls -d -1 /project2/gilad/briana/threeprimeseq/data/normalizedBW/*N* | tail -n +2 > /project2/gilad/briana/threeprimeseq/data/list_bw/list_of_NucNorombigwig.txt


bigWigMerge -inList /project2/gilad/briana/threeprimeseq/data/list_bw/list_of_TotNorombigwig.txt /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.bg


sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.bg > /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.sort.bg


bedGraphToBigWig /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.sort.bg /project2/gilad/briana/genome_anotation_data/chrom.length.txt /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.bw


bigWigMerge -inList /project2/gilad/briana/threeprimeseq/data/list_bw/list_of_NucNorombigwig.txt /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.bg

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.bg > /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.sort.bg

bedGraphToBigWig /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.sort.bg /project2/gilad/briana/genome_anotation_data/chrom.length.txt /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.bw

Use pygenome tracks:

makeTNcompINIfile.sh

#!/bin/bash

#SBATCH --job-name=makeTNcompINIfile
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=makeTNcompINIfile.out
#SBATCH --error=makeTNcompINIfile.err
#SBATCH --partition=broadwl
#SBATCH --mem=10G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env



make_tracks_file --trackFiles /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Nuclear_NormalizedMerged.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW_merged/Total_NormalizedMerged.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18499-N-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18499-T-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-19128-N-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-19128-T-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18516-N-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18516-T-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18912-N-combined-norm.bw /project2/gilad/briana/threeprimeseq/data/normalizedBW/YL-SP-18912-T-combined-norm.bw /project2/gilad/briana/genome_anotation_data/NCBI_refseq_forPyGenTrack_sort.bed  -o /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TotalvNuc_exmple.ini

#/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed

Can add

  • /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt

  • /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt

Would need to convert to bed

try on a region:

DOCK9

get region from iGV for now

pyGenomeTracks --tracks TotalvNuc_exmple.ini --region 13:99,439,464-99,736,383 -out testDock9.png

pyGenomeTracks --tracks TotalvNuc_exmple.ini --region 14:66,968,548-67,658,985 -out testGPHN.png


pyGenomeTracks --tracks TotalvNuc_exmple.ini --region 21:43,766,162-43,772,074 -out testTFF2.png

pyGenomeTracks --tracks TotalvNuc_exmple.ini --region 9:92,252,698-92,336,674 -out testUNQ6495.png

chr14:66,968,548-67,658,985

Create boxplots

TN_TFF2_withMetaBox=TN_TFF2_melt %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% mutate(Fraction=ifelse(grepl("T",Sample_ID), "Total","Nuclear"))
Warning: Column `Sample_ID` joining character vector and factor, coercing
into character vector
ggplot(TN_TFF2_withMetaBox,  aes(x=Fraction, y=MappPM, fill=Fraction)) + geom_boxplot(width=.45) + geom_jitter() + scale_fill_brewer(palette = "YlOrRd") + labs(y="CPM", title="TFF2")

R script:
TvNBoxplot.R

library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)


option_list = list(
  make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
              help="input pheno file"),
  make_option(c("-g", "--gene"), action="store", default=NA, type='character',
              help="gene"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file for plot")
)

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)

metadata=read.table("/project2/gilad/briana/threeprimeseq/data/TNcompExamp/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt",header=T, stringsAsFactors = F) %>% select(Sample_ID,Mapped_noMP )


metadata_melt=melt(metadata, id.vars = c("Sample_ID"), value.name = "MappedRead") %>% mutate(MappPM=MappedRead/10^6)


TNhead=read.table("/project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNCountheader.txt", header = T,stringsAsFactors = F)


TN=read.table(opt$pheno, col.names =colnames(TNhead),stringsAsFactors = F) %>% select(-Chr,-Geneid,-Strand, -Length)

TN$Start=as.character(TN$Start)
TN$End=as.character(TN$End)

TN= TN%>% mutate(PeakLoc= paste(Start,End,sep="-")) %>% select(-Start, -End)

TN_melt=melt(TN, id.vars =c("PeakLoc"), variable.name = "ID", value.name = "PeakCount" ) %>% mutate(Sample_ID=substr(ID, 2, length(ID))) 

TN_withMetaBox=TN_melt %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% inner_join(metadata_melt, by=c("Sample_ID")) %>% mutate(Fraction=ifelse(grepl("T",Sample_ID), "Total","Nuclear"))



outplot=ggplot(TN_TFF2_withMetaBox,  aes(x=Fraction, y=MappPM, fill=Fraction)) + geom_boxplot(width=.45) + geom_jitter() + scale_fill_brewer(palette = "YlOrRd") + labs(y="CPM", title=opt$gene)



ggsave(plot=outplot, filename=opt$output, height=10, width=10)

TvNMakeHeatmapandBoxplot.sh

#!/bin/bash

#SBATCH --job-name=TvNMakeHeatmapandBoxplot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TvNMakeHeatmapandBoxplot.out
#SBATCH --error=TvNMakeHeatmapandBoxplot.err
#SBATCH --partition=broadwl
#SBATCH --mem=18G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

gene=$1


grep ${gene} /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc > /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt


Rscript TvNHeatmap.R -P /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}_heatmap.png

Rscript  TvNBoxplot.R -P /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}.txt -g ${gene} -o /project2/gilad/briana/threeprimeseq/data/TNcompExamp/TNcomp_${gene}_boxplot.png
sbatch TvNMakeHeatmapandBoxplot.sh UNQ6494 

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] reshape2_1.4.3  forcats_0.4.0   stringr_1.4.0   dplyr_0.8.0.1  
 [5] purrr_0.3.1     readr_1.3.1     tidyr_0.8.3     tibble_2.0.1   
 [9] ggplot2_3.1.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] plyr_1.8.4         pillar_1.3.1       compiler_3.5.1    
 [7] git2r_0.24.0       workflowr_1.2.0    tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.13      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.3.1       
[19] cli_1.0.1          rstudioapi_0.9.0   yaml_2.2.0        
[22] haven_2.1.0        xfun_0.5           withr_2.1.2       
[25] xml2_1.2.0         httr_1.4.0         knitr_1.21        
[28] hms_0.4.2          generics_0.0.2     fs_1.2.6          
[31] rprojroot_1.3-2    grid_3.5.1         tidyselect_0.2.5  
[34] glue_1.3.0         R6_2.4.0           readxl_1.3.0      
[37] rmarkdown_1.11     modelr_0.1.4       magrittr_1.5      
[40] whisker_0.3-2      backports_1.1.3    scales_1.0.0      
[43] htmltools_0.3.6    rvest_0.3.2        assertthat_0.2.0  
[46] colorspace_1.4-0   labeling_0.3       stringi_1.3.1     
[49] lazyeval_0.2.1     munsell_0.5.0      broom_0.5.1       
[52] crayon_1.3.4