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File Version Author Date Message
html b9cce4b Briana Mittleman 2018-12-05 Build site.
Rmd e230640 Briana Mittleman 2018-12-05 add code to save relevant figures
html 210b58b Briana Mittleman 2018-11-29 Build site.
Rmd 413c8fd Briana Mittleman 2018-11-29 add filter QTL analysis and start explain pqtl

I want to use this analysis to understand how many genes are not included in the apaQTL analysis due to low coverage and how many are not included due to low variance between individuals. I can do this by modifying the leafcutter prepare_phenotype_table.py to include output this information.

This will take 2 arguments, the ratio file and the outfile with the information for missing info ##Modify Scripts MissingandNoVarGenes.py

#!/usr/bin/env python

import sys
import gzip
import numpy as np
import scipy as sc
import pickle

from optparse import OptionParser
    
from sklearn.decomposition import PCA
from sklearn import preprocessing
from sklearn import linear_model

from scipy.stats import rankdata
from scipy.stats import norm

def qqnorm(x):
    n=len(x)
    a=3.0/8.0 if n<=10 else 0.5
    return(norm.ppf( (rankdata(x)-a)/(n+1.0-2.0*a) ))

def stream_table(f, ss = ''):
    fc = '#'
    while fc[0] == "#":
        fc = f.readline().strip()
        head = fc.split(ss)

    for ln in f:
        ln = ln.strip().split(ss)
        attr = {}

        for i in range(len(head)):
            try: attr[head[i]] = ln[i]
            except: break
        yield attr

def main(ratio_file,out_File, pcs=50):
    outF=open(out_File, "w")
    n_Missing=0
    n_lowVar=0
    dic_pop, fout = {}, {}
    try: open(ratio_file)
    except: 
        sys.stderr.write("Can't find %s..exiting\n"%(ratio_file))
        return

    sys.stderr.write("Starting...\n")
    for i in range(1,23):
        fout[i] = file(ratio_file+".phen_chr%d"%i,'w')
        fout_ave = file(ratio_file+".ave",'w')
    valRows, valRowsnn, geneRows = [], [], []
    finished = False
    header = gzip.open(ratio_file).readline().split()[1:]


    for i in fout:
        fout[i].write("\t".join(["#Chr","start", "end", "ID"]+header)+'\n')

    for dic in stream_table(gzip.open(ratio_file),' '):

        chrom = dic['chrom'].replace("chr",'')
        chr_ = chrom.split(":")[0]
        if chr_ in 'XY': continue
        NA_indices, valRow, aveReads = [], [], []
        tmpvalRow = []

        i = 0
        for sample in header:

            try: count = dic[sample]
            except: print chrom, len(dic)
            num, denom = count.split('/')
            if float(denom) < 1:
                count = "NA"
                tmpvalRow.append("NA")
                NA_indices.append(i)
            else:
                # add a 0.5 pseudocount
                count = (float(num)+0.5)/((float(denom))+0.5)
                tmpvalRow.append(count) 
                aveReads.append(count)


        # If ratio is missing for over 40% of the samples, skip
        if tmpvalRow.count("NA") > len(tmpvalRow)*0.4:
            #this is missing data (i want to write this row to skipped genes)
            n_Missing += 1
            continue

        ave = np.mean(aveReads)

        # Set missing values as the mean of all values
        for c in tmpvalRow:
            if c == "NA": valRow.append(ave)
            else: valRow.append(c)

        # If there is too little variation, skip (there is a bug in fastqtl which doesn't handle cases with no variation)
        if np.std(valRow) < 0.005:
            #this is little variation (i want to write this row to little variation genes)
            n_lowVar +=1 
            continue
    outF.write("MissingData:%d: NoVariation:%d"%(n_Missing,n_lowVar))
    outF.close()

if __name__ == "__main__":

    parser = OptionParser(usage="usage: %prog [-p num_PCs] input_perind.counts.gz")
    parser.add_option("-p", "--pcs", dest="npcs", default = 50, help="number of PCs output")
    (options, args) = parser.parse_args()
    if len(args)==0:
        sys.stderr.write("Error: no ratio file provided... (e.g. python leafcutter/scripts/prepare_phenotype_table.py input_perind.counts.gz\n")
        exit(0)
    main(args[0], args[1], int(options.npcs))

Try this in /project2/gilad/briana/threeprimeseq/data/PASstats

module load python 

python /project2/gilad/briana/threeprimeseq/code/MissingandNoVarGenes.py /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz /project2/gilad/briana/threeprimeseq/data/PASstats/Nuclear_missing_and_noVar.txt

python /project2/gilad/briana/threeprimeseq/code/MissingandNoVarGenes.py /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz /project2/gilad/briana/threeprimeseq/data/PASstats/Total_missing_and_noVar.txt

total (338142): MissingData:1692: NoVariation:112641
Nuclear (338142):
MissingData:472: NoVariation:134521

This is 33% in total and 40% in nuclear with low variation.

It may be interesting to look at how many peaks have counts <5 in at least 40% of the individuals. This could tell me if we need to be filtering more.

LowExpPeaks.py

Input the ratio matrix and a file to output the number of NAs due to low expression. I am looking at the number of peaks with <5 reads in at least 40% of the individuals.

#!/usr/bin/env python

import sys
import gzip
import numpy as np
import scipy as sc
import pickle

from optparse import OptionParser
    
from sklearn.decomposition import PCA
from sklearn import preprocessing
from sklearn import linear_model

from scipy.stats import rankdata
from scipy.stats import norm

def qqnorm(x):
    n=len(x)
    a=3.0/8.0 if n<=10 else 0.5
    return(norm.ppf( (rankdata(x)-a)/(n+1.0-2.0*a) ))

def stream_table(f, ss = ''):
    fc = '#'
    while fc[0] == "#":
        fc = f.readline().strip()
        head = fc.split(ss)

    for ln in f:
        ln = ln.strip().split(ss)
        attr = {}

        for i in range(len(head)):
            try: attr[head[i]] = ln[i]
            except: break
        yield attr

def main(ratio_file,out_File, pcs=50):
    outF=open(out_File, "w")
    n_Missing=0
    n_lowVar=0
    dic_pop, fout = {}, {}
    try: open(ratio_file)
    except: 
        sys.stderr.write("Can't find %s..exiting\n"%(ratio_file))
        return

    sys.stderr.write("Starting...\n")
    for i in range(1,23):
        fout[i] = file(ratio_file+".phen_chr%d"%i,'w')
        fout_ave = file(ratio_file+".ave",'w')
    valRows, valRowsnn, geneRows = [], [], []
    finished = False
    header = gzip.open(ratio_file).readline().split()[1:]


    for i in fout:
        fout[i].write("\t".join(["#Chr","start", "end", "ID"]+header)+'\n')

    for dic in stream_table(gzip.open(ratio_file),' '):

        chrom = dic['chrom'].replace("chr",'')
        chr_ = chrom.split(":")[0]
        if chr_ in 'XY': continue
        NA_indices, valRow, aveReads = [], [], []
        tmpvalRow = []

        i = 0
        for sample in header:

            try: count = dic[sample]
            except: print chrom, len(dic)
            num, denom = count.split('/')
            if float(denom) < 1:
                count = "NA"
                tmpvalRow.append("NA")
                NA_indices.append(i)
            #add condition to make the count NA if the numerator is less than 5 
            if float(num) < 5:
                count = "NA"
                tmpvalRow.append("NA")
                NA_indices.append(i)
            else:
                # add a 0.5 pseudocount
                count = (float(num)+0.5)/((float(denom))+0.5)
                tmpvalRow.append(count) 
                aveReads.append(count)


        # If ratio is missing for over 40% of the samples, skip
        if tmpvalRow.count("NA") > len(tmpvalRow)*0.4:
            #this is missing data (i want to write this row to skipped genes)
            n_Missing += 1
            continue

        ave = np.mean(aveReads)

        # Set missing values as the mean of all values
        for c in tmpvalRow:
            if c == "NA": valRow.append(ave)
            else: valRow.append(c)

        # If there is too little variation, skip (there is a bug in fastqtl which doesn't handle cases with no variation)
        if np.std(valRow) < 0.005:
            #this is little variation (i want to write this row to little variation genes)
            n_lowVar +=1 
            continue
    outF.write("TooLow:%d\n"%(n_Missing))
    outF.close()

if __name__ == "__main__":

    parser = OptionParser(usage="usage: %prog [-p num_PCs] input_perind.counts.gz")
    parser.add_option("-p", "--pcs", dest="npcs", default = 50, help="number of PCs output")
    (options, args) = parser.parse_args()
    if len(args)==0:
        sys.stderr.write("Error: no ratio file provided... (e.g. python leafcutter/scripts/prepare_phenotype_table.py input_perind.counts.gz\n")
        exit(0)
    main(args[0], args[1], int(options.npcs))
module load python 

python /project2/gilad/briana/threeprimeseq/code/LowExpPeaks.py /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz /project2/gilad/briana/threeprimeseq/data/PASstats/Nuclear_LowExp.txt

python /project2/gilad/briana/threeprimeseq/code/LowExpPeaks.py /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz /project2/gilad/briana/threeprimeseq/data/PASstats/Total_LowExp.txt

Total: 258090/338142 Nuclear: 185530/338142

Plot results

library(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(edgeR)
Loading required package: limma
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::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(reshape2)

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

    smiths
fraction=c("Total", "Nuclear")
NoVariation=c(0.33,0.40)
LowExpression=c(0.76,0.55)
PASstats=data.frame(fraction, NoVariation, LowExpression)
PASstats_melt=melt(PASstats)
Using fraction as id variables

plot thid

prefilter_pas_stat=ggplot(PASstats_melt, aes(x=variable, y=value,fill=fraction))+ geom_bar(stat="identity",position="dodge") + labs(y="Proportion of PAS", title="PAS with low variation or low expression", x="Category") + scale_fill_manual(values=c("deepskyblue3","darkviolet"))
prefilter_pas_stat

Version Author Date
210b58b Briana Mittleman 2018-11-29 
ggsave("../output/plots/QC_plots/prefilter_PASstats.png", prefilter_pas_stat)
Saving 7 x 5 in image

Do this de novo

CPM

It may be better to look at mean distribution of reads per peak to look for a cuttoff. I can take counts matrix

  • /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc

  • /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc

It would be good to do this in CPM and look at the densities like you do for RNA seq. In order to do this. I need a vector with the peak lengths.

total:

Load data and convert to CPM with peak length.

totalPeakCov=read.table("../data/PeakCounts/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", header = T, stringsAsFactors = F)

peakLength=totalPeakCov[,6]


totalPeakCov_cpm= cpm(as.matrix(totalPeakCov[,7:ncol(totalPeakCov)]),log=T, gene.length=peakLength)

Plot prefiltered density

plotDensities(totalPeakCov_cpm, legend = "bottomright", main="Pre-filtering")
abline(v = -1, lty = 3)

Version Author Date
210b58b Briana Mittleman 2018-11-29

Looks like filtering around -1 works. I can keep peaks with greater than -1 cpm in 2/3 of the samples (26)

keep.exprs_T=rowSums(totalPeakCov_cpm>-1) >= 26
totalPeakCov_cpm_filt= totalPeakCov_cpm[keep.exprs_T,]

plotDensities(totalPeakCov_cpm_filt, legend = "bottomright", main="Post-filtering")

Version Author Date
210b58b Briana Mittleman 2018-11-29

I can assess this with boxplots. It is interesting to see how different these data are from the RNA seq.

boxplot(totalPeakCov_cpm, main="Log CPM Counts by library prefilter")

Version Author Date
210b58b Briana Mittleman 2018-11-29 
boxplot(totalPeakCov_cpm_filt, main="Log CPM Counts by library filtered")

Version Author Date
210b58b Briana Mittleman 2018-11-29

Nuclear

Load data and convert to CPM with peak length.

nuclearPeakCov=read.table("../data/PeakCounts/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", header = T, stringsAsFactors = F)

peakLengthN=nuclearPeakCov[,6]


nuclearPeakCov_cpm= cpm(as.matrix(nuclearPeakCov[,7:ncol(nuclearPeakCov)]),log=T, gene.length=peakLengthN)

Plot prefiltered density

plotDensities(nuclearPeakCov_cpm, legend = "bottomright", main="Pre-filtering")
abline(v = 0, lty = 3)

Version Author Date
210b58b Briana Mittleman 2018-11-29

Looks like filtering around -4.5 works. I can keep peaks with greater than -4.5 cpm in 2/3 of the samples (26)

keep.exprs_N=rowSums(nuclearPeakCov_cpm>-4.5) >= 26
nuclearPeakCov_cpm_filt= nuclearPeakCov_cpm[keep.exprs_T,]

plotDensities(nuclearPeakCov_cpm_filt, legend = "bottomright", main="Post-filtering")

Version Author Date
210b58b Briana Mittleman 2018-11-29 
boxplot(nuclearPeakCov_cpm, main="Log CPM Counts by library prefilter")

Version Author Date
210b58b Briana Mittleman 2018-11-29 
boxplot(nuclearPeakCov_cpm_filt, main="Log CPM Counts by library filtered")

Version Author Date
210b58b Briana Mittleman 2018-11-29

I had to filter stronger in the total fraction to get similar boxplot distributions. I can compare this method to using raw counts rather than CPM.

With this scheme I have the following number of peaks remaining:
Total: 66,782 Nuclear: 124,196

Raw counts

Total

png("../output/plots/QC_plots/log10TotalPeakCov_prefilter.png")
totalPeakCov_logM=log10(as.matrix(totalPeakCov[,7:ncol(totalPeakCov)]))
plotDensities(totalPeakCov_logM, legend = "bottomright", main="Raw Pre-filtering")
abline(v = .55, lty = 3)
dev.off()
quartz_off_screen 
                2

I can filter 2/3 (26) individuals at .55.

keep.exprs_T_raw=rowSums(totalPeakCov_logM>.55) >= 26
totalPeakCov_raw_filt= totalPeakCov_logM[keep.exprs_T_raw,]

png("../output/plots/QC_plots/log10TotalPeakCov_postfilter.png")
plotDensities(totalPeakCov_raw_filt, legend = "bottomright", main="Post-filtering")
dev.off()
quartz_off_screen 
                2 
boxplot(totalPeakCov_logM, main="log Counts by library prefilter")
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out =
z$out[z$group == : Outlier (-Inf) in boxplot 9 is not drawn

Version Author Date
210b58b Briana Mittleman 2018-11-29 
boxplot(totalPeakCov_raw_filt, main="Log Counts by library filtered")
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out =
z$out[z$group == : Outlier (-Inf) in boxplot 2 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 3 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 4 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 5 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 6 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 12 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 13 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 14 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 15 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 16 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 26 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 27 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 28 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 29 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 35 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 36 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 37 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 38 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 39 is not drawn

Version Author Date
210b58b Briana Mittleman 2018-11-29

This leaves 80554 rows.

Nuclear

png("../output/plots/QC_plots/log10NuclearPeakCov_prefilter.png")

nuclearPeakCov_logM=log10(as.matrix(nuclearPeakCov[,7:ncol(nuclearPeakCov)]))
plotDensities(nuclearPeakCov_logM, legend = "bottomright", main="Raw Pre-filtering")
abline(v = .55, lty = 3)
dev.off()
quartz_off_screen 
                2

I can filter 2/3 (26) individuals at .55.

keep.exprs_N_raw=rowSums(nuclearPeakCov_logM>.55) >= 26
nuclearPeakCov_raw_filt= nuclearPeakCov_logM[keep.exprs_N_raw,]
png("../output/plots/QC_plots/log10NuclearPeakCov_postfilter.png")
plotDensities(nuclearPeakCov_raw_filt, legend = "bottomright", main="Post-filtering")
dev.off()
quartz_off_screen 
                2 
boxplot(nuclearPeakCov_logM, main="log Counts by library prefilter")
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out =
z$out[z$group == : Outlier (-Inf) in boxplot 2 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 32 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 35 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 37 is not drawn
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z$out[z$group == : Outlier (-Inf) in boxplot 38 is not drawn

Version Author Date
210b58b Briana Mittleman 2018-11-29 
boxplot(nuclearPeakCov_raw_filt, main="Log Counts by library filtered")
Warning in bplt(at[i], wid = width[i], stats = z$stats[, i], out =
z$out[z$group == : Outlier (-Inf) in boxplot 1 is not drawn
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Version Author Date
210b58b Briana Mittleman 2018-11-29

This leaves 159308 peaks with coverage.

Filter based on raw coverage

I am going to write a script to filter based on this cutoff so I can see if there is increased power to detect QTLs. I will need to change this when I add individuals.

extraFilterPeaks_39ind.R

#this script will filter the peaks based on peaks with coverage greater than log10(cov)==.55 in 2/3 of the individuals, I will have to fix headers after 

#nonfilter dir:  /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/  
#filter dir: /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/


#total
totalPeak=read.table("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", stringsAsFactors = F, header=T)

totalPeakCov_logM=log10(as.matrix(totalPeak[,7:ncol(totalPeak)]))
keep.exprs_T_raw=rowSums(totalPeakCov_logM>.55) >= 26
totalPeakCov_raw_filt= totalPeak[keep.exprs_T_raw,]

write.table(totalPeakCov_raw_filt,file="/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", quote=F, col.names = T ,row.names = F)

#nuclear 

nuclearPeak=read.table("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", header = T)

nuclearPeakCov_logM=log10(as.matrix(nuclearPeak[,7:ncol(nuclearPeak)]))
keep.exprs_N_raw=rowSums(nuclearPeakCov_logM>.55) >= 26
nuclearPeakCov_raw_filt= nuclearPeak[keep.exprs_N_raw,]

write.table(nuclearPeakCov_raw_filt,file="/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", quote=F, col.names = T,row.names = F )

I can use fileIDs /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript_head.txt and /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript_head.txt

makePhenoRefSeqPeaks_Transcript_Total_extraFilt.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#gene start and end dictionaries: 
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt"):
    chrom, start, end, geneID, score, strand = ln.split('\t')
    gene= geneID.split(":")[1]
    if "-" in gene:
        gene=gene.split("-")[0]
    if gene not in dic_geneS:
        dic_geneS[gene]=int(start)
        dic_geneE[gene]=int(end)
        


#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA) 
fout.write(" ".join(peak + inds_noL) + '\n' )


count_file=open("/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=dic_geneS[id_list[5]]
        end=dic_geneE[id_list[5]]
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

makePhenoRefSeqPeaks_Transcript_Nuclear_extraFilt.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#gene start and end dictionaries: 
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt"):
    chrom, start, end, geneID, score, strand = ln.split('\t')
    gene= geneID.split(":")[1]
    if "-" in gene:
        gene=gene.split("-")[0]
    if gene not in dic_geneS:
        dic_geneS[gene]=int(start)
        dic_geneE[gene]=int(end)
        


#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA) 
fout.write(" ".join(peak + inds_noL) + '\n' )


count_file=open("/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=dic_geneS[id_list[5]]
        end=dic_geneE[id_list[5]]
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

run_makePhen_sep_Transcript_extraFilt.sh

#!/bin/bash

#SBATCH --job-name=run_makepheno_sep_trans_filt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_makepheno_sep_trans_filt.out
#SBATCH --error=run_makepheno_sep_trans_filt.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks_Transcript_Total_extraFilt.py  

python makePhenoRefSeqPeaks_Transcript_Nuclear_extraFilt.py

Prepare for fastQTL

/project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov

module load samtools
#zip file 
gzip Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt

module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py  Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz 

#source activate three-prime-env
sh  Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz_prepare.sh

#run for nuclear as well 
gzip  Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt
#unload anaconda, load python
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py  Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz
#load anaconda and env. 
sh  Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz_prepare.sh





#keep only 2 PCs
head -n 3 Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.PCs > Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs
head -n 3 Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.PCs > Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs

Now I can rerun the modified leafcutter and see if this improves the drop proportion.

module load python 

python /project2/gilad/briana/threeprimeseq/code/MissingandNoVarGenes.py /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz /project2/gilad/briana/threeprimeseq/data/PASstats/Total_missing_and_noVarFilter.txt

python /project2/gilad/briana/threeprimeseq/code/MissingandNoVarGenes.py /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz /project2/gilad/briana/threeprimeseq/data/PASstats/Nuclear_missing_and_noVarFilter.txt




python /project2/gilad/briana/threeprimeseq/code/LowExpPeaks.py /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz /project2/gilad/briana/threeprimeseq/data/PASstats/Nuclear_LowExpFilter.txt

python /project2/gilad/briana/threeprimeseq/code/LowExpPeaks.py /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz /project2/gilad/briana/threeprimeseq/data/PASstats/Total_LowExpFilter.txt

No variation
* Nuclear: 37738/159308
* Total: 8289/80554
Low exp
* Nuclear 8221/159308
* Total: 2808/80554

#total, nuclear
NoVariationFilt=c(0.10,0.23)
LowExpressionFilt=c(0.035,0.052)
PASstatsFilt=data.frame(fraction, NoVariationFilt, LowExpressionFilt)
PASstatsFilt_melt=melt(PASstatsFilt)
Using fraction as id variables

plot thid

postfilter_pas_stat=ggplot(PASstatsFilt_melt, aes(x=variable, y=value,fill=fraction))+ geom_bar(stat="identity",position="dodge") + labs(y="Proportion of Filtered PAS", title="PAS with low variation or low expression after filtering", x="Category") + scale_fill_manual(values=c("deepskyblue3","darkviolet"))
postfilter_pas_stat

Version Author Date
210b58b Briana Mittleman 2018-11-29 
ggsave("../output/plots/QC_plots/postfilter_PASstats.png", postfilter_pas_stat)
Saving 7 x 5 in image

merge the pre and post filter into the same dataframe (did this in excel and saved it)

PasStats_all=read.csv("../data/PeakCounts/pasStats.csv", header=T, stringsAsFactors = F)

preandpostfilter_pas_stat=ggplot(PasStats_all, aes(x=Category, y=Value,fill=Fraction))+ geom_bar(stat="identity",position="dodge")  +labs(y="Proportion of PAS", title="PAS with low variation or low expression before and after filtering", x="Category") + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + facet_grid(~Filter)
preandpostfilter_pas_stat

Version Author Date
210b58b Briana Mittleman 2018-11-29 
ggsave("../output/plots/QC_plots/preandpostfilter_PASstats.png", preandpostfilter_pas_stat)
Saving 7 x 5 in image

Rerun QTL analysis.

I can use the same sample list. /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt"
APAqtl_nominal_transcript_filt.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_transcript_filt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_transcript_filt.out
#SBATCH --error=APAqtl_nominal_transcript_filt.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

permuted:

APAqtl_permuted_transcript_filt.sh


#!/bin/bash


#SBATCH --job-name=APAqtl_permuted_transcript_filt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_permuted_transcript_filt.out
#SBATCH --error=APAqtl_permuted_transcript_filt.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000  --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000  --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/ExtrafiltPeakOppstrand_cov/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

APAqtlpermCorrectQQplot_trans_filt.R

library(dplyr)


##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_permRes.out",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")

#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_transcript_filt.png") 
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_permResBH.out", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_permRes.out",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")


#plot qqplot
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_transcript_filt.png") 
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_permResBH.out", col.names = T, row.names = F, quote = F)

run_APAqtlpermCorrectQQplot_trans_filt.sh

#!/bin/bash


#SBATCH --job-name=run_APAqtlpermCorrectQQplot_trans_filt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_APAqtlpermCorrectQQplot_trans_filt.out
#SBATCH --error=run_APAqtlpermCorrectQQplot_trans_filt.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


Rscript APAqtlpermCorrectQQplot_trans_filt.R

Evaluate results:

Total:

tot.perm= read.table("../data/perm_QTL_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_permResBH.out",head=T, stringsAsFactors=F)

tot.perm %>% filter(-log10(bh) > 1) %>% nrow()
[1] 183

Nuclear:

nuc.perm= read.table("../data/perm_QTL_trans_filt/Extrafiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_permResBH.out",head=T, stringsAsFactors=F)

nuc.perm %>% filter(-log10(bh) > 1) %>% nrow()
[1] 1031

This analysis gives 183 total and 1031 nuclear. (65 more total and 151 more in nuclear)

plot(tot.perm$ppval, tot.perm$bpval, xlab="Direct method", ylab="Beta approximation", main="Total Check plot")
abline(0, 1, col="red")

Version Author Date
210b58b Briana Mittleman 2018-11-29 
plot(nuc.perm$ppval, nuc.perm$bpval, xlab="Direct method", ylab="Beta approximation", main="Nuclear Check plot")
abline(0, 1, col="red")

Version Author Date
210b58b Briana Mittleman 2018-11-29 


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  reshape2_1.4.3  forcats_0.3.0   stringr_1.4.0  
 [5] dplyr_0.7.6     purrr_0.2.5     readr_1.1.1     tidyr_0.8.1    
 [9] tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1 edgeR_3.22.5   
[13] limma_3.36.5    workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4 locfit_1.5-9.1   haven_1.1.2      lattice_0.20-35 
 [5] colorspace_1.3-2 htmltools_0.3.6  yaml_2.2.0       rlang_0.2.2     
 [9] pillar_1.3.0     glue_1.3.0       withr_2.1.2      modelr_0.1.2    
[13] readxl_1.1.0     bindr_0.1.1      plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.13   
[21] labeling_0.3     knitr_1.20       broom_0.5.0      Rcpp_0.12.19    
[25] backports_1.1.2  scales_1.0.0     jsonlite_1.6     fs_1.2.6        
[29] hms_0.4.2        digest_0.6.17    stringi_1.2.4    grid_3.5.1      
[33] rprojroot_1.3-2  cli_1.0.1        tools_3.5.1      magrittr_1.5    
[37] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[41] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.11  
[45] httr_1.3.1       rstudioapi_0.9.0 R6_2.3.0         nlme_3.1-137    
[49] git2r_0.24.0     compiler_3.5.1