Last updated: 2019-02-07

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
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    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/understandPeaks.Rmd
    Modified:   code/Snakefile
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Expand here to see past versions:
    File Version Author Date Message
    Rmd 74276bd Briana Mittleman 2019-02-07 add plot by percentile
    html 83ebe13 Briana Mittleman 2019-02-07 Build site.
    Rmd e41afd9 Briana Mittleman 2019-02-07 average peak diff by exp and cov
    html cc6b1ee Briana Mittleman 2019-02-05 Build site.
    Rmd 06912e9 Briana Mittleman 2019-02-05 initiate ind peak usage diff analysis 

library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(reshape2)
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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── Conflicts ─────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

So far i have been looking at mean peak usage for my filters. As a QC metric, I want to look at the variance in this measurement. I want to understand the reproducibility of the data at a usage percent level. I also want to see if this value is dependent on coverage. I will look at the peaks used in the QTL analysis with 55 individuals and comopute an RNSD value for each gene. This value is computed as \(\sqrt{\sum_{n=1}^N (X-Y)^2}\). Here n is the number of peaks in the gene up to N. X and Y are different individuals. I will plot this value for each gene. I can do this for 2 individuals with low depth and 2 with high depth.

I can start with just the total individuals.

First step is to convert /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs_3UTR/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.3UTR.fixed.pheno_5perc.fc.gz to numeric.

First I will cut the first column to just get the counts:

less /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz | cut -f1 -d" " --complement | sed '1d' > /project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc_counts

5percCovUsageToNumeric.py

def convert(infile, outfile):
  final=open(outfile, "w")
  for ln in open(infile, "r"):
    line_list=ln.split()
    new_list=[]
    for i in line_list:
      num, dem = i.split("/")
      if dem == "0":
        perc = "0.00"
      else:
        perc = int(num)/int(dem)
        perc=round(perc,2)
        perc= str(perc)
      new_list.append(perc)
    final.write("\t".join(new_list)+ '\n')
  final.close()
  
convert("/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc_counts","/project2/gilad/briana/threeprimeseq/data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.numeric.txt")

Get the gene names from the first file:

less /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz | cut -f1 -d" " | sed '1d' > PeakIDs.txt

Merge the files: PeakIDs.txt and the numeric version

paste PeakIDs.txt filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.numeric.txt > filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.numeric.named.txt
names=read.table("../data/PeakUsage_noMP_GeneLocAnno/PeakUsageHeader.txt",stringsAsFactors = F) %>% t %>% as_data_frame()
usageTot=read.table("../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.numeric.named.txt", header=F, stringsAsFactors = F)
colnames(usageTot)= names$V1

I want to use ind based on coverage

metadataTotal=read.table("../data/threePrimeSeqMetaData55Ind.txt", header=T) %>% filter(fraction=="total")

#top
metadataTotal %>% arrange(desc(reads)) %>% slice(1:2)
  Sample_ID  line fraction batch   fqlines    reads   mapped prop_mapped
1   18504_T 18504    total     4 139198896 34799724 25970922 0.746296781
2   18855_T 18855    total     4 139040660 34760165 24532100 0.705753267
  Mapped_noMP prop_MappedwithoutMP Sex  Wake_Up Collection count1 count2
1    14703998          0.422532029   M 10/31/18   11/19/18    1.9   1.44
2    12999618          0.373980331   F 10/31/18   11/19/18    1.6   1.40
  alive1 alive2 alive_avg undiluted_avg Extraction Conentration
1     83     81      82.0          1.67   12.12.18       1984.6
2     71     80      75.5          1.50   12.12.18       2442.9
  ratio260_280 to_use  h20 threeprime_start    Cq cycles library_conc
1         2.07   0.50 9.50         12.17.18 19.67     20        0.402
2         2.08   0.41 9.59         12.17.18 21.00     24        0.353
#bottom
metadataTotal %>% arrange(reads) %>% slice(1:2)
  Sample_ID  line fraction batch  fqlines   reads  mapped prop_mapped
1   19160_T 19160    total     2 30319920 7579980 5473593   0.7221118
2   19101_T 19101    total     4 33766300 8441575 6741550 0.798612818
  Mapped_noMP prop_MappedwithoutMP Sex  Wake_Up Collection count1 count2
1     4009189           0.52891815   M  6/19/18    7/10/18     NA     NA
2     3630954          0.430127553   M 11/26/18   12/14/18  0.976   1.05
  alive1 alive2 alive_avg undiluted_avg Extraction Conentration
1     NA     NA        90         1.100    7.12.18       1287.1
2     76     86        81         1.013   12.16.18       2453.6
  ratio260_280 to_use  h20 threeprime_start    Cq cycles library_conc
1         2.07   0.78 9.22          7.19.18 19.44     20        1.440
2         2.07   0.41 9.59         12.17.18 23.14     24        0.097

2 Top read ind: NA18504, NA18855
2 bottom read ind: NA19160, NA19101

topInd=usageTot %>% select(chrom, NA18504, NA18855) %>% separate(chrom, into=c("chr", "start", "end", "geneInf"), sep =":") %>% separate(geneInf, into=c("gene", "strand", "peak"), sep="_") %>% mutate(val=(NA18504-NA18855)^2) %>% group_by(gene) %>% summarise(sumPeaks=sum(val)) %>% mutate(RNSD=sqrt(sumPeaks)) %>% mutate(Sample="Top") %>% select(Sample, RNSD)
Warning: Expected 3 pieces. Additional pieces discarded in 4 rows [4757,
4758, 4759, 35047].
bottomInd=usageTot %>% select(chrom, NA19160, NA19101) %>% separate(chrom, into=c("chr", "start", "end", "geneInf"), sep =":") %>% separate(geneInf, into=c("gene", "strand", "peak"), sep="_") %>% mutate(val=(NA19160-NA19101)^2) %>% group_by(gene) %>% summarise(sumPeaks=sum(val)) %>% mutate(RNSD=sqrt(sumPeaks)) %>% mutate(Sample="Bottom") %>% select(Sample, RNSD)
Warning: Expected 3 pieces. Additional pieces discarded in 4 rows [4757,
4758, 4759, 35047].
bothInd= data.frame(rbind(topInd, bottomInd))
ggplot(bothInd, aes(x=RNSD, by=Sample, fill=Sample))+geom_density(alpha=.4) +labs(title="RNSD for peak usage in top 2 and bottom 2 individuals by reads")

Expand here to see past versions of unnamed-chunk-9-1.png:
Version Author Date
83ebe13 Briana Mittleman 2019-02-07 

ggplot(bothInd, aes(y=RNSD, x=Sample, fill=Sample))+geom_violin() + labs(title="RNSD for peak usage in top 2 and bottom 2 individuals by reads")

Expand here to see past versions of unnamed-chunk-10-1.png:
Version Author Date
83ebe13 Briana Mittleman 2019-02-07

Change the statistic to increase the interpretability.

I can look at just genes with 2 peaks. I can then sum the absolute value of the individual differences.

TwoPeakGenes_top= usageTot %>% select(chrom, NA18504, NA18855) %>% separate(chrom, into=c("chr", "start", "end", "geneInf"), sep =":") %>% separate(geneInf, into=c("gene", "strand", "peak"), sep="_") %>% group_by(gene) %>% mutate(nPeak=n()) %>% filter(nPeak==2)
Warning: Expected 3 pieces. Additional pieces discarded in 4 rows [4757,
4758, 4759, 35047].
n2Peak=unique(TwoPeakGenes_top$gene) %>% length()

This gives us 3448 genes.

TwoPeakGenes_top_stat= TwoPeakGenes_top %>% mutate(absDiff=abs(NA18504-NA18855)) %>% group_by(gene) %>% select(gene, absDiff) %>% distinct(gene, .keep_all=T) 
Avg2PeakGeneTop=sum(TwoPeakGenes_top_stat$absDiff)/n2Peak
Avg2PeakGeneTop
[1] 0.1925493

Now do this for the bottom 2 ind:

TwoPeakGenes_bottom= usageTot %>% select(chrom, NA19160, NA19101) %>% separate(chrom, into=c("chr", "start", "end", "geneInf"), sep =":") %>% separate(geneInf, into=c("gene", "strand", "peak"), sep="_") %>% group_by(gene) %>% mutate(nPeak=n()) %>% filter(nPeak==2)
Warning: Expected 3 pieces. Additional pieces discarded in 4 rows [4757,
4758, 4759, 35047].
TwoPeakGenes_bottom_stat= TwoPeakGenes_bottom %>% mutate(absDiff=abs(NA19101-NA19160)) %>% group_by(gene) %>% select(gene, absDiff) %>% distinct(gene, .keep_all=T) 
Avg2PeakGeneBottom=sum(TwoPeakGenes_bottom_stat$absDiff)/n2Peak
Avg2PeakGeneBottom
[1] 0.2727929

This demonstrates we may have some noise in the data and have not reached sequencing saturation. However, this could be driven by lowly expressed genes. I will fraction this analysis by the top expressed and bottom expressed genes. To do this I will pull in the count data (before we had usage parameters) and add to this table the mean counts.

Count files:

  • /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.fc
  • /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.fc

I need to filter these for the peaks I kept after the 5% usage filter.

filterCounts_5percCovPeaks.py

#python  

totalokPeaks5perc_file="/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt"

totalokPeaks5perc={}
for ln in open(totalokPeaks5perc_file,"r"):
    peakname=ln.split()[5]
    totalokPeaks5perc[peakname]=""


nuclearokPeaks5perc_file="/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt"
nuclearokPeaks5perc={}
for ln in open(nuclearokPeaks5perc_file,"r"):
    peakname=ln.split()[5]
    nuclearokPeaks5perc[peakname]=""
    
    
totalPhenoBefore=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.fc","r")
totalPhenoAfter=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc", "w")
for num, ln in enumerate(totalPhenoBefore):
    if num == 1:
        totalPhenoAfter.write(ln)
    if num >1:  
        id=ln.split()[0].split(":")[0]
        if id in totalokPeaks5perc.keys():
            totalPhenoAfter.write(ln)
totalPhenoAfter.close()  

nuclearPhenoBefore=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.fc","r")
nuclearPhenoAfter=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.5perc.fc", "w")
for num, ln in enumerate(nuclearPhenoBefore):
    if num ==1:
        nuclearPhenoAfter.write(ln)
    if num > 1:  
        id=ln.split()[0].split(":")[0]
        if id in nuclearokPeaks5perc.keys():
            nuclearPhenoAfter.write(ln)
nuclearPhenoAfter.close()

Pull the filtered counts for the total here and get the genes in TwoPeakGenes_top_stat$gene. For simplicity I will just look at the ind. with the top coverage.

TotalCounts=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc", header=T, stringsAsFactors = F) %>% separate(Geneid, into =c('peak', 'chr', 'start', 'end', 'strand', 'gene'), sep = ":") %>% select(-peak, -chr, -start, -end, -strand, -Chr, -Start, -End, -Strand, -Length) %>% group_by(gene) %>% mutate(PeakCount=n()) %>% filter(PeakCount==2) %>% select(gene, X18504_T) %>% group_by(gene) %>% summarise(Exp=sum(X18504_T))

Top 1000 genes

TotalCounts_top1000= TotalCounts %>% arrange(desc(Exp)) %>% top_n(1000)
Selecting by Exp

Join this with top ind:

TwoPeakGenes_top_stat_top100 = TwoPeakGenes_top_stat %>% inner_join(TotalCounts_top1000, by="gene")

Avg2PeakGeneTopExpHigh=sum(TwoPeakGenes_top_stat_top100$absDiff)/nrow(TwoPeakGenes_top_stat_top100)
Avg2PeakGeneTopExpHigh
[1] 0.09936

Do the same for low expressed genes (not 0)

TotalCounts_bottom1000= TotalCounts %>% filter(Exp > 0) %>% top_n(-1000)
Selecting by Exp
TwoPeakGenes_top_stat_bottom100 = TwoPeakGenes_top_stat %>% inner_join(TotalCounts_bottom1000, by="gene")

Avg2PeakGeneTopExpLow=sum(TwoPeakGenes_top_stat_bottom100$absDiff)/nrow(TwoPeakGenes_top_stat_bottom100)

Avg2PeakGeneTopExpLow
[1] 0.3172211

Do this for the other individuals as wel..

TwoPeakGenes_bottom_stat_top100 = TwoPeakGenes_bottom_stat %>% inner_join(TotalCounts_top1000, by="gene")

Avg2PeakGeneBottomExpHigh=sum(TwoPeakGenes_bottom_stat_top100$absDiff)/nrow(TwoPeakGenes_bottom_stat_top100)
Avg2PeakGeneBottomExpHigh
[1] 0.18876
TwoPeakGenes_bottom_stat_bottom100 = TwoPeakGenes_bottom_stat %>% inner_join(TotalCounts_bottom1000, by="gene")

Avg2PeakGeneBottomExpLow=sum(TwoPeakGenes_bottom_stat_bottom100$absDiff)/nrow(TwoPeakGenes_bottom_stat_bottom100)

Avg2PeakGeneBottomExpLow
[1] 0.354243

Make a plot with these values:

avgPeakUsageTable=data.frame(rbind(c("Top", "High", .1), c("Top", "Low", .32), c("Bottom", "High", .19), c("Bottom", "Low", .35)))
colnames(avgPeakUsageTable)= c("Coverage", "Expression", "AverageUsageDiff")
ggplot(avgPeakUsageTable,aes(y=AverageUsageDiff, x=Coverage, by=Expression, fill=Expression)) + geom_bar(stat="identity",position="dodge") + labs(title="Difference between individual usage \ndepends on expression and coverage")

Expand here to see past versions of unnamed-chunk-22-1.png:
Version Author Date
83ebe13 Briana Mittleman 2019-02-07

I want to look at this based on every 10% of gene expression. First I will remove genes with 0 coverage in this ind.

TotalCounts_no0=TotalCounts %>% filter(Exp>0) 
nrow(TotalCounts_no0)
[1] 3143

I can add a column that is the rank over the sum (or the percentile)

TotalCounts_no0_perc= TotalCounts_no0 %>% mutate(Percentile = percent_rank(Exp)) 
TotalCounts_no0_perc10= TotalCounts_no0_perc %>% filter(Percentile<.1)

TotalCounts_no0_perc20= TotalCounts_no0_perc %>% filter(Percentile<.2, Percentile>.1)

TotalCounts_no0_perc30= TotalCounts_no0_perc %>% filter(Percentile<.3, Percentile>.2)


TotalCounts_no0_perc40= TotalCounts_no0_perc %>% filter(Percentile<.4, Percentile>.3)

TotalCounts_no0_perc50= TotalCounts_no0_perc %>% filter(Percentile<.5, Percentile>.4)

TotalCounts_no0_perc60= TotalCounts_no0_perc %>% filter(Percentile<.6, Percentile>.5)

TotalCounts_no0_perc70= TotalCounts_no0_perc %>% filter(Percentile<.7, Percentile>.6)

TotalCounts_no0_perc80= TotalCounts_no0_perc %>% filter(Percentile<8, Percentile>.7)
TotalCounts_no0_perc90= TotalCounts_no0_perc %>% filter(Percentile<.9, Percentile>.8)

TotalCounts_no0_perc100= TotalCounts_no0_perc %>% filter(Percentile<1, Percentile>.9)

Now I can make a function that takes one of these files, and computes the relevant stat. This takes the usage file and the expression file

getAvgDiffUsage=function(Usage, exp){
  df = Usage %>% inner_join(exp, by="gene")
  value=df=sum(df$absDiff)/nrow(df)
  return(value)
}

Run this for the top coverage at each exp:

AvgUsageDiff_top10=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc10)
AvgUsageDiff_top20=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc20)
AvgUsageDiff_top30=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc30)
AvgUsageDiff_top40=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc40)
AvgUsageDiff_top50=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc50)
AvgUsageDiff_top60=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc60)
AvgUsageDiff_top70=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc70)
AvgUsageDiff_top80=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc80)
AvgUsageDiff_top90=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc90)
AvgUsageDiff_top100=getAvgDiffUsage(TwoPeakGenes_top_stat, exp=TotalCounts_no0_perc100)

AvgUsageTop=c(AvgUsageDiff_top10,AvgUsageDiff_top20,AvgUsageDiff_top30,AvgUsageDiff_top40,AvgUsageDiff_top50,AvgUsageDiff_top60,AvgUsageDiff_top70,AvgUsageDiff_top80,AvgUsageDiff_top90,AvgUsageDiff_top100)

Do this for Bottom coverage:

AvgUsageDiff_bottom10=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc10)
AvgUsageDiff_bottom20=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc20)
AvgUsageDiff_bottom30=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc30)
AvgUsageDiff_bottom40=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc40)
AvgUsageDiff_bottom50=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc50)
AvgUsageDiff_bottom60=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc60)
AvgUsageDiff_bottom70=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc70)
AvgUsageDiff_bottom80=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc80)
AvgUsageDiff_bottom90=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc90)
AvgUsageDiff_bottom100=getAvgDiffUsage(TwoPeakGenes_bottom_stat, exp=TotalCounts_no0_perc100)

AvgUsagebottom=c(AvgUsageDiff_bottom10,AvgUsageDiff_bottom20,AvgUsageDiff_bottom30,AvgUsageDiff_bottom40,AvgUsageDiff_bottom50,AvgUsageDiff_bottom60,AvgUsageDiff_bottom70,AvgUsageDiff_bottom80,AvgUsageDiff_bottom90,AvgUsageDiff_bottom100)

All together:

Percentile=c(10,20,30,40,50,60,70,80,90,100)
allAvgUsage=data.frame(cbind(Percentile,AvgUsageTop,AvgUsagebottom))
colnames(allAvgUsage)=c("Percentile","Top", "Bottom")
allAvgUsage$Top= as.numeric(as.character(allAvgUsage$Top))
allAvgUsage$Bottom= as.numeric(as.character(allAvgUsage$Bottom))
allAvgUsage_melt= melt(allAvgUsage, id.vars = c("Percentile"))
colnames(allAvgUsage_melt)=c("Percentile","Coverage", "AvgDifference")
ggplot(allAvgUsage_melt, aes(x=Percentile, y=AvgDifference,by=Coverage, fill=Coverage)) + geom_bar(stat="identity", position = "dodge") + labs(title="Average Usage Difference between 2 individauls by 3' Seq \nCount percentile and coverage") + scale_x_discrete(limits = c(10,20,30,40,50,60,70,80,90,100))

Session information

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  forcats_0.3.0   stringr_1.3.1   dplyr_0.7.6    
 [5] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
 [9] ggplot2_3.0.0   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] modelr_0.1.2      readxl_1.1.0      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] cellranger_1.1.0  rvest_0.3.2       R.methodsS3_1.7.1
[22] evaluate_0.11     labeling_0.3      knitr_1.20       
[25] broom_0.5.0       Rcpp_0.12.19      backports_1.1.2  
[28] scales_1.0.0      jsonlite_1.5      hms_0.4.2        
[31] digest_0.6.17     stringi_1.2.4     grid_3.5.1       
[34] rprojroot_1.3-2   cli_1.0.1         tools_3.5.1      
[37] magrittr_1.5      lazyeval_0.2.1    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