20190613_PowerAnalysis

workflowr

Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/20190521_eQTL_CrossSpeciesEnrichment_cache/
    Ignored:    analysis_temp/.DS_Store
    Ignored:    code/.DS_Store
    Ignored:    code/snakemake_workflow/.DS_Store
    Ignored:    data/.DS_Store
    Ignored:    data/PastAnalysesDataToKeep/.DS_Store
    Ignored:    docs/.DS_Store
    Ignored:    docs/assets/.DS_Store

Unstaged changes:
    Modified:   analysis/20190613_PowerAnalysis.Rmd

library(tidyverse)
library(knitr)
library("edgeR")
library(corrplot)
library(gplots)
library(pROC)
library(qvalue)
library(reshape2)
library(gridExtra)

39 Chimp heart RNA-seq datasets (from this project as well as from Pavlovic et al 2018) as well as 10 human heart RNA-seq datasets (from Pavlovic et al) and 39 randomly selected GTEx left ventricle heart RNA-seq datasets were trimmed to same read length (single end, 75bp) and aligned to the respective genomes. Gene counts were obtained with subread software using gene annotations based only on orthologous exons (Pavlovic et al 2018). Here I will perform differential gene expression analysis to understand the relationship between read depth and number of individuals (samples) needed to identify cross-species differentially expressed genes.

CountTableChimpFile <- '../output/PowerAnalysisFullCountTable.Chimp.subread.txt.gz'
CountTableChimp <- read.table(gzfile(CountTableChimpFile), header=T, check.names=FALSE, skip=1)
colnames(CountTableChimp) <- paste0("C.", colnames(CountTableChimp))
kable(CountTableChimp[1:10,1:10])

CountTableHumanFile <- '../output/PowerAnalysisFullCountTable.Human.subread.txt.gz'
CountTableHuman <- read.table(gzfile(CountTableHumanFile), header=T, check.names=FALSE, skip=1)
colnames(CountTableHuman) <- paste0("H.", colnames(CountTableHuman))
kable(CountTableHuman[1:10,1:10])

CombinedTable <- inner_join(CountTableChimp[,c(1,7:length(CountTableChimp))], CountTableHuman[,c(1,7:length(CountTableHuman))], by=c("C.Geneid"="H.Geneid")) %>%
  column_to_rownames("C.Geneid") %>% as.matrix()

#Plot depth per sample
CombinedTable %>% colSums() %>% as.data.frame() %>%
  rownames_to_column("Sample") %>%
  mutate(Species=substr(Sample, 1,1)) %>%
  ggplot(aes(x=Sample, y=., fill=Species)) +
  geom_col() +
  scale_y_continuous(expand = c(0, 0)) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, size=6))

Past versions of read-data-1.png

cpm <- cpm(CombinedTable, log=TRUE, prior.count=0.5)
kable(cpm[1:10,1:10])

# Heatmap of correlation. Species segregate as expected.
SpeciesFactor <- colnames(cpm) %>% substr(1,1) %>% factor() %>% unclass() %>% as.character()

cor(cpm, method = c("spearman")) %>%
heatmap.2(trace="none", ColSideColors=SpeciesFactor)

Past versions of read-data-2.png

Unsurprisingly, the samples with the lowest read depth in the human cohort are clear outliers. This might change once I filter out the more lowly expressed genes.

d0 <- DGEList(CombinedTable)

#Calculate normalization factors
d0 <- calcNormFactors(d0)

#Note: calcNormFactors doesn’t normalize the data, it just calculates normalization factors for use downstream.
#Filter low-expressed genes

cutoff <- 6
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,] 
dim(d) # number of genes left

[1] 13754    88

plotMDS(d, col = as.numeric(SpeciesFactor))

Past versions of unnamed-chunk-1-1.png

plotMDS(d, col = as.numeric(SpeciesFactor), dim=c(3,4))

Past versions of unnamed-chunk-1-2.png

cor(cpm(d, log=T, prior.count=0.5), method = c("spearman")) %>%
heatmap.2(trace="none", ColSideColors=SpeciesFactor)

Past versions of unnamed-chunk-1-3.png

mm <- model.matrix(~0 + SpeciesFactor)
y <- voom(d, mm, plot = T, normalize.method="cyclicloess")

Past versions of unnamed-chunk-1-4.png

In fact some of these samples seemed to have gotten worse. I’ll just throw these out of future analysis… I threw out the human samples that were outliers, as well as LittleR which was a clear chimp outlier, then I kept throwing out more human samples (by lowest read depth) until we got a dataset of 38 chimp and 38 humans

HumanSamplesToDrop <- c(c("SRR1507229","SRR603918", "SRR1478149", "SRR598509", "SRR613186"), c("SRR1489693", "SRR598148", "59167", "SRR1478900", "SRR1474730", "61317"))
ChimpSamplesToDrop <- c("Little_R")

Anway, now that voom calculated mean/variance relationship from count values, I will now normalize for gene length differences between species (Convert cpm to rpkm). As per the voom publication (Law et al 2014) log-cpm values output by voom can be converted to log-rpkm by subtracting the log2 gene length in kilobases. For this I will make a matrix of gene lengths based on chimp orthologous exon length and human orthologous exon lengths to subtract the correct length for each species from the count matrix output by voom.

rep.col<-function(x,n){
   matrix(rep(x,each=n), ncol=n, byrow=TRUE)
}

GeneLengths <- inner_join(CountTableChimp[,c("C.Geneid", "C.Length")], CountTableHuman[,c("H.Geneid", "H.Length")], by=c("C.Geneid"="H.Geneid"))
head(kable(GeneLengths))

[1] "C.Geneid           C.Length   H.Length"
[2] "----------------  ---------  ---------"
[3] "ENSG00000273443         707        706"
[4] "ENSG00000217801         824        824"
[5] "ENSG00000237330         289        292"
[6] "ENSG00000223823          94         94"

ggplot(GeneLengths, aes(x=log10(C.Length), y=log10(H.Length))) + geom_point()

Past versions of unnamed-chunk-3-1.png

# accounting for the length differences probably will have negligble effect on results anyway. # Will probably calculate DE genes both ways (cpm and rpkm) to verify

GeneLengthMatrix <- cbind(
  rep.col(log2(GeneLengths$C.Length/1000), length(CountTableChimp)-6),
  rep.col(log2(GeneLengths$H.Length/1000), length(CountTableHuman)-6))
rownames(GeneLengthMatrix) <- GeneLengths$C.Geneid
kable(GeneLengthMatrix[1:10,1:10])

#subtract gene log2(kb) from log2(cpm)
y$E <- y$E - GeneLengthMatrix[rownames(y$E),]

#Now do model fitting, significance testing
fit<- lmFit(y, mm)
plotSA(fit)

Past versions of unnamed-chunk-3-2.png

head(coef(fit))

                SpeciesFactor1 SpeciesFactor2
ENSG00000186891     -0.6204116     -3.1289432
ENSG00000186827      2.2447828      1.5864495
ENSG00000078808      4.9231110      5.2194360
ENSG00000176022      3.0607593      2.9003148
ENSG00000184163     -0.0235633     -0.5707886
ENSG00000160087      2.4483810      2.5814250

head(y$E[,1])

ENSG00000186891 ENSG00000186827 ENSG00000078808 ENSG00000176022 
     -0.4126992       1.6727615       4.9028512       2.8890203 
ENSG00000184163 ENSG00000160087 
     -3.6177805       2.1925140 

contr <- makeContrasts(DE=SpeciesFactor1-SpeciesFactor2, levels = mm)

tmp <- contrasts.fit(fit, contrasts=contr)
FC.NullInterval <- log2(1.0)
True.efit <- treat(tmp, lfc = FC.NullInterval)

summary(decideTests(True.efit))

         DE
Down   4713
NotSig 4352
Up     4689

TrueResponse <- decideTests(True.efit)
plotMD(True.efit, column=1, zero.weights = F)

Past versions of unnamed-chunk-3-3.png

Ok, seems like the above workflow for identifying DE genes is set up reasonably… Now let’s repeat with less samples and see what changes. First, I will wrap all of the above analysis into a function with a sample size parameter:

DE.Subsampled <-function(ChimpCountTableFile, HumanCountTableFile, SubsampleSize, FC.NullInterval, drop, ChimpSampleDrop=NULL, HumanSampleDrop=NULL)
  #if SubsampleSize parameter == 0, use full table, otherwise, subsample from it
  {
   FullChimpData <- read.table(gzfile(ChimpCountTableFile), header=T, check.names=FALSE, skip=1)
   FullHumanData <- read.table(gzfile(HumanCountTableFile), header=T, check.names=FALSE, skip=1)
   
  if (!is.null(ChimpSampleDrop)){
     FullChimpData <- FullChimpData %>% dplyr::select(-ChimpSampleDrop)
   }   
  if (!is.null(HumanSampleDrop)){
     FullHumanData <- FullHumanData %>% dplyr::select(-HumanSampleDrop)
   }

   if (SubsampleSize==0){
     CountTableChimp <- FullChimpData
     colnames(CountTableChimp) <- paste0("C.", colnames(CountTableChimp))
     CountTableHuman <- FullHumanData
     colnames(CountTableHuman) <- paste0("H.", colnames(CountTableHuman))
     
   } else {
     CountTableChimp <- FullChimpData %>% dplyr::select(c(1:6, sample(7:length(FullChimpData), SubsampleSize)))
     colnames(CountTableChimp) <- paste0("C.", colnames(CountTableChimp))
     
     CountTableHuman <- FullHumanData %>% dplyr::select(c(1:6, sample(7:length(FullHumanData), SubsampleSize)))
     colnames(CountTableHuman) <- paste0("H.", colnames(CountTableHuman))
   }
   
CombinedTable <- inner_join(CountTableChimp[,c(1,7:length(CountTableChimp))], CountTableHuman[,c(1,7:length(CountTableHuman))], by=c("C.Geneid"="H.Geneid")) %>%
  column_to_rownames("C.Geneid") %>% as.matrix()

SpeciesFactor <- colnames(CombinedTable) %>% substr(1,1) %>% factor() %>% unclass() %>% as.character()

d0 <- DGEList(CombinedTable)
d0 <- calcNormFactors(d0)
d <- d0[-drop,] 
mm <- model.matrix(~0 + SpeciesFactor)
y <- voom(d, mm, normalize.method="cyclicloess", plot=F)

GeneLengths <- inner_join(CountTableChimp[,c("C.Geneid", "C.Length")], CountTableHuman[,c("H.Geneid", "H.Length")], by=c("C.Geneid"="H.Geneid"))
GeneLengthMatrix <- cbind(
  rep.col(log2(GeneLengths$C.Length/1000), length(CountTableChimp)-6),
  rep.col(log2(GeneLengths$H.Length/1000), length(CountTableHuman)-6))
rownames(GeneLengthMatrix) <- GeneLengths$C.Geneid
y$E <- y$E - GeneLengthMatrix[rownames(y$E),]

fit<- lmFit(y, mm)
contr <- makeContrasts(DE=SpeciesFactor1-SpeciesFactor2, levels = mm)
tmp <- contrasts.fit(fit, contrasts=contr)
efit <- treat(tmp, lfc = FC.NullInterval)
return(efit)
}

Now that I have a function to do all of the DE gene analysis, use the function with a smaller sample size and check results:

#20 samples
Subsampled.DE.results <- DE.Subsampled(CountTableChimpFile,
              CountTableHumanFile,
              20, 0, drop)
summary(decideTests(Subsampled.DE.results))

         DE
Down   3801
NotSig 6098
Up     3855

RocCurveData <- coords(roc(response=as.vector(abs(TrueResponse)), predictor=Subsampled.DE.results$p.value, plot=F))

Setting levels: control = 0, case = 1

Warning in roc.default(response = as.vector(abs(TrueResponse)), predictor
= Subsampled.DE.results$p.value, : Deprecated use a matrix as predictor.
Unexpected results may be produced, please pass a numeric vector.

Setting direction: controls > cases

Warning in coords.roc(roc(response = as.vector(abs(TrueResponse)),
predictor = Subsampled.DE.results$p.value, : An upcoming version of pROC
will set the 'transpose' argument to FALSE by default. Set transpose =
TRUE explicitly to keep the current behavior, or transpose = FALSE to
adopt the new one and silence this warning. Type help(coords_transpose) for
additional information.

plot(1-RocCurveData["specificity",], RocCurveData["sensitivity",])

Past versions of unnamed-chunk-5-1.png

SubSampledResponse <- decideTests(Subsampled.DE.results)

# distribution of effect sizes for true positives
hist(abs(True.efit$coefficients[TrueResponse==SubSampledResponse & SubSampledResponse!=0]), main="|effect size| of true positives")

Past versions of unnamed-chunk-5-2.png

median(abs(True.efit$coefficients[TrueResponse==SubSampledResponse & SubSampledResponse!=0]))

[1] 0.7373539

#5 samples
Subsampled.DE.results <- DE.Subsampled(CountTableChimpFile,
              CountTableHumanFile,
              5, 0, drop)
summary(decideTests(Subsampled.DE.results))

          DE
Down    1076
NotSig 11542
Up      1136

RocCurveData <- coords(roc(response=as.vector(abs(TrueResponse)), predictor=Subsampled.DE.results$p.value, plot=F))

Setting levels: control = 0, case = 1

Warning in roc.default(response = as.vector(abs(TrueResponse)), predictor
= Subsampled.DE.results$p.value, : Deprecated use a matrix as predictor.
Unexpected results may be produced, please pass a numeric vector.

Setting direction: controls > cases

Warning in coords.roc(roc(response = as.vector(abs(TrueResponse)),
predictor = Subsampled.DE.results$p.value, : An upcoming version of pROC
will set the 'transpose' argument to FALSE by default. Set transpose =
TRUE explicitly to keep the current behavior, or transpose = FALSE to
adopt the new one and silence this warning. Type help(coords_transpose) for
additional information.

plot(1-RocCurveData["specificity",], RocCurveData["sensitivity",])

Past versions of unnamed-chunk-5-3.png

SubSampledResponse <- decideTests(Subsampled.DE.results)

# distribution of effect sizes for true positives
hist(abs(True.efit$coefficients[TrueResponse==SubSampledResponse & SubSampledResponse!=0]), main="|effect size| of true positives")

Past versions of unnamed-chunk-5-4.png

median(abs(True.efit$coefficients[TrueResponse==SubSampledResponse & SubSampledResponse!=0]))

[1] 1.23068

Now I will systematically do this for varying sample sizes and make some final plots

#Note there is some randomness in subsampling samples. So much so that sometimes it effects results wherein 4 samples might yield more DE genes than 2 if you get unluck and pick "bad" samples in the 4
set.seed(1)

#Genes to drop. Needs to be re-evaluated after dropping outlier samples
CountTableChimp <- read.table(gzfile(CountTableChimpFile), header=T, check.names=FALSE, skip=1) %>% dplyr::select(-ChimpSamplesToDrop)
CountTableHuman <- read.table(gzfile(CountTableHumanFile), header=T, check.names=FALSE, skip=1) %>% dplyr::select(-HumanSamplesToDrop)
CountTableFull <- inner_join(CountTableChimp[,c(1,7:length(CountTableChimp))], CountTableHuman[,c(1,7:length(CountTableHuman))], by="Geneid") %>%
  column_to_rownames("Geneid") %>% as.matrix() %>% DGEList() %>% calcNormFactors()
cutoff <- 6
drop <- which(apply(cpm(CountTableFull), 1, max) < cutoff)
d <- CountTableFull[-drop,] 

#Interval for null hypothesis
FC.NullInterval <- log2(1.0)

#True results are those using all samples
True.efit <- DE.Subsampled(CountTableChimpFile,
              CountTableHumanFile,
              0, FC.NullInterval, drop, ChimpSampleDrop=ChimpSamplesToDrop, HumanSampleDrop = HumanSamplesToDrop)
TrueResponse <- decideTests(True.efit, p.value=0.05)

SampleSizes <- c(2,4,8,16,24,32,38)
FDRLevels <- c(0.01, 0.05, 0.1)


RocCurveDataToPlot <- data.frame()
DEGeneCountToPlot <- matrix(nrow=length(SampleSizes), ncol=length(FDRLevels))
rownames(DEGeneCountToPlot) <- SampleSizes
EffectSizesToPlot <- data.frame()

for (i in seq_along(SampleSizes)){
  print(paste0("processing sample size = ", SampleSizes[i]))
  Results <- DE.Subsampled(CountTableChimpFile,
              CountTableHumanFile,
              SampleSizes[i], FC.NullInterval, drop, ChimpSampleDrop=ChimpSamplesToDrop, HumanSampleDrop = HumanSamplesToDrop)
  
  RocCurveData <- as.data.frame(coords(roc(response=as.vector(abs(TrueResponse)), quiet=T, predictor=as.numeric(Results$p.value), plot=F), transpose=F))
  RocCurveData$samplesize <- SampleSizes[i]
  RocCurveDataToPlot <- rbind(RocCurveDataToPlot, RocCurveData)
  
  
  for (j in seq_along(FDRLevels)){
    
    SubSampledResponse <- decideTests(Results, p.value=FDRLevels[j])
    
    DEGeneCountToPlot[i,j] <- sum(table(SubSampledResponse)[c("-1","1")])
    
    if (length(table(TrueResponse==SubSampledResponse & SubSampledResponse!=0)) > 1){
      EffectSizes.df <-data.frame(EffectSizes=abs(True.efit$coefficients[TrueResponse==SubSampledResponse & SubSampledResponse!=0]), FDR=FDRLevels[j], SampleSize=SampleSizes[i])
      EffectSizesToPlot <- rbind(EffectSizesToPlot, EffectSizes.df)
    }

    
  }
}

[1] "processing sample size = 2"
[1] "processing sample size = 4"
[1] "processing sample size = 8"
[1] "processing sample size = 16"
[1] "processing sample size = 24"
[1] "processing sample size = 32"
[1] "processing sample size = 38"

RocCurve <- function(RocCurveData.df){
  RocCurveLandmarks<-RocCurveDataToPlot %>%
  group_by(samplesize) %>%
  filter(abs(threshold - 0.01) == min(abs(threshold - 0.01)) |
          abs(threshold - 0.05) == min(abs(threshold - 0.05)) |
          abs(threshold - 0.1) == min(abs(threshold - 0.1))
         ) %>%
  ungroup() %>%
  mutate(threshold=signif(threshold, digits=1)) %>%
  mutate(FDR=factor(threshold))
  
  PlotOut <- ggplot(RocCurveDataToPlot, aes(x=1-specificity, y=sensitivity, color=factor(samplesize))) +
  geom_line() +
  geom_point(data=RocCurveLandmarks, aes(x=1-specificity, y=sensitivity, color=factor(samplesize), shape=FDR)) +
  xlab("false positive rate (1-sensitivity)") +
  ylab("true positive rate (sensitivity)") +
  scale_colour_discrete(name  ="Sample size") +
  theme_bw()
  return(PlotOut)
}


DEGeneCountPlot <- function(DEGeneCount.df){
  DEGeneCountToPlot.df<-as.data.frame(DEGeneCount.df)
colnames(DEGeneCountToPlot.df) <- FDRLevels
DEGeneCountToPlot.df$SampleSize <- rownames(DEGeneCountToPlot.df)
DEGeneCountToPlot.df$SampleSize
DEGeneCountToPlot.df[is.na(DEGeneCountToPlot.df)] <- 0
PlotOut<-DEGeneCountToPlot.df %>% melt() %>%
  dplyr::rename(Number.DE.genes=value, FDR=variable) %>%
ggplot(aes(x=as.numeric(SampleSize), y=Number.DE.genes, color=FDR)) +
  geom_line() +
  geom_point() +
  xlab("Sample size") +
  ylab("Number DE genes") +
  theme_bw()
return(PlotOut)
}


EffectSizesPlot <- function(EffectSizesToPlot.df){
  PlotOut <- ggplot(EffectSizesToPlot.df, aes(x=factor(SampleSize), y=EffectSizes, color=factor(FDR))) +
  # geom_violin()
  geom_boxplot() +
  ylab(expression(atop("Effect-size of true positive DE genes",'|log'[2]*'(FoldChange)|'))) +
    xlab("Sample size") +
  scale_colour_discrete(name  ="FDR") +
  theme_bw()
  return(PlotOut)
}
EffectSizesPlot(EffectSizesToPlot)

Past versions of unnamed-chunk-6-1.png

DEGeneCountPlot(DEGeneCountToPlot)

Using SampleSize as id variables

Past versions of unnamed-chunk-6-2.png

RocCurve(RocCurveDataToPlot)

Past versions of unnamed-chunk-6-3.png

data.frame(coefficients=as.numeric(True.efit$coefficients), pval=as.numeric(True.efit$p.value), signif=decideTests(True.efit)) %>%
ggplot(aes(x=coefficients, y=-log10(pval), color=factor(DE))) +
  geom_point() +
  scale_x_continuous(limits=c(-5,5))

Warning: Removed 150 rows containing missing values (geom_point).

Past versions of unnamed-chunk-6-4.png

I probably also want to explore how read depth plays into this, especially considering some samples were much more sparse than others. Here I’ll try to write a function to subsample without replacement the read counts from the original count table.

sample_species <- function(counts,n) {
  num_species <- length(counts)
  total_count <- sum(counts)
  samples <- sample(1:total_count,n,replace=F)
  samples <- samples[order(samples)]
  result <- array(0,num_species)
  total <- 0
  for (i in 1:num_species) {
    result[i] <- length(which(samples > total & samples <= total+counts[i]))
    total <- total+counts[i]
  }
  return(result)
  # return(apply(t(counts),1,sample_species,1500) )
}

sample_count_table <- function(counts, n){
  return(apply(t(counts),1,sample_species,n) )
}

A<- sample_count_table(CombinedTable,2200000)

This is still a work in progress… It seems that sampling without replcement from a large count table actually takes an unreasonably long time (and probably uses a lot of memory)… Sampling without replacement could be faster in theory, but I’m not sure if that is still reasonable. There are some third party packages out there that do this, maybe I will try them out. Though, I don’t see any that let you pick a number of reads to subsample as opposed to a proportion of reads to subsample (in other words, it wouldn’t be easy to normalize read depth across samples). Maybe it will actually just be easier to realign everything at various read depths and make new count table files for each read depth if I want to incorporate normalizing and adjusting read depths into this analysis.

Update: What I turned out doing is sampling from the original alignment bam file to varying depths, normalizing such that each sample has an equal number of aligned reads. Then count tables were generated (one table for chimp, one for human), and the block of code that makes roc curves and other plots for different numbers of samples was repeated below using those smaller count tables (but still using the full count tabe as to determine “true” responses)

###25M reads for each sample

RocCurveDataToPlot <- data.frame()
DEGeneCountToPlot <- matrix(nrow=length(SampleSizes), ncol=length(FDRLevels))
rownames(DEGeneCountToPlot) <- SampleSizes
EffectSizesToPlot <- data.frame()

ReadDepthControlledChimpCountFile <- "../output/PowerAnalysisCountTable.Chimp.25000000.subread.txt.gz"
ReadDepthControlledHumanCountFile <- "../output/PowerAnalysisCountTable.Human.25000000.subread.txt.gz"

for (i in seq_along(SampleSizes)){
  paste0("processing ", SampleSizes[i])
  Results <- DE.Subsampled(ReadDepthControlledChimpCountFile,
              ReadDepthControlledHumanCountFile,
              SampleSizes[i], FC.NullInterval, drop, ChimpSampleDrop=ChimpSamplesToDrop, HumanSampleDrop = HumanSamplesToDrop)
  
  RocCurveData <- as.data.frame(coords(roc(response=as.vector(abs(TrueResponse)), quiet=T, predictor=as.numeric(Results$p.value), plot=F), transpose=F))
  RocCurveData$samplesize <- SampleSizes[i]
  RocCurveDataToPlot <- rbind(RocCurveDataToPlot, RocCurveData)
  
  for (j in seq_along(FDRLevels)){
    
    SubSampledResponse <- decideTests(Results, p.value=FDRLevels[j])
    
    DEGeneCountToPlot[i,j] <- sum(table(SubSampledResponse)[c("-1","1")])
    
    if (length(table(TrueResponse==SubSampledResponse & SubSampledResponse!=0)) > 1){
      EffectSizes.df <-data.frame(EffectSizes=abs(True.efit$coefficients[TrueResponse==SubSampledResponse & SubSampledResponse!=0]), FDR=FDRLevels[j], SampleSize=SampleSizes[i])
      EffectSizesToPlot <- rbind(EffectSizesToPlot, EffectSizes.df)
    }

    
  }
}

RocCurve(RocCurveDataToPlot)

Past versions of unnamed-chunk-8-1.png

DEGeneCountPlot(DEGeneCountToPlot)

Using SampleSize as id variables

Past versions of unnamed-chunk-8-2.png

EffectSizesPlot(EffectSizesToPlot)

Past versions of unnamed-chunk-8-3.png

###5M reads for each sample

RocCurveDataToPlot <- data.frame()
DEGeneCountToPlot <- matrix(nrow=length(SampleSizes), ncol=length(FDRLevels))
rownames(DEGeneCountToPlot) <- SampleSizes
EffectSizesToPlot <- data.frame()

ReadDepthControlledChimpCountFile <- "../output/PowerAnalysisCountTable.Chimp.10000000.subread.txt.gz"
ReadDepthControlledHumanCountFile <- "../output/PowerAnalysisCountTable.Human.10000000.subread.txt.gz"

for (i in seq_along(SampleSizes)){
  paste0("processing ", SampleSizes[i])
  Results <- DE.Subsampled(ReadDepthControlledChimpCountFile,
              ReadDepthControlledHumanCountFile,
              SampleSizes[i], FC.NullInterval, drop, ChimpSampleDrop=ChimpSamplesToDrop, HumanSampleDrop = HumanSamplesToDrop)
  
  RocCurveData <- as.data.frame(coords(roc(response=as.vector(abs(TrueResponse)), quiet=T, predictor=as.numeric(Results$p.value), plot=F), transpose=F))
  RocCurveData$samplesize <- SampleSizes[i]
  RocCurveDataToPlot <- rbind(RocCurveDataToPlot, RocCurveData)
  
  for (j in seq_along(FDRLevels)){
    
    SubSampledResponse <- decideTests(Results, p.value=FDRLevels[j])
    
    DEGeneCountToPlot[i,j] <- sum(table(SubSampledResponse)[c("-1","1")])
    
    if (length(table(TrueResponse==SubSampledResponse & SubSampledResponse!=0)) > 1){
      EffectSizes.df <-data.frame(EffectSizes=abs(True.efit$coefficients[TrueResponse==SubSampledResponse & SubSampledResponse!=0]), FDR=FDRLevels[j], SampleSize=SampleSizes[i])
      EffectSizesToPlot <- rbind(EffectSizesToPlot, EffectSizes.df)
    }

    
  }
}

RocCurve(RocCurveDataToPlot)

Past versions of unnamed-chunk-9-1.png

DEGeneCountPlot(DEGeneCountToPlot)

Using SampleSize as id variables

Past versions of unnamed-chunk-9-2.png

EffectSizesPlot(EffectSizesToPlot)

Past versions of unnamed-chunk-9-3.png

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

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] gridExtra_2.3   reshape2_1.4.3  qvalue_2.14.1   pROC_1.15.0    
 [5] gplots_3.0.1.1  corrplot_0.84   edgeR_3.24.3    limma_3.38.3   
 [9] knitr_1.23      forcats_0.4.0   stringr_1.4.0   dplyr_0.8.1    
[13] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.3   
[17] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1         locfit_1.5-9.1     lubridate_1.7.4   
 [4] lattice_0.20-38    gtools_3.8.1       assertthat_0.2.1  
 [7] rprojroot_1.3-2    digest_0.6.19      R6_2.4.0          
[10] cellranger_1.1.0   plyr_1.8.4         backports_1.1.4   
[13] evaluate_0.14      httr_1.4.0         highr_0.8         
[16] pillar_1.4.1       rlang_0.3.4        lazyeval_0.2.2    
[19] readxl_1.3.1       rstudioapi_0.10    gdata_2.18.0      
[22] whisker_0.3-2      rmarkdown_1.13     labeling_0.3      
[25] splines_3.5.1      munsell_0.5.0      broom_0.5.2       
[28] compiler_3.5.1     modelr_0.1.4       xfun_0.7          
[31] pkgconfig_2.0.2    htmltools_0.3.6    tidyselect_0.2.5  
[34] workflowr_1.4.0    crayon_1.3.4       withr_2.1.2       
[37] bitops_1.0-6       grid_3.5.1         nlme_3.1-140      
[40] jsonlite_1.6       gtable_0.3.0       git2r_0.25.2      
[43] magrittr_1.5       scales_1.0.0       KernSmooth_2.23-15
[46] cli_1.1.0          stringi_1.4.3      fs_1.3.1          
[49] xml2_1.2.0         generics_0.0.2     tools_3.5.1       
[52] glue_1.3.1         hms_0.4.2          yaml_2.2.0        
[55] colorspace_1.4-1   caTools_1.17.1.2   rvest_0.3.4       
[58] haven_2.1.0