PlotBootstrapDistributions

Description

Plots the generated bootstrap distribution as violin plots. Genes showing significant values are marked in a different color.

Usage

PlotBootstrapDistributions(bootList,
                           reportTables,
                           outputFolder = getwd(),
                           sampleNames = NULL,
                           save = FALSE,
                           scale = 7)

Arguments

Value

A list with ggplot2 objects.

Author(s)

Thomas Wolf, Cristiano Oliveira

Examples

data(sampleReadCounts)
data(referenceReadCounts)
## Gene names should be same size as row columns
geneNames <- row.names(referenceReadCounts)

ampliconNames <- NULL

normalizedReadCounts <- CombinedNormalizedCounts(sampleReadCounts,
                                                 referenceReadCounts,
                                                 ampliconNames = ampliconNames)

# After normalization data sets need to be splitted again to perform bootstrap
samplesNormalizedReadCounts = normalizedReadCounts["samples"][[1]]
referenceNormalizedReadCounts = normalizedReadCounts["reference"][[1]]

# Should be used values above 10000
replicates <- 10

# Perform the bootstrap based analysis
bootList <- BootList(geneNames,
                     samplesNormalizedReadCounts,
                     referenceNormalizedReadCounts,
                     replicates = replicates)

backgroundNoise <- Background(geneNames,
           samplesNormalizedReadCounts,
           referenceNormalizedReadCounts,
           bootList,
           replicates = replicates)

reportTables <- ReportTables(geneNames,
             samplesNormalizedReadCounts,
             referenceNormalizedReadCounts,
             bootList,
             backgroundNoise)

PlotBootstrapDistributions(bootList, reportTables, save = FALSE)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(CNVPanelizer)
Loading required package: GenomicRanges
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/CNVPanelizer/PlotBootstrapDistributions.Rd_%03d_medium.png", width=480, height=480)
> ### Name: PlotBootstrapDistributions
> ### Title: PlotBootstrapDistributions
> ### Aliases: PlotBootstrapDistributions
> 
> ### ** Examples
> 
> 
> data(sampleReadCounts)
> data(referenceReadCounts)
> ## Gene names should be same size as row columns
> geneNames <- row.names(referenceReadCounts)
> 
> ampliconNames <- NULL
> 
> normalizedReadCounts <- CombinedNormalizedCounts(sampleReadCounts,
+                                                  referenceReadCounts,
+                                                  ampliconNames = ampliconNames)
> 
> # After normalization data sets need to be splitted again to perform bootstrap
> samplesNormalizedReadCounts = normalizedReadCounts["samples"][[1]]
> referenceNormalizedReadCounts = normalizedReadCounts["reference"][[1]]
> 
> # Should be used values above 10000
> replicates <- 10
> 
> # Perform the bootstrap based analysis
> bootList <- BootList(geneNames,
+                      samplesNormalizedReadCounts,
+                      referenceNormalizedReadCounts,
+                      replicates = replicates)
> 
> backgroundNoise <- Background(geneNames,
+            samplesNormalizedReadCounts,
+            referenceNormalizedReadCounts,
+            bootList,
+            replicates = replicates)
> 
> reportTables <- ReportTables(geneNames,
+              samplesNormalizedReadCounts,
+              referenceNormalizedReadCounts,
+              bootList,
+              backgroundNoise)
> 
> PlotBootstrapDistributions(bootList, reportTables, save = FALSE)
$Sample_1

$Sample_2

$Sample_3

$Sample_4

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>