Chicago (version 1.0.3)

mergeSamples: Merge samples together.

Description

Merge a number of chicagoData objects together, summarising their counts into a normalised value.

Usage

mergeSamples(cdl, normalise = TRUE, NcolOut = "N", NcolNormPrefix = "NNorm", mergeMethod = c("weightedMean", "mean")[1], repNormCounts = (mergeMethod=="mean"))

Arguments

cdl

A list of chicagoData objects.

normalise

If TRUE, use a normalisation procedure, specified by mergeMethod, to arrive at a normalised count. If FALSE, take the mean number of reads.

NcolOut

The column to store the normalised counts in.

NcolNormPrefix

Each sample gains a normalised count column, that begins with this prefix.

mergeMethod

If mergeMethod == "weightedMean", then NcolOut is the weighted mean of the sample-wise counts adjusted by the samples' respective scaling factors s_k. If mergeMethod == "mean", then sample-specific counts are first normalised by dividing by s_k, and NcolOut is computed as the mean of these normalised counts.

repNormCounts

Report normalised counts for each replicate (by dividing them by s_k) in the . column (by default, NNorm.1, NNorm.2, etc.). This option is on by default when mergeMethod == "mean". However, it can also be used with mergeMethod == "weightedMean" (but the normalised counts will still be produced by dividing the raw counts for each replicate by s_k).

Value

An object of class chicagoData, with a params(cd)$s_k slot added representing the per-sample scaling factors used in normalisation.

See Also

readAndMerge

Examples

filesDir <- file.path(system.file("extdata", package="Chicago"), "unitTestData")
files <- file.path(filesDir, dir(filesDir))
print(files) ##we will read in and merge these files

designDir <- file.path(system.file("extdata", package="Chicago"), "unitTestDesign")

cdA <- setExperiment(designDir=designDir)
cdA <- readSample(files[1], cdA)

cdB <- setExperiment(designDir=designDir)
cdB <- readSample(files[2], cdB)

cdMerged <- mergeSamples(list(cdA, cdB))