Description Usage Arguments Value Note Author(s) See Also Examples
Merge a number of chicagoData
objects together, summarising their counts into a normalised value.
1 2 | mergeSamples(cdl, normalise = TRUE, NcolOut = "N",
NcolNormPrefix = "NNorm", mergeMethod = c("weightedMean", "mean")[1], repNormCounts = (mergeMethod=="mean"))
|
cdl |
A |
normalise |
If |
NcolOut |
The column to store the normalised counts in. |
NcolNormPrefix |
Each sample gains a normalised count column, that begins with this prefix. |
mergeMethod |
If |
repNormCounts |
Report normalised counts for each replicate (by dividing them by s_k) in the <NcolNormPrefix>.<sampleNo> column (by default, NNorm.1, NNorm.2, etc.). This option is on by default when |
An object of class chicagoData
, with a params(cd)$s_k
slot added representing the per-sample scaling factors used in normalisation.
Raw per-sample counts will be stored in the N.<sampleNo> column (N.1, N.2, etc.)
Mikhail Spivakov, Jonathan Cairns, Paula Freire Pritchett
1 2 3 4 5 6 7 8 9 10 11 12 13 | 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))
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