Within-lane normalization for GC-content (or other lane-specific) bias.
A numeric matrix representing the counts or a
A numeric vector representing the covariate to normalize for (if
Method used to normalized. See the details section and the reference below for details.
Should the normalized value be returned as an offset leaving the original counts unchanged?
The number of bins used to stratify the covariate for
If TRUE the normalization returns rounded values (pseudo-counts). Ignored if offset=TRUE.
This method implements four normalizations described in Risso et al. (2011).
loess normalization transforms the data by regressing the counts on
y and subtracting the loess fit from the counts to remove the dependence.
full normalizations are based on the stratification of the genes based on
y. Once the genes are stratified in
num.bins strata, the methods work as follows.
scales the data to have the same median in each bin.
the same but with the upper quartile.
forces the distribution of each stratum to be the same using a non linear full quantile normalization, in the spirit of the one used in microarrays.
signature(x = "matrix", y = "numeric")
It returns a matrix with the normalized counts if
offset=FALSE or with the offset if
signature(x = "SeqExpressionSet", y = "character")
It returns a
SeqExpressionSet with the normalized counts in the
normalizedCounts slot and with the offset in the
offset slot (if
D. Risso, K. Schwartz, G. Sherlock and S. Dudoit (2011). GC-Content Normalization for RNA-Seq Data. Manuscript in Preparation.
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library(yeastRNASeq) data(geneLevelData) data(yeastGC) sub <- intersect(rownames(geneLevelData), names(yeastGC)) mat <- as.matrix(geneLevelData[sub, ]) data <- newSeqExpressionSet(mat, phenoData=AnnotatedDataFrame( data.frame(conditions=factor(c("mut", "mut", "wt", "wt")), row.names=colnames(geneLevelData))), featureData=AnnotatedDataFrame(data.frame(gc=yeastGC[sub]))) norm <- withinLaneNormalization(data, "gc", which="full", offset=FALSE)
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