Normalize RNASeq count data using gene lengths

This function normalizes a count matrix, using the matrix of length, and an appropriate transformation.

Usage

lengthNormalizeRNASeq(
  countMatrix,
  lengthMatrix = NULL,
  normalisationFactor = NULL,
  lengthNormalization = c("TPM", "RPKM", "none"),
  dataTransformation = c("log2", "sqrt", "asin(sqrt)")
)

Arguments

countMatrix

the RNASeq count matrix. Rows and columns should be named.

lengthMatrix

the associated length matrix. Should have the same dimensions as countMatrix, with the same names.

normalisationFactor

normalization factors to scale the raw library sizes, as computed e.g. by calcNormFactors.

lengthNormalization

one of "none", "TPM" (default) or "RPKM". See details.

dataTransformation

one of "log2" (default), "asin(sqrt)" or "sqrt." See details.

Value

A matrix of normalized and transformed counts, with the same dimensions as countMatrix.

Details

The lengthMatrix is used to normalize the counts, using one of the following formulas:

• lengthNormalization="none" : \(CPM_{gi} = \frac{N_{gi} + 0.5}{NF_i \times \sum_{g} N_{gi} + 1} \times 10^6\)

• lengthNormalization="TPM" : \(TPM_{gi} = \frac{(N_{gi} + 0.5) / L_{gi}}{NF_i \times \sum_{g} N_{gi}/L_{gi} + 1} \times 10^6\)

• lengthNormalization="RPKM" : \(RPKM_{gi} = \frac{(N_{gi} + 0.5) / L_{gi}}{NF_i \times \sum_{g} N_{gi} + 1} \times 10^9\)

where \(N_{gi}\) is the count for gene g and sample i, \(L_{gi}\) is the length of gene g in sample i, and \(NF_i\) is the normalization for sample i stored in normalisationFactor.

The function specified by the dataTransformation is then applied to the normalized count matrix.

The "\(+0.5\)" is taken from Law et al 2014, and dropped from the normalization when the transformation is something else than log2.

The "\(\times 10^6\)" and "\(\times 10^9\)" factors are omitted when the asin(sqrt) transformation is taken, as \(asin\) can only be applied to real numbers smaller than 1.

References

Law, C. W., Chen, Y., Shi, W. and Smyth, G. K. (2014), 'voom: precision weights unlock linear model analysis tools for RNA-seq read counts', Genome Biology 15(2), R29.

Bastide, P., Soneson, C., Stern, D. B., Lespinet, O. and Gallopin, M. (2023), 'A Phylogenetic Framework to Simulate Synthetic Interspecies RNA-Seq Data', Molecular Biology and Evolution 40(1), msac269.