DaMiR.iTSnorm: Normalization of Independent Test Set

In BioinfoMonzino/DaMiRseq: Data Mining for RNA-seq data: normalization, feature selection and classification

Description

This function aims to normalize properly an actual independent test set by taking information from the Learning set that will be used to transform the new sample(s).

Usage

DaMiR.iTSnorm(
  Learning_set,
  Ind_Test_set,
  normtype = c("vst", "rlog", "logcpm"),
  method = c("precise", "quick")
)

Arguments

Learning_set	A SummarizedExperiment object or a data frame/matrix of raw count data. The learning set is supposed to be a raw counts dataset of the expressed features (not all features). Rows and Cols should be features and samples, respectively.
Ind_Test_set	A SummarizedExperiment object or a data frame/matrix of raw count data. The independent test set is supposed to be a raw counts dataset with the same features of 'Learning_set'. Rows and Cols should be features and samples, respectively.
normtype	Type of normalization to be applied: varianceStabilizingTransformation (vst), rlog or logcpm are allowed; default is "vst".
method	Type of method to estimate the dispersion, applied to the independent test set to normalize data. Only 'precise' and 'quick' are allowed. In the first case, the dispersion is estimated by the Learning set and applied to the independent test set. In the second case, is estimated from the independent test set. Default is "precise". See details in dispersionFunction

Details

The Learning_set is supposed to be a raw counts dataset of the expressed features. Moreover, the independent test set is supposed to be a raw counts dataset with the same features of 'Learning_set'. The independent test set is normalized, taking into account the dispersion parameter, estimated by the Learning set ('precise' method) or by the independent test set itself ('quick' method).

Value

A matrix containing a normalized expression matrix (log2 scale)

Author(s)

Mattia Chiesa, Luca Piacentini

References

Michael I Love, Wolfgang Huber and Simon Anders (2014): Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. Genome Biology

See Also

varianceStabilizingTransformation, rlog cpm

Examples

# use example data:
data(SE)

BioinfoMonzino/DaMiRseq documentation built on Aug. 22, 2021, 3:11 p.m.