PsiNorm: PsiNorm: scaling normalization based on the Pareto...

In MatteoBlla/PsiNorm: Scaling normalization based on the Pareto distribution

Description

Normalization of a raw counts matrix using the estimate of the shape parameter of the Pareto distribution.

Usage

PsiNorm(x, ...)

## S4 method for signature 'SummarizedExperiment'
PsiNorm(x, whichAssay = 1, assayName = "PsiNorm")

## S4 method for signature 'SingleCellExperiment'
PsiNorm(x, whichAssay = "counts")

## S4 method for signature 'ANY'
PsiNorm(x)

Arguments

x	A SingleCellExperiment/SummarizedExperiment object or a matrix=like object with genes in rows and samples in columns.
...	generic argument
whichAssay	if x is a SingleCellExperiment/SummarizedExperiment the assay with the counts to normalize (default to 1).
assayName	if x is a SummarizedExperiment the name of the assay in which to save the normalized data (default to "PsiNorm").

Value

If the input is a SingleCellExperiment object the function returns the same object adding as sizeFactors those computed by PsiNorm. If the object is a SummarizedExperiment object, the function returns the same objject adding an assay with the normalized count matrix. If the input is a matrix-like object pareto_norm returns a matrix with the same dimensions containing the normalized counts.

Examples

m<-matrix(c(1,0,2,0,2,9,3,0), ncol=2)
sce<-SingleCellExperiment::SingleCellExperiment(assays=list(counts=m))

sce<-PsiNorm(sce) # SingleCellExperiment object
norm.matrix<-PsiNorm(m) # normalized matrix object

MatteoBlla/PsiNorm documentation built on April 26, 2021, 4:04 p.m.