Normalization for RNASeq Data

This is a pipeline function for performing the available normalization methods in this package. The users can perform all the normalization methods at the same time or select the method they prefer to use.

pip.norm(raw, groups, norm.method = "all", QN_filter = FALSE)

Arguments

raw	raw count data in the format of data frame or matrix, with columns for samples and raws for genes.
groups	vector of characters indicating the group for each sample (only 2 groups allowed).
norm.method	the method for normalization selected from all, norm.none norm.TMM, norm.TC, norm.UQ, norm.med, norm.DESeq, norm.PoissonSeq, norm.QN, norm.SVA, norm.RUVr, norm.RUVg, and norm.RUVs. If all is selected, the function applies all provided normalization methods to the data and returns a list normalized counts (and scaling factors or adjusting factors depending on the method).
QN_filter	whether the filtering is performed if method = norm.QN.

Value

list, containing normalized dataset, and scaling factors or adjusting factors. If method="all" the list contains the normalized data set and scaling/adjusting factors for all provided methods.

Examples

res <- pip.norm(data.test, data.group, "norm.TMM")