TMM_Norm: Trimmed Mean of M Values normalization of count data

Description Usage Arguments Details Value Author(s) Examples

Description

The method normalizes count data by the trimmed mean of m values in each sample

Usage

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TMM_Norm(e_data, edata_id, reference = NULL, qm = 0.3, qa = 0.05)

Arguments

e_data

a p \times n data.frame of count data, where p is the number of features and n is the number of samples. Each row corresponds to data for a feature, with the first column giving the feature name.

edata_id

character string indicating the name of the feature identifier. Usually obtained by calling attr(omicsData, "cnames")$edata_cname.

reference

which column in e_data should be used as the reference, default is to use the sample with the least amount of missing data.

qm

percentage by which to trim M values (gene-wise log-fold-changes), default is 0.30 (30%)

qa

percentage by which to trim A values (absolute expression levels), default is 0.05 (5%)

Details

Count data is normalized by the trimmed mean of m values.

Value

List containing 3 elements: norm_data is a data.frame with same structure as e_data that contains the TMM-normalized data, location_param is a numeric vector of the TMM values for each sample, and scale_param is NULL.

Author(s)

Allison Thompson, Lisa Bramer

Examples

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library(mintJansson)
data(cDNA_hiseq_data)
cDNA_TMM <- TMM_Norm(e_data = cDNA_hiseq_data$e_data, edata_id = attr(cDNA_hiseq_data, "cnames")$edata_cname)
norm_factors <- attr(cDNA_TMM,"data_info")$scale_param

pmartR/pmartRseq documentation built on May 25, 2019, 9:20 a.m.