TCGAanalyze_Normalization {TCGAbiolinks} | R Documentation

## normalization mRNA transcripts and miRNA using EDASeq package.

### Description

TCGAanalyze_Normalization allows user to normalize mRNA transcripts and miRNA, using EDASeq package.

Normalization for RNA-Seq Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010).

For istance returns all mRNA or miRNA with mean across all samples, higher than the threshold defined quantile mean across all samples.

TCGAanalyze_Normalization performs normalization using following functions from EDASeq

1. EDASeq::newSeqExpressionSet

2. EDASeq::withinLaneNormalization

3. EDASeq::betweenLaneNormalization

4. EDASeq::counts

### Usage

    TCGAanalyze_Normalization(tabDF, geneInfo, method = "geneLength")

### Arguments

`tabDF` | Rnaseq numeric matrix, each row represents a gene, each column represents a sample
`geneInfo` | Information matrix of 20531 genes about geneLength and gcContent. Two objects are provided: TCGAbiolinks::geneInfoHT,TCGAbiolinks::geneInfo
`method` | is method of normalization such as 'gcContent' or 'geneLength'

### Value

Rnaseq matrix normalized with counts slot holds the count data as a matrix of non-negative integer count values, one row for each observational unit (gene or the like), and one column for each sample.

### Examples

    dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)

[Package TCGAbiolinks version 2.14.0 Index]