TCGAanalyze_Normalization: normalization mRNA transcripts and miRNA using EDASeq...

Description Usage Arguments Value Examples

View source: R/analyze.R


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


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



Rnaseq numeric matrix, each row represents a gene, each column represents a sample


Information matrix of 20531 genes about geneLength and gcContent. Two objects are provided: TCGAbiolinks::geneInfoHT,TCGAbiolinks::geneInfo


is method of normalization such as 'gcContent' or 'geneLength'


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.



TCGAbiolinks documentation built on Nov. 8, 2020, 5:37 p.m.