EDASeq-package: Exploratory Data Analysis and Normalization for RNA-Seq data

Description Details Author(s) References


Numerical summaries and graphical representations of some key features of the data along with implementations of both within-lane normalization methods for GC content bias and between-lane normalization methods to adjust for sequencing depth and possibly other differences in distribution.


The SeqExpressionSet class is used to store gene-level counts along with sample information. It extends the virtual class eSet. See the help page of the class for details.

"Read-level" information is managed via the FastqFileList and BamFileList classes of Rsamtools.

Most used graphic tools for the FastqFileList and BamFileList objects are: 'barplot', 'plotQuality', 'plotNtFrequency'. For SeqExpressionSet objects are: 'biasPlot', 'meanVarPlot', 'MDPlot'.

To perform gene-level normalization use the functions 'withinLaneNormalization' and 'betweenLaneNormalization'.

An 'As' method exists to coerce SeqExpressionSet objects to CountDataSet objects (DESeq package).

See the package vignette for a typical Exploratory Data Analysis example.


Davide Risso and Sandrine Dudoit. Maintainer: Davide Risso <[email protected]>


J. H. Bullard, E. A. Purdom, K. D. Hansen and S. Dudoit (2010). Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics Vol. 11, Article 94.

D. Risso, K. Schwartz, G. Sherlock and S. Dudoit (2011). GC-Content Normalization for RNA-Seq Data. Technical Report No. 291, Division of Biostatistics, University of California, Berkeley, Berkeley, CA.

drisso/EDASeq documentation built on July 28, 2018, 4:35 p.m.