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.
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
BamFileList classes of
Most used graphic tools for the
BamFileList objects are: 'barplot', 'plotQuality', 'plotNtFrequency'. For
SeqExpressionSet objects are: 'biasPlot', 'meanVarPlot', 'MDPlot'.
To perform gene-level normalization use the functions 'withinLaneNormalization' and 'betweenLaneNormalization'.
See the package vignette for a typical Exploratory Data Analysis example.
Davide Risso and Sandrine Dudoit. Maintainer: Davide Risso <email@example.com>
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.
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