EDASeq-package: Exploratory Data Analysis and Normalization for RNA-Seq data
In drisso/EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq
Description
Details
Author(s)
References
Description
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.
Details
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'.
See the package vignette for a typical Exploratory Data Analysis example.
Author(s)
Davide Risso and Sandrine Dudoit.
Maintainer: Davide Risso <risso.davide@gmail.com>
References
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 June 22, 2021, 4:18 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References
Description
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.
Details
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'.
See the package vignette for a typical Exploratory Data Analysis example.
Author(s)
Davide Risso and Sandrine Dudoit. Maintainer: Davide Risso <risso.davide@gmail.com>
References
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.