HTSanalyzeR: HTSanalyzeR Package Overview

In HTSanalyzeR: Gene set over-representation, enrichment and network analyses for high-throughput screens

Description

This package provides classes and methods for gene set over-representation, enrichment and network analyses on high-throughput screens. The over- representation analysis is performed based on hypergeometric tests. The enrichment analysis is based on the GSEA algorithm (Subramanian et al. PNAS 2005). The network analysis identifies enriched subnetworks based on algorithms from the BioNet package (Beisser et al., Bioinformatics 2010). A pipeline is also specifically designed for cellHTS2 object to perform integrative network analyses of high-throughput RNA interference screens. The users can build their own analysis pipeline for their own data set based on this package.

Details

Package:	HTSAnalyzeR
Type:	Package
Version:	2.13.2
Date:	2013-09-29
License:	Artistic-2.0
LazyLoad:	yes

The most important classes in this package are 'GSCA' (Gene Set Collection Analyses) and 'NWA' (NetWork Analyses). As an example, a pipeline (see function 'HTSanalyzeR4cellHTS2') is developed in this package for cellHTS2 screen analyses. Based on these two classes and other functions, users can design their own pipelines specifically for their own data sets.

Full help on classes and associated functions is available from within class help pages.

Introductory information on the use of classes and pipeline are available in the vignette, typeopenVignette().

A full listing of documented topics is available in HTML view by typing help.start() and selecting the HTSanalyzeR package from the Packages menu or via library(help="HTSanalyzeR").

Author(s)

Xin Wang, Camille D.A. Terfve, John C. Rose, and Florian Markowetz Maintainer: Xin Wang <[e-mail]>

References

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S. & Mesirov, J. P. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545-15550.

Beisser D, Klau GW, Dandekar T, Muller T, Dittrich MT. BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics. 2010 Apr 15;26(8):1129-30.

Dittrich MT, Klau GW, Rosenwald A., Dandekar T and Muller T. Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 2008 24(13):i223-i231.

HTSanalyzeR documentation built on Oct. 31, 2019, 7:10 a.m.