HTSanalyzeR - Network analysis of high-throughput screens
Functional genomics has demonstrated considerable success in inferring the inner working of a cell through analysis of its response to various perturbations.
In recent years several technological advances have pushed high-throughput screens (HTS) to the forefront of functional genomics.
The software package HTSanalyzeR combines state-of-the-art analysis methods for HTS in a unified framework. This package provides classes and methods for gene set over-representation, enrichment and network analyses on high-throughput screens. It contains a pipeline specifically designed for cellHTS2 objects. Additionaly, users can build their own analysis pipeline for their particular data format based on the methods in this package. Features of the current release include:
- Bioconductor compliant R package
- Easy to install under R. The package contains a vignette explaining the main functions in detail.
- Works hand in hand with cellHTS2, the leading package for pre-processing of cell-based high-throughput screens.
- Gene set and pathway analysis
- based on gene sets defined in MSigDB and Gene Ontology
- Overrepresentation analysis of hits based on hyper-geometric distribution
- Cutoff free Gene Set Enrichment Analysis (GSEA)
- Comparative gene set enrichment analysis
- identify gene sets that show opposite trends in two phenotypes
- based on comparing enrichment scores in GSEA
- Network enrichment analysis
- Presentation of results
- Output as HTML pages in easy to interpret list and figures.
- Enrichment results are presented in a graph visualization to identify functional themes in overlapping gene sets (similar to Merico et al, 2010)
Download and Installation
You can download the current release from the package's Bioconductor page. Alternatively, you can install the current release directly in R by typing:
source("http://bioconductor.org/biocLite.R")
biocLite("HTSanalyzeR")
References:
-
HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high throughput screens
Xin Wang*, Camille D.A. Terfve*, John C. Rose, Florian Markowetz
Bioinformatics (2011) 27 (6): 879-880
PMID:21258062 | doi:10.1093/bioinformatics/btr028 -
How to understand the cell by breaking it: network analysis of gene perturbation screens
F. Markowetz, PLoS Comp Bio, 2010 Feb 26;6(2):e1000655.
PMID:20195495 | doi:10.1371/journal.pcbi.1000655 | arXiv:0910.2938
