CSFA: Computing connectivity scores with Factor Analysis...
In ewouddt/CSFA: Connectivity Scores with Factor Analysis
Description
CSFA is a wrapper of multiple packages containing a factor analysis method.
These methods are used to derive the the connectivity scores of reference gene signatures with one or multiple query signatures.
CSFA will apply them, output the scores and immediately produce a number of meaningful plots interactively. The included methods are PCA and MFA from the FactoMineR package, FABIA from the fabia package and Sparse PCA/MFA from the elasticnet package.
Further, CSFA also contains an implementation of the Zhang and Gant score.
References
Abdi, H. et al. (2013), "Multiple factor analysis: principal component analysis for multitable and multiblock data sets," WIREs Comput Stat, 1-31.
Hochreiter, S. et al., "FABIA: Factor Analysis for Bicluster acquisition," Bioinformatics, 26, 1520-1527.
Lamb, J. et al. (2006), "The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease," Science, 313, 1929-1934.
Zhang, S.-D. and Gant, T.W. (2008), "A simple and robust method for connecting small-molecule drugs using gene-expression signatures," BMC Bioinformatics, 9, 10.
Working Paper: De Troyer E., Shkedzy Z., Kasim A. and Perualila-Tan N.-J. (2018), Connectivity Mapping Using Multiple Factor Analysis
ewouddt/CSFA documentation built on May 20, 2019, 6:44 p.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
CSFA is a wrapper of multiple packages containing a factor analysis method.
These methods are used to derive the the connectivity scores of reference gene signatures with one or multiple query signatures.
CSFA will apply them, output the scores and immediately produce a number of meaningful plots interactively. The included methods are PCA and MFA from the FactoMineR package, FABIA from the fabia package and Sparse PCA/MFA from the elasticnet package.
Further, CSFA also contains an implementation of the Zhang and Gant score.
References
Abdi, H. et al. (2013), "Multiple factor analysis: principal component analysis for multitable and multiblock data sets," WIREs Comput Stat, 1-31.
Hochreiter, S. et al., "FABIA: Factor Analysis for Bicluster acquisition," Bioinformatics, 26, 1520-1527.
Lamb, J. et al. (2006), "The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease," Science, 313, 1929-1934.
Zhang, S.-D. and Gant, T.W. (2008), "A simple and robust method for connecting small-molecule drugs using gene-expression signatures," BMC Bioinformatics, 9, 10.
Working Paper: De Troyer E., Shkedzy Z., Kasim A. and Perualila-Tan N.-J. (2018), Connectivity Mapping Using Multiple Factor Analysis
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.