Authors: Guixian Lin<email@example.com> and Sheng
Maintainer: Guixian Lin
This software, written in the R language, computes reproducibility probability score to select differentially expressed genes. The Reproducibility Probability Score (RPS), takes into consideration both the replicated data in a particular lab and the measurement variability across labs. The measurement variability is assessed by utilizing the reference gene expression data generated in the Microarray Quality Control (MAQC) project. Specifically, we applied the data generated across replicate gene expression analysis that was conducted in multiple facilities as part of this effort. A larger RPS means a gene is more likely to be differentially expressed; and if similar transcription profiling measurements are made in other laboratories, it is highly likely to be confirmed.
Reproducibility Probability Score: Incorporating Measurement Variability across Laboratories for Gene Selection,
by Guixian Lin, Xuming He, Hanlee Ji, Leming Shi, Ronald W. Davis, and Sheng Zhong.
Download RPS R package (Windows zip file)
RPS R package (Windows zip file)
How to use it?
You can read the help in the RPS package or this introduction.
Last updated: 11/06/2006