PROBABILITY OF EXPRESSION
(POE)
An approach to the
analysis of gene expression microarrays
using three-component
mixtures
PROBABILITY OF EXPRESSION
(POE)
An approach to the
analysis of gene expression microarrays
using three-component
mixtures
Statistical methods for
molecular classification have focused on using high dimensional representations
of molecular profiles to identify subclasses.
These can be noisy, unstable,
and highly platform-specific. We develop statistical approaches to molecular
classification that emphasize simple molecular profiles based on latent
categories signifying under-, over-, and baseline-expression.
Following
this approach we can generate results that are more easily interpretable,
more easily translated into clinical tools, more robust to noise, and less
platform-dependent.
POE is an R package to
facilitate the application of these methods.
R PACKAGE:
POE is available for Linux and Windows. Download version 0.2-7 (compatible with R 1.9.1) here. Please address any comments or suggestions to the POE team below.
PAPERS:
Parmigiani, G, Garrett, ES Anbazhaghan, R, Gabrielson, E A statistical framework for expression-based molecular classification in cancer. JRSS, 64:717-736, 2002 [Full text-PDF]
Garrett ES, and Parmigiani G. POE: Statistical Tools for Molecular profiling, In The analysis of gene expression data: methods and software, (G Parmigiani, ES Garrett, R Irizarry, and SL Zeger eds), New York: Springer. [Book website]
Scharpf R, Garrett ES, Hu J, Parmigiani G. Statistical Modeling and Visualization of Molecular Profiles in Cancer. Biotechniques, 34:S22--S29, 2003. [Full text-PDF]
Garrett ES, Parmigiani G. A nested unsupervised approach to identifying novel molecular subtypes. [Full text-PDF]
A statistical framework for expression-based molecular classification in cancer. ENAR 2002 [PDF] RSS 2002 [PDF]
SUPPLEMENTARY MATERIALS:
A plain text R dump object of the phat.plus and phat.minus matrices used in the RSS paper. Each is a 2897x80 matrix. After downloading, please use dget to load into R. For other formate, please send us an email.
POE team: Elizabeth Garrett esg@jhu.edu, Jiang Hu, Giovanni Parmigiani gp@jhu.edu , Rob Scharpf, Xiaogang Zhong.
CREDITS: Ed
Gabrielson, Anba Ramaswami
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GP, Last updated 04/06/04