Partition Decoupling for Multi-gene Analysis of Gene Expression Profiling Data

Autor:	Braun, Rosemary, Leibon, Gregory, Pauls, Scott, Rockmore, Daniel
Rok vydání:	2010
Předmět:	Quantitative Biology - Quantitative Methods Quantitative Biology - Genomics Quantitative Biology - Molecular Networks Statistics - Computation Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We present the extention and application of a new unsupervised statistical learning technique--the Partition Decoupling Method--to gene expression data. Because it has the ability to reveal non-linear and non-convex geometries present in the data, the PDM is an improvement over typical gene expression analysis algorithms, permitting a multi-gene analysis that can reveal phenotypic differences even when the individual genes do not exhibit differential expression. Here, we apply the PDM to publicly-available gene expression data sets, and demonstrate that we are able to identify cell types and treatments with higher accuracy than is obtained through other approaches. By applying it in a pathway-by-pathway fashion, we demonstrate how the PDM may be used to find sets of mechanistically-related genes that discriminate phenotypes. Comment: Revised
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1002.3946 Zobrazit plný text záznamu View this record from Arxiv