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Multi-group cancer outlier differential gene expression detection.

Abstract：

:It has recently been shown that cancer genes (oncogenes) tend to have heterogeneous expressions across disease samples. So it is reasonable to assume that in a microarray data only a subset of disease samples will be activated (often referred to as outliers), which presents some new challenges for statistical analysis. In this paper, we study the multi-class cancer outlier differential gene expression detection. Statistical methods will be proposed to take into account the expression heterogeneity. Through simulation studies and application to public microarray data, we will show that the proposed methods could provide more comprehensive analysis results and improve upon the traditional differential gene expression detection methods, which often ignore the expression heterogeneity and may loss power. Supplementary information can be found at http://www.biostat.umn.edu/~baolin/research/orf.html.

journal_name

Comput Biol Chem

journal_title

Computational biology and chemistry

authors

Liu F,Wu B

doi

10.1016/j.compbiolchem.2007.02.004

subject

Has Abstract

pub_date

2007-04-01 00:00:00

pages

65-71

issue

2

eissn

1476-9271

issn

1476-928X

pii

S1476-9271(07)00024-2

journal_volume

31

pub_type

杂志文章

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