Assessing the significance of consistently mis-regulated genes in cancer associated gene expression matrices | Zendy

Mattias Wahde | Zendy; Gregory T. Klus | Zendy; Michael Bittner | Zendy; Yidong Chen | Zendy; Zoltán Szállási | Zendy

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Assessing the significance of consistently mis-regulated genes in cancer associated gene expression matrices

Author(s) -

Mattias Wahde,

Gregory T. Klus,

Michael Bittner,

Yidong Chen,

Zoltán Szállási

Publication year - 2002

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/18.3.389

Subject(s) - set (abstract data type) , expression (computer science) , gene , computational biology , computer science , gene expression , sample (material) , statistical power , regulation of gene expression , biology , genetics , mathematics , statistics , chemistry , chromatography , programming language

The simplest level of statistical analysis of cancer associated gene expression matrices is aimed at finding consistently up- or down-regulated genes within a given set of tumor samples. Considering the high level of gene expression diversity detected in cancer, one needs to assess the probability that the consistent mis-regulation of a given gene is due to chance. Furthermore, it is important to determine the required sample number that will ensure the meaningful statistical analysis of massively parallel gene expression measurements.

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