Zipf's Law in Importance of Genes for Cancer Classification Using Microarray Data

Authors: LI W.¹; YANG Y.²

Source: Journal of Theoretical Biology, Volume 219, Number 4, December 2002 , pp. 539-551(13)

Buy & download fulltext article:

Price: $52.63 plus tax (Refund Policy)

Abstract:

Using a measure of how differentially expressed a gene is in two biochemically/phenotypically different conditions, we can rank all genes in a microarray dataset. We have shown that the falling-off of this measure (normalized maximum likelihood in a classification model such as logistic regression) as a function of the rank is typically a power-law function. This power-law function in other similar ranked plots are known as the Zipf's law, observed in many natural and social phenomena. The presence of this power-law function prevents an intrinsic cutoff point between the “important”genes and “irrelevant” genes. We have shown that similar power-law functions are also present in permuted dataset, and provide an explanation from the well-known chi ² distribution of likelihood ratios. We discuss the implication of this Zipf's law on gene selection in a microarray data analysis, as well as other characterizations of the ranked likelihood plots such as the rate of fall-off of the likelihood. Copyright 2002 Elsevier Science Ltd. All rights reserved.

Language: English

Document Type: Research article

DOI: http://dx.doi.org/10.1006/jtbi.2002.3145

Affiliations: 1: Center for Genomics and Human Genetics North Shore LIJ Research Institute, 350 Community Drive, Manhasset, NY, 11030, U.S.A. 2: Laboratory of Statistical Genetics, Rockefeller University, 1230 York Avenue, New York, NY, 10021, U.S.A.

Publication date: 2002-12-01