An Algorithm for Binary and Multi-Class Cancer Classification and Informative Genes Selection

• Activity Number: 367
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Haiyan Wang*+
• Companies: Kansas State University
• Keywords: Classification ; cross-validation ; TSP family classifiers

Abstract

One challenge in classification of cancer tissue samples based on gene expression data is to build an effective classification rule and select a parsimonious set of informative genes. We introduce a computational algorithm named Chisquare-statistic-based Top Scoring Genes (Chi-TSG) classifier that redefines the scores for gene set and the classification rules in TSP family classifiers by incorporating the sample size information. The algorithm automatically reports the total number of informative genes selected with cross validation. We provide the algorithm for both binary and multi-class cancer classification. The algorithm was applied to 9 binary and 10 multi-class gene expression datasets involving human cancers. The TSG classifier outperforms TSP family classifiers by a big margin in most of the 19 datasets. Advantages of our classifier include: easy interpretation, invariant to monotone transformation, resistant to sampling variations due to within sample operations. It offers a useful tool for cancer classification based on numerical molecular data.

The resulting TSG classifier offers a useful tool for cancer classification based on numerical molecular data.