Estimating Dataset Size Requirements for Classifying DNA Microarray Data

J. of Comp. Biol. vol 10, n2, p119-142 (2003). Published: 2003.03.31

Sayan Mukherjee, Pablo Tamayo, Simon Rogers, Ryan Rifkin, Anna Engle, Colin Campbell, Todd R. Golub and Jill P. Mesirov.

Read Manuscript

Abstract

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.

Keywords: Gene expression profiling, molecular pattern recognition, DNA microarrays, microarray analysis, sample size estimation

Thumbnail2

Supplemental Data

Description Link/Filename
Draft Manuscript Sample_size_fin.pdf