A Comparison of Probe-Level and Probeset Models for Small-Sample Gene Expression Data

Authors

• John R. Stevens, Utah State UniversityFollow
• Jason L. Bell, Utah State University
• Kenneth I. Aston, Utah State University
• Kenneth L. White, Utah State University

Document Type

Article

Journal/Book Title/Conference

BMC Bioinformatics

Volume

11

Issue

281

Publisher

BioMed Central Ltd.

Publication Date

5-26-2010

Journal Article Version

Version of Record

First Page

1

Last Page

17

Abstract

Background: Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.

Results: The performance of several probe-level and probeset models was assessed graphically and numerically using three spike-in datasets. Based on the Affymetrix GeneChip, a novel nested factorial model was developed and found to perform competitively on small-sample spike-in experiments.

Conclusions: Statistical methods with test statistics related to the estimated log fold change tend to be more consistent in their performance on small-sample gene expression data. For such small-sample experiments, the nested factorial model can be a useful statistical tool. This method is implemented in freely-available R code (affyNFM), available with a tutorial document at http://www.stat.usu.edu/~jrstevens.

Recommended Citation

Stevens, J.R., Bell, J.L., Aston, K.I. et al. A comparison of probe-level and probeset models for small-sample gene expression data. BMC Bioinformatics 11, 281 (2010). https://doi.org/10.1186/1471-2105-11-281