A comparison of probe-level and probeset models for small-sample gene expression data

作者：Stevens John R^*; Bell Jason L; Aston Kenneth I; White Kenneth L

来源：BMC Bioinformatics, 2010, 11: 281.

DOI：10.1186/1471-2105-11-281

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/similar to jrstevens.

• 出版日期2010-5-26