A comparison of probe-level and probeset models for small-sample gene expression data.
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.
Stevens
John R
JR
Department of Mathematics and Statistics, Utah State University, Logan, UT 84322, USA. john.r.stevens@usu.edu
Bell
Jason L
JL
Aston
Kenneth I
KI
White
Kenneth L
KL
D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D018401:Sample Size