A benchmark for statistical microarray data analysis that preserves actual biological and technical variance.

ABSTRACT:

Background

Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods.

Results

Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality.

Conclusions

Performance analysis refined the results from benchma

SUBMITTER: De Hertogh B 

PROVIDER: S-EPMC2831002 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature