In:
BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2010-12)
Abstract:
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 benchmarks published previously. We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better. Availability The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/ .
Type of Medium:
Online Resource
ISSN:
1471-2105
DOI:
10.1186/1471-2105-11-17
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2010
detail.hit.zdb_id:
2041484-5
SSG:
12
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