Papers on normalization, variable selection, classification or clustering of microarray data

Abstract

Over the last decade or so, there have been large numbers of methods published on approaches for normalization, variable (gene) selection, classification, and clustering of microarray data. As indicated in the scope document for Bioinformatics, this requires papers describing new methods for these problems to meet a very high standard, showing important improvement in results for real biological data, as well as novelty. In this editorial, we describe some standards that need to be met for papers in these areas to be seriously considered. We ask that prospective authors consider these points carefully before submission of their papers to Bioinformatics. The Role of Simulation. Simulation can be useful in investigating the properties of various methods of data analysis. Yet there are important barriers to credible use of simulation in microarray studies, largely due to what we don’t know about the statistical distribution of measured gene expression levels. First, the distribution across transcripts of true expression values is dependent on the biological state of the tissue or cell, and for a given state this is unknown, even in distributional form, and may further exhibit genespecific and platform-specific effects. Second, the correlation within biological replicates of true expression is unknown, and is likely unknowable in detail given that it is