Reducing the algorithmic variability in transcriptome-based inference.

MOTIVATION: High-throughput measurements of mRNA abundances from microarrays involve several stages of preprocessing. At each stage, a user has access to a large number of algorithms with no universally agreed guidance on which of these to use. We show that binary representations of gene expressions, retaining only information on whether a gene is expressed or not, reduces the variability in results caused by algorithmic choice, while also improving the quality of inference drawn from microarray studies.
RESULTS: Binary representation of transcriptome data has the desirable property of reducing the variability introduced at the preprocessing stages due to algorithmic choice. We compare the effect of the choice of algorithms on different problems and suggest that using binary representation of microarray data with Tanimoto kernel for support vector machine reduces the effect of the choice of algorithm and simultaneously improves the performance of classification of phenotypes.