%0 Journal Article
%T poolMC: Smart pooling of mRNA samples in microarray experiments
%A Raghunandan M Kainkaryam
%A Angela Bruex
%A Anna C Gilbert
%A John Schiefelbein
%A Peter J Woolf
%J BMC Bioinformatics
%D 2010
%I BioMed Central
%R 10.1186/1471-2105-11-299
%X A theoretical framework to perform smart pooling of mRNA samples in microarray experiments was established and the software implementation of the pooling and decoding algorithms was developed in MATLAB. A proof-of-concept smart pooled experiment was performed using validated biological samples on commercially available gene chips. Differential-expression analysis of the smart pooled data was performed and compared against the unpooled control experiment.The theoretical developments and experimental demonstration in this paper provide a useful starting point to investigate smart pooling of mRNA samples in microarray experiments. Although the smart pooled experiment did not compare favorably with the control, the experiment highlighted important conditions for the successful implementation of smart pooling - linearity of measurements, sparsity in data, and large experiment size.Presently, pooling in microarray experiments refers to the act of mixing messenger RNA (mRNA) collected from several biological-replicate samples, before hybridization onto a microarray chip [1-6]. This form of pooling may be used to reduce biological variation, to lower costs by reducing the number of microarray chips used, and to overcome the problem of limited sample availability.In this paper, we describe a different pooling strategy; a smart pooling strategy based on compression algorithms from digital communication theory. The smart pooling strategy is applied to a large number of diverse biological samples, not necessarily biological replicates, which are pooled and tested on several microarray chips based on a pre-specified pooling design. The mathematical properties of smart pooling designs ensure that each sample is tested on multiple chips, but always in pools made up of a different set of samples, such that, data from all the chips taken together capture the same information as the standard one-sample-one-chip approach. Because of the convolution step involved in testing pools of sa
%U http://www.biomedcentral.com/1471-2105/11/299