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Open Access Research article

poolMC: Smart pooling of mRNA samples in microarray experiments

Raghunandan M Kainkaryam1, Angela Bruex2, Anna C Gilbert3, John Schiefelbein2 and Peter J Woolf14*

Author Affiliations

1 Department of Chemical Engineering, University of Michigan, Ann Arbor MI 48109, USA

2 Department of Molecular, Cellular, and Developmental Biology, University of Michigan Ann Arbor MI 48109, USA

3 Department of Mathematics, University of Michigan, Ann Arbor MI 48109, USA

4 Bioinformatics Program, University of Michigan, Ann Arbor MI 48109, USA

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BMC Bioinformatics 2010, 11:299  doi:10.1186/1471-2105-11-299

Published: 2 June 2010

Abstract

Background

Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart pooling strategy in which different samples, not necessarily biological replicates, are pooled in an information theoretic efficient way. Further, each sample is tested on multiple chips, but always in pools made up of different samples. The end goal is to exploit the compressibility of microarray data to reduce the number of chips used and increase the robustness to noise in measurements.

Results

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.

Conclusions

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.