poolMC: Smart pooling of mRNA samples in microarray experiments

<p>Abstract</p> <p>Background</p> <p>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 d...

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Main Authors: Schiefelbein John, Gilbert Anna C, Bruex Angela, Kainkaryam Raghunandan M, Woolf Peter J
Format: Article
Language:English
Published: BMC 2010-06-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/11/299
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author Schiefelbein John
Gilbert Anna C
Bruex Angela
Kainkaryam Raghunandan M
Woolf Peter J
author_facet Schiefelbein John
Gilbert Anna C
Bruex Angela
Kainkaryam Raghunandan M
Woolf Peter J
author_sort Schiefelbein John
collection DOAJ
description <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>
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spelling doaj.art-584d8345507e46f88d5025fde7d12cd52022-12-22T03:06:42ZengBMCBMC Bioinformatics1471-21052010-06-0111129910.1186/1471-2105-11-299poolMC: Smart pooling of mRNA samples in microarray experimentsSchiefelbein JohnGilbert Anna CBruex AngelaKainkaryam Raghunandan MWoolf Peter J<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>http://www.biomedcentral.com/1471-2105/11/299
spellingShingle Schiefelbein John
Gilbert Anna C
Bruex Angela
Kainkaryam Raghunandan M
Woolf Peter J
poolMC: Smart pooling of mRNA samples in microarray experiments
BMC Bioinformatics
title poolMC: Smart pooling of mRNA samples in microarray experiments
title_full poolMC: Smart pooling of mRNA samples in microarray experiments
title_fullStr poolMC: Smart pooling of mRNA samples in microarray experiments
title_full_unstemmed poolMC: Smart pooling of mRNA samples in microarray experiments
title_short poolMC: Smart pooling of mRNA samples in microarray experiments
title_sort poolmc smart pooling of mrna samples in microarray experiments
url http://www.biomedcentral.com/1471-2105/11/299
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