Dataset Information

PoolMC: smart pooling of mRNA samples in microarray experiments.

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.

SUBMITTER: Kainkaryam RM

PROVIDER: S-EPMC2900278 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

poolMC: smart pooling of mRNA samples in microarray experiments.

Kainkaryam Raghunandan M RM Bruex Angela A Gilbert Anna C AC Schiefelbein John J Woolf Peter J PJ

BMC bioinformatics 20100602

<h4>Background</h4>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 compre ...[more]

PMID: 20525223

Similar Datasets

Project description:In this study 3 pooling experiments were performed. In each of the 3 cohorts, a 'case' and a 'control' blood pool was compared - the goal being to identify single nucleotide polymorphisms with significantly different estimated pooling allele frequencies between cases and controls. For cohort 1, 100 individuals with blue eye color were placed in one pool (the 'control' pool) and 100 individuals with brown eye color were placed in another pool ( the 'case' pool). In cohort 2, 131 individuals with age-related macular degeneration were placed in one pool, with 216 control individuals in another pool. In cohort 3, 100 individuals with pseudoexfoliation syndrome were placed in a case pool - in this case the cohort 2 control sample was used as 'controls'. The blue/brown pools were hybridized to Illumina HumanHap550 arrays. The cohort 2 and 3 pools were hybridized to Illumina 1M arrays. After processing, the raw data are summarized to give pooling allele frequency estimates for each pool. The abstract from the paper describing these data is as follows: Genome-wide association studies (GWAS) have now successfully identified important genetic variants associated with many human traits and diseases. The high cost of genotyping arrays in large datasets remains the major barrier to wider utilization of GWAS. We have developed a novel method in which whole blood from cases and controls respectively is pooled prior to DNA extraction for genotyping. We demonstrate proof of principle by clearly identifying the associated variants for eye color, age-related macular degeneration and pseudoexfoliation syndrome in cohorts not previously studied. Blood pooling has the potential to reduce GWAS cost by several orders of magnitude and dramatically shorten gene discovery time. This method has profound implications for translation of modern genetic approaches to a multitude of diseases and traits yet to be analysed by GWAS, and will enable developing nations to participate in GWAS.