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MAID : an effect size based model for microarray data integration across laboratories and platforms.

ABSTRACT: BACKGROUND: Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions. RESULTS: Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model (MAID) can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different laboratories and platforms. The model uses two standardized indices, the standard effect size score for experiments with two groups of data, and a new standardized index that measures the difference in gene expression between treatment and control groups for one sample data with replicate arrays. The statistical significance of treatment effect across studies for each gene is determined by appropriate permutation methods depending on the type of data integrated. We apply our method to three different expression datasets from two different laboratories generated using three different array platforms and two different experimental designs. Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models. We also show that genes found to be significant using our data integration method are of direct biological relevance to the three experiments integrated. CONCLUSION: High-throughput genomics data provide a rich and complex source of information that could play a key role in deciphering intricate molecular networks behind disease. Here we propose an extension of the traditional effect size model to allow the integration of as many array experiments as possible with the aim of increasing the statistical power for identifying differentially expressed genes.

SUBMITTER: Borozan I

PROVIDER: S-EPMC2483727 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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MAID : an effect size based model for microarray data integration across laboratories and platforms.

Borozan Ivan I Chen Limin L Paeper Bryan B Heathcote Jenny E JE Edwards Aled M AM Katze Michael M Zhang Zhaolei Z McGilvray Ian D ID

BMC bioinformatics 20080710

<h4>Background</h4>Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that ...[more]

PMID: 18616827

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Project description:Using array comparative genomic hybridization (aCGH), a large number of deleted genomic regions have been identified in human cancers. However, subsequent efforts to identify target genes selected for inactivation in these regions have often been challenging. We integrated here genome-wide copy number data with gene expression data and non-sense mediated mRNA decay rates in breast cancer cell lines to prioritize gene candidates that are likely to be tumour suppressor genes inactivated by bi-allelic genetic events. The candidates were sequenced to identify potential mutations. This integrated genomic approach led to the identification of RIC8A at 11p15 as a putative candidate target gene for the genomic deletion in the ZR-75-1 breast cancer cell line. We identified a truncating mutation in this cell line, leading to loss of expression and rapid decay of the transcript. We screened 127 breast cancers for RIC8A mutations, but did not find any pathogenic mutations. No promoter hypermethylation in these tumours was detected either. However, analysis of gene expression data from breast tumours identified a small group of aggressive tumours that displayed low levels of RIC8A transcripts. Real-time PCR analysis of 38 breast tumours showed a strong association between low RIC8A expression and the presence of TP53 mutations (P=0.006). We demonstrate a data integration strategy leading to the identification of RIC8A as a gene undergoing a classical double-hit genetic inactivation in a breast cancer cell line, as well as in vivo evidence of loss of RIC8A expression in a subgroup of aggressive TP53 mutant breast cancers. Gene expression data: Samples GSM388181-GSM388198. The experiment utilized six breast cancer cell lines: MDA-MB-468, MDA-MB-231, ZR-75-1, MCF7, BT-474 and T-47D, and three non-malignant cell lines: HMECs (non-malignant human mammary epithelial cells), IMR90 (normal lung fibroblasts) and WS1 (normal skin fibroblasts). All cell lines were obtained from American Type Culture Collection and grown in accordance with the distributor's instructions. Both malignant and non-malignant cell lines were treated with the translation inhibitor emetine dihydrochloride hydrate. For each cell line, parallel cell cultures were grown in 175 cm2 flasks until 70-80 % confluence. Half of the subconfluent cultures were treated with 100 μg ml-1 of emetine dihydrochloride hydrate while the other half were left as untreated controls. Genome-wide copy number data: Samples GSM388211-GSM388216. The experiment utilized six breast cancer cell lines; MDA-MB-468, MDA-MB-231, ZR-75-1, MCF7, BT-474 and T-47D. All cell lines were obtained from American Type Culture Collection and grown in accordance with the distributor's instructions. All samples were hybridized once on 44k Agilent Human Genome CGH microarrays according to manufacturers instructions. Genomic DNA pooled from healthy female donors was used as a reference in all hybridizations. DNA from cell line samples were labeled with Cy5 and DNA from reference were labeled with Cy3.

Dataset Information

MAID : an effect size based model for microarray data integration across laboratories and platforms.

Publications

MAID : an effect size based model for microarray data integration across laboratories and platforms.

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