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Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes.

ABSTRACT: Microarray technologies are widely used to quantify the abundance of transcripts corresponding to thousands of genes. To maximise the robustness of transcriptome results, we have tested the performance and reproducibility of rat and mouse gene expression data obtained with Affymetrix, Illumina and Operon platforms.We present a thorough analysis of the degree of reproducibility provided by analysing the transcriptomic profile of the same animals of several experimental groups under different popular microarray technologies in different tissues. Concordant results from inter- and intra-platform comparisons were maximised by testing many popular computational methods for generating fold changes and significances and by only considering oligonucleotides giving high expression levels. The choice of Affymetrix signal extraction technique was shown to have the greatest effect on the concordance across platforms. In both species, when choosing optimal methods, the agreement between data generated on the Affymetrix and Illumina was excellent; this was verified using qRT-PCR on a selection of genes present on all platforms.This study provides an extensive assessment of analytical methods best suited for processing data from different microarray technologies and can assist integration of technologically different gene expression datasets in biological systems.

SUBMITTER: Wilder SP

PROVIDER: S-EPMC2652496 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes.

Wilder Steven P SP Kaisaki Pamela J PJ Argoud Karène K Salhan Anita A Ragoussis Jiannis J Bihoreau Marie-Thérèse MT Gauguier Dominique D

BMC genomics 20090205

<h4>Background</h4>Microarray technologies are widely used to quantify the abundance of transcripts corresponding to thousands of genes. To maximise the robustness of transcriptome results, we have tested the performance and reproducibility of rat and mouse gene expression data obtained with Affymetrix, Illumina and Operon platforms.<h4>Results</h4>We present a thorough analysis of the degree of reproducibility provided by analysing the transcriptomic profile of the same animals of several exper ...[more]

PMID: 19196459

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Project description:BACKGROUND AND PURPOSE:Diabetes mellitus (DM) is the most common metabolic disorder that is characterized by hyperglycemia, it can be categorized by T1DM and T2DM. T1DM is also reported to cause bone loss. However, most reports regarding this aspect of T1DM have only investigated a single site; a comparison of bone loss from different areas of the body is still lacking. METHODS:Thirty-five 12-week-old Sprague Dawley® (SD) rats were separated to seven groups. Five rats were euthanized without any surgery at 0 weeks for histological examination and determination of baseline characteristics. In 15 of the rats, DM was induced via Streptozotocin (STZ)-injection, and they were separated to 3 groups (4 weeks, 8 weeks and 12 weeks after STZ-injection). The remaining 15 rats were used as the control group (4 weeks, 8 weeks and 12 weeks after saline-injection). We tested bone-mass loss at four skeletal sites, the tibia, the femur greater trochanter, the spine, and the mandibular bones using micro-computed tomography (CT) and histological tests. RESULTS:Tibia was influenced the most obvious(BV/TV decreased by 27.3%, 52.5%, and 81.2% at 4 weeks, 8 weeks, and 12 weeks, respectively. p<0.05). In contrast, the other three sites were influenced to a lesser extent and bone loss became prominent at a later time point according to the histological and micro-CT tests(Femur: BV/TV did not decrease significantly at the first month or second month. However, and decreased by 49.4% at the third month, P<0.05. Mandible: the BV/TV only decreased by 6.5% at 1 month after STZ-injection. There was still a significant difference between the second and third months. The BV/TV decreased by 47.0% and 68.1% at 2 months and 3 months, respectively, (p<0.05) Spine: the BV/TV only decreased by 6.7%. However, significant change was observed in the spine at the second month and third month after STZ injection. The BV/TV decreased by 45.4% and 64.3%, respectively, p<0.05). CONCLUSION:The results indicate that T1DM can severely influence the bone structure of the 4 skeletal sites. Further, areas with dense trabecular bones were influenced less and at a later time point in comparison to the tibial region. CLINICAL RELEVANCE:Our research can serve as a guide to help increase the success rate of implant treatment, and help decrease the fracture risk in different bone types with greater accuracy.

Project description:BackgroundWith the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.ResultsTwo Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.ConclusionThe Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.

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Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes.

Publications

Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes.

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