Unknown,Transcriptomics,Genomics,Proteomics

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A simple method to integrate different versions of Affymetrix microarrays using duplicate samples

ABSTRACT: Background: As costs decline, the size and scope of microarray experiments have increased. In multi-centre studies there is a need to ensure consistency of data pre-processing across centres. Similarly, in smaller scale studies the evolution of microarray platforms means that there is often a need to compare data generated on earlier microarrays to that generated on newer ones. It is important in such studies to ensure that platform-dependent biases are removed so that meta-analysis of different datasets can be performed reliably. In both these cases the optimal scenario is to have a small subset of samples repeated at each site or on each platform. These replicates can then be used to learn a relationship between probe intensities on the two platforms. Results: I introduce here a simple, linear-modelling-based method for normalizing data from multiple-platforms by using replicate hybridizations. A dataset of 20 rat liver samples is used as a benchmark. Eight samples are hybridized to two separate versions of Affymetrix microarrays, while the other 12 are hybridized to one, for a total of 28 arrays. Our linear modelling method removes platform bias as assessed using both unsupervised machine-learning and two-group statistical analyses. The method is computationally efficient and works well for data pre-processed by the GCRMA, RMA and MAS5 algorithms and using either default or alternative probe-mappings. The method is very stable towards the number of replicate samples used, with even two replicates greatly reducing platform-specific bias. Conclusions: A simple linear-modelling method can remove platform-specific bias independent of the pre-processing algorithm and ProbeSet-mapping used. This technique can readily be extended to multi-site experiments, and suggests the benefits of including a small number of replicate hybridizations in each new study as a normalization control. Twenty rats livers were processed, eight on both RAE230-A and RAE230-2 arrays, 8 on only RAE230-A arrays, and 4 on RAE230-2 arrays only.

ORGANISM(S): Rattus norvegicus

SUBMITTER: Paul Boutros

PROVIDER: E-GEOD-16458 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Aims of study: (1) To identify systemic differences in osteoarthritic (OA) bone that contribute to OA pathogenesis. (2) Identify novel osteoporotic (OP) bone-related disease genes. Study involved comparison of trabecular bone extracted from the intertrochanteric (IT) region of the proximal femur (PF) from OA, OP and normal/control (CTL) individuals. Bone was obtained from OA and OP individuals at surgery for total hip replacement and from CTL individuals at autopsy. Keywords: Bone tissue comparison, diseased versus non-diseased, factorial design, linear modelling Four sets of sample comparisons (39 comparisons in total) were made in this study. These comprised 10 OA-CTL female, 10 OA-CTL male, 10 OA-OP female and 9 OP-CTL female comparisons. A Compugen Human 19K-oligo library spotted onto glass slides by the Adelaide Microarray facility (AMF) was used in this study. The slides were interrogated by competitive hybridisation of Cy3 and Cy5 labelled pairs of OA-CTL, OA-OP or OP-CTL amplified RNA samples. Sample pairs were age-matched as closely as possible. A biological dye-swap strategy was employed. After hybridisation and washing of the slides they were scanned using a GenePix 4000B Scanner driven by GenePix Pro 4.0. All analyses were performed using the statistical programming and graphics environment R. The “SPOT” software package was used to identify spots by adaptive segmentation method and subtract backgrounds utilising morphological opening approach. Data analysis was performed in R using Bioconductor. Loess print tip method was used to correct for dye-bias and intensity within each group of adjacent spots printed by one pin. Linear modelling was performed with the Limma package of Bioconductor. Linear models were used to incorporate all available data into a single analysis. This allowed the use of both direct and indirect comparisons in calculation of expression ratios as well as improved accuracy in estimation of variance for each gene. Factorial design utilised the following factors: medical condition (OA or OP or CTL); sex (male or female) for OA patients only.

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A simple method to integrate different versions of Affymetrix microarrays using duplicate samples

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