Coincidence between transcriptome analyses on different microarray platforms using a parametric framework

• Published in: PloS one Vol. 3; no. 10; p. e3555
• Main Authors: Konishi, Tomokazu, Konishi, Fumikazu, Takasaki, Shigeru, Inoue, Kohei, Nakayama, Koji, Konagaya, Akihiko
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29.10.2008; Public Library of Science (PLoS)
• Subjects: Acids; Animals; Automatic Data Processing - methods; Automatic Data Processing - standards; Bias; Bioinformatics; Biophysics/Experimental Biophysical Methods; Carcinogens; Cell Biology/Gene Expression; Computational Biology - methods; Computer science; Data acquisition; Data analysis; Data processing; Datasets; DNA microarrays; Gene expression; Gene Expression Profiling - methods; Gene Regulatory Networks; Generalized linear models; Genes; Genetics and Genomics/Bioinformatics; Genetics and Genomics/Gene Expression; Genomes; Hybridization; Laboratories; Male; Metabolic Networks and Pathways - genetics; Molecular Biology/Bioinformatics; Noise; Normal distribution; Oligonucleotide Array Sequence Analysis - instrumentation; Oligonucleotide Array Sequence Analysis - methods; Platforms; Rats; Rats, Inbred F344; Software; Studies; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0003555

A parametric framework for the analysis of transcriptome data is demonstrated to yield coincident results when applied to data acquired using two different microarray platforms. Microarrays are widely employed to acquire transcriptome information, and several platforms of chips are currently in use. However, discrepancies among studies are frequently reported, particularly among those performed using different platforms, casting doubt on the reliability of collected data. The inconsistency among observations can be largely attributed to differences among the analytical frameworks employed for data analysis. The existing frameworks are based on different philosophies and yield different results, but all involve normalization against a standard determined from the data to be analyzed. In the present study, a parametric framework based on a strict model for normalization is applied to data acquired using several slide-glass-type chips and GeneChip. The model is based on a common statistical characteristic of microarray data, and each set of chip data is normalized on the basis of a linear relationship with this model. In the proposed framework, the expressional changes observed and genes selected are coincident between platforms, achieving superior universality of data compared to other frameworks.