Integrated analysis of the heterogeneous microarray data

Background As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting...

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Vydáno v:BMC bioinformatics Ročník 12; číslo Suppl 5; s. S3
Hlavní autoři: Yi, Sung Gon, Park, Taesung
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 27.07.2011
Témata:
Algorithms
Analysis of Variance
Bioinformatics
Biomedical and Life Sciences
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Life Sciences
Linear Models
Liver Neoplasms - genetics
Microarrays
Oligonucleotide Array Sequence Analysis
Proceedings
ANOVA Model
Microarray Data
Linear Mixed Effect Model
Covariance Structure
Statistical Test Procedure
ISSN:1471-2105, 1471-2105
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Shrnutí:Background As the magnitude of the experiment increases, it is common to combine various types of microarrays such as paired and non-paired microarrays from different laboratories or hospitals. Thus, it is important to analyze microarray data together to derive a combined conclusion after accounting for heterogeneity among data sets. One of the main objectives of the microarray experiment is to identify differentially expressed genes among the different experimental groups. We propose the linear mixed effect model for the integrated analysis of the heterogeneous microarray data sets. Results The proposed linear mixed effect model was illustrated using the data from 133 microarrays collected at three different hospitals. Though simulation studies, we compared the proposed linear mixed effect model approach with the meta-analysis and the ANOVA model approaches. The linear mixed effect model approach was shown to provide higher powers than the other approaches. Conclusions The linear mixed effect model has advantages of allowing for various types of covariance structures over ANOVA model. Further, it can handle easily the correlated microarray data such as paired microarray data and repeated microarray data from the same subject.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-12-S5-S3