Integrated analysis of the various types of microarray data using linear-mixed effects models

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 hetero...

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Bibliographic Details
Main Authors: Sung Gon Yi, Taesung Park
Format: Conference Proceeding
Language:English
Subjects:
Analysis of variance
Analytical models
Covariance matrix
Data models
Heterogeneous Microarray data sets
Hospitals
Linear Mixed effect model
Mathematical model
Tumors
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Summary: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. The proposed LMe model was illustrated using the data from 133 microarrays collected at three different hospitals. Though simulation studies, we compared the proposed LMe model approach with the meta-analysis and the ANOVA model approaches. The LMe model approach was shown to provide higher powers than the other approaches.
DOI:10.1109/BIBM.2010.5706594