Multi-level mixed effects models for bead arrays

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Motivation: Bead arrays are becoming a popular platform for high-throughput expression arrays. However, the number of the beads targeting a transcript and the variation of their intensities differ from sample to sample in these arrays. This property results in different accuracy of expression intensities of a transcript across arrays. Results: We provide evidence, with publicly available spike-in data, that the false discovery rate of differential expression is reduced by modeling bead-level variability with a multi-level mixed effects model. We compare the performance of our proposed model to existing analysis methods for bead arrays: the unweighted t-test and other weighted methods. Additionally, we provide theoretical insights into when the multi-level mixed effects model outperforms other methods. Finally, we provide a software program for differential expression analysis using the multi-level mixed effects model that analyzes tens of thousands of genes efficiently.

Original languageEnglish (US)
Article numberbtq708
Pages (from-to)633-640
Number of pages8
JournalBioinformatics
Volume27
Issue number5
DOIs
StatePublished - Mar 2011

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Mixed Effects Model
Differential Expression
Software
t-test
Spike
Genes
Throughput
High Throughput
Gene
Modeling

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability
  • Medicine(all)

Cite this

Multi-level mixed effects models for bead arrays. / Kim, Ryung S.; Lin, Juan.

In: Bioinformatics, Vol. 27, No. 5, btq708, 03.2011, p. 633-640.

Research output: Contribution to journalArticle

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