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• W2115552022 abstract "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. Availability: The software program is freely available on web at http://ephpublic.aecom.yu.edu/sites/rkim/Supplementary. Contact: ryung.kim@einstein.yu.edu Supplementary information: Supplementary data are available at Bioinformatics online." @default.
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• W2115552022 date "2010-12-17" @default.
• W2115552022 modified "2023-09-23" @default.
• W2115552022 title "Multi-level mixed effects models for bead arrays" @default.
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• W2115552022 doi "https://doi.org/10.1093/bioinformatics/btq708" @default.
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