Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-30191-9_15
Title: Iterative piecewise linear regression to accurately assess statistical significance in batch confounded differential expression analysis
Authors: Li, J.
Choi, K.P. 
Karuturi, R.K.M.
Issue Date: 2012
Citation: Li, J.,Choi, K.P.,Karuturi, R.K.M. (2012). Iterative piecewise linear regression to accurately assess statistical significance in batch confounded differential expression analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7292 LNBI : 153-164. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-30191-9_15
Abstract: Batch dependent variation in microarray experiments may be manifested through systematic shift in expression measurements from batch to batch. Such a systematic shift could be taken care of by using an appropriate model for differential expression analysis. However, it poses greater challenge in the estimation of statistical significance and false discovery rate (FDR), if the batches are confounded (collinear) with the biological groups of interest. Batch confounding problem occurs commonly in the analysis of time-course data or data from different laboratories. We demonstrate that batch confounding may lead to incorrect estimation of the expected statistics. In this paper, we propose an iterative piecewise linear regression (iPLR) method, a major extension of our previously published Stepped Linear Regression (SLR) method, in the context of SAM to re-estimate the expected statistics and FDR. iPLR can be applied to one-sided or two-sided statistics based tests. We demonstrate the efficacy of iPLR on both simulated and real microarray datasets. iPLR also provides a better interpretation of the linear model parameters. © 2012 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/105473
ISBN: 9783642301902
ISSN: 03029743
DOI: 10.1007/978-3-642-30191-9_15
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