Iterative Piecewise Linear Regression to Accurately Assess Statistical Significance in Batch Confounded Differential Expression Analysis
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.
KeywordsFalse Discovery Rate Null Distribution Biological Group False Discovery Rate Estimation Linear Model Parameter
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- 1.Li, C., Wong, W.H.: Dna-chip analyzer (dchip). In: The Analysis of Gene Expression Data: Methods and Software, pp. 28–46. Springer, Heidelberg (2003)Google Scholar
- 6.Smyth, G.K.: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3(1) (2004)Google Scholar
- 8.Li, J., Liu, J., Karuturi, R.K.M.: Stepped linear regression to accurately assess statistical significance in batch confounded differential expression analysis. Bioinformatics Research and Applications, 481–491 (2008)Google Scholar
- 9.Chu, G., Narasimhan, B., Tibshirani, R., Tusher, V.: SAM, significance analysis of microarrays. Users guide and technical documentGoogle Scholar