A Regression-Based Differential Expression Detection Algorithm for Microarray Studies with Ultra-Low Sample Size

• Published in: PloS one Vol. 10; no. 3; p. e0118198
• Main Authors: Vasiliu, Daniel, Clamons, Samuel, McDonough, Molly, Rabe, Brian, Saha, Margaret
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.03.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Bioengineering; Bioinformatics; Biological activity; Biological productivity; Biology; Cancer; Computer simulation; Data analysis; Data processing; Datasets; DNA microarrays; Drosophila; Embryos; Experiments; Gene expression; Gene Expression Profiling - methods; Genes; Hypothesis testing; Insects; Laboratories; Medical research; Molecular genetics; Neurogenesis; Notch protein; Oligonucleotide Array Sequence Analysis - methods; Regression Analysis; Reproducibility of Results; Ribonucleic acid; RNA; Sample Size; Signal transduction; Signaling; Simulation; Statistical models; Statistics as Topic - methods; Web sites; Websites; Xenopus; Xenopus laevis - genetics; United States; United States > US; Virginia; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0118198

Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.