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remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets.
Researchers at the College of William and Mary have developed 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). Their 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, they use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Their 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.
The researchers apply their 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. This method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. This method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.
Availability – Code and documentation for PED-based selection are available at https://github.com/sclamons/PED.