Identifying the relevant genes (or other genomic features such as transcripts, miRNAs, lncRNAs, etc.) across the conditions (e.g. tumor and non-tumor tissue samples) is a common research interest in gene-expression studies. In this gene selection, researchers are often interested in detecting a small set of genes for diagnostic purpose in medicine that involves identification of the minimal subset of genes that achieves maximal predictive performance. biomarker discovery and classification problem.
VoomDDA is a decision support tool developed for RNA-Sequencing datasets to assist researchers in their decisions for diagnostic biomarker discovery and classification problem. VoomDDA consists both sparse and non-sparse statistical learning classifiers adapted with voom method. Voom is a recent method that estimates the mean and variance relationship of the log-counts of RNA-Seq data (log counts per million, log-cpm) at observational level. It also provides precision weights for each observation that can be incorporated with the log-cpm values for further analysis. Algorithms in our tool incorporates the log-cpm values and the corresponding precision weights into biomarker discovery and classification problem. For this purpose, these algorithms use weighted statistics in estimating the discriminating functions of the used statistical learning algorithms.
VoomNSC is a sparse classifier that is developed to bring together two powerful methods for RNA-Seq classification:
- to extend voom method for RNA-Seq classification studies,
- to make nearest shrunken centroids (NSC) algorithm available for RNA-Seq technology. VoomNSC both provides fast, accurate and sparser classification results for RNA-Seq data. More details can be found in the research paper. This tool also includes RNA-Seq extensions of diagonal linear and diagonal quadratic discriminant classifiers: (i) voomDLDA and (ii) voomDQDA.
This tool freely available through http://www.biosoft.hacettepe.edu.tr/voomDDA/.