title: ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data.
abstract: Relief-F is a nonparametric, nearest-neighbor machine learning method that has been successfully used to identify relevant variables that may interact in complex multivariate models to explain phenotypic variation. While several tools have been developed for assessing differential expression in sequence-based transcriptomics, the detection of statistical interactions between transcripts has received less attention in the area of RNA-seq analysis. We describe a new extension and assessment of Relief-F for feature selection in RNA-seq data. The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. We compare this gene-wise adaptive-k (gwak) Relief-F method with standard RNA-seq feature selection tools, such as DESeq and edgeR, and with the popular machine learning method Random Forests. We demonstrate performance on a panel of simulated data that have a range of distributional properties reflected in real mRNA-seq data including multiple transcripts with varying sizes of main effects and interaction effects. For simulated main effects, gwak-Relief-F feature selection performs comparably to standard tools DESeq and edgeR for ranking relevant transcripts. For gene-gene interactions, gwak-Relief-F outperforms all comparison methods at ranking relevant genes in all but the highest fold change/highest signal situations where it performs similarly. The gwak-Relief-F algorithm outperforms Random Forests for detecting relevant genes in all simulation experiments. In addition, Relief-F is comparable to the other methods based on computational time. We also apply ReliefSeq to an RNA-Seq study of smallpox vaccine to identify gene expression changes between vaccinia virus-stimulated and unstimulated samples. ReliefSeq is an attractive tool for inclusion in the suite of tools used for analysis of mRNA-Seq data; it has power to detect both main effects and interaction effects. Software Availability: http://insilico.utulsa.edu/ReliefSeq.php.
authors: Tandy School of Computer Science, Department of Mathematics, University of Tulsa, Tulsa, Oklahoma, United States of America ; Laureate Institute for Brain Research, Tulsa, Oklahoma, United States of America.
authors: Brett A
authors: BA
authors: McKinney
authors: Bill C
authors: BC
authors: White
authors: Diane E
authors: DE
authors: Grill
authors: Peter W
authors: PW
authors: Li
authors: Richard B
authors: RB
authors: Kennedy
authors: Gregory A
authors: GA
authors: Poland
authors: Ann L
authors: AL
authors: Oberg
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D004843:Epistasis, Genetic / Q000235:genetics*; D020869:Gene Expression Profiling; D008957:Models, Genetic; D012333:RNA, Messenger / Q000235:genetics; D017423:Sequence Analysis, RNA; D012900:Smallpox Vaccine / Q000235:genetics
chemical_list: D012333:RNA, Messenger; D012900:Smallpox Vaccine