Accurate and fast feature selection workflow for high-dimensional omics data

• Published in: PloS one Vol. 12; no. 12; p. e0189875
• Main Authors: Perez-Riverol, Yasset, Kuhn, Max, Vizcaíno, Juan Antonio, Hitz, Marc-Phillip, Audain, Enrique
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20.12.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Big data; Bioinformatics; Biology and Life Sciences; Biomedical data; Breast cancer; Cardiology; Cardiovascular disease; Classification; Computer and Information Sciences; Computer applications; Congenital diseases; Data analysis; Data management; Databases as Topic; Datasets; Euclidean space; Gene expression; Genes; Genetics; Genomes; Genomics - methods; Humans; Laboratories; Learning algorithms; Machine learning; Molecular biology; Multivariate Analysis; Pediatrics; Physical Sciences; Principal Component Analysis; Proteins; Proteomics; Research and Analysis Methods; Software; Studies; Support Vector Machine; Variables; Workflow
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0189875

We are moving into the age of 'Big Data' in biomedical research and bioinformatics. This trend could be encapsulated in this simple formula: D = S * F, where the volume of data generated (D) increases in both dimensions: the number of samples (S) and the number of sample features (F). Frequently, a typical omics classification includes redundant and irrelevant features (e.g. genes or proteins) that can result in long computation times; decrease of the model performance and the selection of suboptimal features (genes and proteins) after the classification/regression step. Multiple algorithms and reviews has been published to describe all the existing methods for feature selection, their strengths and weakness. However, the selection of the correct FS algorithm and strategy constitutes an enormous challenge. Despite the number and diversity of algorithms available, the proper choice of an approach for facing a specific problem often falls in a 'grey zone'. In this study, we select a subset of FS methods to develop an efficient workflow and an R package for bioinformatics machine learning problems. We cover relevant issues concerning FS, ranging from domain's problems to algorithm solutions and computational tools. Finally, we use seven different proteomics and gene expression datasets to evaluate the workflow and guide the FS process.