title: Distance metric learning using random forest for cytometry data.
abstract: Visualization and clustering of single-cell mass cytometry (CyTOF) data are analytic techniques to identify different cell types. Most of such techniques, such as Euclidean norm, lose their effectiveness when the data dimension increases due to the curse of dimensionality. In this paper, we propose a new cell distance (called CytoRFD) that works based on Random Forest (RF) concept. The experimental results show that the proposed distance can achieve a much higher quality and effectiveness in large data analysis than traditional metrics specially for CyTOF data.
authors.forename: M Baran
authors.initials: MB
authors.lastname: Pouyan
authors.forename: J
authors.initials: J
authors.lastname: Birjandtalab
authors.forename: M
authors.initials: M
authors.lastname: Nourani
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism; D002469:Cell Separation / Q000379:methods*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D006801:Humans; D000069550:Machine Learning; D015233:Models, Statistical; D015203:Reproducibility of Results
chemical_list: D015415:Biomarkers