Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA
Meelad Amouzgar, David R. Glass, Reema Baskar, Inna Averbukh, Samuel C. Kimmey, Albert G. Tsai, Felix J. Hartmann, Sean C. Bendall
Abstract
classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.
Topics & Concepts
Dimensionality reductionReduction (mathematics)Artificial intelligencePattern recognition (psychology)Computer scienceMathematicsGeometrySingle-cell and spatial transcriptomicsCell Image Analysis TechniquesAdvanced Fluorescence Microscopy Techniques