Data-driven comparison of multiple high-dimensional single-cell expression profiles
Daigo Okada, Jian Hao Cheng, Cheng Zheng, Ryo Yamada
Abstract
Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets.
Topics & Concepts
Computational biologyExpression (computer science)Data setComputer scienceSet (abstract data type)Gene expressionArtificial intelligenceData miningBiologyGeneGeneticsProgramming languageSingle-cell and spatial transcriptomicsGene Regulatory Network AnalysisGene expression and cancer classification