matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments

摘要: Single-cell transcriptomics allows the identification of cellular types, subtypes and states through cell clustering. In this process, similar cells are grouped before determining co-expressed marker genes for phenotype inference. The performance computational tools is directly associated to their accuracy, but lack an optimal solution challenges a systematic method comparison. Moreover, phenotypes from different studies challenging integrate, due varying resolution, methodology experimental design. work we introduce matchSCore (https://github.com/elimereu/matchSCore), approach match populations fast across tools, experiments technologies. We compared 14 methods evaluated accuracy in clustering gene simulated data sets. further used project type identities mouse human atlas projects. Despite originating technologies, could be matched sets, allowing assignment clusters reference maps annotation.