Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets.

ABSTRACT: Modern single-cell data analysis relies on statistical testing (e.g. differential expression testing) to identify genes or proteins that are up-or down-regulated in relation to cell-types or clinical outcomes. However, existing algorithms for such statistical testing are often limited by technical noise and cellular heterogeneity, which lead to false-positive results. To constrain the analysis to a compact and phenotype-related cell population, differential abundance (DA) testing methods were employed to identify subgroups of cells whose abundance changed significantly in response to disease progression, or experimental perturbation. Despite the effectiveness of DA testing algorithms of identifying critical cell-states, there are no systematic benchmarking or comparative studies to compare

SUBMITTER: Yi H 

PROVIDER: S-EPMC10002703 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature