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

ABSTRACT:

Background

To analyze the large volume of data generated by single-cell technologies and to identify cellular correlates of particular clinical or experimental outcomes, differential abundance analyses are often applied. These algorithms identify subgroups of cells whose abundances change significantly in response to disease progression, or to an experimental perturbation. Despite the effectiveness of differential abundance analyses in identifying critical cell-states, there is currently no systematic benchmarking study to compare their applicability, usefulness, and accuracy in practice across single-cell modalities.

Results

Here, we perform a comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-ar

SUBMITTER: Yi H 

PROVIDER: S-EPMC10762948 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature