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Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample

Haidong Yi¹, Alec Plotkin², Natalie Stanley^3,4

¹Department of Computer Science, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA.

|January 4, 2024

Summary

This summary is machine-generated.

This study benchmarks differential abundance (DA) testing methods for single-cell data. It provides recommendations for choosing the best DA analysis tools based on dataset characteristics and technical noise for accurate cell-state identification.

Keywords:

Benchmarking Clinical phenotyping Differential abundance (DA)Single-cell bioinformatics

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Area of Science:

Computational Biology
Single-cell Genomics
Bioinformatics

Background:

Differential abundance (DA) analyses are crucial for identifying cell subgroups with altered frequencies in single-cell studies.
These methods help link cellular changes to clinical outcomes or experimental perturbations.
A systematic comparison of existing DA methods across single-cell modalities is lacking.

Purpose of the Study:

To comprehensively benchmark and compare state-of-the-art DA testing methods for single-cell data.
To evaluate the performance, accuracy, and usability of different DA approaches.
To provide data-driven recommendations for practical application of DA methods.

Main Methods:

Benchmarking of six single-cell DA testing methods.
Evaluation using both synthetic and real single-cell datasets.
Assessment of performance on tasks including true positive identification, batch effect handling, runtime, and hyperparameter robustness.

Main Results:

Objective comparison of the benefits and drawbacks of current DA testing methods.
Identification of method performance across various practical tasks.
Dataset-specific suggestions for optimal DA method selection and usage.

Conclusions:

Recommendations are provided for the practical application of single-cell DA testing methods.
Guidance considers factors like technical noise (e.g., batch effects), dataset size, and hyperparameter sensitivity.
The study aims to improve the reliability and accuracy of cell-state identification in single-cell analyses.