Benchmarking single cell transcriptome matching methods for incremental growth of reference atlases.

ABSTRACT:

Background

The advancement of single cell technologies has driven significant progress in constructing a multiscale, pan-organ Human Reference Atlas (HRA) for healthy human cells, though challenges remain in harmonizing cell types and unifying nomenclature. Multiple machine learning and artificial intelligence methods, including pre-trained and fine-tuned models on large-scale atlas data, are publicly available for the single cell community users to computationally annotate and match their cell clusters to the reference atlas.

Results

This study benchmarks four computational tools for cell type annotation and matching - Azimuth, CellTypist, scArches, and FR-Match - using two lung atlas datasets, the Human Lung Cell Atlas (HLCA) and the LungMAP single-cell reference (CellRef)

SUBMITTER: Hu J 

PROVIDER: S-EPMC12190912 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature