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Evaluating the Utilities of Foundation Models in Single-cell Data Analysis.
- Source :
-
BioRxiv : the preprint server for biology [bioRxiv] 2024 Feb 27. Date of Electronic Publication: 2024 Feb 27. - Publication Year :
- 2024
-
Abstract
- Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing ten different single-cell FMs with task-specific methods, we found that single-cell FMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of FMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell FMs and points to their constraints and avenues for future development.
Details
- Language :
- English
- Database :
- MEDLINE
- Journal :
- BioRxiv : the preprint server for biology
- Accession number :
- 38464157
- Full Text :
- https://doi.org/10.1101/2023.09.08.555192