Cognitive Sciences and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, The Netherlands.
Abstract: Single-cell RNA sequencing data is among the most interesting and impactful data of today and the sizes of the available datasets are increasing drastically. There is a substantial need for learning from large datasets, causing nontrivial challenges, especially in hardware. Loading even a single dataset into the memory of an ordinary, off-the-shelf computer can be infeasible, and using computing servers might not always be an option. This paper presents continual learning as a solution to such hardware bottlenecks. The findings of cell-type classification demonstrate that XGBoost and Catboost algorithms, when implemented in a continual learning framework, exhibit superior performance compared to the best-performing static classifier. We achieved up to 10% higher median F1 scores than the state-of-the-art on the most challenging datasets. On the other hand, these algorithms can suffer from variations in data characteristics across diverse datasets, pointing out indications of the catastrophic forgetting problem.
*** [1] G. Saygili and B. Ozgode Yigin, "Continual learning approaches for single cell RNA sequencing data", Nature Scientific Reports, 2023.***
*** Please cite our paper [1] in case you use the code.***
Created by Busra Ozgode Yigin and Gorkem Saygili on 14-09-23.
Datasets can be obtained from:
- Intra and Inter Datasets by Abdelaal et al., 2019: https://doi.org/10.5281/zenodo.3357167
- PBMC-eQTL by Michielsen et al. 2021: https://zenodo.org/record/3736493#.ZGZgXHZBxPY
- HLCA latent space Michielsen at al. 2022: https://zenodo.org/record/6337966#.YqmGIidBx3g
Important Note: This code is under MIT License:
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