arXiv:2506.01883v3 Announce Type: replace Abstract: Training deep learning models on single-cell datasets with hundreds of millions of cells requires loading data from disk, as these datasets exceed available memory. While random sampling provides the data diversity needed for effective training, it is prohibitively slow due to the random access pattern overhead, whereas sequential streaming achieves high throughput but introduces biases that degrade model performance. We present scDataset, a PyTorch data loader that enables efficient training from on-disk data with seamless integration across
The rapid increase in size and complexity of single-cell omics datasets necessitates novel deep learning data loading solutions to overcome computational bottlenecks.
This development addresses a critical technical challenge in applying deep learning to very large biological datasets, accelerating discovery in synthetic biology and medicine.
Deep learning models can now more efficiently process extremely large single-cell omics datasets, enabling more scalable and unbiased biological research.
- · Biotech researchers
- · Pharmaceutical R&D
- · AI/ML biotech startups
- · Genomics companies
- · Traditional bioinformatics pipelines
- · Companies reliant on in-memory data processing
More sophisticated deep learning models can be trained on larger biological datasets.
This will accelerate drug discovery, biomarker identification, and understanding of disease mechanisms.
The enhanced computational capabilities could lead to novel therapies and personalized medicine approaches at scale.
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