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bioRxiv · 10.64898/2026.09.03.748766

Atlas-scale single-cell analysis beyond in-memory paradigm with scAtlasPy

Xu, H.·Ye, Y.·Zhang, S.·Xie, R.·Li, J.·Lin, J.·Hu, Y.·Gao, L.
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Abstract

Single-cell atlases are rapidly outgrowing the memory capacity of standard workstations, challenging the in-memory paradigm underlying mainstream computational ecosystems. Here, scAtlasPy decouples scale of atlas from memory capacity by leveraging the disk-resident computing. It enables full-resolution analysis of a 100-million-cell atlas with only 42.9 GB peak memory, whereas state-of-the-art platforms are limited at 3 million cells with 512 GB memory. scAtlasPy achieves 137,745 cells/s, 10.4x faster than scDataset with 82.6% lower memory usage for random minibatch retrieval. Its extensible architecture offers a flexible platform for diverse atlas-scale analytical tasks, facilitating the discovery of complex cellular heterogeneity and functions in massive cell atlases.

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Xu, H., Ye, Y., Zhang, S., Xie, R., Li, J., Lin, J., Hu, Y., Gao, L.. 2026-09-08. Atlas-scale single-cell analysis beyond in-memory paradigm with scAtlasPy. https://doi.org/10.64898/2026.09.03.748766

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