{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scdataset-scalable-data-loading-for-deep","title":"scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics","arxiv_id":"2506.01883","date":"2025-06-02","proceeding":null,"authors":["Davide D'Ascenzo","Sebastiano Cultrera di Montesano"],"abstract":"Modern single-cell datasets now comprise hundreds of millions of cells, presenting significant challenges for training deep learning models that require shuffled, memory-efficient data loading. While the AnnData format is the community standard for storing single-cell datasets, existing data loading solutions for AnnData are often inadequate: some require loading all data into memory, others convert to dense formats that increase storage demands, and many are hampered by slow random disk access. We present scDataset, a PyTorch IterableDataset that operates directly on one or more AnnData files without the need for format conversion. The core innovation is a combination of block sampling and batched fetching, which together balance randomness and I/O efficiency. On the Tahoe 100M dataset, scDataset achieves up to a 48$\\times$ speed-up over AnnLoader, a 27$\\times$ speed-up over HuggingFace Datasets, and an 18$\\times$ speed-up over BioNeMo in single-core settings. These advances democratize large-scale single-cell model training for the broader research community.","url_abs":"https://arxiv.org/abs/2506.01883v1","url_pdf":"https://arxiv.org/pdf/2506.01883v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scdataset-scalable-data-loading-for-deep","repo_url":"https://github.com/Kidara/scDataset","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2506.01883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.01883"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Kidara/scDataset","reach":null}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"16efea15782fe30c","entry":"SamplingStrategy","repo":"Kidara/scDataset","repo_kind":"official","path":"src/scdataset/scdataset.py","file_url":"https://github.com/Kidara/scDataset/blob/HEAD/src/scdataset/scdataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"16efea15782fe30c"}},{"code_sha256_prefix":"fc9f6ecc908b5e56","entry":"scDataset","repo":"Kidara/scDataset","repo_kind":"official","path":"src/scdataset/scdataset.py","file_url":"https://github.com/Kidara/scDataset/blob/HEAD/src/scdataset/scdataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fc9f6ecc908b5e56"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}