pyALRA: python implementation of low-rank zero-preserving approximation of single cell RNA-seq.

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This re-implementation achieves similar prediction performance using corresponding python methods and allows both speed and RAM consumption improvements.\nAvailability and implementation: pyALRA is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/alexandrelanau/pyALRA and on Zenodo at https://doi.org/10.5281/zenodo.15730914.", "claims": [{"id": "claim-method", "statement": "We applied the Adaptively thresholded Low-Rank Approximation (ALRA) method to several single-cell RNA sequencing (scRNA-seq) datasets to evaluate its performance in imputing expression values while preserving biological zeros.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41323649", "locator": "sections[2] \"2 Implementation\" paragraph 2", "quote": "We applied the Adaptively thresholded Low-Rank Approximation (ALRA) method to several single-cell RNA sequencing (scRNA-seq) datasets to evaluate its performance in imputing expression values while preserving biological zeros."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article were downloaded from EBI Single Cell Expression atlas. 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