Work

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

Year: 2024

Type: article

Abstract: Motivation: Some recently published methods for single-cell RNA-seq preprocessing and correction are not necessarily available in both Python and R, which limits the accessibility of these tools to the wider community. Results: We present pyALRA, an efficient python implementation of the (r-)ALRA R package conceived to impute drop out values using a low-rank zero-preserving approximation for single cell RNA-seq. This re-implementation achieves similar prediction performance using corresponding python methods and allows both speed and RAM consumption improvements. Availability 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.

Source: Bioinformatics Advances

DOI: 10.1093/bioadv/vbaf279

Authors: Alexandre Lanau, Joshua J Waterfall

Institutions: Inserm, Université Paris Sciences et Lettres, Institut Curie

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,917

Funders: Institut National de la Santé et de la Recherche Médicale, Fondation ARC pour la Recherche sur le Cancer