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pyALRA: python implementation of low-rank zero-preserving approximation of single cell RNA-seq
Alexandre Lanau1,2, Joshua J Waterfall1,2
1INSERM U1330, Institut Curie Research Center, PSL Université, 26 rue d'Ulm, Paris, 75005, France.
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.
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