LIGER2: Scalable Single-Cell Integration with On-Disk Datasets

Abstract

Correcting batch effects and integrating single-cell sequencing datasets has been a crucial step in large-scale biological studies. Many methods have been published for this task, and excel in various scenarios. Our previous work, LIGER, leveraging integrative non-negative matrix factorization (iNMF), stands out in providing an interpretable low-dimensional representation. To adapt to the modern need for integrating millions of cells, we developed a highly-optimized parallel factorization solution with on-demand loading from disk. The upgraded LIGER algorithm shows significant improvements in time and memory efficiency for single-cell data integration. We also developed a new downstream embedding alignment method significantly improved performance in conserving biological variation while still aligning corresponding cell types across datasets.

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Cite

BibTeX RIS

BibTeX

@misc{wang2026liger2,
  title = {{LIGER2: Scalable Single-Cell Integration with On-Disk Datasets}},
  author = {Y. Wang and A. Robbins and G. Gadhvi and J. D. Welch},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.08.750130},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.08.750130v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - LIGER2: Scalable Single-Cell Integration with On-Disk Datasets
AU  - Y. Wang
AU  - A. Robbins
AU  - G. Gadhvi
AU  - J. D. Welch
PY  - 2026
DA  - 2026/09/08
PB  - bioRxiv
DO  - 10.64898/2026.09.08.750130
UR  - https://www.biorxiv.org/content/10.64898/2026.09.08.750130v1
AB  - Correcting batch effects and integrating single-cell sequencing datasets has been a crucial step in large-scale biological studies. Many methods have been published for this task, and excel in various scenarios. Our previous work, LIGER, leveraging integrative non-negative matrix factorization (iNMF), stands out in providing an interpretable low-dimensional representation. To adapt to the modern need for integrating millions of cells, we developed a highly-optimized parallel factorization solution with on-demand loading from disk. The upgraded LIGER algorithm shows significant improvements in time and memory efficiency for single-cell data integration. We also developed a new downstream embedding alignment method significantly improved performance in conserving biological variation while still aligning corresponding cell types across datasets.
ER  -

Abstract

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