LIGER2: Scalable Single-Cell Integration with On-Disk Datasets
Wang, Y.; Robbins, A.; Gadhvi, G.; Welch, J. D.
Show 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Reconstruction of clone- and haplotype-specific cancer genome karyotypes from bulk tumor samples 94%
- Highly accurate reference and method selection for universal cross-dataset cell type annotation with CAMUS 94%
- SHARP: Single-cell RNA-seq Hyper-fast and Accurate Processing via Ensemble Random Projection 94%
Similar papers in this journal
Similar papers in this journal
- scReadSim: a single-cell RNA-seq and ATAC-seq read simulator 95%
- Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing 95%
- Constructing Ensemble Gene Functional Networks Capturing Tissue/condition-specific Co-expression from Unlabled Transcriptomic Data with TEA-GCN 95%
Similar papers in this journal
- Single-Cell Signature Explorer for comprehensive visualization of single cell signatures across scRNA-seq data sets 95%
- LCA robustly reveals subtle diversity in large-scale single-cell RNA-seq data 94%
- CelLink: integrating single-cell multi-omics data with weak feature linkage and imbalanced cell populations 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.