A memory-efficient implementation for integrating gene expression data
from single-cell RNA sequencing experiments. Uses a C++ backend with
thin R wrappers to enable analysis of large-scale single-cell datasets. The
package supports multiple data modalities including count matrices, paired
data (splicing, RNA velocity, CITE-seq), and binary indicators. It implements
a latent variable model with block coordinate descent optimization for
dimensionality reduction and batch effect correction. The method is
described in Mikaeili Namini et al. (2026)