SCEMENT: scalable and memory efficient integration of large-scale single-cell RNA-sequencing data.

ABSTRACT:

Motivation

Integrative analysis of large-scale single-cell data collected from diverse cell populations promises an improved understanding of complex biological systems. While several algorithms have been developed for single-cell RNA-sequencing data integration, many lack the scalability to handle large numbers of datasets and/or millions of cells due to their memory and run time requirements. The few tools that can handle large data do so by reducing the computational burden through strategies such as subsampling of the data or selecting a reference dataset to improve computational efficiency and scalability. Such shortcuts, however, hamper the accuracy of downstream analyses, especially those requiring quantitative gene expression information.

Results

We present SCEMENT,

SUBMITTER: Chockalingam SP 

PROVIDER: S-EPMC12013815 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature