Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2.

Area of Science:

• Single-cell biology
• Computational biology
• Bioinformatics

Background:

• Single-cell studies generate vast datasets, but integrating multi-sample, multi-condition, and multi-cohort data remains challenging.
• Existing methods struggle with the scale and technological diversity of modern single-cell experiments.

Purpose of the Study:

• To develop a scalable algorithm, scMerge2, for effective data integration of atlas-scale single-cell studies.
• To generalize scMerge2 for merging millions of cells from various single-cell technologies.
• To demonstrate scMerge2's utility in analyzing large-scale, multi-condition datasets.

Main Methods:

• scMerge2 algorithm development and generalization.
• Application to a large COVID-19 single-cell RNA sequencing dataset (>5 million cells, 1000+ individuals).
• Validation across diverse single-cell profiling technologies including CyTOF, imaging mass cytometry, and CITE-seq.

Main Results:

• scMerge2 successfully integrated multi-sample, multi-condition single-cell RNA sequencing data from multiple cohorts.
• The integrated data revealed cell-type expression signatures more accurately discriminating COVID-19 disease progression.
• scMerge2 effectively removed dataset-specific variability in CyTOF, imaging mass cytometry, and CITE-seq data.

Conclusions:

• scMerge2 is a scalable and versatile tool for integrating large-scale single-cell data.
• The algorithm enhances biological discovery by enabling robust analysis across diverse experimental conditions and technologies.
• scMerge2 facilitates deeper insights into complex biological systems, such as disease progression.