Integrating multiple single-cell multi-omics samples with Smmit.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell multi-omics datasets enable large-scale analysis of gene expression, regulatory activities, and protein abundances.
• Integrating data across multiple samples and modalities presents computational challenges, including batch effect removal.

Purpose of the Study:

• To develop a novel computational method, Smmit, for effective integration of multi-sample single-cell multi-omics data.
• To improve upon existing methods by enhancing batch effect removal while preserving biological signals.

Main Methods:

• Smmit is an R software package designed for integrating multi-omics data across samples and modalities.
• The method focuses on robust batch effect correction and biological information preservation.

Main Results:

• Smmit demonstrates superior data integration outcomes compared to existing methods.
• The developed method effectively removes batch effects while retaining crucial biological information.
• Smmit offers improved computational efficiency and seamless integration with existing pipelines.

Conclusions:

• Smmit provides a powerful and efficient solution for integrating complex single-cell multi-omics datasets.
• The R package facilitates advanced analysis of gene expression, regulatory activities, and protein abundances.
• Smmit is freely available on Github, promoting accessibility for researchers.