SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Analyzing single-cell multi-omics data presents challenges due to data sparsity and heterogeneity.
• A gap exists between the rapid growth of single-cell multi-omics data and effective integrative analysis methods.

Purpose of the Study:

• To develop a novel method for integrating single-cell RNA-sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data.
• To identify coherent functional regulatory signals and target genes from joint multi-omics datasets.

Main Methods:

• Developed the Single-cell Multi-omics Gene co-Regulatory algorithm (SMGR).
• Utilized a generalized linear regression model to identify latent representations from zero-inflated Negative Binomial distributions.
• Integrated scRNA-seq and scATAC-seq data from different samples.

Main Results:

• SMGR accurately detects co-regulatory programs and elucidates regulatory mechanisms.
• Demonstrated superior performance over existing methods using simulation and experimental data.
• Identified a mixed-phenotype acute leukemia (MPAL)-specific regulatory program with significant peak-gene links.

Conclusions:

• SMGR effectively bridges the gap in analyzing sparse and heterogeneous single-cell multi-omics data.
• The method enhances understanding of regulatory mechanisms in complex diseases like MPAL.
• Identified potential therapeutic targets within the MPAL-specific regulatory program.