Multi-omics Integration for Single-cell Data Analysis

Overview: Our project focuses on developing innovative computational methods for integrating various single-cell multi-omics data types. We aim to combine three-dimensional chromatin interaction (scHi-C) data with transcriptomics and proteomics data (scRNA-seq and CITE-seq) to gain comprehensive insights into cellular regulation and function.

Motivation: The integration of multiple single-cell data modalities presents unique opportunities and challenges in understanding cellular heterogeneity. Our research addresses these challenges through several key innovations:

1. scGAD (single-cell Gene Associating Domain) Scores:

   • A dimension reduction and exploratory analysis tool for scHi-C data
   • Enables gene-level summarization while accounting for genomic biases
   • Facilitates integration with other single-cell modalities
   • Projects 3D genomics data onto reference embeddings

2. Key Features and Applications:

   • Captures cell clustering based on 3D chromatin structure
   • Identifies significant chromatin interactions within and between cell types
   • Enables automated and refined cell-type annotation
   • Provides multi-modal data integration framework

Our integrative approach enables:

• Comprehensive understanding of cellular states
• Improved cell type identification
• Better characterization of regulatory mechanisms
• Cross-modality validation of biological findings

Publications

⁺: co-corresponding author *: co-first author

1. Shen S, Zheng Y⁺, Keles S⁺. scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data. Bioinformatics. (2022).

2. Germanos AA, Arora S, Zheng Y, Goddard ET, Coleman IM, Ku AT, Wilkinson S, Amezquita RA, Zager M, Long A, Yang YC, Bielas J, Gottardo R, Ghajar C, Nelson P, Sowalsky A, Setty M, Hsieh A. Defining cellular population dynamics at single cell resolution during prostate cancer progression. eLife. (2022).

3. Liao R*, Zheng Y*, Liu X, Zhang Y, Seim G, Tanimura N, Wilson G, Hematti P, Coon J, Fan J, Xu J, Keles S⁺ and Bresnick E⁺. Discovering How Heme Controls Genome Function Through Heme-omics. Cell Reports. (2020).

4. Soukup AA, Zheng Y, Mehta C, Liu P, Hofmann I, Zhou Y, Zhang J, Choi K, Johnson KD, Keles S, Bresnick EH. Single-nucleotide human disease mutation inactivates a blood-regenerative GATA2 enhancer. Journal of Clinical Investigation. (2019).

5. Tanimura N, Liao R, Wilson GM, Dent MR, Cao M, Burstyn JN, Hematti P, Liu X, Zhang Y, Zheng Y, Keles S, Xu J, Coon J, Bresnick E. GATA/Heme Multi-omics Reveals a Trace Metal-dependent Cellular Differentiation Mechanism. Developmental Cell. (2018).

The project’s success has led to robust tools that are being widely adopted by the single-cell research community, contributing to our understanding of cellular heterogeneity and regulation at unprecedented resolution.