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Updated: Oct 17, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

Jointly Embedding Multiple Single-Cell Omics Measurements.

Jie Liu¹, Yuanhao Huang¹, Ritambhara Singh²

¹Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

Algorithms in Bioinformatics : ... International Workshop, WABI ..., Proceedings. WABI (Workshop)

|October 11, 2021

Summary

This summary is machine-generated.

This study introduces MMD-MA, a novel algorithm for integrating diverse single-cell sequencing data. MMD-MA enables computational co-assays, aligning multiple measurements from the same cell population without requiring cell or feature correspondence.

Keywords:

Applied computing → Computational biology Computing methodologies → Dimensionality reduction and manifold learning Computing methodologies → Machine learning algorithms Computing methodologies → Unsupervised learning Manifold alignment single-cell sequencing

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Single-cell sequencing technologies offer unprecedented biological insights.
Integrating multiple data types from the same cell population remains a significant challenge.

Purpose of the Study:

To develop an unsupervised algorithm for integrating multiple single-cell measurements from disjoint cell aliquots.
To enable computational co-assays by aligning cells measured through different modalities into a shared latent space.

Main Methods:

Proposed MMD-MA, an unsupervised manifold alignment algorithm.
Optimized an objective function with Maximum Mean Discrepancy (MMD), distortion, and penalty terms.
Aligned single-cell data from multiple domains without requiring cell or feature correspondence.

Main Results:

MMD-MA successfully integrates heterogeneous single-cell data types, including gene expression, DNA accessibility, and methylation.
Demonstrated utility through simulation experiments and a real-world dataset of single-cell gene expression and methylation.
The algorithm effectively embeds cells measured differently into a learned latent space.

Conclusions:

MMD-MA provides a powerful tool for integrating multi-modal single-cell data.
The method's weak distributional requirements allow for flexible integration of diverse biological measurements.
Facilitates deeper understanding of cellular heterogeneity and function through combined data analysis.