Integrated analysis of multimodal single-cell data with structural similarity.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Multimodal single-cell sequencing offers rich cellular heterogeneity data.
• Joint analysis often suffers from noise, leading to modality overfitting and suboptimal clustering.
• Efficiently leveraging multi-omics data for cell state identification is a key computational challenge.

Purpose of the Study:

• To develop an efficient, robust, and flexible deep learning framework for multi-modal single-cell data analysis.
• To address the challenge of noise and overfitting in joint analysis of multiple genomic readouts.
• To enable accurate cell state delineation and meaningful signal identification from single-cell multi-omics data.

Main Methods:

• Proposed SAILERX, a deep learning framework utilizing a variational autoencoder with invariant representation learning.
• Implemented a multimodal data alignment mechanism that encourages similarity in local structures rather than hard projection.
• Employed pairwise similarity measures to maintain modality-specific information while integrating data.

Main Results:

• SAILERX demonstrates robustness against noise and overfitting, outperforming traditional single-modality analyses.
• The framework facilitates downstream analyses including clustering, imputation, and marker gene detection.
• Invariant representation learning enables integrative analysis of both multi- and single-modal datasets.

Conclusions:

• SAILERX provides an effective solution for analyzing noisy multimodal single-cell data.
• The proposed alignment strategy enhances the reliability and flexibility of multi-omics integration.
• SAILERX is a scalable tool applicable to diverse single-cell multi-omics scenarios.