Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets

TL;DR

This paper introduces scGCM, a new method for integrating complex single-cell data with missing information, improving accuracy and consistency.

Contribution

The novel contribution is a flexible integration framework, scGCM, based on variational autoencoder for multimodal mosaic data.

Findings

scGCM outperforms existing methods in clustering accuracy and data consistency.

The framework effectively handles high dimensionality, sparsity, and batch effects in multimodal datasets.

Abstract

As single-cell sequencing technology became widely used, scientists found that single-modality data alone could not fully meet the research needs of complex biological systems. To address this issue, researchers began simultaneously collect multi-modal single-cell omics data. But different sequencing technologies often result in datasets where one or more data modalities are missing. Therefore, mosaic datasets are more common when we analyze. However, the high dimensionality and sparsity of the data increase the difficulty, and the presence of batch effects poses an additional challenge. To address these challenges, we proposes a flexible integration framework based on Variational Autoencoder called scGCM. The main task of scGCM is to integrate single-cell multimodal mosaic data and eliminate batch effects. This method was conducted on multiple datasets, encompassing different modalities of single-cell data. The results demonstrate that, compared to state-of-the-art multimodal data integration methods, scGCM offers significant advantages in clustering accuracy and data consistency. The source code of scGCM can be accessed at https://github.com/closmouz/scCGM.

Figures