SCREAM: Single-cell Clustering using Representation Autoencoder of Multiomics.

ABSTRACT:

Motivation

Single-cell multiomics technologies offer unprecedented opportunities to study cellular heterogeneity. But, integrating information across different omics modalities remains a major challenge due to high dimensionality, sparsity, and modality-specific noise characteristics. To address this, we develop SCREAM (Single-cell Clustering using Representation Autoencoder of Multiomics), a novel deep learning framework for the robust integration and clustering of multimodal single-cell data. SCREAM leverages Stacked Autoencoders (SAEs) to generate robust latent representations for each omics modality as well as for their fusion. Subsequently, borrowing Deep Embedding Clustering (DEC), SCREAM iteratively fine tunes the integrated mulitomics latent space and single-cell cluster as

SUBMITTER: Chrysinas P 

PROVIDER: S-EPMC12632474 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature