ScRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types.

ABSTRACT: Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often face the challenges of high dimensionality, large number of samples, high sparsity and sample imbalance. The traditional methods of cell type recognition have been challenged. The authors propose a deep residual generation model based on semi-supervised learning (scRSSL) to address these challenges. ScRSSL creatively introduces residual networks into semi-supervised generative models. The authors take advantage of its semi-supervised learning to solve the problem of sample imbalance. During the training of the model, the authors use a residual ne

SUBMITTER: Gao Y 

PROVIDER: S-EPMC12033026 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature