Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) technology is one of the most cost-effective and efficacious methods for revealing cellular heterogeneity and diversity. Precise identification of cell types is essential for establishing a robust foundation for downstream analyses and is a prerequisite for understanding heterogeneous mechanisms. However, the accuracy of existing methods warrants improvement, and highly accurate methods often impose stringent equipment requirements. Moreover, most unsupervised learning-based approaches are constrained by the need to input the number of cell types a prior, which limits their widespread application. In this paper, we propose a novel algorithm framework named WLGG. Initially, to capture the underlying nonlinear information, we introduce a weighted distan

SUBMITTER: Zhang W 

PROVIDER: S-EPMC11562834 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature