CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data.

ABSTRACT: A key challenge in analyzing single-cell RNA sequencing data is the large number of false zeros, known as "dropout zeros", which are caused by technical limitations such as shallow sequencing depth or inefficient mRNA capture. To address this challenge, we propose a novel imputation model called CPARI, which combines cell partitioning with our designed absolute and relative imputation methods. Initially, CPARI employs a new approach to select highly variable genes and constructs an average consensus matrix using C-mean fuzzy clustering-based blockchain technology to obtain results at different resolutions. Hierarchical clustering is then applied to further refine these blocks, resulting in well-defined cellular partitions. Subsequently, CPARI identifies dropout events and determines the im

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11666288 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature