ScRNMF: An imputation method for single-cell RNA-seq data by robust and non-negative matrix factorization.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can i

SUBMITTER: Qian Y 

PROVIDER: S-EPMC11338450 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature