ArticleCell reports methods2022
A Bayesian factorization method to recover single-cell RNA sequencing data.
Article in Cell reports methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- CNVeil resolves haplotype-specific copy number and uncovers subclonal architecture hidden from total copy number profiling in single-cell cancer genomes.bioRxiv : the preprint server for biology · 2026Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- scDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information.Briefings in bioinformatics · 2024Article
- DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.Genome biology · 2023Article
- ADEPT: Autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering.iScience · 2023Article
- Text mining of gene-phenotype associations reveals new phenotypic profiles of autism-associated genes.Scientific reports · 2021Article
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Authors and funding
5 authors.
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Abstract
Single-cell RNA sequencing (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. In this article, we introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute the final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than ten other publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene- or cell-related information that users provide to increase performance.
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