A Bayesian factorization method to recover single-cell RNA sequencing data

Zi-Hang Wen, Jeremy L. Langsam, Lu Zhang, Wenjun Shen*, Xin Zhou*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Article number100133
Number of pages18
JournalCell Reports Methods
Issue number1
Publication statusPublished - 24 Jan 2022

Scopus Subject Areas

  • Genetics
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Biochemistry
  • Radiology Nuclear Medicine and imaging
  • Biotechnology
  • Computer Science Applications

User-Defined Keywords

  • Bayesian factorization
  • imputation
  • RNA-seq
  • single cell


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