Zero-preserving imputation of single-cell RNA-seq data

A key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets.

• NI
  National Institutes of Health

  Awards: UM1DA051410, R01GM135928, U01DA053628, R01GM131642, P50CA121974, R01HG008383
• NI
  National Institute on Drug Abuse

  Award: UM1DA051410
• NH
  National Human Genome Research Institute

  Awards: F30HG010102, R01HG008383
• NC
  National Cancer Institute

  Award: P50CA121974
• NI
  National Institute of General Medical Sciences

  Awards: R01GM135928, R01GM131642