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Version 2. F1000Res. 2018 Jan 3 [revised 2018 Jan 24];7:8. doi: 10.12688/f1000research.13511.2. eCollection 2018.

netSmooth: Network-smoothing based imputation for single cell RNA-seq.

Author information

Scientific Bioinformatics Platform, Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Berlin, 13125, Germany.


Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at:


genomics; imputation; networks; scRNA-seq; single-cell

Conflict of interest statement

No competing interests were disclosed.

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