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

ABSTRACT: 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: https://github.com/BIMSBbioinfo/netSmooth.

SUBMITTER: Ronen J

PROVIDER: S-EPMC5814748 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Ronen Jonathan J Akalin Altuna A

F1000Research 20180103

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 ...[more]

PMID: 29511531

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