Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data.

ABSTRACT: We introduce an iterative normalization and clustering method for single-cell gene expression data. The emerging technology of single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is confounded by technical variation emanating from experimental errors and cell type-specific biases. Current approaches perform a global normalization prior to analyzing biological signals, which does not resolve missing data or variation dependent on latent cell types. Our model is formulated as a hierarchical Bayesian mixture model with cell-specific scalings that aid the iterative normalization and clustering of cells, teasing apart technical variation from biological signals. We demonstrate that this

SUBMITTER: Prabhakaran S 

PROVIDER: S-EPMC6004614 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature