The Annals of Applied Statistics

Graphical models for zero-inflated single cell gene expression

Andrew McDavid, Raphael Gottardo, Noah Simon, and Mathias Drton

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Abstract

Bulk gene expression experiments relied on aggregations of thousands of cells to measure the average expression in an organism. Advances in microfluidic and droplet sequencing now permit expression profiling in single cells. This study of cell-to-cell variation reveals that individual cells lack detectable expression of transcripts that appear abundant on a population level, giving rise to zero-inflated expression patterns. To infer gene coregulatory networks from such data, we propose a multivariate Hurdle model. It is comprised of a mixture of singular Gaussian distributions. We employ neighborhood selection with the pseudo-likelihood and a group lasso penalty to select and fit undirected graphical models that capture conditional independences between genes. The proposed method is more sensitive than existing approaches in simulations, even under departures from our Hurdle model. The method is applied to data for T follicular helper cells, and a high-dimensional profile of mouse dendritic cells. It infers network structure not revealed by other methods, or in bulk data sets. A R implementation is available at https://github.com/amcdavid/HurdleNormal.

Article information

Source
Ann. Appl. Stat., Volume 13, Number 2 (2019), 848-873.

Dates
Received: October 2016
Revised: March 2018
First available in Project Euclid: 17 June 2019

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1560758430

Digital Object Identifier
doi:10.1214/18-AOAS1213

Mathematical Reviews number (MathSciNet)
MR3963555

Keywords
Gene network single cell gene expression graphical model group lasso

Citation

McDavid, Andrew; Gottardo, Raphael; Simon, Noah; Drton, Mathias. Graphical models for zero-inflated single cell gene expression. Ann. Appl. Stat. 13 (2019), no. 2, 848--873. doi:10.1214/18-AOAS1213. https://projecteuclid.org/euclid.aoas/1560758430


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Supplemental materials

  • Derivations and methods. Supplemental derivations and methods for simulation and data preprocessing.