Graphical models for zero-inflated single cell gene expression

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Dokumenter

  • Andrew McDavid
  • Raphael Gottardo
  • Noah Simon
  • Mathias Drton

Bulk gene expression experiments relied on aggregations of thousands of cells to measure the average expression in an organism. Advances in mi-crofluidic 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 coreg-ulatory 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 inde-pendences 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.

OriginalsprogEngelsk
TidsskriftAnnals of Applied Statistics
Vol/bind13
Udgave nummer2
Sider (fra-til)848-873
Antal sider26
ISSN1932-6157
DOI
StatusUdgivet - 2019

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