Graphical models for zero-inflated single cell gene expression

TY - JOUR

T1 - Graphical models for zero-inflated single cell gene expression

AU - McDavid, Andrew

AU - Gottardo, Raphael

AU - Simon, Noah

AU - Drton, Mathias

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Gene network

KW - Graphical model

KW - Group lasso

KW - Single cell gene expression

UR - http://www.scopus.com/inward/record.url?scp=85068503200&partnerID=8YFLogxK

U2 - 10.1214/18-aoas1213

DO - 10.1214/18-aoas1213

M3 - Journal article

C2 - 31388390

AN - SCOPUS:85068503200

SN - 1932-6157

VL - 13

SP - 848

EP - 873

JO - Annals of Applied Statistics

JF - Annals of Applied Statistics

IS - 2

ER -