ArticleThe annals of applied statistics2019
GRAPHICAL MODELS FOR ZERO-INFLATED SINGLE CELL GENE EXPRESSION.
Article in The annals of applied statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fusion of spatiotemporal and network models to prioritize multiscale effects in single-cell perturbations.Briefings in bioinformatics · 2025Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.Nature communications · 2023Article
- Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.G3 (Bethesda, Md.) · 2023Article
- Directed Graphical Models and Causal Discovery for Zero-Inflated Data.Proceedings of machine learning research · 2023Article
- Accounting for technical noise in Bayesian graphical models of single-cell RNA-sequencing data.Biostatistics (Oxford, England) · 2022Article
- Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data.Journal of the American Statistical Association · 2022Article
- Joint Microbial and Metabolomic Network Estimation with the Censored Gaussian Graphical Model.Statistics in biosciences · 2021Article
- Network analysis methods for studying microbial communities: A mini review.Computational and structural biotechnology journal · 2021Review
- Compositional zero-inflated network estimation for microbiome data.BMC bioinformatics · 2020Article
- GRAPHICAL MODELS FOR ZERO-INFLATED SINGLE CELL GENE EXPRESSION.The annals of applied statistics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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 co-regulatory 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. An R implementation is available at https://github.com/amcdavid/HurdleNormal.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.