Graphical models for zero-inflated single cell gene expression

DOI10.1214/18-AOAS1213zbMath1423.62148arXiv1610.05857OpenAlexW3106175892WikidataQ92462615 ScholiaQ92462615MaRDI QIDQ2318662

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

Publication date: 15 August 2019

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.05857

zbMATH Keywords

group Lassographical modelgene networksingle cell gene expression

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Related Items (7)

Prioritizing Autism Risk Genes Using Personalized Graphical Models Estimated From Single-Cell RNA-seq Data ⋮ Structure learning for zero-inflated counts with an application to single-cell RNA sequencing data ⋮ An R package AZIAD for analysing zero-inflated and zero-altered data ⋮ HurdleNormal ⋮ Graphical models for zero-inflated single cell gene expression ⋮ Estimating heterogeneous gene regulatory networks from zero-inflated single-cell expression data ⋮ Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks

Uses Software

• GitHub
• HdBCS
• MAST

Cites Work

• Lectures on algebraic statistics
• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• Sparse graphical models for exploring gene expression data
• Graphical models for zero-inflated single cell gene expression
• High-dimensional graphs and variable selection with the Lasso
• Standardization and the Group Lasso Penalty
• Variable Selection with Error Control: Another Look at Stability Selection
• Selection and estimation for mixed graphical models
• Strong Rules for Discarding Predictors in Lasso-Type Problems

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