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Zhong W, Dong L, Poston TB, et al. Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs. Front Genet. 2020;11:8doi: 10.3389/fgene.2020.00008.
Zhong, W., Dong, L., Poston, T. B., Darville, T., Spracklen, C. N., Wu, D., Mohlke, K. L., Li, Y., Li, Q., & Zheng, X. (2020). Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs. Frontiers in genetics, 118. https://doi.org/10.3389/fgene.2020.00008
Zhong, Wujuan, et al. "Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs." Frontiers in genetics vol. 11 (2020): 8. doi: https://doi.org/10.3389/fgene.2020.00008
Zhong W, Dong L, Poston TB, Darville T, Spracklen CN, Wu D, Mohlke KL, Li Y, Li Q, Zheng X. Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs. Front Genet. 2020 Feb 07;11:8. doi: 10.3389/fgene.2020.00008. eCollection 2020. PMID: 32127796; PMCID: PMC7038820.
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Front Genet. 2020 Feb 07;11:8. doi: 10.3389/fgene.2020.00008. eCollection 2020.

Inferring Regulatory Networks From Mixed Observational Data Using Directed Acyclic Graphs.

Frontiers in genetics

Wujuan Zhong, Li Dong, Taylor B Poston, Toni Darville, Cassandra N Spracklen, Di Wu, Karen L Mohlke, Yun Li, Quefeng Li, Xiaojing Zheng

Affiliations

  1. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  2. Department of Pediatrics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  3. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  4. Department of Oral and Craniofacial Health Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

PMID: 32127796 PMCID: PMC7038820 DOI: 10.3389/fgene.2020.00008

Abstract

Construction of regulatory networks using cross-sectional expression profiling of genes is desired, but challenging. The Directed Acyclic Graph (DAG) provides a general framework to infer causal effects from observational data. However, most existing DAG methods assume that all nodes follow the same type of distribution, which prohibit a joint modeling of continuous gene expression and categorical variables. We present a new mixed DAG (mDAG) algorithm to infer the regulatory pathway from mixed observational data containing both continuous variables (e.g. expression of genes) and categorical variables (e.g. categorical phenotypes or single nucleotide polymorphisms). Our method can identify upstream causal factors and downstream effectors closely linked to a variable and generate hypotheses for causal direction of regulatory pathways. We propose a new permutation method to test the conditional independence of variables of mixed types, which is the key for mDAG. We also utilize an

Copyright © 2020 Zhong, Dong, Poston, Darville, Spracklen, Wu, Mohlke, Li, Li and Zheng.

Keywords: causal regulatory pathways; continuous and categorical variables; directed acyclic graphs; mixed observational data; regulatory network

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