ArticlePLoS computational biology2010
Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 79 citations in OpenAlex.
- Expression Quantitative Trait Locus of Wood Formation-Related Genes inInternational journal of molecular sciences · 2023Article
- A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.Life (Basel, Switzerland) · 2023Article
- Estimation of high-dimensional directed acyclic graphs with surrogate intervention.Biostatistics (Oxford, England) · 2020Article
- Modeling cross-regulatory influences on monolignol transcripts and proteins under single and combinatorial gene knockdowns in Populus trichocarpa.PLoS computational biology · 2020Article
- Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations.BMC bioinformatics · 2020Article
- Inference of differential gene regulatory networks based on gene expression and genetic perturbation data.Bioinformatics (Oxford, England) · 2020Article
- Inferring Gene Regulatory Networks from a Population of Yeast Segregants.Scientific reports · 2019Article
- Condition-adaptive fused graphical lasso (CFGL): An adaptive procedure for inferring condition-specific gene co-expression network.PLoS computational biology · 2018Article
- Differential Analysis of Directed Networks.Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence · 2018Article
- A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations.Journal of machine learning research : JMLR · 2018Article
- An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.PLoS computational biology · 2017Article
- Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer.BMC medical research methodology · 2017Article
- Statistical Methods in Integrative Genomics.Annual review of statistics and its application · 2016Article
- Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data.Journal of biomedical informatics · 2016Article
- The Wright stuff: reimagining path analysis reveals novel components of the sex determination hierarchy in Drosophila melanogaster.BMC systems biology · 2015Article
- Integrative random forest for gene regulatory network inference.Bioinformatics (Oxford, England) · 2015Article
- Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.Genetics · 2015Article
- Causal inference of gene regulation with subnetwork assembly from genetical genomics data.Nucleic acids research · 2014Article
- An expression quantitative trait loci-guided co-expression analysis for constructing regulatory network using a rice recombinant inbred line population.Journal of experimental botany · 2014Article
- Learning gene networks under SNP perturbations using eQTL datasets.PLoS computational biology · 2014Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL), which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1), and genes involved in endocytosis (RCY1), the spindle checkpoint (BUB2), sulfonate catabolism (JLP1), and cell-cell communication (PRM7). Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.
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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.