ArticlePloS one2016
Natural Cubic Spline Regression Modeling Followed by Dynamic Network Reconstruction for the Identification of Radiation-Sensitivity Gene Association Networks from Time-Course Transcriptome Data.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- DNA methylation predicts the outcome of COVID-19 patients with acute respiratory distress syndrome.Journal of translational medicine · 2022Trial
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Resolving early embryonic metabolism in Drosophila through single-embryo metabolomics and transcriptomics.Nature metabolism · 2025Article
- Dynamics in zebrafish development define transcriptomic specificity after angiogenesis inhibitor exposure.Archives of toxicology · 2025Article
- Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer.Genome medicine · 2024Article
- Unraveling the dynamic transcriptomic changes during the dimorphic transition ofFrontiers in microbiology · 2024Article
- Continuous transcriptome analysis reveals novel patterns of early gene expression inCell genomics · 2023Article
- Time-restricted eating alters the 24-hour profile of adipose tissue transcriptome in men with obesity.Obesity (Silver Spring, Md.) · 2023Article
- Integrative omics approaches for biosynthetic pathway discovery in plants.Natural product reports · 2022Review
- Rewiring of the Liver Transcriptome across Multiple Time-Scales Is Associated with the Weight Loss-Independent Resolution of NAFLD Following RYGB.Metabolites · 2022Article
- Clinical, genomic, and transcriptomic correlates of response to immune checkpoint blockade-based therapy in a cohort of patients with angiosarcoma treated at a single center.Journal for immunotherapy of cancer · 2022Article
- Integrated time-series transcriptomic and metabolomic analyses reveal different inflammatory and adaptive immune responses contributing to host resistance to PRRSV.Frontiers in immunology · 2022Article
- TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data.Nucleic acids research · 2021Article
- Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model.Scientific reports · 2021Article
- Jonckheere-Terpstra-Kendall-based non-parametric analysis of temporal differential gene expression.NAR genomics and bioinformatics · 2021Article
- Review
- Inhibition of FGF and TGF-β Pathways in hESCs Identify STOX2 as a Novel SMAD2/4 Cofactor.Biology · 2020Article
- Expansion of the circadian transcriptome ineLife · 2020Article
- rmRNAseq: differential expression analysis for repeated-measures RNA-seq data.Bioinformatics (Oxford, England) · 2020Article
- HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis.BMC bioinformatics · 2019Article
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10 authors.
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Abstract
Gene expression time-course experiments allow to study the dynamics of transcriptomic changes in cells exposed to different stimuli. However, most approaches for the reconstruction of gene association networks (GANs) do not propose prior-selection approaches tailored to time-course transcriptome data. Here, we present a workflow for the identification of GANs from time-course data using prior selection of genes differentially expressed over time identified by natural cubic spline regression modeling (NCSRM). The workflow comprises three major steps: 1) the identification of differentially expressed genes from time-course expression data by employing NCSRM, 2) the use of regularized dynamic partial correlation as implemented in GeneNet to infer GANs from differentially expressed genes and 3) the identification and functional characterization of the key nodes in the reconstructed networks. The approach was applied on a time-resolved transcriptome data set of radiation-perturbed cell culture models of non-tumor cells with normal and increased radiation sensitivity. NCSRM detected significantly more genes than another commonly used method for time-course transcriptome analysis (BETR). While most genes detected with BETR were also detected with NCSRM the false-detection rate of NCSRM was low (3%). The GANs reconstructed from genes detected with NCSRM showed a better overlap with the interactome network Reactome compared to GANs derived from BETR detected genes. After exposure to 1 Gy the normal sensitive cells showed only sparse response compared to cells with increased sensitivity, which exhibited a strong response mainly of genes related to the senescence pathway. After exposure to 10 Gy the response of the normal sensitive cells was mainly associated with senescence and that of cells with increased sensitivity with apoptosis. We discuss these results in a clinical context and underline the impact of senescence-associated pathways in acute radiation response of normal cells. The workflow of this novel approach is implemented in the open-source Bioconductor R-package splineTimeR.
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