ArticleNucleic acids research2018
Impulse model-based differential expression analysis of time course sequencing data.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers.
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Who cites it
95 citing papers in PubMed.
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- Cochlear nucleus spatial transcriptomes of normal and hearing loss mice reveal a critical role of Spp1 in bushy cells.Cell research · 2026Article
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- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Spatial transcriptomics reveals expression gradients in developing wheat inflorescences at cellular resolution.The Plant cell · 2026Article
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Pseudotimecascade Visualizes Gene Expression Cascade in Pseudotime Analysis.Computational and structural biotechnology journal · 2026Article
- Persistent dopamine-dependent remodeling of the neural transcriptome in response to pregnancy and postpartum.bioRxiv : the preprint server for biology · 2025Article
- Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations.STAR protocols · 2025Review
- A high-resolution model of gene expression during Gossypium hirsutum (cotton) fiber development.BMC genomics · 2025Article
35 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Temporal changes to the concentration of molecular species such as mRNA, which take place in response to various environmental cues, can often be modeled as simple continuous functions such as a single pulse (impulse) model. The simplicity of such functional representations can provide an improved performance on fundamental tasks such as noise reduction, imputation and differential expression analysis. However, temporal gene expression profiles are often studied with models that treat time as a categorical variable, neglecting the dependence between time points. Here, we present ImpulseDE2, a framework for differential expression analysis that combines the power of the impulse model as a continuous representation of temporal responses along with a noise model tailored specifically to sequencing data. We compare the simple categorical models to ImpulseDE2 and to other continuous models based on natural cubic splines and demonstrate the utility of the continuous approach for studying differential expression in time course sequencing experiments. A unique feature of ImpulseDE2 is the ability to distinguish permanently from transiently up- or down-regulated genes. Using an in vitro differentiation dataset, we demonstrate that this gene classification scheme can be used to highlight distinct transcriptional programs that are associated with different phases of the differentiation process.
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