ArticleBioinformatics (Oxford, England)2017
ImpulseDE: detection of differentially expressed genes in time series data using impulse models.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Generating Functional and Highly Proliferative Melanocytes Derived from Human Pluripotent Stem Cells: A Promising Tool for Biotherapeutic Approaches to Treat Skin Pigmentation Disorders.International journal of molecular sciences · 2023Article
- Integrative systems immunology uncovers molecular networks of the cell cycle that stratify COVID-19 severity.Journal of medical virology · 2023Article
- Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime.Bioinformatics (Oxford, England) · 2022Article
- Plant glutamate receptors mediate a bet-hedging strategy between regeneration and defense.Developmental cell · 2022Article
- Multispecies transcriptomes reveal core fruit development genes.Frontiers in plant science · 2022Article
- Nanopore Assay Reveals Cell-Type-Dependent Gene Expression of Vesicular Stomatitis Indiana Virus and Differential Host Cell Response.Pathogens (Basel, Switzerland) · 2021Article
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- Amynthas corticis genome reveals molecular mechanisms behind global distribution.Communications biology · 2021Article
- Time Course Transcriptomic Study Reveals the Gene Regulation During Liver Development and the Correlation With Abdominal Fat Weight in Chicken.Frontiers in genetics · 2021Article
- Article
- TimeMeter assesses temporal gene expression similarity and identifies differentially progressing genes.Nucleic acids research · 2020Article
- Lag penalized weighted correlation for time series clustering.BMC bioinformatics · 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
- Identification and Massively Parallel Characterization of Regulatory Elements Driving Neural Induction.Cell stem cell · 2019Article
- Simulating multiple faceted variability in single cell RNA sequencing.Nature communications · 2019Article
- Bayesian statistical learning for big data biology.Biophysical reviews · 2019Review
- A descriptive marker gene approach to single-cell pseudotime inference.Bioinformatics (Oxford, England) · 2019Article
- Impulse model-based differential expression analysis of time course sequencing data.Nucleic acids research · 2018Article
- Computational approaches for interpreting scRNA-seq data.FEBS letters · 2017Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Summary: Perturbations in the environment lead to distinctive gene expression changes within a cell. Observed over time, those variations can be characterized by single impulse-like progression patterns. ImpulseDE is an R package suited to capture these patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes across time points from a single or between two time courses from two experiments. To optimize running time, the code uses clustering and multi-threading. By applying ImpulseDE , we demonstrate its power to represent underlying biology of gene expression in microarray and RNA-Seq data. Availability and Implementation: ImpulseDE is available on Bioconductor ( https://bioconductor.org/packages/ImpulseDE/ ). Contact: niryosef@berkeley.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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