ImpulseDE：使用脉冲模型检测时间序列数据中的差异表达基因。
ImpulseDE: detection of differentially expressed genes in time series data using impulse models.
作者信息
Sander Jil, Schultze Joachim L, Yosef Nir
机构信息
Genomics and Immunoregulation, LIMES-Institute, University of Bonn, Bonn, 53115, Germany.
Single Cell Genomics and Epigenomics Unit at the University of Bonn and the German Center for Neurodegenerative Diseases, Bonn, Germany.
出版信息
Bioinformatics. 2017 Mar 1;33(5):757-759. doi: 10.1093/bioinformatics/btw665.
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
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
摘要
环境扰动会导致细胞内基因表达发生独特变化。随着时间的推移，这些变化可以通过单一脉冲样的进展模式来表征。ImpulseDE是一个R包，适用于在高通量时间序列数据集中捕捉这些模式。通过为每个基因拟合一个代表性的脉冲模型，它可以报告来自单个时间进程或两个实验的两个时间进程中各时间点的差异表达基因。为了优化运行时间，该代码使用了聚类和多线程技术。通过应用ImpulseDE，我们展示了它在微阵列和RNA测序数据中表征基因表达潜在生物学特性的能力。
可用性和实现方式
ImpulseDE可在Bioconductor上获取（https://bioconductor.org/packages/ImpulseDE/）。
联系方式
补充信息
补充数据可在《生物信息学》在线版获取。
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本文引用的文献
1
High-resolution chromatin dynamics during a yeast stress response.酵母应激反应过程中的高分辨率染色质动力学
Mol Cell. 2015 Apr 16;58(2):371-86. doi: 10.1016/j.molcel.2015.02.002. Epub 2015 Mar 19.
2
Immunogenetics. Dynamic profiling of the protein life cycle in response to pathogens.免疫遗传学。病原体刺激下蛋白质生命周期的动态分析。
Science. 2015 Mar 6;347(6226):1259038. doi: 10.1126/science.1259038. Epub 2015 Feb 12.
3
High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies.高分辨率测序与建模识别出不同的动态RNA调控策略。
Cell. 2014 Dec 18;159(7):1698-710. doi: 10.1016/j.cell.2014.11.015. Epub 2014 Dec 11.
4
Immunogenetics. Chromatin state dynamics during blood formation.免疫遗传学。血液形成过程中的染色质状态动态。
Science. 2014 Aug 22;345(6199):943-9. doi: 10.1126/science.1256271. Epub 2014 Aug 7.
5
Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation.RNA-seq 数据的时间序列分析方法及其在人 Th17 细胞分化中的应用。
Bioinformatics. 2014 Jun 15;30(12):i113-20. doi: 10.1093/bioinformatics/btu274.
6
Single-cell RNA-seq reveals dynamic paracrine control of cellular variation.单细胞 RNA 测序揭示了细胞变异的动态旁分泌控制。
Nature. 2014 Jun 19;510(7505):363-9. doi: 10.1038/nature13437. Epub 2014 Jun 11.
7
A method to identify differential expression profiles of time-course gene data with Fourier transformation.基于傅里叶变换的时间序列基因数据差异表达谱识别方法
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8
A comparison of methods for differential expression analysis of RNA-seq data.RNA-seq 数据差异表达分析方法的比较。
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Dynamic regulatory network controlling TH17 cell differentiation.动态调控网络控制 TH17 细胞分化。
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A validated regulatory network for Th17 cell specification.Th17 细胞分化的调控网络的验证。
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