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Transcript Time Course Analysis TTCA

Online

Analyses sparse and heterogeneous time course data with high detection sensitivity and transparency. TTCA is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognize the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response.

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TTCA classification

TTCA specifications

Software type:
Package/Module
Restrictions to use:
None
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Source code URL:
https://cran.r-project.org/src/contrib/TTCA_0.1.0.tar.gz
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
License:
Other
Version:
0.1.0
Requirements:
Matrix, quantreg, tcltk2, RISmed, VennDiagram, MASS
Maintained:
Yes

TTCA distribution

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TTCA support

Documentation

Maintainer

  • Marco Albrecht <>

Credits

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Publications

Institution(s)

Complex Biological Systems Group (BIOMS/IWR), Heidelberg, Germany; Systems Biology Group, Université du Luxembourg, Belvaux, Luxembourg; CCU Neuropathology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany; Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany; Systems Biology of Signal Transduction Group, German Cancer Research Center (DKFZ), Heidelberg, Germany; Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany; Medical Research Center, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany; Frankfurt Institute for Advanced Studies (FIAS), Goethe University Frankfurt, Frankfurt am Main, Germany

Funding source(s)

This work was supported by the Horizon 2020 MSCA grant agreement No 642295, by a grant from the Center for Modelling and Simulation in the Biosciences (BIOMS) of the Heidelberg University, by a grant from the BMBF (LungSysII, FKZ 0316042B), and by the German Center for Lung Research (DZL, 82DZL00404).

Link to literature

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