TMixClust-package: The main usages of TMixClust
In cbg-ethz/TMixClust: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines
Description
Overview
Author(s)
References
Description
Description of package TMixClust
Overview
TMixClust is a soft-clustering method which employs mixed-effects models
with nonparametric smoothing spline fitting and is able to
robustly stratify genes by their complex time series patterns.
The package has, besides the main clustering method, a set of
functionalities assisting the user to visualise and assess the
clustering results, and to choose the most optimal clustering solution.
Author(s)
Monica Golumbeanu, monica.golumbeanu@bsse.ethz.ch
References
Golumbeanu M, Desfarges S, Hernandez C, Quadroni M, Rato S,
Mohammadi P, Telenti A, Beerenwinkel N, Ciuffi A. (2017) Dynamics of
Proteo-Transcriptomic Response to HIV-1 Infection.
cbg-ethz/TMixClust documentation built on May 30, 2019, 8:28 a.m.
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Description Overview Author(s) References
Description
Description of package TMixClust
Overview
TMixClust is a soft-clustering method which employs mixed-effects models with nonparametric smoothing spline fitting and is able to robustly stratify genes by their complex time series patterns. The package has, besides the main clustering method, a set of functionalities assisting the user to visualise and assess the clustering results, and to choose the most optimal clustering solution.
Author(s)
Monica Golumbeanu, monica.golumbeanu@bsse.ethz.ch
References
Golumbeanu M, Desfarges S, Hernandez C, Quadroni M, Rato S, Mohammadi P, Telenti A, Beerenwinkel N, Ciuffi A. (2017) Dynamics of Proteo-Transcriptomic Response to HIV-1 Infection.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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