Package: TMixClust 1.27.0

Monica Golumbeanu

TMixClust: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines

Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets.

Authors:Monica Golumbeanu <[email protected]>

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NEWS

# Install 'TMixClust' in R:
install.packages('TMixClust', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • best_clust_toy_obj - TMixClust object containing the optimal clustering solution for the toy data with 3 clusters.
  • best_clust_yeast_obj - TMixClust object containing the optimal clustering solution for the yeast data.
  • toy_data_df - Simulated time-series gene expression data

On BioConductor:TMixClust-1.27.0(bioc 3.20)TMixClust-1.26.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

7 exports 0.71 score 23 dependencies 2 mentions

Last updated 2 months agofrom:41dbf6657b

Exports:analyse_stabilitygenerate_TMixClust_reportget_time_series_dfget_time_series_df_bioplot_silhouetteplot_time_series_dfTMixClust

Dependencies:BHBiobaseBiocGenericsBiocParallelclassclustercodetoolscpp11flexclustformatRfutile.loggerfutile.optionsgsslambda.rlatticeMASSmodeltoolsmvtnormRsolnpsnowSPEMtruncnormzoo

Clustering time series gene expression data with TMixClust

Rendered fromTMixClust.Rnwusingknitr::knitron Jun 30 2024.

Last update: 2018-02-01
Started: 2017-07-11