Description Usage Arguments Value Author(s) References Examples

View source: R/external_TMixClust.R

`analyse_stability`

Performs multiple clustering runs
with TMixClust, analyses the agreement between runs
with the Rand index and returns the clustering solution with the largest
likelihood.
A plot of agreement probability between all the runs and the run with the
maximum likelihood is produced.

1 2 3 | ```
analyse_stability(time_series_df, time_points = seq_len(ncol(time_series_df)),
nb_clusters = 2, em_iter_max = 1000, mc_em_iter_max = 10,
em_ll_convergence = 0.001, nb_clustering_runs = 3, nb_cores = 1)
``` |

`time_series_df` |
data frame containing the time series. Each row is a time series comprised of the time series name which is also the row name, and the time series values at each time point. |

`time_points` |
vector containing numeric values for the time points.
Default: |

`nb_clusters` |
desired number of clusters |

`em_iter_max` |
maximum number of iterations for the expectation-maximization (EM) algorithm. Default: 1000. |

`mc_em_iter_max` |
maximum number of iterations for Monte-Carlo resampling. Default is 10. |

`em_ll_convergence` |
convergence threshold for likelihood improvement. Default is 0.001. |

`nb_clustering_runs` |
number of times the clustering procedure is repeated on the input data. Default is 3. |

`nb_cores` |
number of cores to be used to run the separate clustering operations in parallel. Default is 1. |

TMixClust object with the highest likelihood. Renders a plot showing the overall distribution of the Rand index, which allows the user to assess clustering stability.

Monica Golumbeanu, monica.golumbeanu@bsse.ethz.ch

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# Load the toy time series data provided with the TMixClust package
data(toy_data_df)
# Identify the most optimal clustering solution with 3 clusters
best_clust_obj = analyse_stability(toy_data_df, nb_clusters = 3,
nb_clustering_runs = 4, nb_cores = 1)
# Plot the time series from each cluster
for (i in seq_len(3)) {
# Extract the time series in the current cluster and plot them
c_df=toy_data_df[which(best_clust_obj$em_cluster_assignment==i),]
plot_time_series_df(c_df, plot_title = paste("cluster",i))
}
``` |

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