## ----init, eval=TRUE, results='hide', echo=FALSE, cache=FALSE----------------- knitr::opts_chunk$set(cache=FALSE, echo=FALSE, eval=TRUE) ## ----style-knitr, results="asis"------------------------------------------- BiocStyle::latex() ## ----load-package-and-data, echo=TRUE, results='markup'-------------------- # load the package library(TMixClust) # load the simulated time series data set data(toy_data_df) # display the first rows of the data frame print(head(toy_data_df)) # plot the time series of the data set plot_time_series_df(toy_data_df) ## ----load-data-from-file, echo=TRUE, results='markup'---------------------- # retrieve the time series data frame from a text file toy_data_file = system.file("extdata", "toy_time_series.txt", package = "TMixClust") toy_data = get_time_series_df(toy_data_file) # display the first rows of the data frame print(head(toy_data)) ## ----load-data-from-biobase, echo=TRUE, results='hide'--------------------- # Load the SOS pathway data from Bioconductor package SPEM library(SPEM) data(sos) sos_data = get_time_series_df_bio(sos) # Print the first lines of the retrieved time series data frame print(head(sos_data)) ## ----cluster-toy-data, echo=TRUE, results='hide'--------------------------- # cluster the time series in 3 groups cluster_obj = TMixClust(toy_data_df, nb_clusters = 3) ## ----analyse-stability, echo=TRUE, results='markup'------------------------ # command used for running clustering 10 times with K=3 # and obtaining the result stored in best_clust_toy_obj # best_clust_toy_obj = analyse_stability(toy_data_df, nb_clusters = 3, # nb_clustering_runs = 10, # nb_cores = 3) # load the optimal clustering result following stability analysis data("best_clust_toy_obj") # display the likelihood of the best clustering solution print(paste("Likelihood of the best solution:", best_clust_toy_obj$em_ll[length(best_clust_toy_obj$em_ll)])) # plot the time series in each cluster for (i in 1:3) { # extract the time series in the current cluster and plot them c_df=toy_data_df[which(best_clust_toy_obj$em_cluster_assignment==i),] plot_time_series_df(c_df, plot_title = paste("cluster",i)) } ## ----generate-silhouette3, echo=TRUE, results='hide'----------------------- # silhouette plot of the best clustering solution for K=3 plot_silhouette(best_clust_toy_obj) ## ----generate-silhouette2, echo=TRUE, results='hide'----------------------- # cluster the data in 4 groups and generate a silhouette plot cluster_obj_2 = TMixClust(toy_data_df, nb_clusters = 4) plot_silhouette(cluster_obj_2) ## ----generate-report, echo=TRUE, results='hide'---------------------------- # generate a TMixClust report with detailed clustering results # (not executed here) # generate_TMixClust_report(cluster_obj) ## ----cluster-real-data, echo=TRUE, results='hide'-------------------------- # retrieve the yeast time series data frame from a text file yeast_data_file = system.file("extdata", "yeast_time_series.txt", package = "TMixClust") yeast_data = get_time_series_df(yeast_data_file) # plot the time series of the data set plot_time_series_df(yeast_data) # command used for running clustering 10 times with K=4 # and obtaining the result stored in best_clust_yeast_obj # best_clust_yeast_obj = analyse_stability(yeast_data, # time_points = c(0,24,48,63,87), # nb_clusters = 4, # nb_clustering_runs = 10, # nb_cores = 3) # load the optimal clustering object for the yeast dataset data("best_clust_yeast_obj") # plot the identified 4 time series clusters: for (i in 1:4) { # extract the time series in the current cluster and plot them c_df=yeast_data[which(best_clust_yeast_obj$em_cluster_assignment==i),] plot_time_series_df(c_df, plot_title = paste("cluster",i)) } ## ----getting-help, echo=TRUE, results='hide'------------------------------- ?TMixClust ?generate_TMixClust_report ?get_time_series_df ?plot_time_series_df ?plot_silhouette ?analyse_stability ## ----session-info, results="asis"------------------------------------------ toLatex(sessionInfo()) ## ----citation, echo=TRUE, results='hide'----------------------------------- citation('TMixClust')