choose_features_clustering
                        choose_features_clustering
clustering_angular_distance
                        clustering_angular_distance
detect_insertion        detect_insertion
dpt_test                dpt_test
filter_bases            filter_bases
get_distribution        get_distribution
get_heteroplasmy        get_heteroplasmy
get_raw_counts_allele   get_raw_counts_allele
get_wilcox_test         get_wilcox_test
plot_allele_frequency   plot_allele_frequency
plot_base_coverage      plot_base_coverage
plot_batch              plot_batch
plot_cells_coverage     plot_cells_coverage
plot_condition          plot_condition
plot_coordinate_cluster
                        plot_coordinate_cluster
plot_coordinate_heteroplasmy
                        plot_coordinate_heteroplasmy
plot_correlation_bases
                        plot_correlation_bases
plot_distance_matrix    plot_distance_matrix
plot_distribution       plot_distribution
plot_dpt                plot_dpt
plot_genome_coverage    plot_genome_coverage
plot_heatmap            plot_heatmap
plot_heteroplasmy       plot_heteroplasmy
plot_heteroplasmy_variability
                        plot_heteroplasmy_variability
plot_spider_chart       plot_spider_chart
vi_comparison           vi_comparison We compute the variation of
                        information (VI) between the partition provided
                        by _new_classification_ and
                        _old_classification_. The VI between a random
                        partitions (obtained with re-shuffle from
                        original labels in _old_classification_) and
                        _old_classification_ is also computed. A
                        distribution of VI values from random
                        partitions is built. Finally, from the
                        comparison with this distribution, an empirical
                        p value is given to the VI of the unsupervised
                        cluster analysis.
