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. |