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. |