scegot package

Submodules

scegot.scegot module

Module contents

scegot.is_notebook() → bool

class scegot.scEGOT(X, day_names=None, verbose=True, adata_day_key=None)

Bases: object

animate_gene_expression(target_gene_name, mode='pca', interpolate_interval=11, n_samples=5000, x_range=None, y_range=None, c_range=None, x_label=None, y_label=None, cmap='gnuplot2', save=False, save_path=None)

animatie_interpolated_distribution(x_range=None, y_range=None, interpolate_interval=11, cmap='gnuplot2', save=False, save_path=None)

apply_umap(n_neighbors, n_components=2, random_state=None, min_dist=0.1, umap_other_params={})

bures_wasserstein_distance(m_0, m_1, sigma_0, sigma_1)

calculate_cell_velocities()

calculate_grns(selected_clusters=None, alpha_range=(-2, 2), cv=3, ridge_cv_fit_intercept=False, ridge_fit_intercept=False)

calculate_mut_st(gmm_source, gmm_target, t)

calculate_normalized_solutions(gmm_models, reg=0.01, numItermax=10000000000, method='sinkhorn_epsilon_scaling', tau=100000000.0, stopThr=1e-09, sinkhorn_other_params={})

calculate_solution(gmm_source, gmm_target, reg=0.01, numItermax=10000000000, method='sinkhorn_epsilon_scaling', tau=100000000.0, stopThr=1e-09, sinkhorn_other_params={})

calculate_solutions(gmm_models, reg=0.01, numItermax=10000000000, method='sinkhorn_epsilon_scaling', tau=100000000.0, stopThr=1e-09, sinkhorn_other_params={})

calculate_waddington_potential(n_neighbors=100, knn_other_params={})

egot(pi_0, pi_1, mu_0, mu_1, S_0, S_1, reg=0.01, numItermax=10000000000, method='sinkhorn_epsilon_scaling', tau=100000000.0, stopThr=1e-09, sinkhorn_other_params={})

fit_gmm(n_components_list, covariance_type='full', max_iter=2000, n_init=10, random_state=None, gmm_other_params={})

fit_predict_gmm(n_components_list, covariance_type='full', max_iter=2000, n_init=10, random_state=None, gmm_other_params={})

gaussian_mixture_density(mu, sigma, alpha, x)

generate_cluster_names_with_day(cluster_names=None)

get_gaussian_map(m_0, m_1, sigma_0, sigma_1, x)

get_gmm_means()

get_positive_gmm_mean_gene_values_per_cluster(gmm_means, cluster_names=None)

make_cell_state_graph(cluster_names, mode='pca', threshold=0.05)

make_interpolation_data(gmm_source, gmm_target, t, columns=None, n_samples=2000, seed=0)

plot_cell_state_graph(G, cluster_names, tf_gene_names=None, tf_gene_pick_num=5, save=False, save_path=None)

plot_cell_velocity(velocities, mode='pca', color_points='gmm', size_points=30, cmap='tab20', cluster_names=None, save=False, save_path=None)

color_points = “gmm”, “day”, or None

plot_fold_change(cluster_names, cluster1, cluster2, tf_gene_names=None, threshold=1.0, save=False, save_path=None)

plot_gene_expression_2d(gene_name, mode='pca', col=None, save=False, save_path=None)

plot_gene_expression_3d(gene_name, col=None, save=False, save_path=None)

plot_gmm_predictions(mode='pca', figure_labels=None, x_range=None, y_range=None, figure_titles_without_gmm=None, figure_titles_with_gmm=None, plot_gmm_means=False, cmap='plasma', save=False, save_paths=None)

plot_grn_graph(grns, ridge_cvs, selected_genes, threshold=0.01, save=False, save_paths=None, save_format='png')

plot_interpolation_of_cell_velocity(velocities, mode='pca', color_streams=False, color_points='gmm', cluster_names=None, x_range=None, y_range=None, cmap='gnuplot2', linspace_num=300, save=False, save_path=None)

color_points = “gmm”, “day”, or None

plot_pathway_gene_expressions(cluster_names, pathway_names, selected_genes, tf_gene_names=None, save=False, save_path=None)

plot_pathway_mean_var(cluster_names, pathway_names, tf_gene_names=None, threshold=1.0, save=False, save_path=None)

plot_pathway_single_gene_2d(gene_name, mode='pca', col=None, save=False, save_path=None)

plot_pathway_single_gene_3d(gene_name, col=None, save=False, save_path=None)

plot_simple_cell_state_graph(G, layout='normal', order=None, save=False, save_path=None)

layout = “normal” or “hierarchy” order = None or “weight”

plot_true_and_interpolation_distributions(interpolate_index, mode='pca', n_samples=2000, t=0.5, plot_source_and_target=True, alpha_true=0.5, x_col_name=None, y_col_name=None, x_range=None, y_range=None, save=False, save_path=None)

plot_waddington_potential(waddington_potential, mode='pca', gene_name=None, save=False, save_path=None)

plot_waddington_potential_surface(waddington_potential, mode='pca', save=False, save_path=None)

predict_gmm_label(X_item, gmm_model)

predict_gmm_labels(X, gmm_models)

preprocess(pca_n_components, recode_params={}, umi_target_sum=10000.0, pca_random_state=None, pca_other_params={}, apply_recode=True, apply_normalization_log1p=True, apply_normalization_umi=True, select_genes=True, n_select_genes=2000)

replace_gmm_labels(converter)