def test_init_tree_human_alpha_beta(): import os import pandas as pd from tcrdist.repertoire import TCRrep from tcrdist.tree import TCRtree df = pd.read_csv("dash_human.csv").sample(10).reset_index(drop = True) tr = TCRrep(cell_df = df, organism = 'human', chains = ['alpha', 'beta'], db_file = 'alphabeta_gammadelta_db.tsv') tcrtree = TCRtree(tcrrep = tr, html_name = 'dash.human.ab.tree.html') tcrtree.build_tree() assert os.path.isfile('dash.human.ab.tree.html')
def test_init_tree_beta(): import os import pandas as pd from tcrdist.repertoire import TCRrep from tcrdist.tree import TCRtree df = pd.read_csv("dash.csv").sample(10).reset_index(drop = True) tr = TCRrep(cell_df = df, organism = 'mouse', chains = ['beta'], db_file = 'alphabeta_gammadelta_db.tsv') tcrtree = TCRtree(tcrrep = tr, html_name = 'dash.mouse.b.tree.html') tcrtree.default_plot_hclust_props['tooltip_cols'].append('ref_size_olga_beta') tcrtree.default_plot_hclust_props['tooltip_cols'].append('ref_unique_olga_beta') tcrtree.default_plot_hclust_props['tooltip_cols'].append('percent_missing_olga_beta') tcrtree.build_tree() assert os.path.isfile('dash.mouse.b.tree.html')
def test_example_tree_args(): import os import pandas as pd from tcrdist.repertoire import TCRrep from tcrdist.tree import TCRtree df = pd.read_csv("dash.csv").sample(100, random_state=1).reset_index(drop=True) tr = TCRrep(cell_df=df, organism='mouse', chains=['beta'], db_file='alphabeta_gammadelta_db.tsv') tcrtree = TCRtree(tcrrep=tr, html_name='dash.mouse.b.tree.html') tcrtree.default_hcluster_diff_kwargs = \ {'clone_df': None, 'pwmat': None, 'x_cols': ['epitope'], 'Z': None, 'count_col': 'count', 'subset_ind': None, 'hclust_method': 'complete', 'optimal_ordering': True, 'test_method': 'fishers'} tcrtree.default_member_summ_kwargs = \ {'key_col': 'neighbors_i', 'count_col': 'count', 'addl_cols': ['subject'], 'addl_n': 1} tcrtree.default_plot_hclust_props = \ {'title': '', 'alpha_col': 'pvalue', 'alpha': 0.05, 'tooltip_cols': ['subject', 'mean_dist', 'pct_dist_75', 'pct_dist_50', 'pct_dist_25', 'fuzzy_simpson_diversity_75', 'fuzzy_simpson_diversity_50', 'fuzzy_simpson_diversity_25', 'cdr3_b_aa', 'v_b_gene', 'j_b_gene', 'svg_beta', 'svg_raw_beta', 'ref_size_beta', 'ref_unique_beta', 'percent_missing_beta']} tcrtree.build_tree()
def test_example_tree(): """ An example showing how to create an interactive tree from a sample of mouse TCRs """ import os import pandas as pd from tcrdist.repertoire import TCRrep from tcrdist.tree import TCRtree df = pd.read_csv("dash.csv").sample(100, random_state=1).reset_index(drop=True) tr = TCRrep(cell_df=df, organism='mouse', chains=['beta'], db_file='alphabeta_gammadelta_db.tsv') tcrtree = TCRtree(tcrrep=tr, html_name='dash.mouse.b.tree.html') tcrtree.build_tree() assert os.path.isfile('dash.mouse.b.tree.html')
def test_example_tree_args(): import os import pandas as pd from tcrdist.repertoire import TCRrep from tcrdist.tree import TCRtree df = pd.read_csv("dash.csv").sample(100, random_state=1).reset_index(drop = True) tr = TCRrep(cell_df = df, organism = 'mouse', chains = ['beta'], db_file = 'alphabeta_gammadelta_db.tsv') tcrtree = TCRtree(tcrrep = tr, html_name = 'dash.mouse.b.tree.html') tcrtree.default_hcluster_diff_kwargs['x_cols'] = ['epitope'] tcrtree.default_member_summ_kwargs['addl_cols'] : ['subject', 'epitope'] tcrtree.default_plot_hclust_props['alpha_col'] = 'pvalue' tcrtree.default_plot_hclust_props['alpha'] = 1.0 tcrtree.build_tree()
count_col='count', other_frequency_threshold=0.01) IPython.display.SVG(data=svg) svg = plot_pairings(cell_df=tra.clone_df, cols=['v_a_gene', 'j_a_gene'], count_col='count', other_frequency_threshold=0.01) IPython.display.SVG(data=svg) #t = TCRsampler() # t.download_background_file('ruggiero_mouse_sampler.zip') #tcrsampler_beta = TCRsampler(default_background='ruggiero_mouse_beta_t.tsv.sampler.tsv') #tcrsampler_alpha = TCRsampler(default_background='ruggiero_mouse_alpha_t.tsv.sampler.tsv') tcrtree = TCRtree(tcrrep=tra, html_name=opj(proj_folder, 'Gil_alpha_hierdiff.html')) tcrtree.default_hcluster_diff_kwargs['x_cols'] = ['cohort'] tcrtree.default_plot_hclust_props['alpha'] = 0.05 tcrtree.default_plot_hclust_props['tooltip_cols'].append('ref_size_olga_alpha') tcrtree.default_plot_hclust_props['tooltip_cols'].append( 'ref_unique_olga_alpha') tcrtree.default_plot_hclust_props['tooltip_cols'].append( 'percent_missing_olga_alpha') tcrtree.build_tree() tcrtree = TCRtree(tcrrep=trb, html_name=opj(proj_folder, 'Gil_beta_hierdiff.html')) tcrtree.default_hcluster_diff_kwargs['x_cols'] = ['cohort'] tcrtree.default_plot_hclust_props['alpha'] = 0.05 tcrtree.default_plot_hclust_props['tooltip_cols'].append('ref_size_olga_beta') tcrtree.default_plot_hclust_props['tooltip_cols'].append(