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(