scphylo.tl.booster#

scphylo.tl.booster(df_input, alpha, beta, solver='SCITE', sample_on='muts', sample_size=10, n_samples=10, begin_index=0, n_jobs=10, dep_weight=50, time_limit=120, n_iterations=500000, subsample_dir=None, disable_tqdm=False, no_subsampling=False, no_dependencies=False, no_reconstruction=False)[source]#

Trisicell-Boost solver.

For more details of available tools that work on binary matrices, read [ReviewBinary].

Parameters

  • df_input (pandas.DataFrame) – input noisy dataframe

  • alpha (float) – false positive rate

  • beta (float) – false negative rate

  • solver (str, optional) – which tool is boosted {“SCITE”, “PhISCS”}, by default “SCITE”

  • sample_on (str, optional) – on which dimension is subsampled {“muts”, “cells”}, by default “muts”

  • sample_size (int, optional) – number of subsampled mutations or cells depends on sample_on, by default 10

  • n_samples (int, optional) – number of samples, by default 10

  • begin_index (int, optional) – start index of intermediate file names, by default 0

  • n_jobs (int, optional) – number of jobs, by default 10

  • dep_weight (int, optional) – weight multiplier, by default 50

  • time_limit (int, optional) – time out needed for PhISCS running on each instance, by default 120

  • n_iterations (int, optional) – number of iterations needed for SCITE running, by default 500000

  • subsample_dir (str, optional) – for keeping the intermediate subsamples CFMatrices, by default None

  • disable_tqdm (bool, optional) – disable progress bar, by default False

  • no_subsampling (bool, optional) – subsampling (step 1/3) gets off, by default False

  • no_dependencies (bool, optional) – dependencies calculation (step 2/3) gets off, by default False

  • no_reconstruction (bool, optional) – reconstruction of big tree (step 3/3) gets off, by default False

Returns

A conflict-free matrix in which rows are cells and columns are mutations. Values inside this matrix show the presence (1) and absence (0).

Return type

pandas.DataFrame

See also

scphylo.tl.scite(), scphylo.tl.phiscsb()

Examples#

Reconstruct tree by Trisicell-Boost

Reconstruct tree by Trisicell-Boost