trisicell.tl.booster

trisicell.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

trisicell.tl.scite(), trisicell.tl.phiscsb()

Examples

Construct lienage tree using Trisicell-Boost

Construct lienage tree using Trisicell-Boost