import time
import scphylo as scp
from scphylo.tl.solver.booster._dependencies import prepare_dependencies
from scphylo.tl.solver.booster._reconstruct_big_tree import reconstruct_big_tree
from scphylo.tl.solver.booster._subsamples import subsampling
[docs]def 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,
):
"""Trisicell-Boost solver.
For more details of available tools that work on binary matrices, read
:cite:`ReviewBinary`.
Parameters
----------
df_input : :class:`pandas.DataFrame`
input noisy dataframe
alpha : :obj:`float`
false positive rate
beta : :obj:`float`
false negative rate
solver : :obj:`str`, optional
which tool is boosted {"SCITE", "PhISCS"}, by default "SCITE"
sample_on : :obj:`str`, optional
on which dimension is subsampled {"muts", "cells"}, by default "muts"
sample_size : :obj:`int`, optional
number of subsampled mutations or cells depends on `sample_on`, by default 10
n_samples : :obj:`int`, optional
number of samples, by default 10
begin_index : :obj:`int`, optional
start index of intermediate file names, by default 0
n_jobs : :obj:`int`, optional
number of jobs, by default 10
dep_weight : :obj:`int`, optional
weight multiplier, by default 50
time_limit : :obj:`int`, optional
time out needed for PhISCS running on each instance, by default 120
n_iterations : :obj:`int`, optional
number of iterations needed for SCITE running, by default 500000
subsample_dir : :obj:`str`, optional
for keeping the intermediate subsamples CFMatrices, by default None
disable_tqdm : :obj:`bool`, optional
disable progress bar, by default False
no_subsampling : :obj:`bool`, optional
subsampling (step 1/3) gets off, by default False
no_dependencies : :obj:`bool`, optional
dependencies calculation (step 2/3) gets off, by default False
no_reconstruction : :obj:`bool`, optional
reconstruction of big tree (step 3/3) gets off, by default False
Returns
-------
:class:`pandas.DataFrame`
A conflict-free matrix in which rows are cells and columns are mutations.
Values inside this matrix show the presence (1) and absence (0).
See Also
--------
:func:`scphylo.tl.scite`.
:func:`scphylo.tl.phiscsb`
"""
if subsample_dir is not None:
tmpdir = scp.ul.mkdir(subsample_dir)
else:
# tmpdir = scp.ul.tmpdirsys(suffix=".booster")
# tmpdir = tmpdir.name
tmpdir = scp.ul.tmpdir(suffix=".booster")
n_muts = df_input.shape[1]
if sample_size is None:
sample_size = int(dep_weight * (n_muts**2) / (sample_size**2))
detail = {}
df_input.index.name = "cellIDxmutID"
s_time = time.time()
# subsampling matrices and solving them
if not no_subsampling:
subsampling(
df_input,
alpha=alpha,
beta=beta,
solver=solver,
sample_on=sample_on,
sample_size=sample_size,
n_samples=n_samples,
begin_sample=begin_index,
n_jobs=n_jobs,
time_limit=time_limit,
n_iterations=n_iterations,
tmpdir=tmpdir,
disable_tqdm=disable_tqdm,
)
# preparing dependencies file
if not no_dependencies:
max_num_submatrices = int(dep_weight * (n_muts**2) / (sample_size**2))
prepare_dependencies(
df_input.columns,
tmpdir,
f"{tmpdir}/_booster.dependencies",
max_num_submatrices,
disable_tqdm,
)
# building the final CFMatrix
if not no_reconstruction:
scp.io.write(df_input, f"{tmpdir}/_input.SC")
detail["TREE_SCORE"] = reconstruct_big_tree(
f"{tmpdir}/_booster.dependencies",
f"{tmpdir}/_input.SC",
alpha,
beta,
f"{tmpdir}/_booster",
disable_tqdm,
)
df_output = scp.io.read(
f"{tmpdir}/_booster.dnc.CFMatrix",
)
df_output = df_output.loc[df_input.index, df_input.columns]
else:
df_output = None
e_time = time.time()
running_time = e_time - s_time
if subsample_dir is None:
scp.ul.cleanup(tmpdir)
if df_output is not None:
scp.ul.stat(df_input, df_output, alpha, beta, running_time)
for k, v in detail.items():
scp.logg.info(f"{k}: {v}")
return df_output