import pathlib as pl
from argparse import ArgumentParser
from typing import Literal, Tuple
import numpy as np
import pandas as pd
_NORMMETHOD = Literal["rank", "log_rank", "log", "custom"]
_SCORERNAME = Literal["ssgsea", "average"]
[docs]
class AvgBulkScorer:
"""This class creates a scorer that takes the average of the (std or not) bulk gex as
a proxy of signature score"""
def __init__(self, std: bool):
"""
Args:
name: name of the scorer
std: if True, the bulk gex will be standardized before the average is computed
Returns:
None
"""
self.std = std
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def score(self, bulk_values: pd.DataFrame, metasig: np.ndarray) -> pd.Series:
"""The main scoring function
Args:
bulk_values: a df of size (n_samples, n_genes) with the bulk gene expression
metasig: a list of genes representing the signature to score
Returns:
a series with the score for each patient
"""
intersection = bulk_values.columns.intersection(metasig)
if len(intersection) == 0:
raise ValueError(
"There is no common gene between the metasignature and the bulk DataFrame"
)
else:
df = bulk_values.loc[:, intersection]
if self.std:
df = (df - df.mean()) / df.std()
return df.mean(axis=1)
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def get_data(
bulk_file: pl.Path,
metasignature_file: pl.Path,
truesignature_file: pl.Path,
) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
"""Helper function to download files
Args:
bulk_file: path to file with the bulk gex
purity_file: path to the file with the purity info
metasignature_file: path to the file with the metasignature genes
truesignature_file: path to the file with the true signature genes
Returns:
a scorer object
"""
bulk_values = pd.read_csv(bulk_file, index_col=0)
bulk_values = bulk_values.loc[~bulk_values.index.duplicated()]
metasignatures = pd.read_csv(metasignature_file)
truesignatures = pd.read_csv(truesignature_file)
return bulk_values, metasignatures, truesignatures
[docs]
def get_all_scores(
bulk_values: pd.DataFrame,
metasignatures: pd.DataFrame,
truesignatures: pd.DataFrame
) -> pd.DataFrame:
"""Computes the scores for all metasignatures and true signatures using
the appropriate scorer function
Args:
bulk_values: df of bulk gex of size (n_samples, n_genes)
purity: series with the purity information per patient + cancer type of size (n_samples, 2)
metasignatures: df with n_metasignatures columns, containing in each column the
list of genes that constitute the metasignature
truesignatures: df with n_true signatures columns, containing in each column the
list of genes that constitute the true signature
std: only used if scorer name is average, if True the bulk gex will be standardized
before computing the average
sample_norm_method: only used if scorer name is ssgsea, what method to use
for sample norm in ssgsea (see ssgsea doc for more info)
Returns:
the dataframe of size (n_samples, n_metasignatures + n_true signatures + 1 + 1),
containing all scores on the metasignatures, the true signatuers, the purity information,
and TCGA the cancer type the scoring was performed on
"""
scorer = AvgBulkScorer(std=True)
all_scores = {}
for sig in truesignatures.columns:
all_scores[sig] = scorer.score(
bulk_values=bulk_values, metasig=truesignatures[sig].ravel()
)
for sig in metasignatures.columns:
all_scores[sig] = scorer.score(
bulk_values=bulk_values, metasig=metasignatures[sig].ravel()
)
all_scores = pd.concat(all_scores, axis=1)
return all_scores
[docs]
def score_dataset(
bulk_file: pl.Path,
metasignature_file: pl.Path,
truesignature_file: pl.Path
) -> pd.DataFrame:
"""Main function, computes the bulk score for metasignatures and reference signatures
Args:
bulk_file: path to file with the bulk gex
metasignature_file: path to the file with the metasignature genes
truesignature_file: path to the file with the true signature genes
scorer_name: which scoring to use
std: only used if scorer name is average, if True the bulk gex will be standardized
before computing the average
sample_norm_method: only used if scorer name is ssgsea, what method to use
for sample norm in ssgsea (see ssgsea doc for more info)
Returns:
the dataframe of size (n_samples, n_metasignatures + n_true signatures + 1 + 1),
containing all scores on the metasignatures, the true signatuers, the purity information,
and TCGA the cancer type the scoring was performed on
"""
bulk_values, metasignatures, truesignatures = get_data(
bulk_file=bulk_file,
metasignature_file=metasignature_file,
truesignature_file=truesignature_file,
)
all_scores = get_all_scores(
bulk_values=bulk_values,
metasignatures=metasignatures,
truesignatures=truesignatures,
)
return all_scores
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def get_args():
parser = ArgumentParser()
parser.add_argument("--data-path", "-d", type=str)
parser.add_argument("--annotation-path", "-a", type=str)
parser.add_argument("--metasig-path", "-m", type=str)
return parser.parse_args()
[docs]
def main():
args = get_args()
scores = score_dataset(bulk_file=args.data_path,
truesignature_file=args.annotation_path,
metasignature_file=args.metasig_path)
if __name__ == '__main__':
main()
