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"""Calculate scores based on the expression of gene lists.
import numpy as np
import pandas as pd
from scipy.sparse import issparse
from .. import logging as logg
from ..logging import _settings_verbosity_greater_or_equal_than
def score_genes(
use_raw=False): # we use the scikit-learn convention of calling the seed "random_state"
"""Score a set of genes [Satija15]_.
The score is the average expression of a set of genes subtracted with the
average expression of a reference set of genes. The reference set is
randomly sampled from the `gene_pool` for each binned expression value.
This reproduces the approach in Seurat [Satija15]_ and has been implemented
for Scanpy by Davide Cittaro.
adata : :class:`~anndata.AnnData`
The annotated data matrix.
gene_list : iterable
The list of gene names used for score calculation.
ctrl_size : `int`, optional (default: 50)
Number of reference genes to be sampled. If `len(gene_list)` is not too
low, you can set `ctrl_size=len(gene_list)`.
gene_pool : `list` or `None`, optional (default: `None`)
Genes for sampling the reference set. Default is all genes.
n_bins : `int`, optional (default: 25)
Number of expression level bins for sampling.
score_name : `str`, optional (default: `'score'`)
Name of the field to be added in `.obs`.
random_state : `int`, optional (default: 0)
The random seed for sampling.
copy : `bool`, optional (default: `False`)
Copy `adata` or modify it inplace.
use_raw : `bool`, optional (default: `False`)
Use `raw` attribute of `adata` if present.
Depending on `copy`, returns or updates `adata` with an additional field
See this `notebook <>`__.
"""'computing score \'{}\''.format(score_name), r=True)
adata = adata.copy() if copy else adata
if random_state:
gene_list_in_var = []
var_names = adata.raw.var_names if use_raw else adata.var_names
for gene in gene_list:
if gene in var_names:
logg.warn('gene: {} is not in adata.var_names and will be ignored'.format(gene))
gene_list = set(gene_list_in_var[:])
if not gene_pool:
gene_pool = list(var_names)
gene_pool = [x for x in gene_pool if x in var_names]
# Trying here to match the Seurat approach in scoring cells.
# Basically we need to compare genes against random genes in a matched
# interval of expression.
_adata = adata.raw if use_raw else adata
# TODO: this densifies the whole data matrix for `gene_pool`
if issparse(_adata.X):
obs_avg = pd.Series(
_adata[:, gene_pool].X.toarray(), axis=0), index=gene_pool) # average expression of genes
obs_avg = pd.Series(
np.nanmean(_adata[:, gene_pool].X, axis=0), index=gene_pool) # average expression of genes
obs_avg = obs_avg[np.isfinite(obs_avg)] # Sometimes (and I don't know how) missing data may be there, with nansfor
n_items = int(np.round(len(obs_avg) / (n_bins - 1)))
obs_cut = obs_avg.rank(method='min') // n_items
control_genes = set()
# now pick `ctrl_size` genes from every cut
for cut in np.unique(obs_cut.loc[gene_list]):
r_genes = np.array(obs_cut[obs_cut == cut].index)
control_genes.update(set(r_genes[:ctrl_size])) # uses full r_genes if ctrl_size > len(r_genes)
# To index, we need a list - indexing implies an order.
control_genes = list(control_genes - gene_list)
gene_list = list(gene_list)
X_list = _adata[:, gene_list].X
if issparse(X_list): X_list = X_list.toarray()
X_control = _adata[:, control_genes].X
if issparse(X_control): X_control = X_control.toarray()
X_control = np.nanmean(X_control, axis=1)
if len(gene_list) == 0:
# We shouldn't even get here, but just in case
'could not add \n'
' \'{}\', score of gene set (adata.obs)'.format(score_name))
return adata if copy else None
elif len(gene_list) == 1:
score = _adata[:, gene_list].X - X_control
score = np.nanmean(X_list, axis=1) - X_control
adata.obs[score_name] = pd.Series(np.array(score).ravel(), index=adata.obs_names)' finished', time=True, end=' ' if _settings_verbosity_greater_or_equal_than(3) else '\n')
' \'{}\', score of gene set (adata.obs)'.format(score_name))
return adata if copy else None
def score_genes_cell_cycle(
"""Score cell cycle genes [Satija15]_.
Given two lists of genes associated to S phase and G2M phase, calculates
scores and assigns a cell cycle phase (G1, S or G2M). See
:func:`~scanpy.api.score_genes` for more explanation.
adata : :class:`~anndata.AnnData`
The annotated data matrix.
s_genes : `list`
List of genes associated with S phase.
g2m_genes : `list`
List of genes associated with G2M phase.
copy : `bool`, optional (default: `False`)
Copy `adata` or modify it inplace.
**kwargs : optional keyword arguments
Are passed to :func:`~scanpy.api.score_genes`. `ctrl_size` is not
possible, as it's set as `min(len(s_genes), len(g2m_genes))`.
Depending on `copy`, returns or updates `adata` with the following fields.
S_score : `adata.obs`, dtype `object`
The score for S phase for each cell.
G2M_score : `adata.obs`, dtype `object`
The score for G2M phase for each cell.
phase : `adata.obs`, dtype `object`
The cell cycle phase (`S`, `G2M` or `G1`) for each cell.
See also
See this `notebook <>`__.
"""'calculating cell cycle phase')
adata = adata.copy() if copy else adata
ctrl_size = min(len(s_genes), len(g2m_genes))
# add s-score
score_genes(adata, gene_list=s_genes, score_name='S_score', ctrl_size=ctrl_size, **kwargs)
# add g2m-score
score_genes(adata, gene_list=g2m_genes, score_name='G2M_score', ctrl_size=ctrl_size, **kwargs)
scores = adata.obs[['S_score', 'G2M_score']]
# default phase is S
phase = pd.Series('S', index=scores.index)
# if G2M is higher than S, it's G2M
phase[scores.G2M_score > scores.S_score] = 'G2M'
# if all scores are negative, it's G1...
phase[np.all(scores < 0, axis=1)] = 'G1'
adata.obs['phase'] = phase
logg.hint(' \'phase\', cell cycle phase (adata.obs)')
return adata if copy else None