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data_utils.py
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data_utils.py
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import pandas as pd
import numpy as np
import scanpy as sc
from random import shuffle
sc.settings.verbosity = 0
from tqdm import tqdm
import requests
import os, sys
import warnings
warnings.filterwarnings("ignore")
from .utils import parse_single_pert, parse_combo_pert, parse_any_pert, print_sys
def rank_genes_groups_by_cov(
adata,
groupby,
control_group,
covariate,
pool_doses=False,
n_genes=50,
rankby_abs=True,
key_added='rank_genes_groups_cov',
return_dict=False,
):
gene_dict = {}
cov_categories = adata.obs[covariate].unique()
for cov_cat in cov_categories:
#name of the control group in the groupby obs column
control_group_cov = '_'.join([cov_cat, control_group])
#subset adata to cells belonging to a covariate category
adata_cov = adata[adata.obs[covariate]==cov_cat]
#compute DEGs
sc.tl.rank_genes_groups(
adata_cov,
groupby=groupby,
reference=control_group_cov,
rankby_abs=rankby_abs,
n_genes=n_genes,
use_raw=False
)
#add entries to dictionary of gene sets
de_genes = pd.DataFrame(adata_cov.uns['rank_genes_groups']['names'])
for group in de_genes:
gene_dict[group] = de_genes[group].tolist()
adata.uns[key_added] = gene_dict
if return_dict:
return gene_dict
def get_DE_genes(adata, skip_calc_de):
adata.obs.loc[:, 'dose_val'] = adata.obs.condition.apply(lambda x: '1+1' if len(x.split('+')) == 2 else '1')
adata.obs.loc[:, 'control'] = adata.obs.condition.apply(lambda x: 0 if len(x.split('+')) == 2 else 1)
adata.obs.loc[:, 'condition_name'] = adata.obs.apply(lambda x: '_'.join([x.cell_type, x.condition, x.dose_val]), axis = 1)
adata.obs = adata.obs.astype('category')
if not skip_calc_de:
rank_genes_groups_by_cov(adata,
groupby='condition_name',
covariate='cell_type',
control_group='ctrl_1',
n_genes=len(adata.var),
key_added = 'rank_genes_groups_cov_all')
return adata
def get_dropout_non_zero_genes(adata):
# calculate mean expression for each condition
unique_conditions = adata.obs.condition.unique()
conditions2index = {}
for i in unique_conditions:
conditions2index[i] = np.where(adata.obs.condition == i)[0]
condition2mean_expression = {}
for i, j in conditions2index.items():
condition2mean_expression[i] = np.mean(adata.X[j], axis = 0)
pert_list = np.array(list(condition2mean_expression.keys()))
mean_expression = np.array(list(condition2mean_expression.values())).reshape(len(adata.obs.condition.unique()), adata.X.toarray().shape[1])
ctrl = mean_expression[np.where(pert_list == 'ctrl')[0]]
## in silico modeling and upperbounding
pert2pert_full_id = dict(adata.obs[['condition', 'condition_name']].values)
pert_full_id2pert = dict(adata.obs[['condition_name', 'condition']].values)
gene_id2idx = dict(zip(adata.var.index.values, range(len(adata.var))))
gene_idx2id = dict(zip(range(len(adata.var)), adata.var.index.values))
non_zeros_gene_idx = {}
top_non_dropout_de_20 = {}
top_non_zero_de_20 = {}
non_dropout_gene_idx = {}
for pert in adata.uns['rank_genes_groups_cov_all'].keys():
p = pert_full_id2pert[pert]
X = np.mean(adata[adata.obs.condition == p].X, axis = 0)
non_zero = np.where(np.array(X)[0] != 0)[0]
zero = np.where(np.array(X)[0] == 0)[0]
true_zeros = np.intersect1d(zero, np.where(np.array(ctrl)[0] == 0)[0])
non_dropouts = np.concatenate((non_zero, true_zeros))
top = adata.uns['rank_genes_groups_cov_all'][pert]
gene_idx_top = [gene_id2idx[i] for i in top]
non_dropout_20 = [i for i in gene_idx_top if i in non_dropouts][:20]
non_dropout_20_gene_id = [gene_idx2id[i] for i in non_dropout_20]
non_zero_20 = [i for i in gene_idx_top if i in non_zero][:20]
non_zero_20_gene_id = [gene_idx2id[i] for i in non_zero_20]
non_zeros_gene_idx[pert] = np.sort(non_zero)
non_dropout_gene_idx[pert] = np.sort(non_dropouts)
top_non_dropout_de_20[pert] = np.array(non_dropout_20_gene_id)
top_non_zero_de_20[pert] = np.array(non_zero_20_gene_id)
non_zero = np.where(np.array(X)[0] != 0)[0]
zero = np.where(np.array(X)[0] == 0)[0]
true_zeros = np.intersect1d(zero, np.where(np.array(ctrl)[0] == 0)[0])
non_dropouts = np.concatenate((non_zero, true_zeros))
adata.uns['top_non_dropout_de_20'] = top_non_dropout_de_20
adata.uns['non_dropout_gene_idx'] = non_dropout_gene_idx
adata.uns['non_zeros_gene_idx'] = non_zeros_gene_idx
adata.uns['top_non_zero_de_20'] = top_non_zero_de_20
return adata
class DataSplitter():
"""
Class for handling data splitting. This class is able to generate new
data splits and assign them as a new attribute to the data file.
"""
def __init__(self, adata, split_type='single', seen=0):
self.adata = adata
self.split_type = split_type
self.seen = seen
def split_data(self, test_size=0.1, test_pert_genes=None,
test_perts=None, split_name='split', seed=None, val_size = 0.1,
train_gene_set_size = 0.75, combo_seen2_train_frac = 0.75, only_test_set_perts = False):
"""
Split dataset and adds split as a column to the dataframe
Note: split categories are train, val, test
"""
np.random.seed(seed=seed)
unique_perts = [p for p in self.adata.obs['condition'].unique() if
p != 'ctrl']
if self.split_type == 'simulation':
train, test, test_subgroup = self.get_simulation_split(unique_perts,
train_gene_set_size,
combo_seen2_train_frac,
seed, test_perts, only_test_set_perts)
train, val, val_subgroup = self.get_simulation_split(train,
0.9,
0.9,
seed)
## adding back ctrl to train...
train.append('ctrl')
elif self.split_type == 'simulation_single':
train, test, test_subgroup = self.get_simulation_split_single(unique_perts,
train_gene_set_size,
seed, test_perts, only_test_set_perts)
train, val, val_subgroup = self.get_simulation_split_single(train,
0.9,
seed)
elif self.split_type == 'no_test':
print('test_pert_genes',str(test_pert_genes))
print('test_perts',str(test_perts))
train, val = self.get_split_list(unique_perts,
test_pert_genes=test_pert_genes,
test_perts=test_perts,
test_size=test_size)
else:
train, test = self.get_split_list(unique_perts,
test_pert_genes=test_pert_genes,
test_perts=test_perts,
test_size=test_size)
train, val = self.get_split_list(train, test_size=val_size)
map_dict = {x: 'train' for x in train}
map_dict.update({x: 'val' for x in val})
if self.split_type != 'no_test':
map_dict.update({x: 'test' for x in test})
map_dict.update({'ctrl': 'train'})
self.adata.obs[split_name] = self.adata.obs['condition'].map(map_dict)
if self.split_type == 'simulation':
return self.adata, {'test_subgroup': test_subgroup,
'val_subgroup': val_subgroup
}
else:
return self.adata
def get_simulation_split_single(self, pert_list, train_gene_set_size = 0.85, seed = 1, test_set_perts = None, only_test_set_perts = False):
unique_pert_genes = self.get_genes_from_perts(pert_list)
pert_train = []
pert_test = []
np.random.seed(seed=seed)
if only_test_set_perts and (test_set_perts is not None):
ood_genes = np.array(test_set_perts)
train_gene_candidates = np.setdiff1d(unique_pert_genes, ood_genes)
else:
## a pre-specified list of genes
train_gene_candidates = np.random.choice(unique_pert_genes,
int(len(unique_pert_genes) * train_gene_set_size), replace = False)
if test_set_perts is not None:
num_overlap = len(np.intersect1d(train_gene_candidates, test_set_perts))
train_gene_candidates = train_gene_candidates[~np.isin(train_gene_candidates, test_set_perts)]
ood_genes_exclude_test_set = np.setdiff1d(unique_pert_genes, np.union1d(train_gene_candidates, test_set_perts))
train_set_addition = np.random.choice(ood_genes_exclude_test_set, num_overlap, replace = False)
train_gene_candidates = np.concatenate((train_gene_candidates, train_set_addition))
## ood genes
ood_genes = np.setdiff1d(unique_pert_genes, train_gene_candidates)
pert_single_train = self.get_perts_from_genes(train_gene_candidates, pert_list,'single')
unseen_single = self.get_perts_from_genes(ood_genes, pert_list, 'single')
assert len(unseen_single) + len(pert_single_train) == len(pert_list)
return pert_single_train, unseen_single, {'unseen_single': unseen_single}
def get_simulation_split(self, pert_list, train_gene_set_size = 0.85, combo_seen2_train_frac = 0.85, seed = 1, test_set_perts = None, only_test_set_perts = False):
unique_pert_genes = self.get_genes_from_perts(pert_list)
pert_train = []
pert_test = []
np.random.seed(seed=seed)
if only_test_set_perts and (test_set_perts is not None):
ood_genes = np.array(test_set_perts)
train_gene_candidates = np.setdiff1d(unique_pert_genes, ood_genes)
else:
## a pre-specified list of genes
train_gene_candidates = np.random.choice(unique_pert_genes,
int(len(unique_pert_genes) * train_gene_set_size), replace = False)
if test_set_perts is not None:
num_overlap = len(np.intersect1d(train_gene_candidates, test_set_perts))
train_gene_candidates = train_gene_candidates[~np.isin(train_gene_candidates, test_set_perts)]
ood_genes_exclude_test_set = np.setdiff1d(unique_pert_genes, np.union1d(train_gene_candidates, test_set_perts))
train_set_addition = np.random.choice(ood_genes_exclude_test_set, num_overlap, replace = False)
train_gene_candidates = np.concatenate((train_gene_candidates, train_set_addition))
## ood genes
ood_genes = np.setdiff1d(unique_pert_genes, train_gene_candidates)
pert_single_train = self.get_perts_from_genes(train_gene_candidates, pert_list,'single')
pert_combo = self.get_perts_from_genes(train_gene_candidates, pert_list,'combo')
pert_train.extend(pert_single_train)
## the combo set with one of them in OOD
combo_seen1 = [x for x in pert_combo if len([t for t in x.split('+') if
t in train_gene_candidates]) == 1]
pert_test.extend(combo_seen1)
pert_combo = np.setdiff1d(pert_combo, combo_seen1)
## randomly sample the combo seen 2 as a test set, the rest in training set
np.random.seed(seed=seed)
pert_combo_train = np.random.choice(pert_combo, int(len(pert_combo) * combo_seen2_train_frac), replace = False)
combo_seen2 = np.setdiff1d(pert_combo, pert_combo_train).tolist()
pert_test.extend(combo_seen2)
pert_train.extend(pert_combo_train)
## unseen single
unseen_single = self.get_perts_from_genes(ood_genes, pert_list, 'single')
combo_ood = self.get_perts_from_genes(ood_genes, pert_list, 'combo')
pert_test.extend(unseen_single)
## here only keeps the seen 0, since seen 1 is tackled above
combo_seen0 = [x for x in combo_ood if len([t for t in x.split('+') if
t in train_gene_candidates]) == 0]
pert_test.extend(combo_seen0)
assert len(combo_seen1) + len(combo_seen0) + len(unseen_single) + len(pert_train) + len(combo_seen2) == len(pert_list)
return pert_train, pert_test, {'combo_seen0': combo_seen0,
'combo_seen1': combo_seen1,
'combo_seen2': combo_seen2,
'unseen_single': unseen_single}
def get_split_list(self, pert_list, test_size=0.1,
test_pert_genes=None, test_perts=None,
hold_outs=True):
"""
Splits a given perturbation list into train and test with no shared
perturbations
"""
single_perts = [p for p in pert_list if 'ctrl' in p and p != 'ctrl']
combo_perts = [p for p in pert_list if 'ctrl' not in p]
unique_pert_genes = self.get_genes_from_perts(pert_list)
hold_out = []
if test_pert_genes is None:
test_pert_genes = np.random.choice(unique_pert_genes,
int(len(single_perts) * test_size))
# Only single unseen genes (in test set)
# Train contains both single and combos
if self.split_type == 'single' or self.split_type == 'single_only':
test_perts = self.get_perts_from_genes(test_pert_genes, pert_list,
'single')
if self.split_type == 'single_only':
# Discard all combos
hold_out = combo_perts
else:
# Discard only those combos which contain test genes
hold_out = self.get_perts_from_genes(test_pert_genes, pert_list,
'combo')
elif self.split_type == 'combo':
if self.seen == 0:
# NOTE: This can reduce the dataset size!
# To prevent this set 'holdouts' to False, this will cause
# the test set to have some perturbations with 1 gene seen
single_perts = self.get_perts_from_genes(test_pert_genes,
pert_list, 'single')
combo_perts = self.get_perts_from_genes(test_pert_genes,
pert_list, 'combo')
if hold_outs:
# This just checks that none of the combos have 2 seen genes
hold_out = [t for t in combo_perts if
len([t for t in t.split('+') if
t not in test_pert_genes]) > 0]
combo_perts = [c for c in combo_perts if c not in hold_out]
test_perts = single_perts + combo_perts
elif self.seen == 1:
# NOTE: This can reduce the dataset size!
# To prevent this set 'holdouts' to False, this will cause
# the test set to have some perturbations with 2 genes seen
single_perts = self.get_perts_from_genes(test_pert_genes,
pert_list, 'single')
combo_perts = self.get_perts_from_genes(test_pert_genes,
pert_list, 'combo')
if hold_outs:
# This just checks that none of the combos have 2 seen genes
hold_out = [t for t in combo_perts if
len([t for t in t.split('+') if
t not in test_pert_genes]) > 1]
combo_perts = [c for c in combo_perts if c not in hold_out]
test_perts = single_perts + combo_perts
elif self.seen == 2:
if test_perts is None:
test_perts = np.random.choice(combo_perts,
int(len(combo_perts) * test_size))
else:
test_perts = np.array(test_perts)
else:
if test_perts is None:
test_perts = np.random.choice(combo_perts,
int(len(combo_perts) * test_size))
train_perts = [p for p in pert_list if (p not in test_perts)
and (p not in hold_out)]
return train_perts, test_perts
def get_perts_from_genes(self, genes, pert_list, type_='both'):
"""
Returns all single/combo/both perturbations that include a gene
"""
single_perts = [p for p in pert_list if ('ctrl' in p) and (p != 'ctrl')]
combo_perts = [p for p in pert_list if 'ctrl' not in p]
perts = []
if type_ == 'single':
pert_candidate_list = single_perts
elif type_ == 'combo':
pert_candidate_list = combo_perts
elif type_ == 'both':
pert_candidate_list = pert_list
for p in pert_candidate_list:
for g in genes:
if g in parse_any_pert(p):
perts.append(p)
break
return perts
def get_genes_from_perts(self, perts):
"""
Returns list of genes involved in a given perturbation list
"""
if type(perts) is str:
perts = [perts]
gene_list = [p.split('+') for p in np.unique(perts)]
gene_list = [item for sublist in gene_list for item in sublist]
gene_list = [g for g in gene_list if g != 'ctrl']
return np.unique(gene_list)