def experiment_seurat_ari(data_names, namespace):
datasets, genes_list, n_cells = load_names(data_names, norm=False)
datasets, genes = merge_datasets(datasets, genes_list)
X = vstack(datasets)
X_dimred = reduce_dimensionality(normalize(X))
name = 'data/{}'.format(namespace)
Ns = [500, 1000, 2000, 5000, 10000]
if not os.path.isfile('{}/matrix.mtx'.format(name)):
save_mtx(name, csr_matrix(X), genes)
log('Seurat clustering full dataset...')
cluster_labels_full = seurat_cluster(name)
log('Seurat clustering done.')
for N in Ns:
gs_idx = gs(X_dimred, N)
save_mtx(name + '/gs{}'.format(N), csr_matrix(X[gs_idx, :]), genes)
log('Seurat clustering GS N = {}...'.format(N))
seurat_labels = seurat_cluster(name + '/gs{}'.format(N))
log('Seurat clustering GS N = {} done.'.format(N))
cluster_labels = label_approx(X_dimred, X_dimred[gs_idx],
seurat_labels)
log('N = {}, GS ARI = {}'.format(
N, adjusted_rand_score(cluster_labels_full, cluster_labels)))
uni_idx = uniform(X_dimred, N)
save_mtx(name + '/uni{}'.format(N), csr_matrix(X[uni_idx, :]), genes)
log('Seurat clustering uniform N = {}...'.format(N))
seurat_labels = seurat_cluster(name + '/uni{}'.format(N))
log('Seurat clustering uniform N = {} done.'.format(N))
cluster_labels = label_approx(X_dimred, X_dimred[uni_idx],
seurat_labels)
log('N = {}, Uniform ARI = {}'.format(
N, adjusted_rand_score(cluster_labels_full, cluster_labels)))
def load_data():
data_names = [
'data/norman2019_k562',
]
[ X ], [ genes ], n_cells = load_names(data_names, norm=False)
qc_idx, perturbs = load_meta(data_names[0])
X = X[qc_idx]
X = normalize(X, norm='l1') * 1e5
X = X.log1p()
adata = AnnData(X)
adata.var_names = genes
adata.var_names_make_unique()
adata.obs['perturb'] = perturbs
sc.pp.highly_variable_genes(adata, n_top_genes=5000)
return adata
def kl_divergence(cell_labels, samp_idx, expected):
cluster_labels = cell_labels[samp_idx]
clusters = sorted(set(cell_labels))
max_cluster = max(clusters)
cluster_hist = np.zeros(max_cluster + 1)
for c in range(max_cluster + 1):
if c in clusters:
cluster_hist[c] = np.sum(cluster_labels == c)
cluster_hist /= np.sum(cluster_hist)
return scipy.stats.entropy(cluster_hist, expected)
if __name__ == '__main__':
datasets, genes_list, n_cells = load_names(data_names, norm=False)
datasets, genes = merge_datasets(datasets, genes_list)
X = vstack(datasets)
k = DIMRED
U, s, Vt = pca(normalize(X), k=k)
X_dimred = U[:, :k] * s[:k]
Xs = []
labels = []
translate = X_dimred.max(0)
for i in range(3):
rand_idx = np.random.choice(X.shape[0],
size=int(X.shape[0] / (10**i)),
replace=False)
Xs.append(X_dimred[rand_idx, :] + (translate * 2 * i))
data_names = [
'data/mouse_brain/nuclei',
'data/mouse_brain/dropviz/Cerebellum_ALT',
'data/mouse_brain/dropviz/Cortex_noRep5_FRONTALonly',
'data/mouse_brain/dropviz/Cortex_noRep5_POSTERIORonly',
'data/mouse_brain/dropviz/EntoPeduncular',
'data/mouse_brain/dropviz/GlobusPallidus',
'data/mouse_brain/dropviz/Hippocampus',
'data/mouse_brain/dropviz/Striatum',
'data/mouse_brain/dropviz/SubstantiaNigra',
'data/mouse_brain/dropviz/Thalamus',
]
if __name__ == '__main__':
datasets, genes_list, n_cells = load_names(data_names)
datasets, genes = merge_datasets(datasets, genes_list, ds_names=data_names)
datasets_dimred, genes = process_data(datasets, genes, verbose=True)
t0 = time()
datasets_dimred = assemble(
datasets_dimred,
batch_size=BATCH_SIZE,
)
print('Integrated panoramas in {:.3f}s'.format(time() - t0))
t0 = time()
datasets_dimred, datasets, genes = correct(datasets,
genes_list,
from scipy.sparse import vstack
from sklearn.preprocessing import normalize, LabelEncoder
import sys
from process import load_names
NAMESPACE = 'polarized'
data_names = [
'data/macrophage/gmcsf_day6_1',
'data/macrophage/gmcsf_day6_2',
'data/macrophage/mcsf_day6_1',
'data/macrophage/mcsf_day6_2',
]
if __name__ == '__main__':
datasets, genes_list, n_cells = load_names(data_names, log1p=True)
datasets, genes = merge_datasets(datasets, genes_list)
datasets_dimred, genes = process_data(datasets, genes)
labels = []
names = []
curr_label = 0
for i, a in enumerate(datasets):
labels += list(np.zeros(a.shape[0]) + curr_label)
names.append(data_names[i])
curr_label += 1
labels = np.array(labels, dtype=int)
polarized_genes = ['PRDX1']
#embedding = visualize(datasets_dimred,
import numpy as np
from process import load_names, merge_datasets
from utils import *
NAMESPACE = 'human_cordblood_ica'
DIMRED = 100
DR_METHOD = 'svd'
data_names = [
'data/ica/ica_cord_blood_h5',
]
namespaces = [
'ica_cord_blood',
]
[X], [genes], _ = load_names(data_names)
umi_sum = np.sum(X, axis=1)
gt_idx = [i for i, s in enumerate(umi_sum) if s >= 500]
low_idx = [
idx for idx, gene in enumerate(genes)
if gene.startswith('RPS') or gene.startswith('RPL')
]
lt_idx = [
i for i, s in enumerate(np.sum(X[:, low_idx], axis=1) / umi_sum)
if s <= 0.5
]
qc_idx = sorted(set(gt_idx) & set(lt_idx))
X = csr_matrix(X[qc_idx])
from process import load_names
from scanorama import *
NAMESPACE = 'hsc'
data_names = [
'data/hsc/hsc_mars',
'data/hsc/hsc_ss2',
]
# Computes the probability that the corrected SS2 dataset
# comes from the original SS2 distribution or from the same
# distribution as the corrected MARS-Seq dataset.
if __name__ == '__main__':
# Load data.
datasets, genes_list, n_cells = load_names(data_names, verbose=False)
datasets, genes = merge_datasets(datasets, genes_list, verbose=False)
datasets, genes = process_data(datasets, genes)
datasets = [normalize(ds, axis=1) for ds in datasets]
# Fit initial mixture models.
gm_ss2 = (GaussianMixture(n_components=3, n_init=3).fit(datasets[1]))
# Do batch correction.
datasets = assemble(datasets,
verbose=False,
knn=KNN,
sigma=SIGMA,
approx=APPROX)
datasets = [normalize(ds, axis=1) for ds in datasets]
from pancreas_tests import *
from process import load_names
NAMESPACE = 'pancreas'
data_names = [
'data/pancreas/pancreas_inDrop',
'data/pancreas/pancreas_multi_celseq2_expression_matrix',
'data/pancreas/pancreas_multi_celseq_expression_matrix',
'data/pancreas/pancreas_multi_fluidigmc1_expression_matrix',
'data/pancreas/pancreas_multi_smartseq2_expression_matrix',
]
if __name__ == '__main__':
datasets, genes_list, n_cells = load_names(data_names)
labels = []
names = []
curr_label = 0
for i, a in enumerate(datasets):
labels += list(np.zeros(a.shape[0]) + curr_label)
names.append(data_names[i])
curr_label += 1
labels = np.array(labels, dtype=int)
datasets_dimred, datasets, genes = correct(datasets,
genes_list,
ds_names=data_names,
return_dimred=True)
