def preproc_data(data):
"""
basic data preprocessing
"""
import uncurl
from uncurl.preprocessing import log1p, cell_normalize
from sklearn.decomposition import TruncatedSVD
gene_subset = uncurl.max_variance_genes(data)
data_subset = data[gene_subset, :]
tsvd = TruncatedSVD(8)
data_tsvd = tsvd.fit_transform(log1p(cell_normalize(data_subset)).T)
return data_tsvd
def one_vs_rest_t(data,
labels,
eps=1.0,
calc_pvals=True,
test='t',
normalize=False,
use_fdr=False):
"""
Computes 1-vs-rest t-test for all clusters and genes.
If calc_pvals is False, the pvals will be all 0.
test is either 't' or 'u'.
't' indicates that the 2-sample t-test will be used,
and 'u' is the Mann-Whitney U test.
Returns:
scores (dict): map of labels to tuples (array of gene ids, array of ratios) sorted by descending ratio
pvals (dict): map of labels to tuples (array of gene ids, array of pvals) sorted by ascending pval
"""
data_csc = sparse.csc_matrix(data)
if normalize:
from uncurl.preprocessing import cell_normalize
data_csc = cell_normalize(data_csc)
genes, cells = data.shape
if cells != len(labels):
raise Exception('length of data not equal to length of labels')
labels_array = np.zeros(len(labels), dtype=int)
# map from label names to indices
labels_map = {}
for i, l in enumerate(sorted(list(set(labels)))):
labels_map[l] = i
for i, l in enumerate(labels):
labels_array[i] = labels_map[l]
if test == 't':
test_func = csc_unweighted_1_vs_rest_t_test
elif test == 'u':
test_func = csc_unweighted_1_vs_rest_rank_sum_test
scores, pvals = test_func(data_csc.data, data_csc.indices, data_csc.indptr,
labels_array, cells, genes, eps, calc_pvals)
# map back to original label set?
new_scores = {}
new_pvals = {}
for i, l in labels_map.items():
new_scores[i] = scores[l]
new_pvals[i] = pvals[l]
if use_fdr:
from statsmodels.stats.multitest import fdrcorrection
for k, v in new_pvals.items():
pv = v[1]
v[1] = fdrcorrection(pv)[1]
return new_scores, new_pvals
def load_data(paths,
genes_path,
genes_subset_path=None,
normalize=True,
log=True):
"""
Returns:
data - cells x genes csr matrix
genes - array of gene names
all_labels - all cell labels
"""
all_matrices = []
all_labels = []
for data_path in paths:
for f in os.listdir(data_path):
if f.endswith('.mtx.gz'):
file_path = os.path.join(data_path, f)
label = f.split('.')[0]
print(label)
data = scipy.io.mmread(file_path)
data = sparse.csc_matrix(data)
all_matrices.append(data)
all_labels.extend([label] * data.shape[1])
print('num cells: ', data.shape[1])
all_matrices = sparse.hstack(all_matrices)
all_labels = np.array(all_labels)
n_genes, n_cells = all_matrices.shape
print('data matrix shape:', all_matrices.shape)
print('number of cells:', all_labels.shape)
from uncurl import preprocessing
if normalize:
all_matrices = preprocessing.cell_normalize(all_matrices,
multiply_means=False)
if log:
all_matrices = preprocessing.log1p(all_matrices)
all_matrices = sparse.csr_matrix(all_matrices.T)
print('matrices normalized')
genes = np.loadtxt(genes_path, dtype=str)
if genes_subset_path is not None:
all_matrices, genes = load_genes_subset(all_matrices, genes,
genes_subset_path)
return all_matrices, genes, all_labels
def pairwise_t(data,
w_or_labels,
eps=1.0,
calc_pvals=True,
normalize=False,
use_fdr=False):
"""
Computes pairwise t-test between all pairs of clusters and all genes.
If calc_pvals is False, the pvals will be all 0.
Returns:
ratios, pvals - two arrays of shape (k, k, genes), where k is the number of clusters.
"""
data_csc = sparse.csc_matrix(data)
if normalize:
from uncurl.preprocessing import cell_normalize
data_csc = cell_normalize(data_csc)
genes, cells = data.shape
if len(w_or_labels.shape) == 2:
scores, pvals = csc_weighted_t_test(data_csc.data, data_csc.indices,
data_csc.indptr, w_or_labels,
cells, genes, calc_pvals)
else:
labels_array = np.zeros(len(w_or_labels), dtype=int)
if cells != len(labels_array):
raise Exception('length of data not equal to length of labels')
labels_map = {}
for i, l in enumerate(sorted(list(set(w_or_labels)))):
labels_map[l] = i
for i, l in enumerate(w_or_labels):
labels_array[i] = labels_map[l]
scores, pvals = csc_unweighted_t_test(data_csc.data, data_csc.indices,
data_csc.indptr, labels_array,
cells, genes, eps, calc_pvals)
# TODO: re-map keys? or would that mess up the c-score calculation?
if use_fdr:
from statsmodels.stats.multitest import fdrcorrection
new_pvals = np.zeros(pvals.shape)
for k1 in range(pvals.shape[0]):
for k2 in range(pvals.shape[1]):
new_pvals[k1, k2, :] = fdrcorrection(pvals[k1, k2, :])[1]
pvals = new_pvals
return scores, pvals
def preproc_data(data, gene_subset=False, **kwargs):
"""
basic data preprocessing before running gap score
Assumes that data is a matrix of shape (genes, cells).
Returns a matrix of shape (cells, 8), using the first 8 SVD
components. Why 8? It's an arbitrary selection...
"""
import uncurl
from uncurl.preprocessing import log1p, cell_normalize
from sklearn.decomposition import TruncatedSVD
data_subset = data
if gene_subset:
gene_subset = uncurl.max_variance_genes(data)
data_subset = data[gene_subset, :]
tsvd = TruncatedSVD(min(8, data_subset.shape[0] - 1))
data_tsvd = tsvd.fit_transform(log1p(cell_normalize(data_subset)).T)
return data_tsvd
def identify_mnns(data1, data2, k=20, n_components=20, metric='cosine'):
"""
Identify mutual nearest neighbors in the two datasets.
Args:
data1, data2: sparse (csc) matrices of shape genes x cells. data1 is the target matrix and data2 is the reference matrix.
k (int): number of nearest neighbors
n_components (int): number of components for tsvd
metric (str): cosine or euclidean
Returns:
a list of lists, indicating MNNs every cell in data1
"""
# 1. compute tsvd on sparse matrices
data_combined = scipy.sparse.hstack([data1, data2])
cell_indices = np.array([1]*data1.shape[1] + [2]*data2.shape[1])
U, Sigma, VT = randomized_svd(log1p(cell_normalize(data_combined)).T,
n_components)
data_reduced = U*Sigma
# 2. build index for euclidean nearest neighbors using nmslib
# space can be cosinesiml or l2
metric_to_space = {'cosine': 'cosinesimil', 'euclidean': 'l2'}
index1 = nmslib.init(method='hnsw', space=metric_to_space[metric])
index1.addDataPointBatch(data_reduced[:, cell_indices==1])
index2 = nmslib.init(method='hnsw', space=metric_to_space[metric])
index2.addDataPointBatch(data_reduced[:, cell_indices==2])
neighbors1 = []
neighbors2 = []
for i in range(data1.shape[1]):
points = index2.knnQueryBatch(data_reduced[i,:], k=k)
points = set([n[0][0] for n in points])
neighbors1.append(points)
for i in range(data2.shape[1]):
index = data1.shape[1] + i
points = index1.knnQueryBatch(data_reduced[index,:], k=k)
points = set([n[0][0] for n in points])
neighbors2.append(points)
mnns = [[] for i in range(data1.shape[1])]
for i in range(data1.shape[1]):
for p in neighbors1[i]:
if i in neighbors2[p]:
mnns[i].append(p)
return mnns
def bnpy_select_clusters(data, max_cells=50000):
"""
Args:
data: matrix of shape genes x cells
Returns:
selected k based on converged Gaussian DPMM, and
the assigned labels.
"""
# TODO: randomly sub-select max_cells
selected_cell_ids = list(range(data.shape[1]))
if max_cells < data.shape[1]:
import random
selected_cell_ids = random.sample(selected_cell_ids, max_cells)
data = data[:, selected_cell_ids]
tsvd = TruncatedSVD(8)
data_tsvd = tsvd.fit_transform(log1p(cell_normalize(data)).T)
data_dense_bnpy = bnpy.data.XData(data_tsvd)
trained_model, info_dict = bnpy.run(
data_dense_bnpy,
'DPMixtureModel',
'Gauss',
'memoVB',
#doSaveToDisk=False,
doWriteStdOut=False,
output_path='./temp',
nLap=100,
nTask=1,
nBatch=1,
sF=0.1,
ECovMat='eye',
K=10,
initname='randexamples',
moves='birth,merge,shuffle',
m_startLap=5,
b_startLap=2,
b_Kfresh=4)
selected_k = info_dict['K_history'][-1]
results = trained_model.calc_local_params(data_dense_bnpy)
cluster_labels = results['resp'].argmax(1)
return selected_k, cluster_labels
if __name__ == '__main__':
import uncurl
from uncurl.state_estimation import objective
from uncurl.preprocessing import cell_normalize, log1p
import scipy.io
from sklearn.cluster import KMeans
from sklearn.metrics.cluster import normalized_mutual_info_score as nmi
mat = scipy.io.loadmat('data/10x_pooled_400.mat')
actual_labels = mat['labels'].squeeze()
X = mat['data'].toarray().astype(np.float32)
genes = uncurl.max_variance_genes(X, 5, 0.2)
X_subset = X[genes, :]
X_log_norm = log1p(cell_normalize(X_subset)).astype(np.float32)
uncurl_net = UncurlNet(X_log_norm,
8,
use_reparam=False,
use_decoder=False,
use_batch_norm=True,
hidden_layers=2,
hidden_units=200,
loss='mse')
m_init = torch.tensor(uncurl_net.M)
uncurl_net.pre_train_encoder(None, lr=1e-3, n_epochs=20, log_interval=10)
uncurl_net.train_model(None, lr=1e-3, n_epochs=50, log_interval=10)
w = uncurl_net.w_net.get_w(X_log_norm).transpose(1, 0)
m = uncurl_net.w_net.get_m()
mw = torch.matmul(m, w)
)
genes_seqwell = table_seqwell.index
genes_10x = table_10x.index
genes_set = set(genes_seqwell).intersection(genes_10x)
genes_list = list(genes_set)
data_seqwell = table_seqwell.loc[genes_list].values
data_10x = table_10x.loc[genes_list].values
batch_list = [0] * data_seqwell.shape[1]
batch_list += [1] * data_10x.shape[1]
data_total = np.hstack([data_seqwell, data_10x])
X_log_norm = log1p(cell_normalize(data_total)).astype(np.float32)
"""
# TODO: create networks
net1 = UncurlNet(X_log_norm, 10,
use_reparam=False, use_decoder=False,
use_batch_norm=True,
hidden_layers=2,
hidden_units=400,
loss='mse')
net1.train_1(X_log_norm, log_interval=10)
# TODO: test clustering?
w = net1.w_net.get_w(X_log_norm).transpose(1, 0)
print(w.argmax(0))
w = w.numpy().T
tsne = TSNE(2)
def testCellNormalize(self):
sparse_cell_norm = cell_normalize(self.data_sparse)
dense_cell_norm = cell_normalize(self.data_dense)
diff = dense_cell_norm - sparse_cell_norm.toarray()
diff = np.sqrt(np.sum(diff**2))
self.assertTrue(diff < 1e-6)
