accs_names = ['Calinski-Harabaz', 'Silhouette (euc)', 'Silhouette (corr)', 'Silhouette (jacc)', 'ARI']
accs = np.zeros((5, len(mixtures), len(num_cluster)))
for i in range(len(num_cluster)):
for j in range(len(mixtures)):
print('Iteration k={0} mix={1}')
trg_k = num_cluster[i]
mix = mixtures[j]
# --------------------------------------------------
# 3.1. SETUP SOURCE DATA NMF CLUSTERING
# --------------------------------------------------
src_clustering = None
if src_data is not None:
src_clustering = NmfClustering(src_data, src_gene_ids, num_cluster=arguments.src_k)
src_clustering.add_cell_filter(src_cell_filter_fun)
src_clustering.add_gene_filter(src_gene_filter_fun)
src_clustering.set_data_transformation(src_data_transf_fun)
# --------------------------------------------------
# 3.2. SETUP TARGET DATA CLUSTERING
# --------------------------------------------------
if arguments.method is 'NMF' and src_data is not None:
print('Transfer learning method is NMF.')
trg_clustering = DaNmfClustering(src_clustering, trg_data, trg_gene_ids, num_cluster=trg_k)
trg_clustering.add_cell_filter(trg_cell_filter_fun)
trg_clustering.add_gene_filter(trg_gene_filter_fun)
trg_clustering.set_data_transformation(trg_data_transf_fun)
trg_clustering.apply(mix=mix, alpha=arguments.nmf_alpha, l1=arguments.nmf_l1,
max_iter=arguments.nmf_max_iter, rel_err=arguments.nmf_rel_err)
num_cluster = map(np.int, arguments.cluster_range.split(","))
accs_names = ['KTA (linear)', 'ARI']
accs = np.zeros((2, len(num_cluster)))
for i in range(len(num_cluster)):
k = num_cluster[i]
print('Iteration {0}, num-cluster={1}'.format(i, k))
# --------------------------------------------------
# 3.1. SETUP SOURCE DATA NMF CLUSTERING
# --------------------------------------------------
if labels is None:
# No source labels are provided, generate them via NMF clustering
nmf_labels = None
nmf_labels = NmfClustering(data, gene_ids, num_cluster=k, labels=[])
nmf_labels.add_cell_filter(cell_filter_fun)
nmf_labels.add_gene_filter(gene_filter_fun)
nmf_labels.set_data_transformation(data_transf_fun)
nmf_labels.apply(k=k, alpha=arguments.nmf_alpha, l1=arguments.nmf_l1, max_iter=arguments.nmf_max_iter, rel_err=arguments.nmf_rel_err)
labels = nmf_labels.cluster_labels
# Use perfect number of latent states for nmf and sc3
src_labels = np.array(labels, dtype=np.int)
src_lbl_set = np.unique(src_labels)
k_now = src_lbl_set.size
nmf = None
nmf = NmfClustering_initW(data, gene_ids, labels=labels, num_cluster=k_now)
nmf.add_cell_filter(cell_filter_fun)
nmf.add_gene_filter(gene_filter_fun)
nmf.set_data_transformation(data_transf_fun)
# Cell and gene filter and transformation within the procedure
cell_filter_fun = partial(sc.cell_filter,
num_expr_genes=min_expr_genes,
non_zero_threshold=non_zero_threshold)
gene_filter_fun = partial(sc.gene_filter,
perc_consensus_genes=perc_consensus_genes,
non_zero_threshold=non_zero_threshold)
data_transf_fun = sc.data_transformation_log2
# Generating labels from complete dataset
print "Train complete data"
complete_nmf = None
complete_nmf = NmfClustering(data,
np.arange(data.shape[0]),
num_cluster=num_cluster)
complete_nmf.add_cell_filter(cell_filter_fun)
complete_nmf.add_gene_filter(gene_filter_fun)
complete_nmf.set_data_transformation(data_transf_fun)
complete_nmf.apply(k=num_cluster,
alpha=nmf_alpha,
l1=nmf_l1,
max_iter=nmf_max_iter,
rel_err=nmf_rel_err)
# Get labels
desc, target_nmf, trg_lbls_pred, mixed_data = method_sc3_filter(
complete_nmf,
data, [],
cell_filter=cell_filter_fun,
gene_filter=gene_filter_fun,
transformation=data_transf_fun,
mix=0.0,
cell_inds = sc.cell_filter(data, num_expr_genes=min_expr_genes, non_zero_threshold=non_zero_threshold) data = data[:,cell_inds] # labels = labels[cell_inds] gene_inds = sc.gene_filter(data, perc_consensus_genes=perc_consensus_genes, non_zero_threshold=non_zero_threshold) data = data[gene_inds, :] data = sc.data_transformation_log2(data) cell_filter_fun = partial(sc.cell_filter, num_expr_genes=0, non_zero_threshold=-1) gene_filter_fun = partial(sc.gene_filter, perc_consensus_genes=1, non_zero_threshold=-1) data_transf_fun = sc.no_data_transformation print "data dimensions after preprocessing: genes x cells: ", data.shape # Generating labels from complete dataset print "Train complete data" complete_nmf = None complete_nmf = NmfClustering(data, np.arange(data.shape[0]), num_cluster=num_cluster) complete_nmf.add_cell_filter(cell_filter_fun) complete_nmf.add_gene_filter(gene_filter_fun) complete_nmf.set_data_transformation(data_transf_fun) complete_nmf.apply(k=num_cluster, alpha=nmf_alpha, l1=nmf_l1, max_iter=nmf_max_iter, rel_err=nmf_rel_err) # Get NMF labels labels_NMF = complete_nmf.cluster_labels label_names, label_counts = np.unique(labels_NMF, return_counts = True) print "Labels NMF: ", label_names print "Counts NMF: ", label_counts # Get SC3 labels desc, target_nmf, trg_lbls_pred, mixed_data = method_sc3_filter(complete_nmf, data, [], cell_filter=cell_filter_fun, gene_filter=gene_filter_fun, transformation=data_transf_fun, mix=0.0, metric='euclidean', use_da_dists=False, n_trg_cluster=num_cluster) labels_SC3 = trg_lbls_pred label_names, label_counts = np.unique(labels_SC3, return_counts = True) print "Labels SC3: ", label_names
accs_names = [
'KTA (linear)', 'Silhouette (euc)', 'Silhouette (pearson)',
'Silhouette (spearman)', 'ARI'
]
accs = np.zeros((5, len(num_cluster)))
for i in range(len(num_cluster)):
k = num_cluster[i]
print('Iteration {0}, num-cluster={0}'.format(i, k))
# --------------------------------------------------
# 3.1. SETUP SOURCE DATA NMF CLUSTERING
# --------------------------------------------------
nmf = None
nmf = NmfClustering(data, gene_ids, num_cluster=k)
nmf.add_cell_filter(cell_filter_fun)
nmf.add_gene_filter(gene_filter_fun)
nmf.set_data_transformation(data_transf_fun)
nmf.apply(k=k,
alpha=arguments.nmf_alpha,
l1=arguments.nmf_l1,
max_iter=arguments.nmf_max_iter,
rel_err=arguments.nmf_rel_err)
# --------------------------------------------------
# 3.2. EVALUATE CLUSTER ASSIGNMENT
# --------------------------------------------------
print('\nUnsupervised evaluation:')
accs[0, i] = unsupervised_acc_kta(nmf.pp_data,
nmf.cluster_labels,
kernel='linear')
data,
labels,
mode=1,
target_ncells=n_trg,
source_ncells=n_src[s])
src_labels = np.array(src_labels, dtype=np.int)
#src_labels_SC3 = np.array(src_labels_SC3, dtype=np.int)
# 3.c. train source once per repetition
print "Train source data of rep {0}".format(r + 1)
source_nmf = None
source_nmf = NmfClustering(src,
np.arange(src.shape[0]),
num_cluster=num_cluster)
source_nmf.add_cell_filter(cell_filter_fun)
source_nmf.add_gene_filter(gene_filter_fun)
source_nmf.set_data_transformation(data_transf_fun)
source_nmf.apply(k=num_cluster,
alpha=nmf_alpha,
l1=nmf_l1,
max_iter=nmf_max_iter,
rel_err=nmf_rel_err)
# Calculate ARIs and KTAs
source_aris[s, r] = metrics.adjusted_rand_score(
src_labels[source_nmf.remain_cell_inds], source_nmf.cluster_labels)
print 'SOURCE ARI Labels NMF, Method NMF = ', source_aris[s, r]
r += 1
