def test_approx(datasets_dimred, genes, labels, idx, distr, xlabels):
integrated = assemble(datasets_dimred, approx=False)
X = np.concatenate(integrated)
distr.append(sil(X[idx, :], labels[idx]))
xlabels.append('Exact NN')
plt.figure()
plt.boxplot(distr, showmeans=True, whis='range')
plt.xticks(range(1, len(xlabels) + 1), xlabels)
plt.ylabel('Silhouette Coefficient')
plt.ylim((-1, 1))
plt.savefig('param_sensitivity_{}.svg'.format('approx'))
def test_alpha(datasets_dimred, genes, labels, idx, distr, xlabels):
alphas = [0, 0.05, 0.20, 0.30]
for alpha in alphas:
integrated = assemble(datasets_dimred, alpha=alpha)
X = np.concatenate(integrated)
distr.append(sil(X[idx, :], labels[idx]))
xlabels.append(str(alpha))
plt.figure()
plt.boxplot(distr, showmeans=True, whis='range')
plt.xticks(range(1, len(xlabels) + 1), xlabels)
plt.ylabel('Silhouette Coefficient')
plt.ylim((-1, 1))
plt.savefig('param_sensitivity_{}.svg'.format('alpha'))
def test_sigma(datasets_dimred, genes, labels, idx, distr, xlabels):
sigmas = [10, 50, 100, 200]
for sigma in sigmas:
integrated = assemble(datasets_dimred, sigma=sigma)
X = np.concatenate(integrated)
distr.append(sil(X[idx, :], labels[idx]))
xlabels.append(str(sigma))
plt.figure()
plt.boxplot(distr, showmeans=True, whis='range')
plt.xticks(range(1, len(xlabels) + 1), xlabels)
plt.ylabel('Silhouette Coefficient')
plt.ylim((-1, 1))
plt.savefig('param_sensitivity_{}.svg'.format('sigma'))
def test_learn_rate(datasets_dimred, genes, labels, idx, distr, xlabels):
X = np.concatenate(datasets_dimred)
learn_rates = [50., 100., 500., 1000.]
for learn_rate in learn_rates:
embedding = fit_tsne(X, learn_rate=learn_rate)
distr.append(sil(embedding[idx, :], labels[idx]))
xlabels.append(str(learn_rate))
plt.figure()
plt.boxplot(distr, showmeans=True, whis='range')
plt.xticks(range(1, len(xlabels) + 1), xlabels)
plt.ylabel('Silhouette Coefficient')
plt.ylim((-1, 1))
plt.savefig('param_sensitivity_{}.svg'.format('learn_rate'))
def test_perplexity(datasets_dimred, genes, labels, idx, distr, xlabels):
X = np.concatenate(datasets_dimred)
perplexities = [10, 100, 500, 2000]
for perplexity in perplexities:
embedding = fit_tsne(X, perplexity=perplexity)
distr.append(sil(embedding[idx, :], labels[idx]))
xlabels.append(str(perplexity))
plt.figure()
plt.boxplot(distr, showmeans=True, whis='range')
plt.xticks(range(1, len(xlabels) + 1), xlabels)
plt.ylabel('Silhouette Coefficient')
plt.ylim((-1, 1))
plt.savefig('param_sensitivity_{}.svg'.format('perplexity'))
def test_dimred(datasets, genes, labels, idx, distr, xlabels):
dimreds = [10, 20, 50, 200, 6000]
for dimred in dimreds:
datasets_dimred, genes = process_data(datasets, genes, dimred=dimred)
datasets_dimred = assemble(datasets_dimred)
X = np.concatenate(datasets_dimred)
distr.append(sil(X[idx, :], labels[idx]))
xlabels.append(str(dimred))
xlabels[-1] = 'No SVD'
plt.figure()
plt.boxplot(distr, showmeans=True, whis='range')
plt.xticks(range(1, len(xlabels) + 1), xlabels)
plt.ylabel('Silhouette Coefficient')
plt.ylim((-1, 1))
plt.savefig('param_sensitivity_{}.svg'.format('dimred'))
plt.xticks(range(1, len(xlabels) + 1), xlabels)
plt.ylabel('Silhouette Coefficient')
plt.ylim((-1, 1))
plt.savefig('param_sensitivity_{}.svg'.format('learn_rate'))
if __name__ == '__main__':
with open('conf/panorama.txt') as f:
data_names = f.read().split()
labels = np.array(open('data/cell_labels/all.txt').read().rstrip().split())
idx = np.random.choice(labels.shape[0], replace=False)
# scran MNN baseline.
X = np.loadtxt('data/corrected_mnn.txt')
sil_mnn = sil(X[idx, :], labels[idx])
print(np.median(sil_mnn))
# Seurat CCA baseline.
X = np.loadtxt('data/corrected_seurat.txt')
sil_cca = sil(X[idx, :], labels[idx])
print(np.median(sil_cca))
datasets, genes_list, n_cells = load_names(data_names)
datasets, genes = merge_datasets(datasets, genes_list)
datasets_dimred, genes = process_data(datasets, genes)
# Baseline without correction.
X = np.concatenate(datasets_dimred)
sil_non = sil(X[idx, :], labels[idx])
print(np.median(sil_non))