def main():
gn = Granatum()
adata = gn.ann_data_from_assay(gn.get_import('assay'))
random_seed = gn.get_arg('random_seed')
sc.tl.tsne(adata, random_state=random_seed)
X_tsne = adata.obsm['X_tsne']
plt.figure()
plt.scatter(X_tsne[:, 0], X_tsne[:, 1], 5000 / adata.shape[0])
plt.xlabel('t-SNE dim. 1')
plt.ylabel('t-SNE dim. 2')
plt.tight_layout()
gn.add_current_figure_to_results('t-SNE plot: each dot represents a cell',
dpi=75)
pca_export = {
'dimNames': ['t-SNE dim. 1', 't-SNE dim. 2'],
'coords': {
sample_id: X_tsne[i, :].tolist()
for i, sample_id in enumerate(adata.obs_names)
},
}
gn.export_statically(pca_export, 't-SNE coordinates')
gn.commit()
def main():
gn = Granatum()
adata = gn.ann_data_from_assay(gn.get_import("assay"))
num_top_comps = gn.get_arg("num_top_comps")
sc.pp.pca(adata, 20)
variance_ratios = adata.uns["pca"]["variance_ratio"]
pc_labels = ["PC{}".format(x + 1) for x in range(len(variance_ratios))]
plt.figure()
plt.bar(pc_labels, variance_ratios)
plt.tight_layout()
gn.add_current_figure_to_results(
"Explained variance (ratio) by each Principal Component (PC)",
height=350,
dpi=75)
X_pca = adata.obsm["X_pca"]
for i, j in combinations(range(num_top_comps), 2):
xlabel = "PC{}".format(i + 1)
ylabel = "PC{}".format(j + 1)
plt.figure()
plt.scatter(X_pca[:, i], X_pca[:, j], s=5000 / adata.shape[0])
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.tight_layout()
gn.add_current_figure_to_results("PC{} vs. PC{}".format(i + 1, j + 1),
dpi=75)
pca_export = {
"dimNames": [xlabel, ylabel],
"coords": {
sample_id: X_pca[k, [i, j]].tolist()
for k, sample_id in enumerate(adata.obs_names)
},
}
gn.export(pca_export,
"PC{} vs. PC{}".format(i + 1, j + 1),
kind="sampleCoords",
meta={})
gn.commit()
def main():
gn = Granatum()
adata = gn.ann_data_from_assay(gn.get_import("assay"))
num_cells_to_sample = gn.get_arg("num_cells_to_sample")
random_seed = gn.get_arg("random_seed")
np.random.seed(random_seed)
num_cells_before = adata.shape[0]
num_genes_before = adata.shape[1]
if num_cells_to_sample > 0 and num_cells_to_sample < 1:
num_cells_to_sample = round(num_cells_before * num_cells_to_sample)
else:
num_cells_to_sample = round(num_cells_to_sample)
if num_cells_to_sample > num_cells_before:
num_cells_to_sample = num_cells_before
if num_cells_to_sample < 1:
num_cells_to_sample = 1
sampled_cells_idxs = np.sort(np.random.choice(num_cells_before, num_cells_to_sample, replace=False))
adata = adata[sampled_cells_idxs, :]
gn.add_result(
"\n".join(
[
"The assay before down-sampling has **{}** cells and {} genes.".format(
num_cells_before, num_genes_before
),
"",
"The assay after down-sampling has **{}** cells and {} genes.".format(adata.shape[0], adata.shape[1]),
]
),
type="markdown",
)
gn.export(gn.assay_from_ann_data(adata), "Down-sampled Assay", dynamic=False)
gn.commit()
def main():
gn = Granatum()
adata = gn.ann_data_from_assay(gn.get_import("assay"))
min_cells_expressed = gn.get_arg("min_cells_expressed")
min_mean = gn.get_arg("min_mean")
max_mean = gn.get_arg("max_mean")
min_disp = gn.get_arg("min_disp")
max_disp = gn.get_arg("max_disp")
num_genes_before = adata.shape[1]
sc.pp.filter_genes(adata, min_cells=min_cells_expressed)
filter_result = sc.pp.filter_genes_dispersion(
adata.X, flavor='seurat', min_mean=math.log(min_mean), max_mean=math.log(max_mean), min_disp=min_disp, max_disp=max_disp,
)
adata = adata[:, filter_result.gene_subset]
sc.pl.filter_genes_dispersion(filter_result)
gn.add_current_figure_to_results(
"Each dot represent a gene. The gray dots are the removed genes. The x-axis is log-transformed.",
zoom=3,
dpi=50,
height=400,
)
gn.add_result(
"\n".join(
[
"Number of genes before filtering: **{}**".format(num_genes_before),
"",
"Number of genes after filtering: **{}**".format(adata.shape[1]),
]
),
type="markdown",
)
gn.export(gn.assay_from_ann_data(adata), "Filtered Assay", dynamic=False)
gn.commit()
def main():
gn = Granatum()
adata = gn.ann_data_from_assay(gn.get_import('assay'))
outliers = gn.get_arg('outliers')
num_cells_before = adata.shape[0]
kept_cell_ids = adata.obs_names.drop(outliers, errors='ignore').values
adata = adata[kept_cell_ids, :]
gn.export_statically(gn.assay_from_ann_data(adata), 'Outlier removed assay')
gn.add_result(
'You removed {} outliers from {} cells, the result assay has {} cells (and {} genes).'.format(
len(outliers), num_cells_before, adata.shape[0], adata.shape[1]
),
type='markdown'
)
gn.commit()
def main():
gn = Granatum()
adata = gn.ann_data_from_assay(gn.get_import('assay'))
sample_coords = gn.get_import('sampleCoords')
random_seed = gn.get_arg('random_seed')
sc.pp.neighbors(adata, n_neighbors=20, use_rep='X', method='gauss')
sc.tl.louvain(adata, random_state=random_seed)
cluster_assignment = dict(
zip(adata.obs_names,
['Cluster {}'.format(int(c) + 1) for c in adata.obs['louvain']]))
gn.export_statically(cluster_assignment, 'Cluster assignment')
dim_names = sample_coords.get('dimNames')
coords_dict = sample_coords.get('coords')
plt.figure()
clusters = adata.obs['louvain'].cat.categories
for c in clusters:
cell_ids = adata.obs_names[adata.obs['louvain'] == c]
coords = [coords_dict.get(x) for x in cell_ids]
coords_x = [x[0] for x in coords]
coords_y = [x[1] for x in coords]
plt.scatter(coords_x, coords_y, label='Cluster {}'.format(int(c) + 1))
plt.xlabel(dim_names[0])
plt.ylabel(dim_names[1])
plt.legend()
plt.tight_layout()
gn.add_current_figure_to_results(
'Scatter-plot using imported cell coordinates. Each dot represents a cell. The colors indicate the indentified cell clusters.',
dpi=75)
gn.commit()
