def main():
gn = Granatum()
set1 = gn.get_import('set1')
set2 = gn.get_import('set2')
set3 = gn.get_import('set3')
maxScore = gn.get_arg('maxScore')
minScore = gn.get_arg('minScore')
labelSet1 = gn.get_arg("labelSet1")
labelSet2 = gn.get_arg("labelSet2")
labelSet3 = gn.get_arg("labelSet3")
wordcloud = gn.get_arg("wordcloud")
filtered_set1 = dict(filter(lambda elem: (isinstance(elem[1], numbers.Number) & (not isnan(elem[1]))) & (elem[1] >= minScore) & (elem[1] <= maxScore), set1.items()))
filtered_set2 = dict(filter(lambda elem: (isinstance(elem[1], numbers.Number) & (not isnan(elem[1]))) & (elem[1] >= minScore) & (elem[1] <= maxScore), set2.items()))
filtered_set3 = dict(filter(lambda elem: (isinstance(elem[1], numbers.Number) & (not isnan(elem[1]))) & (elem[1] >= minScore) & (elem[1] <= maxScore), set3.items()))
merged_frequencies = {**filtered_set1, **filtered_set2, **filtered_set3}
packedsets = [set(filtered_set1.keys()), set(filtered_set2.keys()), set(filtered_set3.keys())]
fig, ax = plt.subplots(1,1)
fig.set_size_inches(5,4)
caption = (
'The area weighted Venn diagram is shown for the gene sets matching the criteria'
)
if wordcloud:
out = venn3_wordcloud(packedsets, set_labels=(labelSet1, labelSet2, labelSet3), wordcloud_kwargs=dict(max_font_size=36), word_to_frequency=merged_frequencies, ax=ax)
for text in out.set_labels:
if text:
text.set_fontsize(18)
for text in out.subset_labels:
if text:
text.set_fontsize(16)
text.set_path_effects([path_effects.SimpleLineShadow(), path_effects.Normal()])
else:
out = venn3(packedsets, set_labels=(labelSet1, labelSet2, labelSet3))
venn3_circles(packedsets, linestyle='dashed', linewidth=1, color="black")
for text in out.set_labels:
if text:
text.set_fontsize(18)
for text in out.subset_labels:
if text:
text.set_fontsize(16)
text.set_path_effects([path_effects.SimpleLineShadow(), path_effects.Normal()])
gn.add_current_figure_to_results(caption)
gn.commit()
def main():
tic = time.perf_counter()
gn = Granatum()
assay = gn.pandas_from_assay(gn.get_import('assay'))
groups = gn.get_import('groups')
reflabels = gn.get_import('reflabels')
remove_cells = gn.get_arg('remove_cells')
inv_map = {}
for k, v in groups.items():
inv_map[v] = inv_map.get(v, []) + [k]
inv_map_ref = {}
for k, v in reflabels.items():
inv_map_ref[v] = inv_map_ref.get(v, []) + [k]
group_relabel = {}
mislabelled_cells = []
for k, v in inv_map.items():
vset = set(v)
label_scores = {}
for kref, vref in inv_map_ref.items():
label_scores[kref] = len(set(vref).intersection(vset))
group_relabel[k] = max(label_scores, key=label_scores.get)
mislabelled_cells = mislabelled_cells + list(
vset.difference(set(inv_map_ref[group_relabel[k]])))
if remove_cells:
gn.add_result(
"Dropping {} mislabelled cells".format(len(mislabelled_cells)),
"markdown")
assay = assay.drop(mislabelled_cells, axis=1)
groups = {
key: val
for key, val in groups.items() if not key in mislabelled_cells
}
for cell in groups:
groups[cell] = group_relabel[groups[cell]]
toc = time.perf_counter()
time_passed = round(toc - tic, 2)
gn.export_statically(gn.assay_from_pandas(assay), "Corresponded assay")
gn.export_statically(groups, "Corresponded labels")
timing = "* Finished sample coloring step in {} seconds*".format(
time_passed)
gn.add_result(timing, "markdown")
gn.commit()
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():
tic = time.perf_counter()
gn = Granatum()
df = gn.pandas_from_assay(gn.get_import('assay'))
mingenes = gn.get_arg('min_genes_per_cell')
maxgenes = gn.get_arg('max_genes_per_cell')
mt_percent = gn.get_arg('mt_genes_percent')/100.0
uniquegenecount = df.astype(bool).sum(axis=0)
totalgenecount = df.sum(axis=0)
mtrows = df[df.index.str.startswith('MT')]
mtgenecount = mtrows.sum(axis=0)
mtpercent = mtgenecount.div(totalgenecount)
colsmatching = uniquegenecount.T[(uniquegenecount.T >= mingenes) & (uniquegenecount.T <= maxgenes) & (mtpercent.T <= mt_percent)].index.values
adata = df.loc[:, colsmatching]
num_orig_cells = uniquegenecount.T.index.size
num_filtered_cells = len(colsmatching)
num_lt_min = uniquegenecount.T[(uniquegenecount.T < mingenes)].index.size
num_gt_max = uniquegenecount.T[(uniquegenecount.T > maxgenes)].index.size
num_gt_mt = uniquegenecount.T[(mtpercent.T > mt_percent)].index.size
gn.add_result("Number of cells is now {} out of {} original cells with {} below min genes, {} above max genes, and {} above mt percentage threshold.".format(num_filtered_cells, num_orig_cells, num_lt_min, num_gt_max, num_gt_mt), "markdown")
plt.figure()
plt.subplot(2, 1, 1)
plt.title('Unique gene count distribution')
sns.distplot(uniquegenecount, bins=int(200), color = 'darkblue', kde_kws={'linewidth': 2})
plt.ylabel('Frequency')
plt.xlabel('Gene count')
plt.subplot(2, 1, 2)
plt.title('MT Percent Distribution')
sns.distplot(mtpercent*100.0, bins=int(200), color = 'darkblue', kde_kws={'linewidth': 2})
plt.ylabel('Frequency')
plt.xlabel('MT Percent')
plt.tight_layout()
caption = (
'The distribution of expression levels for each cell with various metrics.'
)
gn.add_current_figure_to_results(caption, zoom=1, dpi=75)
gn.export(gn.assay_from_pandas(adata), "Filtered Cells Assay", dynamic=False)
toc = time.perf_counter()
time_passed = round(toc - tic, 2)
timing = "* Finished cell filtering step in {} seconds*".format(time_passed)
gn.add_result(timing, "markdown")
gn.commit()
def main():
gn = Granatum()
sample_coords = gn.get_import("viz_data")
df = gn.pandas_from_assay(gn.get_import("assay"))
gene_ids = parse(gn.get_arg("gene_ids"))
groups = gn.get_import("groups")
alpha = 1.0 - gn.get_arg("confint") / 100.0
min_zscore = st.norm.ppf(gn.get_arg("confint"))
min_dist = 0.1
coords = sample_coords.get("coords")
dim_names = sample_coords.get("dimNames")
inv_map = {}
for k, v in groups.items():
inv_map[v] = inv_map.get(v, []) + [k]
for gene in gene_ids:
plt.figure()
# First form a statistic for all values, also puts out plot
params = plot_fits(df.loc[gene, :].dropna().to_list(),
color="r",
alpha=alpha,
min_dist=min_dist,
min_zscore=min_zscore,
label="All")
for k, v in inv_map.items():
plt.subplot(1, 1, 1)
plt.title('Gene expression level distribution for each cluster')
plot_predict(df.loc[gene, v].dropna().to_list(), params, label=k)
# sns.distplot(df.loc[gene,:].to_list(), bins=int(100), color = 'darkblue', kde_kws={'linewidth': 2})
plt.ylabel('Frequency')
plt.xlabel('Gene expression')
plt.legend()
plt.tight_layout()
caption = (
"The distribution of expression levels for gene {}.".format(gene))
gn.add_current_figure_to_results(caption, zoom=1, dpi=75)
gn.commit()
def main():
gn = Granatum()
assay = gn.get_import('assay')
matrix = np.array(assay.get('matrix'))
sample_ids = assay.get('sampleIds')
num_samples = matrix.shape[1]
# ---- PCA --------------------------------------------------------------------
X = np.transpose(matrix)
model = PCA(n_components=2)
Y_pca = model.fit_transform(X)
pca_export = {
'dimNames': ['PCA-1', 'PCA-2'],
'coords': {
sample_id: Y_pca[i, :].tolist()
for i, sample_id in enumerate(sample_ids)
},
}
gn.export_statically(pca_export, 'pca')
plt.figure()
plt.scatter(Y_pca[:, 0], Y_pca[:, 1], 5000 / num_samples)
plt.tight_layout()
gn.add_current_figure_to_results(
'Principal Component Analysis (PCA) scatter-plot', dpi=75)
# ---- T-SNE ------------------------------------------------------------------
X = np.transpose(matrix)
model = TSNE(n_jobs=multiprocessing.cpu_count())
Y_tsne = model.fit_transform(X)
tsne_export = {
'dimNames': ['tSNE-1', 'tSNE-2'],
'coords': {
sample_id: Y_tsne[i, :].tolist()
for i, sample_id in enumerate(sample_ids)
},
}
gn.export_statically(tsne_export, 'tsne')
plt.figure()
plt.scatter(Y_tsne[:, 0], Y_tsne[:, 1], s=5000 / num_samples)
plt.tight_layout()
gn.add_current_figure_to_results(
't-Distributed Stochastic Neighbor Embedding (t-SNE) scatter-plot',
dpi=75)
gn.commit()
def main():
gn = Granatum()
df = gn.pandas_from_assay(gn.get_import("assay"))
epsilon = gn.get_arg('epsilon')
min_cells_expressed = gn.get_arg('min_cells_expressed')
filter_df = pd.DataFrame({'gene': df.index})
filter_df['sum_expr'] = [sum(df.values[i, :]) for i in range(df.shape[0])]
filter_df['avg_expr'] = filter_df['sum_expr'] / df.shape[1]
filter_df['num_expressed_genes'] = [
sum([x > epsilon for x in df.values[i, :]]) for i in range(df.shape[0])
]
filter_df[
'removed'] = filter_df['num_expressed_genes'] < min_cells_expressed
new_df = df.loc[np.logical_not(filter_df['removed'].values), :]
gn.add_result(
"\n".join([
"Number of genes before filtering: **{}**".format(df.shape[0]),
"",
"Number of genes after filtering: **{}**".format(new_df.shape[0]),
]),
type="markdown",
)
if filter_df.shape[0] > 0:
filter_df_deleted = filter_df.loc[filter_df['removed'].values, :].drop(
'removed', axis=1)
gn.add_result(
{
'title': f"Removed genes ({filter_df_deleted.shape[0]})",
'orient': 'split',
'columns': filter_df_deleted.columns.values.tolist(),
'data': filter_df_deleted.values.tolist(),
},
data_type='table',
)
else:
gn.add_result(
f"No genes were removed. All {df.shape[0]} genes were kept. "
f"See attachment **gene_selection.csv** for detail.",
'markdown',
)
gn.export(filter_df.to_csv(index=False),
'gene_selection.csv',
kind='raw',
meta=None,
raw=True)
gn.export(gn.assay_from_pandas(new_df), "Filtered Assay", dynamic=False)
gn.commit()
def main():
gn = Granatum()
sample_meta_true = gn.get_import("sample_meta_true")
sample_meta_predicted = gn.get_import("sample_meta_predicted")
# Using pandas series to align the two metas in case they have different sample IDs
rand_score = adjusted_rand_score(pd.Series(sample_meta_true),
pd.Series(sample_meta_predicted))
mutual_info_score = adjusted_mutual_info_score(
pd.Series(sample_meta_true), pd.Series(sample_meta_predicted))
results_markdown = "\n".join([
"Adjusted Rand score: **{}**".format(rand_score),
"",
"Adjusted mutual information score: **{}**".format(mutual_info_score),
])
gn.add_result(results_markdown, "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()
def main():
gn = Granatum()
assay_df = gn.pandas_from_assay(gn.get_import('assay'))
grdict = gn.get_import('groupVec')
phe_dict = pd.Series(gn.get_import('groupVec'))
groups = set(parse(gn.get_arg('groups')))
inv_map = {}
for k, v in grdict.items():
if v in groups:
inv_map[v] = inv_map.get(v, []) + [k]
cells = []
for k, v in inv_map.items():
cells.extend(v)
assay_df = assay_df.loc[:, cells]
assay_df = assay_df.sparse.to_dense().fillna(0)
#assay_mat = r['as.matrix'](pandas2ri.py2ri(assay_df))
# assay_mat = r['as.matrix'](conversion.py2rpy(assay_df))
phe_vec = phe_dict[assay_df.columns]
r.source('./drive_DESeq2.R')
ret_r = r['run_DESeq'](assay_df, phe_vec)
ret_r_as_df = r['as.data.frame'](ret_r)
# ret_py_df = pandas2ri.ri2py(ret_r_as_df)
# TODO: maybe rename the columns to be more self-explanatory?
result_df = ret_r_as_df
result_df = result_df.sort_values('padj')
result_df.index.name = 'gene'
gn.add_pandas_df(result_df.reset_index(),
description='The result table as returned by DESeq2.')
gn.export(result_df.to_csv(), 'DESeq2_results.csv', raw=True)
significant_genes = result_df.loc[
result_df['padj'] < 0.05]['log2FoldChange'].to_dict()
gn.export(significant_genes, 'Significant genes', kind='geneMeta')
gn.commit()
def main():
gn = Granatum()
assay = gn.get_import('assay')
matrix = np.array(assay.get('matrix'))
transformed_matrix = matrix - matrix.mean(axis=1, keepdims=True)
assay['matrix'] = transformed_matrix.tolist()
plot_distribution_comparison(matrix, transformed_matrix, gn)
gn.export_statically(assay, 'Gene centered assay')
gn.commit()
def main():
gn = Granatum()
assay = gn.get_import('assay')
matrix = np.array(assay.get('matrix'))
take_log = gn.get_arg('take_log')
log_base = gn.get_arg('logBase')
epsilon = gn.get_arg('epsilon')
transformed_matrix = (matrix + epsilon) / (1 - matrix + epsilon)
if take_log:
transformed_matrix = np.log(transformed_matrix) / np.log(log_base)
non_zero_values_before = matrix.flatten()
non_zero_values_before = non_zero_values_before[(
non_zero_values_before > np.percentile(non_zero_values_before, 5))]
non_zero_values_after = transformed_matrix.flatten()
non_zero_values_after = non_zero_values_after[(
non_zero_values_after > np.percentile(non_zero_values_after, 5))]
plt.figure()
plt.subplot(2, 1, 1)
plt.title('Before beta-to-m transformation')
plt.hist(non_zero_values_before, bins=100)
plt.ylabel('Frequency')
plt.xlabel('Expression level')
plt.subplot(2, 1, 2)
plt.title('After beta-to-m transformation')
plt.hist(non_zero_values_after, bins=100)
plt.ylabel('Frequency')
plt.xlabel('Expression level')
plt.tight_layout()
caption = (
'The distribution of expression level before and after beta-to-m transformation. Only the values greater '
'than the 5 percentile (usually zero in single-cell data) and lower than 95 percentile are considered.'
)
gn.add_current_figure_to_results(caption, zoom=2, dpi=50)
assay['matrix'] = transformed_matrix.tolist()
gn.export_statically(assay, 'Beta-to-m transformed assay')
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()
assay = gn.get_import('assay')
args_for_init = {
'selected_embedding': gn.get_arg('selectedEmbedding'),
'selected_clustering': gn.get_arg('selectedClustering'),
'n_components': gn.get_arg('nComponents'),
'n_clusters': gn.get_arg('nClusters'),
'find_best_number_of_cluster': gn.get_arg('findBestNumberOfCluster'),
}
args_for_fit = {
'matrix': np.transpose(np.array(assay.get('matrix'))),
'sample_ids': assay.get('sampleIds'),
}
granatum_clustering = GranatumDeepClustering(**args_for_init)
fit_results = granatum_clustering.fit(**args_for_fit)
fit_exp = fit_results.get('clusters')
gn.export_statically(fit_exp, 'Cluster assignment')
newdictstr = ['"'+str(k)+'"'+", "+str(v) for k, v in fit_exp.items()]
gn.export("\n".join(newdictstr), 'Cluster assignment.csv', kind='raw', meta=None, raw=True)
md_str = f"""\
## Results
* Cluster array: `{fit_results.get('clusters_array')}`
* Cluster array: `{fit_results.get('clusters_array')}`
* nClusters: {fit_results.get('n_clusters')}
* Number of components: {fit_results.get('n_components')}
* Outliers: {fit_results.get('outliers')}"""
# gn.add_result(md_str, 'markdown')
gn.add_result(
{
'orient': 'split',
'columns': ['Sample ID', 'Cluster Assignment'],
'data': [{'Sample ID':x, 'Cluster Assignment':y} for x, y in zip(assay.get('sampleIds'), fit_results.get('clusters_array'))],
},
'table',
)
gn.commit()
def main():
gn = Granatum()
df = gn.pandas_from_assay(gn.get_import('assay'))
n_steps = gn.get_arg('n_steps')
min_theta = gn.get_arg('min_theta')
max_theta = gn.get_arg('max_theta')
jammit = JAMMIT.from_dfs([df])
jammit.scan(
thetas=np.linspace(min_theta, max_theta, n_steps),
calculate_fdr=True,
n_perms=10,
verbose=1,
convergence_threshold=0.000000001,
)
jammit_result = jammit.format(columns=['theta', 'alpha', 'n_sigs', 'fdr'])
jammit_result['theta'] = jammit_result['theta'].round(3)
jammit_result['alpha'] = jammit_result['alpha'].round(3)
plt.plot(jammit_result['alpha'], jammit_result['fdr'])
plt.xlabel('alpha')
plt.ylabel('FDR')
gn.add_current_figure_to_results('FDR plotted against alpha', height=400)
gn.add_result(
{
'pageSize':
n_steps,
'orient':
'split',
'columns': [{
'name': h,
'type': 'number',
'round': 3
} for h in jammit_result.columns],
'data':
jammit_result.values.tolist(),
},
data_type='table',
)
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():
tic = time.perf_counter()
gn = Granatum()
df = gn.pandas_from_assay(gn.get_import('assay'))
n_neighbors = gn.get_arg('n_neighbors')
min_dist = gn.get_arg('min_dist')
metric = gn.get_arg('metric')
random_seed = gn.get_arg('random_seed')
embedding = umap.UMAP(n_neighbors=n_neighbors,
min_dist=min_dist,
metric=metric,
random_state=random_seed).fit_transform(df.values.T)
plt.figure()
plt.scatter(embedding[:, 0], embedding[:, 1], min(5000 / df.shape[0],
36.0))
plt.xlabel('UMAP dim. 1')
plt.ylabel('UMAP dim. 2')
plt.tight_layout()
gn.add_current_figure_to_results('UMAP plot: each dot represents a cell',
dpi=75)
pca_export = {
'dimNames': ['UMAP dim. 1', 'UMAP dim. 2'],
'coords': {
sample_id: embedding[i, :].tolist()
for i, sample_id in enumerate(df.columns)
},
}
gn.export_statically(pca_export, 'UMAP coordinates')
toc = time.perf_counter()
time_passed = round(toc - tic, 2)
timing = "* Finished UMAP step in {} seconds*".format(time_passed)
gn.add_result(timing, "markdown")
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()
gene_scores = gn.get_import("gene_scores")
species = gn.get_arg("species")
gset_group_id = gn.get_arg("gset_group_id")
n_repeats = gn.get_arg("n_repeats")
alterChoice = gn.get_arg("alterChoice")
if alterChoice=="pos":
gene_scores = dict(filter(lambda elem: elem[1] >= 0.0, gene_scores.items()))
elif alterChoice=="neg":
gene_scores = dict(filter(lambda elem: elem[1] < 0.0, gene_scores.items()))
gene_scores = { k: abs(v) for k, v in gene_scores.items() }
gene_ids = gene_scores.keys()
gene_scores = gene_scores.values()
gene_id_type = guess_gene_id_type(list(gene_ids)[:5])
if gene_id_type != 'symbol':
gene_ids = convert_gene_ids(gene_ids, gene_id_type, 'symbol', species)
if species == "human":
pass
elif species == "mouse":
gene_ids = zgsea.to_human_homolog(gene_ids, "mouse")
else:
raise ValueError()
result_df = zgsea.gsea(gene_ids, gene_scores, gset_group_id, n_repeats=n_repeats)
if result_df is None:
gn.add_markdown('No gene set is enriched with your given genes.')
else:
result_df = result_df[["gset_name", "gset_size", "nes", "p_val", "fdr"]]
gn.add_pandas_df(result_df)
gn.export(result_df.to_csv(index=False), 'gsea_results.csv', kind='raw', meta=None, raw=True)
newdict = dict(zip(result_df.index.tolist(), result_df['nes'].tolist()))
print(newdict, flush=True)
gn.export(newdict, 'nes', 'geneMeta')
gn.commit()
def main():
gn = Granatum()
df = gn.pandas_from_assay(gn.get_import("assay"))
frob_norm = np.linalg.norm(df.values)
df = df / frob_norm
gn.add_result(
f"""\
The original assay had Frobenius norm of {frob_norm}, after normalization its
Frobenius norm is now {np.linalg.norm(df.values)}""",
'markdown',
)
gn.export(gn.assay_from_pandas(df),
"Frobenius normalized 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()
n_neighbors = gn.get_arg('nNeighbors', 15)
neighbor_method = gn.get_arg('neighborMethod', 'gauss')
assay = gn.get_import('assay')
adata = sc.AnnData(np.array(assay.get('matrix')).transpose())
adata.var_names = assay.get('geneIds')
adata.obs_names = assay.get('sampleIds')
sc.pp.neighbors(adata,
n_neighbors=n_neighbors,
use_rep='X',
method=neighbor_method)
sc.tl.dpt(adata, n_branchings=1)
gn._pickle(adata, 'adata')
# dpt_groups
for spec in [{
'col': 'dpt_order',
'caption': 'Cell order'
}, {
'col': 'dpt_groups',
'caption': 'Cell groups'
}]:
fig = plt.figure()
sc.pl.diffmap(adata, color=spec['col'])
gn.add_current_figure_to_results(spec['caption'])
gn.export_statically(
dict(
zip(adata.obs_names.tolist(),
adata.obs[spec['col']].values.tolist())), spec['col'])
gn.commit()
def main():
gn = Granatum()
assay = gn.get_import('assay')
x = np.array(assay.get('matrix')).astype(np.float)
log_base = gn.get_arg('log_base')
n_top = gn.get_arg('n_top')
n_bottom = gn.get_arg('n_bottom')
which_mid = gn.get_arg('which_mid')
gene_df = pd.DataFrame(
{
'row_num': range(x.shape[0]),
'gene_id': assay.get('geneIds'),
'exp_mean': np.mean(x, axis=1),
'exp_std': np.std(x, axis=1),
}
)
gene_df = gene_df.sort_values('exp_mean', ascending=False)
top_gene_row = gene_df.head(n_top).sort_values('exp_std', ascending=False).iloc[0]
bottom_gene_row = gene_df.tail(n_bottom).sort_values('exp_std').iloc[0]
hk_gene = np.clip(x[top_gene_row['row_num'], :], a_min=0.00001, a_max=None)
neg_gene = x[bottom_gene_row['row_num'], :]
if which_mid == 'mean':
alphabk = np.mean(neg_gene[:])
elif which_mid == 'median':
alphabk = np.median(neg_gene[:])
else:
raise ValueError()
loghkdatabk = np.log(hk_gene - alphabk) / np.log(log_base)
# Drop NAN values
loghkdatabk = loghkdatabk[~np.isnan(loghkdatabk)]
c = (np.std(neg_gene[:], ddof=1) / np.std(loghkdatabk, ddof=1))**2
xbk = x - alphabk
transformed_matrix = np.log((xbk + np.sqrt(xbk**2 + c)) / 2) / np.log(log_base)
gn.add_result(
'\n'.join(
[
f"Selected benchmarking genes:",
f" * housekeeping gene: **{top_gene_row['gene_id']}** "
f"(mean: {top_gene_row['exp_mean']}, std: {top_gene_row['exp_std']}) ",
f" * negative control gene: **{bottom_gene_row['gene_id']}**"
f"(mean: {bottom_gene_row['exp_mean']}, std: {bottom_gene_row['exp_std']})",
f"",
f"Final formula is `y = log{log_base}((z + sqrt(z^2 + c))/2)`, where `z = x - {alphabk}` and `c = {c}`."
]
), 'markdown'
)
non_zero_values_before = x.flatten()
non_zero_values_before = non_zero_values_before[(non_zero_values_before > np.percentile(non_zero_values_before, 5))]
non_zero_values_after = transformed_matrix.flatten()
non_zero_values_after = non_zero_values_after[(non_zero_values_after > np.percentile(non_zero_values_after, 5))]
plt.figure()
plt.subplot(2, 1, 1)
plt.title('Before glog transformation')
plt.hist(non_zero_values_before, bins=100)
plt.ylabel('Frequency')
plt.xlabel('Expression level')
plt.subplot(2, 1, 2)
plt.title('After glog transformation')
plt.hist(non_zero_values_after, bins=100)
plt.ylabel('Frequency')
plt.xlabel('Expression level')
plt.tight_layout()
caption = (
'The distribution of expression level before and after glog transformation. Only the values greater '
'than the 5 percentile (usually zero in single-cell data) and lower than 95 percentile are considered.'
)
gn.add_current_figure_to_results(caption, zoom=2, dpi=50)
assay['matrix'] = transformed_matrix.tolist()
gn.export_statically(assay, 'GLog transformed assay')
gn.commit()
def main():
gn = Granatum()
tb1 = gn.pandas_from_assay(gn.get_import('assay1'))
tb2 = gn.pandas_from_assay(gn.get_import('assay2'))
label1 = gn.get_arg('label1')
label2 = gn.get_arg('label2')
direction = gn.get_arg('direction')
normalization = gn.get_arg('normalization')
if direction == 'samples':
tb1 = tb1.T
tb2 = tb2.T
overlapped_index = set(tb1.index) & set(tb2.index)
tb1.index = [
f"{label1}_{x}" if x in overlapped_index else x for x in tb1.index
]
tb2.index = [
f"{label2}_{x}" if x in overlapped_index else x for x in tb2.index
]
if normalization == 'none':
tb = pd.concat([tb1, tb2], axis=0)
elif normalization == 'frobenius':
ntb1 = np.linalg.norm(tb1)
ntb2 = np.linalg.norm(tb2)
ntb = np.mean([ntb1, ntb2])
fct1 = ntb / ntb1
fct2 = ntb / ntb2
tb = pd.concat([tb1 * fct1, tb2 * fct2], axis=0)
gn.add_markdown(f"""\
Normalization info:
- Assay **{label1}** is multiplied by {fct1}
- Assay **{label2}** is multiplied by {fct2}
""")
elif normalization == 'mean':
ntb1 = np.mean(tb1)
ntb2 = np.mean(tb2)
ntb = np.mean([ntb1, ntb2])
fct1 = ntb / ntb1
fct2 = ntb / ntb2
tb = pd.concat([tb1 * fct1, tb2 * fct2], axis=0)
gn.add_markdown(f"""\
Normalization info:",
- Assay **{label1}** is multiplied by {fct1}
- Assay **{label2}** is multiplied by {fct2}
""")
else:
raise ValueError()
if direction == 'samples':
tb = tb.T
gn.add_markdown(f"""\
You combined the following assays:
- Assay 1 (with {tb1.shape[0]} genes and {tb1.shape[1]} cells)
- Assay 2 (with {tb2.shape[0]} genes and {tb2.shape[1]} cells)
into:
- Combined Assay (with {tb.shape[0]} genes and {tb.shape[1]} cells)
""")
gn.export_statically(gn.assay_from_pandas(tb), 'Combined assay')
if direction == 'samples':
meta_type = 'sampleMeta'
elif direction == 'genes':
meta_type = 'geneMeta'
else:
raise ValueError()
gn.export(
{
**{x: label1
for x in tb1.index},
**{x: label2
for x in tb2.index}
}, 'Assay label', meta_type)
gn.commit()
def main():
tic = time.perf_counter()
gn = Granatum()
assay = gn.pandas_from_assay(gn.get_import('assay'))
# Groups is {"cell":"cluster}
groups = gn.get_import('groups')
certainty = gn.get_arg('certainty')
alpha = 1 - certainty / 100.0
min_zscore = st.norm.ppf(gn.get_arg("certainty") / 100.0)
min_dist = 0.1
# Likely we want to filter genes before we get started, namely if we cannot create a good statistic
norms_df = assay.apply(np.linalg.norm, axis=1)
assay = assay.loc[norms_df.T >= min_dist, :]
inv_map = {}
inv_map_rest = {}
for k, v in groups.items():
inv_map[v] = inv_map.get(v, []) + [k]
clist = inv_map_rest.get(v, list(assay.columns))
clist.remove(k)
inv_map_rest[v] = clist
# Inv map is {"cluster": ["cell"]}
print("Completed setup", flush=True)
cols = list(inv_map.keys())
colnames = []
for coli in cols:
for colj in cols:
if coli != colj:
colnames.append("{} vs {}".format(coli, colj))
for coli in cols:
colnames.append("{} vs rest".format(coli))
# Instead of scoring into a dataframe, let's analyze each statistically
# Dict (gene) of dict (cluster) of dict (statistics)
# { "gene_name" : { "cluster_name" : { statistics data } }}
# Export would be percentage more/less expressed in "on" state
# For example gene "XIST" expresses at least 20% more in cluster 1 vs cluster 4 with 95% certainty
total_genes = len(assay.index)
print("Executing parallel for {} genes".format(total_genes), flush=True)
results = Parallel(
n_jobs=math.floor(multiprocessing.cpu_count() * 2 * 9 / 10))(
delayed(compref)(gene, assay.loc[gene, :], colnames, inv_map,
inv_map_rest, alpha, min_dist, min_zscore)
for gene in tqdm(list(assay.index)))
result = pd.concat(results, axis=0)
gn.export_statically(gn.assay_from_pandas(result.T),
'Differential expression sets')
gn.export(result.to_csv(),
'differential_gene_sets.csv',
kind='raw',
meta=None,
raw=True)
toc = time.perf_counter()
time_passed = round(toc - tic, 2)
timing = "* Finished differential expression sets step in {} seconds*".format(
time_passed)
gn.add_result(timing, "markdown")
gn.commit()
def main():
gn = Granatum()
sample_coords = gn.get_import("viz_data")
df = gn.pandas_from_assay(gn.get_import("assay"))
gene_ids = gn.get_arg("gene_ids")
overlay_genes = gn.get_arg("overlay_genes")
max_colors = gn.get_arg("max_colors")
min_level = gn.get_arg("min_level")
max_level = gn.get_arg("max_level")
convert_to_zscore = gn.get_arg("convert_to_zscore")
min_marker_area = gn.get_arg("min_marker_area")
max_marker_area = gn.get_arg("max_marker_area")
min_alpha = gn.get_arg("min_alpha")
max_alpha = gn.get_arg("max_alpha")
grey_level = gn.get_arg("grey_level")
coords = sample_coords.get("coords")
dim_names = sample_coords.get("dimNames")
cmaps = []
if overlay_genes:
if max_colors == "":
numcolors = len(gene_ids.split(','))
cycol = cycle('bgrcmk')
for i in range(numcolors):
cmaps = cmaps + [
LinearSegmentedColormap("fire",
produce_cdict(next(cycol),
grey=grey_level,
min_alpha=min_alpha,
max_alpha=max_alpha),
N=256)
]
else:
for col in max_colors.split(','):
col = col.strip()
cmaps = cmaps + [
LinearSegmentedColormap("fire",
produce_cdict(col,
grey=grey_level,
min_alpha=min_alpha,
max_alpha=max_alpha),
N=256)
]
else:
if max_colors == "":
cmaps = cmaps + [LinearSegmentedColormap("fire", cdict, N=256)]
else:
for col in max_colors.split(','):
col = col.strip()
cmaps = cmaps + [
LinearSegmentedColormap("fire",
produce_cdict(col,
grey=grey_level,
min_alpha=min_alpha,
max_alpha=max_alpha),
N=256)
]
colorbar_height = 10
plot_height = 650
num_cbars = 1
if overlay_genes:
num_cbars = len(gene_ids.split(','))
cbar_height_ratio = plot_height / (num_cbars * colorbar_height)
fig, ax = plt.subplots(
1 + num_cbars,
1,
gridspec_kw={'height_ratios': [cbar_height_ratio] + [1] * num_cbars})
gene_index = -1
for gene_id in gene_ids.split(','):
gene_id = gene_id.strip()
gene_index = gene_index + 1
if gene_id in df.index:
if not overlay_genes:
plt.clf()
fig, ax = plt.subplots(
1 + num_cbars,
1,
gridspec_kw={
'height_ratios': [cbar_height_ratio] + [1] * num_cbars
})
transposed_df = df.T
mean = transposed_df[gene_id].mean()
stdev = transposed_df[gene_id].std(ddof=0)
if convert_to_zscore:
scatter_df = pd.DataFrame(
{
"x": [a[0] for a in coords.values()],
"y": [a[1] for a in coords.values()],
"value": (df.loc[gene_id, :] - mean) / stdev
},
index=coords.keys())
else:
scatter_df = pd.DataFrame(
{
"x": [a[0] for a in coords.values()],
"y": [a[1] for a in coords.values()],
"value": df.loc[gene_id, :]
},
index=coords.keys())
values_df = np.clip(scatter_df["value"],
min_level,
max_level,
out=None)
min_value = np.nanmin(values_df)
max_value = np.nanmax(values_df)
scaled_marker_size = (max_marker_area - min_marker_area) * (
values_df - min_value) / (max_value -
min_value) + min_marker_area
scaled_marker_size = scaled_marker_size * scaled_marker_size
# s = 5000 / scatter_df.shape[0]
scatter = ax[0].scatter(
x=scatter_df["x"],
y=scatter_df["y"],
s=scaled_marker_size,
c=values_df,
cmap=cmaps[gene_index % len(cmaps)]) #Amp_3.mpl_colormap)
cbar = fig.colorbar(scatter,
cax=ax[1 + (gene_index % num_cbars)],
orientation='horizontal',
aspect=40)
cbar.set_label(gene_id, rotation=0)
ax[0].set_xlabel(dim_names[0])
ax[0].set_ylabel(dim_names[1])
if not overlay_genes:
gn.add_current_figure_to_results(
"Scatter-plot of {} expression".format(gene_id), dpi=75)
else:
# if the gene ID entered is not present in the assay
# Communicate it to the user and output a table of available gene ID's
description = 'The selected gene is not present in the assay. See the step that generated the assay'
genes_in_assay = pd.DataFrame(
df.index.tolist(),
columns=['Gene unavailable in assay: choose from below'])
gn.add_pandas_df(genes_in_assay, description)
if overlay_genes:
gn.add_current_figure_to_results(
"Scatter-plot of {} expression".format(gene_ids),
height=650 + 100 * len(gene_ids.split(',')),
dpi=75)
gn.commit()
def main():
gn = Granatum()
df = gn.pandas_from_assay(gn.get_import('assay'))
alpha = gn.get_arg('alpha')
jammit = JAMMIT.from_dfs([df])
res = jammit.run_for_one_alpha(
alpha,
verbose=1,
convergence_threshold=0.000000001,
)
u = res['u']
v = res['v']
gn.export(dict(zip(df.index, u)), 'Genes loadings', kind='geneMeta')
gn.export(dict(zip(df.columns, v)), 'Sample scores', kind='sampleMeta')
gene_df = pd.DataFrame({
'id_': df.index,
'abs_loading': abs(u),
'loading': u
})
gene_df = gene_df[['id_', 'abs_loading', 'loading']]
gene_df = gene_df.loc[gene_df['loading'].abs() > EPSILON]
gene_df = gene_df.sort_values('abs_loading', ascending=False)
gn.add_result(
{
'title': f"Signal genes ({len(gene_df)})",
'orient': 'split',
'columns': gene_df.columns.values.tolist(),
'data': gene_df.values.tolist(),
},
data_type='table',
)
gn.export(gene_df.to_csv(index=False),
'signal_genes.csv',
kind='raw',
meta=None,
raw=True)
sample_df = pd.DataFrame({
'id_': df.columns,
'abs_score': abs(v),
'score': v
})
sample_df = sample_df[['id_', 'abs_score', 'score']]
sample_df = sample_df.loc[sample_df['score'].abs() > EPSILON]
sample_df = sample_df.sort_values('abs_score', ascending=False)
gn.add_result(
{
'title': f"Signal samples ({len(sample_df)})",
'orient': 'split',
'columns': sample_df.columns.values.tolist(),
'data': sample_df.values.tolist(),
},
data_type='table',
)
gn.export(sample_df.to_csv(index=False),
'signal_samples.csv',
kind='raw',
meta=None,
raw=True)
subset_df = df.loc[gene_df['id_'], sample_df['id_']]
gn.export(gn.assay_from_pandas(subset_df),
'Assay with only signal genes and samples',
kind='assay')
sns.clustermap(subset_df, cmap='RdBu')
gn.add_current_figure_to_results(
description='Cluster map of the signal genes and signal samples',
zoom=2,
width=750,
height=850,
dpi=50,
)
plt.close()
plt.figure()
plt.scatter(range(len(u)), u, s=2, c='red')
plt.xlabel('index')
plt.ylabel('value in u')
gn.add_current_figure_to_results(
description=
'The *u* vector (loadings for genes) plotted as a scatter plot.',
zoom=2,
width=750,
height=450,
dpi=50,
)
plt.close()
plt.figure()
plt.plot(range(len(v)), v)
plt.scatter(range(len(v)), v, s=6, c='red')
plt.xlabel('index')
plt.ylabel('value in v')
gn.add_current_figure_to_results(
description=
'The *v* vector (scores for samples) plotted as a line plot.',
zoom=2,
width=750,
height=450,
dpi=50,
)
plt.close()
# gn.export_current_figure(
# 'cluster_map.pdf',
# zoom=2,
# width=750,
# height=850,
# dpi=50,
# )
gn.commit()
def main():
tic = time.perf_counter()
gn = Granatum()
sample_coords = gn.get_import("viz_data")
value = gn.get_import("value")
coloring_type = gn.get_arg("coloring_type")
bounding_stdev = gn.get_arg("bounding_stdev")
label_location = gn.get_arg("label_location")
label_transform = gn.get_arg("label_transform")
labelXaxis = gn.get_arg("labelXaxis")
labelYaxis = gn.get_arg("labelYaxis")
sigfigs = gn.get_arg("sigfigs")
numticks = gn.get_arg("numticks")
font = gn.get_arg('font')
coords = sample_coords.get("coords")
dim_names = sample_coords.get("dimNames")
seed = gn.get_arg('random_seed')
random.seed(seed)
np.random.seed(seed)
df = pd.DataFrame(
{
"x": [a[0] for a in coords.values()],
"y": [a[1] for a in coords.values()],
"value": pd.Series(value)
},
index=coords.keys())
target_dpi = 300
target_width = 7.5 # inches
target_height = 6.5 # inches
font_size_in_in = font / 72.0 # inches
font_size_in_px = font_size_in_in * target_dpi
try:
if coloring_type == "categorical":
uniq = df["value"].unique()
uniq.sort(kind="stable")
num = uniq.shape[0]
COLORS2 = plt.get_cmap('gist_rainbow')
carr = [0] * df.shape[0]
listcats = list(df["value"])
miny = min(list(df["y"]))
maxy = max(list(df["y"]))
scaley = (maxy - miny) / (target_height * target_dpi)
print("Scaley = {}".format(scaley))
colorhash = {}
colorstep = np.ceil(256.0 / num)
coffset = randrange(colorstep)
grouptocolor = np.random.choice(np.arange(num), num, replace=False)
for i, cat in enumerate(uniq):
dff = df[df["value"] == cat]
xs = list(dff["x"])
ys = list(dff["y"])
#avgx = sum(dff["x"]) / len(dff["x"])
#avgy = sum(dff["y"]) / len(dff["y"])
#plt.scatter(x=dff["x"], y=dff["y"], s=5000 / df.shape[0], c=COLORS[i].hex_l, label=cat)
#plt.scatter(x=dff["x"], y=dff["y"], s=5000 / df.shape[0], c=[abs(hash(cat)) % 256]*len(dff["x"]), cmap=COLORS2, label=cat)
#plt.scatter(x=dff["x"], y=dff["y"], s=5000 / df.shape[0], c=abs(hash(cat)) % 256, cmap=COLORS2, label=cat)
#abs(hash(cat))
colorindex = (coffset + grouptocolor[i] * colorstep) % 256
colorhash[cat] = colorindex
craw = COLORS2((colorindex + 0.0) / 256.0)
clr = [craw[0], craw[1], craw[2], 0.2]
whitetransparent = [1.0, 1.0, 1.0, 0.5]
coloropaque = [craw[0], craw[1], craw[2], 1.0]
if len(xs) > 3:
pts = list(zip(xs, ys))
cent = np.mean(pts, axis=0)
lengs = list(
map(
lambda p: math.sqrt(
(p[0] - cent[0]) * (p[0] - cent[0]) +
(p[1] - cent[1]) * (p[1] - cent[1])), pts))
avgleng = st.mean(lengs)
stdleng = st.stdev(lengs) * bounding_stdev
rpts = []
if (stdleng > 0.0):
for j, ln in enumerate(lengs):
if (ln - avgleng < stdleng):
rpts.append(pts[j])
pts = rpts
cent = np.mean(pts, axis=0)
hull = ConvexHull(pts)
ptslist = []
for pt in hull.simplices:
ptslist.append(pts[pt[0]])
ptslist.append(pts[pt[1]])
ptslist.sort(key=lambda p: np.arctan2(
p[1] - cent[1], p[0] - cent[0]))
ptslist = ptslist[0::2]
ptslist.insert(len(ptslist), ptslist[0])
lowestpt = ptslist[0]
if label_location == 'bottom':
for pt in ptslist:
if (pt[1] < lowestpt[1]):
lowestpt = pt
else:
lowestpt = ptslist[randrange(len(ptslist))]
if (bounding_stdev >= 0.0):
poly = Polygon(1.1 * (np.array(ptslist) - cent) + cent,
facecolor=clr)
poly.set_capstyle('round')
plt.gca().add_patch(poly)
poly.set_color(clr)
label_text = cat
if label_transform == "numbers":
label_text = re.sub("[^0-9]", "", cat)
txt = plt.text(lowestpt[0],
lowestpt[1] -
scaley * font_size_in_px * 1.2,
label_text,
fontsize=font,
fontname="Arial",
ha="center",
va="center",
color="black",
bbox=dict(boxstyle="round",
fc=whitetransparent,
ec=coloropaque))
# plt.gca().add_artist(txt)
for j, x in enumerate(listcats):
if x == cat:
carr[j] = colorhash[cat]
#carr[j] = colorhash[cat] / 256.0
#int(abs(hash(cat)) % 256)
plt.scatter(x=df["x"],
y=df["y"],
s=5000 / df.shape[0],
c=carr,
cmap=COLORS2)
lgd = plt.legend(markerscale=6,
loc='upper center',
bbox_to_anchor=(0.5, -0.05),
ncol=5)
#60 / (5000 / df.shape[0])
elif coloring_type == "continuous":
plt.scatter(x=df["x"],
y=df["y"],
s=5000 / df.shape[0],
c=df["value"],
cmap="Reds")
plt.colorbar()
xmin, xmax = plt.gca().get_xlim()
ymin, ymax = plt.gca().get_ylim()
# stepsizex=(xmax-xmin)/numticks
# stepsizey=(ymax-ymin)/numticks
xtickArray = resetArray(xmin, xmax, numticks, sigfigs)
ytickArray = resetArray(ymin, ymax, numticks, sigfigs)
# plt.xticks(np.arange(xmin, xmax+stepsizex, step=stepsizex), fontsize=font, fontname="Arial")
# plt.yticks(np.arange(ymin, ymax+stepsizey, step=stepsizey), fontsize=font, fontname="Arial")
plt.xlim(xtickArray[0], xtickArray[-1])
plt.ylim(ytickArray[0], ytickArray[-1])
plt.xticks(xtickArray, fontsize=font, fontname="Arial")
plt.yticks(ytickArray, fontsize=font, fontname="Arial")
if labelXaxis == "":
plt.xlabel(dim_names[0], fontsize=font, fontname="Arial")
else:
plt.xlabel(labelXaxis, fontsize=font, fontname="Arial")
if labelYaxis == "":
plt.ylabel(dim_names[1], fontsize=font, fontname="Arial")
else:
plt.ylabel(labelYaxis, fontsize=font, fontname="Arial")
# plt.tight_layout()
gn.add_current_figure_to_results(
"Scatter-plot",
dpi=target_dpi,
width=target_width * target_dpi,
height=target_height * target_dpi,
savefig_kwargs={'bbox_inches': 'tight'})
toc = time.perf_counter()
time_passed = round(toc - tic, 2)
timing = "* Finished sample coloring step in {} seconds*".format(
time_passed)
gn.add_result(timing, "markdown")
gn.commit()
except Exception as e:
plt.figure()
plt.text(
0.05, 0.7,
'Values used as colors and type of sample metadata are incompatible with each other'
)
if coloring_type == 'categorical':
new_coloring_type = 'continuous'
else:
new_coloring_type = 'categorical'
plt.text(
0.05, 0.5, 'Retry the step with ' + new_coloring_type +
' instead of ' + coloring_type)
plt.axis('off')
gn.add_current_figure_to_results('Scatter-plot')
gn.commit()
def main():
tic = time.perf_counter()
gn = Granatum()
assay = gn.pandas_from_assay(gn.get_import('assay'))
groups = gn.get_import('groups')
min_zscore = gn.get_arg('min_zscore')
max_zscore = gn.get_arg('max_zscore')
min_expression_variation = gn.get_arg('min_expression_variation')
inv_map = {}
for k, v in groups.items():
inv_map[v] = inv_map.get(v, []) + [k]
low_mean_dfs = []
high_mean_dfs = []
mean_dfs = []
std_dfs = []
colnames = []
for k, v in inv_map.items():
group_values = assay.loc[:, v]
lowbound_clust = {}
highbound_clust = {}
for index, row in group_values.iterrows():
meanbounds = sms.DescrStatsW(row).tconfint_mean()
lowbound_clust[index] = meanbounds[0]
highbound_clust[index] = meanbounds[1]
low_mean_dfs.append(pd.DataFrame.from_dict(lowbound_clust, orient="index", columns=[k]))
high_mean_dfs.append(pd.DataFrame.from_dict(highbound_clust, orient="index", columns=[k]))
mean_dfs.append(group_values.mean(axis=1))
std_dfs.append(group_values.std(axis=1))
colnames.append(k)
mean_df = pd.concat(mean_dfs, axis=1)
mean_df.columns = colnames
low_mean_df = pd.concat(low_mean_dfs, axis=1)
low_mean_df.columns = colnames
high_mean_df = pd.concat(high_mean_dfs, axis=1)
high_mean_df.columns = colnames
std_df = pd.concat(std_dfs, axis=1)
std_df.columns = colnames
print(std_df)
minvalues = std_df.min(axis=1).to_frame()
minvalues.columns=["min"]
print("Minvalues>>")
print(minvalues, flush=True)
genes_below_min = list((minvalues[minvalues["min"]<min_expression_variation]).index)
print("{} out of {}".format(len(genes_below_min), len(minvalues.index)), flush=True)
mean_df = mean_df.drop(genes_below_min, axis=0)
low_mean_df = low_mean_df.drop(genes_below_min, axis=0)
high_mean_df = high_mean_df.drop(genes_below_min, axis=0)
std_df = std_df.drop(genes_below_min, axis=0)
assay = assay.drop(genes_below_min, axis=0)
print("Filtered assay to get {} columns by {} rows".format(len(assay.columns), len(assay.index)), flush=True)
mean_rest_dfs = []
std_rest_dfs = []
colnames = []
for k, v in inv_map.items():
rest_v = list(set(list(assay.columns)).difference(set(v)))
mean_rest_dfs.append(assay.loc[:, rest_v].mean(axis=1))
std_rest_dfs.append(assay.loc[:, rest_v].std(axis=1))
colnames.append(k)
mean_rest_df = pd.concat(mean_rest_dfs, axis=1)
mean_rest_df.columns = colnames
std_rest_df = pd.concat(std_rest_dfs, axis=1)
std_rest_df.columns = colnames
zscore_dfs = []
cols = colnames
colnames = []
for coli in cols:
for colj in cols:
if coli != colj:
# Here we should check significance
# Fetch most realistic mean comparison set, what is smallest difference between two ranges
mean_diff_overlap_low_high = (low_mean_df[coli]-high_mean_df[colj])
mean_diff_overlap_high_low = (high_mean_df[coli]-low_mean_df[colj])
diff_df = mean_diff_overlap_low_high.combine(mean_diff_overlap_high_low, range_check)
zscore_dfs.append((diff_df/(std_df[colj]+std_df[coli]/4)).fillna(0).clip(-max_zscore, max_zscore))
colnames.append("{} vs {}".format(coli, colj))
for coli in cols:
zscore_dfs.append(((mean_df[coli]-mean_rest_df[colj])/(std_rest_df[colj]+std_rest_df[coli]/4)).fillna(0).clip(-max_zscore, max_zscore))
colnames.append("{} vs rest".format(coli))
zscore_df = pd.concat(zscore_dfs, axis=1)
zscore_df.columns = colnames
norms_df = zscore_df.apply(np.linalg.norm, axis=1)
colsmatching = norms_df.T[(norms_df.T >= min_zscore)].index.values
return_df = zscore_df.T[colsmatching]
gn.export_statically(gn.assay_from_pandas(return_df), 'Differential expression sets')
gn.export(return_df.T.to_csv(), 'differential_gene_sets.csv', kind='raw', meta=None, raw=True)
toc = time.perf_counter()
time_passed = round(toc - tic, 2)
timing = "* Finished differential expression sets step in {} seconds*".format(time_passed)
gn.add_result(timing, "markdown")
gn.commit()
def main():
tic = time.perf_counter()
gn = Granatum()
assay = gn.get_import('assay')
sample_ids = assay.get('sampleIds')
group_dict = gn.get_import('groupVec')
group_vec = pd.Categorical([group_dict.get(x) for x in sample_ids])
num_groups = len(group_vec.categories)
figheight = 400 * (math.floor((num_groups - 1) / 7) + 1)
adata = sc.AnnData(np.array(assay.get('matrix')).transpose())
adata.var_names = assay.get('geneIds')
adata.obs_names = assay.get('sampleIds')
adata.obs['groupVec'] = group_vec
sc.pp.neighbors(adata, n_neighbors=20, use_rep='X', method='gauss')
try:
sc.tl.rank_genes_groups(adata, 'groupVec', n_genes=100000)
sc.pl.rank_genes_groups(adata, n_genes=20)
gn.add_current_figure_to_results('One-vs-rest marker genes',
dpi=75,
height=figheight)
gn._pickle(adata, 'adata')
rg_res = adata.uns['rank_genes_groups']
for group in rg_res['names'].dtype.names:
genes_names = [str(x[group]) for x in rg_res['names']]
scores = [float(x[group]) for x in rg_res['scores']]
newdict = dict(zip(genes_names, scores))
gn.export(newdict,
'Marker score ({} vs. rest)'.format(group),
kind='geneMeta')
newdictstr = [
'"' + str(k) + '"' + ", " + str(v) for k, v in newdict.items()
]
gn.export("\n".join(newdictstr),
'Marker score {} vs rest.csv'.format(group),
kind='raw',
meta=None,
raw=True)
# cluster_assignment = dict(zip(adata.obs_names, adata.obs['louvain'].values.tolist()))
# gn.export_statically(cluster_assignment, 'cluster_assignment')
toc = time.perf_counter()
time_passed = round(toc - tic, 2)
timing = "* Finished marker gene identification step in {} seconds*".format(
time_passed)
gn.add_result(timing, "markdown")
gn.commit()
except Exception as e:
plt.figure()
plt.text(0.01, 0.5,
'Incompatible group vector due to insufficent cells')
plt.text(0.01, 0.3,
'Please retry the step with a different group vector')
plt.axis('off')
gn.add_current_figure_to_results('One-vs-rest marker genes')
gn.add_result('Error = {}'.format(e), "markdown")
gn.commit()
