"""Written by @Jinerhal, adapted by @Xiaojieqiu.
"""
import numpy as np
import pandas as pd
import seaborn as sns
from anndata import AnnData
[docs]def polarity(
adata: AnnData,
gene_dict: dict,
region_key: str,
mode: str = "density",
):
"""Simple function to visualize expression level varies along regions.
Args:
adata (AnnData): _description_
gene_dict (dict): _description_
region_key (str): _description_
mode (str, optional): _description_. Defaults to "density".
Returns:
_type_: _description_
"""
digi_region = np.array([])
gene_list = np.array([])
gene_mean = np.array([])
if mode == "exp":
for i in np.unique(adata.obs[region_key]):
adata_tmp = adata[adata.obs[region_key] == i, :]
for anno in list(gene_dict.keys()):
for gene in gene_dict[anno]:
gene_mean_tmp = adata_tmp[:, gene].X.toarray().T[0]
digi_region = np.append(digi_region, np.repeat(i, len(adata_tmp)))
gene_list = np.append(gene_list, np.repeat(gene + " " + anno, len(adata_tmp)))
gene_mean = np.append(gene_mean, gene_mean_tmp)
df_plt = pd.DataFrame({region_key: digi_region, "Gene": gene_list, "Mean expression": gene_mean})
ax = sns.lineplot(data=df_plt, x=region_key, y="Mean expression", hue="Gene")
elif mode == "density":
for i in np.unique(adata.obs[region_key]):
adata_tmp = adata[adata.obs[region_key] == i, :]
for anno in list(gene_dict.keys()):
for gene in gene_dict[anno]:
digi_region.append(i)
gene_list.append(gene + " " + anno)
gene_mean.append(np.mean(adata_tmp[:, gene].X))
df_plt = pd.DataFrame({region_key: digi_region, "Gene": gene_list, "Mean expression": gene_mean})
p = sns.kdeplot(data=df_plt, x=region_key, weights="Mean expression", hue="Gene")
p.set_xlim(0, max(adata.obs[region_key]))
return ax