import os,sys
import scanpy as sc
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from mebocost import mebocost1. 创建 mebocost 对象
adata = sc.read_h5ad('data/demo/raw_scRNA/demo_HNSC_200cell.h5ad')
## check adata (cells, genes)
print(adata.shape)
## initiate the mebocost object
### import expression data by scanpy adata object
mebo_obj = mebocost.create_obj(
adata = adata,
group_col = ['celltype'],
met_est = 'mebocost',
config_path = './mebocost.conf',
exp_mat=None,
cell_ann=None,
species='human',
met_pred=None,
met_enzyme=None,
met_sensor=None,
met_ann=None,
scFEA_ann=None,
compass_met_ann=None,
compass_rxn_ann=None,
cutoff_exp='auto',
cutoff_met='auto',
cutoff_prop=0.25,
sensor_type=['Receptor', 'Transporter', 'Nuclear Receptor'],
thread=8
)
2. 代谢通讯推断
## initiate the mebocost object
mebo_obj = mebocost.create_obj(
adata = adata,
group_col = ['celltype'],
met_est = 'mebocost',
config_path = './mebocost.conf',
exp_mat=None,
cell_ann=None,
species='human',
met_pred=None,
met_enzyme=None,
met_sensor=None,
met_ann=None,
scFEA_ann=None,
compass_met_ann=None,
compass_rxn_ann=None,
cutoff_exp='auto',
cutoff_met='auto',
cutoff_prop=0.25,
sensor_type=['Receptor', 'Transporter', 'Nuclear Receptor'],
thread=8
)## metabolic communication inference, this step takes a while
commu_res = mebo_obj.infer_commu(
n_shuffle=1000,
seed=12345,
Return=True,
thread=None,
save_permuation=False,
min_cell_number = 1,
pval_method='permutation_test_fdr',
pval_cutoff=0.05
)
3. 可视化
a. 查看每种细胞类型sender和receiver数量
## sender and receiver event number
mebo_obj.eventnum_bar(
sender_focus=[],
metabolite_focus=[],
sensor_focus=[],
receiver_focus=[],
xorder=[],
and_or='and',
pval_method='permutation_test_fdr',
pval_cutoff=0.05,
comm_score_col='Commu_Score',
comm_score_cutoff = 0,
cutoff_prop = 0.25,
figsize='auto',
save=None,
show_plot=True,
show_num = True,
include=['sender-receiver'],
group_by_cell=True,
colorcmap='tab20',
return_fig=False
)
b. 不同细胞类型的通讯情况
## circle plot to show communications between cell groups
mebo_obj.commu_network_plot(
sender_focus=[],
metabolite_focus=[],
sensor_focus=[],
receiver_focus=[],
and_or='and',
pval_method='permutation_test_fdr',
pval_cutoff=0.05,
node_cmap='tab20',
figsize='auto',
line_cmap='bwr',
line_color_vmin=None,
line_color_vmax=None,
linewidth_norm=(0.2, 1),
linewidth_value_range = None,
node_size_norm=(50, 200),
node_value_range = None,
adjust_text_pos_node=True,
node_text_hidden = False,
node_text_font=10,
save=None,
show_plot=True,
comm_score_col='Commu_Score',
comm_score_cutoff=0,
text_outline=True,
return_fig=False
)
### the "overall score" represent the sum of -log10(FDR) of detected metabolite-sensor communications between a pair of cell types
### dot plot to show the number of communications between cells
mebo_obj.count_dot_plot(
pval_method='permutation_test_fdr',
pval_cutoff=0.05,
cmap='bwr',
figsize='auto',
save=None,
dot_size_norm =(20, 200),
dot_value_range = None,
dot_color_vmin=None,
dot_color_vmax=None,
show_plot=True,
comm_score_col='Commu_Score',
comm_score_cutoff=0,
dendrogram_cluster=True,
sender_order=[],
receiver_order=[],
return_fig = False
)
c. 详细的通讯情况（sender-receiver vs metabolite-sensor），可以指定receiver_focus/sensor_focus查看特定细胞类型。
## Malignant cell was focused, use receiver_focus=[] to include all cell types
mebo_obj.commu_dotmap(
sender_focus=[],
metabolite_focus=[],
sensor_focus=[],
receiver_focus=['Malignant'],
and_or='and',
pval_method='permutation_test_fdr',
pval_cutoff=0.05,
figsize='auto',
cmap='bwr',
cmap_vmin = None,
cmap_vmax = None,
cellpair_order=[],
met_sensor_order=[],
dot_size_norm=(10, 150),
save=None,
show_plot=True,
comm_score_col='Commu_Score',
comm_score_range = None,
comm_score_cutoff=0,
swap_axis = False,
return_fig = False
)
d. 通信流可视化
## Malignant cell was focused, use receiver_focus=[] to include all cell types
mebo_obj.FlowPlot(
pval_method='permutation_test_fdr',
pval_cutoff=0.05,
sender_focus=[],
metabolite_focus=[],
sensor_focus=[],
receiver_focus=['Malignant'],
remove_unrelevant = False,
and_or='and',
node_label_size=8,
node_alpha=0.6,
figsize='auto',
node_cmap='Set1',
line_cmap='bwr',
line_cmap_vmin = None,
line_cmap_vmax = 15.5,
node_size_norm=(20, 150),
node_value_range = None,
linewidth_norm=(0.5, 5),
linewidth_value_range = None,
save='test.pdf',
show_plot=True,
comm_score_col='Commu_Score',
comm_score_cutoff=0,
text_outline=False,
return_fig = False
)
e. 可视化细胞亚群的代谢物水平
## violin plot to show the estimated metabolite abundance of informative metabolties in communication
### here we show five significant metabolites,
### users can pass several metabolites of interest by provide a list
commu_df = mebo_obj.commu_res.copy()
good_met = commu_df[(commu_df['permutation_test_fdr']<=0.05)]['Metabolite_Name'].sort_values().unique()
mebo_obj.violin_plot(
sensor_or_met=good_met[:5], ## only top 5 as example
cell_focus=[],
cell_order = [],
row_zscore = False,
cmap=None,
vmin=None,
vmax=None,
figsize='auto',
cbar_title='',
save=None,
show_plot=True
)
f. 可视化细胞亚群的senor水平
## violin plot to show the expression of informative sensors in communication
good_sensor = commu_df[(commu_df['permutation_test_fdr']<=0.05)]['Sensor'].sort_values().unique()
mebo_obj.violin_plot(
sensor_or_met=good_sensor[:5],## only top 5 as example
cell_focus=[],
cell_order = [],
row_zscore = False,
cmap=None,
vmin=None,
vmax=None,
figsize='auto',
cbar_title='',
save=None,
show_plot=True
)
参考：MEBOCOST/Demo_Communication_Prediction.ipynb at master · zhengrongbin/MEBOCOST (github.com)
