空间转录组网络图的分步绘制(Xenium)
2024-06-04 本文已影响0人
单细胞空间交响乐
作者，Evil Genius
工作要劳逸结合，我们来画画图吧。
很多人对公司更新生信分析内容感兴趣，其实公司的更新就是要超前化、专业化、自动化、流程化，当然还有调研很多的方法实现个性化。
先加载
from matplotlib import patches as mpatches
from mpl_chord_diagram import chord_diagram
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
from scipy.stats import f_oneway
from statsmodels.stats.multicomp import pairwise_tukeyhsd
import matplotlib as mpl
import matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
import seaborn as sns
import squidpy as sq
from matplotlib.lines import Line2D
from scipy import stats
有一些定义的函数，我们放在最后
读取数据
adata = sc.read('./EAE_rev_ingest_only_new_v4.h5ad')
adata
基础分析全部做了，包括注释、距离分析等等
数据注释
adata.obsm["spatial"] = adata.obs[["x", "y"]].copy().to_numpy()
adata = adata[adata.obs['Annotation 3.5'] != 'Unknown']
mapping = {'Astro': 'Astro',
'CAM': 'CAM',
'DA-Astro': 'DA-Astr',
'DA-MOL2': 'DA-MOL2',
'DA-MOL5/6': 'DA-MOL5/6',
'DA-MiGL': 'DA-MiGL',
'DA-OPC/COP': 'DA-OPC/COP',
'DC': 'DC',
'Ep': 'Ep',
'Ep-Neu': 'Ep',
'MC/d': 'MC/d',
'MOL2': 'MOL2',
'MOL5/6': 'MOL5/6',
'MiGL': 'MiGL',
'NFOL/MFOL': 'NFOL/MFOL',
'Neu-Chol': 'Neuron',
'Neu-Ex-Glu': 'Neuron',
'Neu-In-GABA': 'Neuron',
'Neu-In-Gly': 'Neuron',
'OPC/COP': 'OPC/COP',
'Per': 'Vascular',
'Schw': 'Schw',
'T-cell': 'T-cell',
'VEC': 'Vascular',
'VEC-Per': 'Vascular',
'VLMC': 'Vascular'}
adata.obs['interaction_annotations'] = adata.obs['Annotation 3.5'].map(mapping)
color_interaction_anno = {'MOL2': '#7c4dffff','MOL5/6': '#311b92ff','DA-MOL5/6': '#58104dff','DA-MOL2': '#941b81ff','DA-Astr': '#25cc44ff','DA-MiGL': '#ff9300ff','MiGL': '#ffdb00ff','Astro': '#009051ff','DA-MiGL': '#ff9300ff','MC/d': '#ff009dff','DC': '#b8860bff','T-cell': '#f8ff00ff','Neuron': '#133c55ff','NFOL/MFOL': '#7d1b94ff','Vascular': '#ff0000ff','Schw': '#929000ff','CAM': '#ff00f5ff','OPC/COP': '#9e8ac0','DA-OPC/COP': '#ff87bbff','Ep': '#ffc0cbff'}
adata.obs['interaction_annotations_colors'] = adata.obs['interaction_annotations'].map(color_interaction_anno)
sc.pl.umap(adata, color = 'interaction_annotations', palette = color_interaction_anno)
计算邻域
sq.gr.spatial_neighbors(adata, library_key = 'sample_id', coord_type="generic", delaunay=False, n_neighs=5)
每个样本的空间网络图
ad_list = []
for sample in adata.obs.sample_id.unique():
ad_sp = adata[adata.obs.sample_id == sample]
if '_B_' in sample:
radius = 300
size = 20
else:
radius = 180
size = 60
sq.pl.spatial_scatter(ad_sp,
color = 'interaction_annotations',
#coords=adata.obsm['spatial'],
# crop_coord=region_subset_dict[sample],
size= size,shape=None,
figsize=(20, 20),
connectivity_key = 'spatial_connectivities',
)#save = sample+'.svg')
plt.savefig('%s.spatial.graph.png'%(sample))
ad_list.append(ad_sp)
subset，为什么要做这个呢，因为一张Xenium芯片上了多个样本（毕竟太贵了），所以要把单个样本提取出来
region_subset_dict = {
'R03_L_EAE_EARLY': [ 2500, 4500,10000, 12000],
'R02_L_EAE_PEAK': [7500, 9500, 7750, 9750],
'R05_L_EAE_LATE': [11000,13000,9500,11500]
}
compartment_dictionary = {
'CNTRL':['WM','GM','Corpus callosum'],
'EAE':['WM',
'GM',
'PL',
'LR',
'LC',
'LL',
'Corpus callosum',
'DA-region',]
}
adata.obs['interaction_grouping'] = adata.obs.type.astype(str) + '_' + adata.obs.batch.astype(str) + '_' +adata.obs.compartment.astype(str)
interaction_grouping = ['CNTRL_spinal cord_WM', 'CNTRL_spinal cord_GM', 'EAE_spinal cord_WM','EAE_spinal cord_GM','EAE_spinal cord_PL', 'EAE_spinal cord_LR', 'EAE_spinal cord_LC', 'EAE_spinal cord_LL', 'EAE_brain_DA-region','EAE_brain_Corpus callosum']
level_ = 'interaction_annotations'
region_subset_dict = {'R1_L_EAE_PEAK': [(9500,11000,16000,18000)],'R6_L_EAE_LATE':[(3000,5000,16000,18000)]}
ad_sp.obs[['x','y']]
region_subset_dict = {'R1_B_EAE_PEAK':[23000, 25000, 25000, 27000],'R1_C_EAE_PEAK':[16000, 18000, 11000, 13000], 'R2_T_EAE_PEAK':[7500, 9500, 800, 2800],'R2_L_EAE_PEAK':[12000, 14000, 3500, 5500],'R5_L_EAE_LATE':[12000, 14000, 10000, 12000]}
adata_SC = adata[adata.obs.region.isin(['L'])]
adata_B = adata[adata.obs.batch == 'brain']
adata_SC.obs.time.unique()
adata_SC_peak = adata_SC[adata_SC.obs.time == 'PEAK']
adata_SC_peak = adata_SC[adata_SC.obs.time == 'PEAK']
def get_range(list_int):
import numpy as np
# Define your list of floats
data = list_int
# Determine the range of values in the data set
data_range = max(data) - min(data)
# Calculate the bin size based on the desired number of bins
num_bins = 5
bin_size = data_range / num_bins
# Use the histogram function to get the frequency counts and bin edges
counts, edges = np.histogram(data, bins=num_bins)
# Create a list of representative integers based on the bin edges
integers = [int(round(edge)) for edge in edges]
# Print the results
print(f"Counts: {counts}")
print(f"Bin Edges: {integers}")
return integers
path = './'
ad_list_2 = {}
for int_group in ['EAE']:
print(int_group)
adata_int2 = adata_SC[adata_SC.obs['type'] == int_group]
for time in adata_int2.obs.time.unique():
print(time)
adata_int = adata_int2[adata_int2.obs['time'] == time]
sq.gr.nhood_enrichment(adata_int, cluster_key=level_)
sq.pl.nhood_enrichment(adata_int, cluster_key=level_)
adata_int.uns[level_+'_nhood_enrichment']['zscore'] = np.nan_to_num(adata_int.uns[level_+'_nhood_enrichment']['zscore'])
colors =pd.DataFrame(dict(zip(adata.obs['interaction_annotations'].cat.categories,adata.uns['interaction_annotations_colors'])).values())#pd.DataFrame(adata_int.uns[level_+'_colors'])
for_eneritz = pd.DataFrame(adata_int.uns[level_+"_nhood_enrichment"]["zscore"])
for_eneritz.index = adata_int.obs['interaction_annotations'].cat.categories
for_eneritz.columns = adata_int.obs['interaction_annotations'].cat.categories
#for_eneritz.to_csv(path+'SC_'+int_group+'_'+time+'_interaction_matrix.csv')
size = pd.DataFrame(adata_int.obs[level_].value_counts())
print(size)
# create network plot
import networkx as nx
import matplotlib.pyplot as plt
G = nx.Graph()
nodes = adata_int.obs[level_].cat.categories
categories = pd.DataFrame(adata_int.obs[level_].cat.categories)
colors['cells'] = categories
nodes2 = []
for i,node in enumerate(((nodes))):
for j in range(i+1, len(nodes)):
zscore = adata_int.uns[level_+"_nhood_enrichment"]["zscore"][i, j]
pval = stats.norm.sf(abs(zscore))*2
if zscore>1:
G.add_edge(nodes[i], nodes[j], weight=(zscore))
pos = nx.spring_layout(G, k=0.5, seed=42)
size = size[size.index.isin(pos.keys())]
size = size.sort_index()
colors = colors[colors.cells.isin(pos.keys())]
colors = dict(zip(colors['cells'], colors[0]))
edge_widths = [d['weight'] for u, v, d in G.edges(data=True)]
size = dict(zip(size.index, size['interaction_annotations']))
node_size = [size[node] for node in G.nodes()]
node_colors = [colors[node] for node in G.nodes()]
plt.figure(figsize=(10, 10))
sc = nx.draw_networkx_nodes(G, pos, node_color=node_colors, alpha=0.5, node_size=np.array(node_size)/2)
nx.draw_networkx_edges(G, pos, edge_color="black", alpha=0.5, width=0.25*(np.array(edge_widths)/5))
nx.draw_networkx_labels(G, pos, font_size=15, font_color="black")
plt.axis("off")
legend1 = plt.legend(*sc.legend_elements("sizes", num=6),
bbox_to_anchor=(1, 1),
prop={'size': 70},
title = '# cells in cluster',
frameon = False)
lines = []
edges_weight_list = sorted(np.array(edge_widths))
integers = get_range(edges_weight_list)
for i, width in enumerate(integers):
lines.append(Line2D([],[], linewidth=0.25*(width/5), color='black'))
legend2 = plt.legend(lines,integers,prop={'size': 20}, bbox_to_anchor=(0, 0.5), frameon=False, )
plt.gca().add_artist(legend1)
plt.gca().add_artist(legend2)
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['svg.fonttype'] = 'none'
plt.savefig(path+'SC_'+int_group+'_'+time+'_nhood_enrichment.svg', bbox_inches="tight", dpi = 500)
plt.show()
sq.gr.interaction_matrix(adata_int, cluster_key=level_, normalized = False)
#sq.pl.interaction_matrix(adata_int, cluster_key=level_,)# vmax = 5000, method="ward",)
df = pd.DataFrame(adata_int.uns[level_+'_interactions'])
df_filt = df#[df.sum() > df.sum().quantile(0.6)]
df_filt = df_filt.T
df_filt = df_filt[df_filt.index.isin(df_filt.columns)]
colors =pd.DataFrame(adata_int.uns[level_+'_colors'])
colors = colors[colors.index.isin(df_filt.columns)][0]
categories = pd.DataFrame(adata_int.obs[level_].cat.categories)
categories = categories[categories.index.isin(df_filt.columns)][0]
df_filt.index = categories
df_filt.columns = categories
import random
randomlist = []
for i in range(0,19):
n = random.uniform(0,1,)
randomlist.append(n)
#df.index= adata_int.obs.level3.cat.categories
#df.columns= adata_int.obs.level3.cat.categories
with plt.rc_context({'figure.figsize': (10, 10), 'figure.dpi':100}):
chord_diagram(df_filt, names = list(categories),
rotate_names = True, fontcolor = 'black',
fontsize=10,colors = list(colors), alpha = 0.90,
sort = 'distance', use_gradient= True, show= False)
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['svg.fonttype'] = 'none'
plt.savefig(path+'SC_'+int_group+'_'+time+'_interaction_matrix.svg', bbox_inches="tight")
plt.show()
ad_list_2 = {}
for int_group in ['EAE']:#,#'CNTRL']:
print(int_group)
adata_int2 = adata_B[adata_B.obs['type'] == int_group]
for region in adata_int2.obs.region.unique():
print(region)
adata_int = adata_int2[adata_int2.obs['region'] == region]
sq.gr.nhood_enrichment(adata_int, cluster_key=level_)
sq.pl.nhood_enrichment(adata_int, cluster_key=level_)
adata_int.uns[level_+'_nhood_enrichment']['zscore'] = np.nan_to_num(adata_int.uns[level_+'_nhood_enrichment']['zscore'])
colors =pd.DataFrame(dict(zip(adata.obs['interaction_annotations'].cat.categories,adata.uns[level_+'_colors'])).values())#pd.DataFrame(adata_int.uns[level_+'_colors'])
for_eneritz = pd.DataFrame(adata_int.uns[level_+"_nhood_enrichment"]["zscore"])
for_eneritz.index = adata_int.obs['interaction_annotations'].cat.categories
for_eneritz.columns = adata_int.obs['interaction_annotations'].cat.categories
for_eneritz.to_csv(path+'B_'+int_group+'_'+time+'_interaction_matrix.csv')
size = pd.DataFrame(adata_int.obs[level_].value_counts())
print(size)
# create network plot
import networkx as nx
import matplotlib.pyplot as plt
G = nx.Graph()
nodes = adata_int.obs[level_].cat.categories
categories = pd.DataFrame(adata_int.obs[level_].cat.categories)
colors['cells'] = categories
nodes2 = []
for i,node in enumerate(((nodes))):
for j in range(i+1, len(nodes)):
pval = adata_int.uns[level_+"_nhood_enrichment"]["zscore"][i, j]
if pval>-1:
G.add_edge(nodes[i], nodes[j], weight=(pval))
pos = nx.spring_layout(G, k=0.15,seed=42)
size = size[size.index.isin(pos.keys())]
size = size.sort_index()
colors = colors[colors.cells.isin(pos.keys())]
colors = dict(zip(colors['cells'], colors[0]))
edge_widths = [d['weight'] for u, v, d in G.edges(data=True)]
size = dict(zip(size.index, size[level_]))
node_size = [size[node] for node in G.nodes()]
node_colors = [colors[node] for node in G.nodes()]
plt.figure(figsize=(20, 20))
sc = nx.draw_networkx_nodes(G, pos, node_color=node_colors, alpha=0.5, node_size=np.array(node_size)/2)
nx.draw_networkx_edges(G, pos, edge_color="black", alpha=0.5, width=0.25*(np.array(edge_widths)/5))
nx.draw_networkx_labels(G, pos, font_size=20, font_color="black")
plt.axis("off")
legend1 = plt.legend(*sc.legend_elements("sizes", num=6),
bbox_to_anchor=(1, 1),
prop={'size': 80},
title = '# cells in cluster',
frameon = False)
lines = []
edges_weight_list = sorted(np.array(edge_widths))
integers = get_range(edges_weight_list)
for i, width in enumerate(integers):
lines.append(Line2D([],[], linewidth=0.25*(width/5), color='black'))
legend2 = plt.legend(lines,integers,prop={'size': 20}, bbox_to_anchor=(0, 0.5), frameon=False, )
plt.gca().add_artist(legend1)
plt.gca().add_artist(legend2)
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['svg.fonttype'] = 'none'
plt.savefig(path+'B_'+int_group+'_nhood_enrichment.svg', bbox_inches="tight", dpi = 500)
sq.gr.interaction_matrix(adata_int, cluster_key=level_, normalized = False)
#sq.pl.interaction_matrix(adata_int, cluster_key=level_,)# vmax = 5000, method="ward",)
df = pd.DataFrame(adata_int.uns[level_+'_interactions'])
df_filt = df#[df.sum() > df.sum().quantile(0.6)]
df_filt = df_filt.T
df_filt = df_filt[df_filt.index.isin(df_filt.columns)]
colors =pd.DataFrame(adata_int.uns[level_+'_colors'])
colors = colors[colors.index.isin(df_filt.columns)][0]
categories = pd.DataFrame(adata_int.obs[level_].cat.categories)
categories = categories[categories.index.isin(df_filt.columns)][0]
df_filt.index = categories
df_filt.columns = categories
import random
randomlist = []
for i in range(0,19):
n = random.uniform(0,1,)
randomlist.append(n)
#df.index= adata_int.obs.level3.cat.categories
#df.columns= adata_int.obs.level3.cat.categories
with plt.rc_context({'figure.figsize': (10, 10), 'figure.dpi':100}):
chord_diagram(df_filt, names = list(categories),
rotate_names = True, fontcolor = 'black',
fontsize=10,colors = list(colors), alpha = 0.90,
sort = 'distance', use_gradient= True, show= False)
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['svg.fonttype'] = 'none'
plt.savefig(path+'B_'+int_group+'_'+time+'_interaction_matrix.svg', bbox_inches="tight")
plt.show()
