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Parallel cholinergic circuit in oculomotor nucleus to control eye movements and REM sleep.

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  1. [1] § Methods › Calcium imaging and analysis ↔ Fig4. Network_analysis1.ipynb, lines 190–248 · score 0.64 · correlation coefficient matrix, linkage, cluster, node, network
  2. [2] § Results › Functionally distinct nIIIChAT subnetworks ↔ Fig4. Network_analysis1.ipynb, lines 190–248 · score 0.53 · correlation coefficient matrices, Ward, linkage, clustering, peak, network

Paper

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The authors' code

Jupyter notebook · 697 lines · 30 KB · MIT · 2 matches

  1. # %%
  2. import numpy as np
  3. import pandas as pd
  4. import matplotlib.pyplot as plt
  5. import matplotlib
  6. import seaborn as sns
  7. from itertools import groupby
  8. from operator import itemgetter
  9. import networkx as nx
  10. from scipy.sparse import csr_matrix
  11. from sklearn.metrics.pairwise import cosine_similarity
  12. import os
  13. from scipy.cluster.hierarchy import linkage, fcluster, dendrogram, optimal_leaf_ordering
  14. from scipy.stats import mode
  15. import warnings
  16. import sys
  17. sys.path.insert(0, r'..\tools')
  18. from zscore import zscore
  19. from smooth import smooth,ins_smooth
  20. from color_convert import color_convert_plt
  21. from raster_plot import raster_plot
  22. from color_convert import color_convert_plt
  23. from matplotlib_venn import venn3,venn2
  25. # %%
  26. path=r'.\rawdata\network_analysis'
  27. REM_active=pd.read_excel(path+'\\REM_activeNeurons.xlsx')
  28. REM_active_cellname=REM_active.cell_name2
  29. is_save='no'
  31. EM_active=pd.read_excel(path+'\\em_define.xlsx')
  32. EM_positive=EM_active.cell_name[EM_active.rem_em==1]
  33. EM_negative=EM_active.cell_name[EM_active.rem_em==-1]
  34. EM_po_ne_cellname=EM_active.cell_name[EM_active.rem_em!=0]
  36. EM_po_inREM=EM_positive[np.isin(EM_positive,REM_active_cellname)]
  37. EM_ne_inREM=EM_negative[np.isin(EM_negative,REM_active_cellname)]
  38. EM_po_ne_inREM=EM_po_ne_cellname[np.isin(EM_po_ne_cellname,REM_active_cellname)]
  40. ###取REM active和EM active细胞的并集
  41. union_series = pd.concat([REM_active_cellname, EM_po_ne_cellname]).drop_duplicates().reset_index(drop=True)
  42. print(f'REM active cell number: {len(REM_active_cellname)},number of union: {len(union_series)}')
  44. # %%
  45. ###读取每个细胞眼动的数据
  46. root_EM=r'.\rawdata\out_put\Ca_acrossStage'
  47. df=pd.read_csv(root_EM+'\\R-matrix.csv',index_col='cell_name')
  48. df_arr=df[np.isin(df.index,EM_po_ne_cellname)]
  49. f, ax1 = plt.subplots(figsize=(8, 5), nrows=1)
  50. sns.heatmap(np.array(df_arr), yticklabels = df_arr.index, ax=ax1, vmax=5, vmin=-5, cmap='bwr') ##
  51. plt.show()
  53. # %%
  54. ###计算不同REM episode持续时间下细胞响应的比例
  55. def median(data):
  56. sorted_data=sorted(data)
  57. half = len(data) // 2
  58. meadia_data=(sorted_data[half]+sorted_data[half+1])/2
  59. return meadia_data
  61. if __name__ == '__main__':
  62. path=r'.\process_data'
  63. new_path=r'.\rawdata\network_analysis'
  64. file_list=[i for i in os.listdir(path) if i.startswith('Ca')]
  65. cell_info=pd.read_excel(path+'\\sleep_define.xlsx',index_col=0)
  66. all_cell={'cell_name':[],'cell':[]}
  67. is_save='yes'
  69. print(cell_info.sleep_ID[cell_info.index])
  70. max_epoch=15
  71. ###这一段代码目前用不上
  72. average_Ca=pd.read_excel(path+'\\stages_average_bystates.xlsx',index_col=0)
  73. thre=average_Ca.apply(lambda x:median(x),axis=0)
  74. thre={'0':thre['wake_activity'],'5':thre['nrem_activity'],'10':thre['rem_activity']}
  76. for file in file_list:
  77. prefix=file[2:]
  78. ##睡眠状态
  79. stage=pd.read_csv(path+f'\\Stage{prefix}',sep=',').Stage
  80. Ca_data=pd.read_csv(path+f'\\{file}',sep=',')
  82. episodes= {'0':[],'5':[],'10':[]}
  83. wake_index=[x[0] for x in enumerate(stage) if x[1]==0]
  84. nrem_index=[x[0] for x in enumerate(stage) if x[1]==5]
  85. rem_index=[x[0] for x in enumerate(stage) if x[1]==10]
  87. # print(f"W_index:{wake_index}")
  88. for k, g in groupby(enumerate(wake_index),lambda x:x[0]-x[1]):
  89. group=list(map(itemgetter(1), g))
  90. episodes['0'].append((group[0], group[-1]+1))
  91. for k, g in groupby(enumerate(nrem_index),lambda x:x[0]-x[1]):
  92. group=list(map(itemgetter(1), g))
  93. episodes['5'].append((group[0], group[-1]+1))
  94. for k, g in groupby(enumerate(rem_index),lambda x:x[0]-x[1]):
  95. group=list(map(itemgetter(1), g))
  96. episodes['10'].append((group[0], group[-1]+1))
  98. ###计算状态转变的次数
  99. trans_number=[]
  100. for start, end in episodes['10'][:-1]: ###不同转变状态需修改
  101. if stage[end]!=10:
  102. trans_number.append(end-start)
  104. ###按照REM持续时间排列REM起始点
  105. order = np.argsort(trans_number)
  106. grouped_id= np.array(episodes['10'])[order]
  108. em_fre=pd.read_csv(path+f'\\em_fre{prefix}',sep=',').em_fre.values
  109. em_fre=np.array(em_fre)
  111. em_index=pd.read_csv(path+f'\\em_index{prefix}',sep=',').em_index.values
  112. em_index_list=np.array(em_index).reshape((-1,512))
  114. valid_grouped_id,valid_trans_number=[],[]
  115. for start, end in grouped_id[:-1]: ###不同转变状态需修改
  116. idx =[idx_i for idx_i in np.arange(start, end)]
  117. epoch_n=[]
  118. ###转变前
  119. for num in idx:
  120. if em_fre[num]!=0: ## and (len(np.where(Ca_cell[num]>thre_pos)[0])>0 or len(np.where(Ca_cell[num]<thre_neg)[0])>0)
  121. epoch_n.append(1)
  122. else:
  123. epoch_n.append(0)
  125. if sum(epoch_n)!=0 and 2<=len(idx)<=max_epoch and stage[end]!=10: ###不同转变状态需修改sum(epoch_n)<len(idx)*0.5 and 2<=len(idx)<=max_epoch..
  126. valid_grouped_id.append([start,end])
  127. valid_trans_number.append(end-start)
  129. print(f'valid trnaistion number: {valid_trans_number}')
  131. ###REM duration
  132. unique_values, indices, counts=np.unique(trans_number,return_index=True,return_counts=True)
  134. for i in Ca_data.columns: ## Ca_data.columns:
  135. # print(i)
  137. cell_arr=[]
  138. Ca_cell=smooth(np.array(Ca_data[i]),11)
  139. mean_cell,std_cell=np.mean(Ca_cell),np.std(Ca_cell)
  140. thre_pos,thre_neg=mean_cell+1.96*std_cell,mean_cell-1.96*std_cell
  141. Ca_celli=Ca_cell.reshape((-1,64))
  143. valid_episode=0
  144. for data_ind, [start, end] in enumerate(valid_grouped_id): ###不同转变状态需修改
  145. if end-start:
  146. valid_episode=valid_episode+1
  147. stage_data = Ca_celli[end-1:end+1].flatten()
  148. cell_arr.append(stage_data)
  149. if valid_episode!=0:
  150. cell_mean=np.mean(cell_arr,axis=0)
  151. all_cell['cell'].append(cell_mean)
  152. all_cell['cell_name'].append(i)
  153. else:
  154. continue
  156. # %%
  157. overlap=EM_po_ne_cellname[np.isin(EM_po_ne_cellname,all_cell['cell_name'])]
  158. print(f'cell number of EM active and REM active: {len(overlap)}')
  159. print('length of REM active: {}'.format(len(all_cell['cell_name'])))
  160. ###眼动和睡眠的长度一致，且细胞ID一致
  161. sum_arr={'REM_name':all_cell['cell_name'], 'REM_active':all_cell['cell'], 'EM_name':all_cell['cell_name'], 'EM_active':[df.loc[i].values.tolist() for i in all_cell['cell_name']] }
  163. ###correlation matrix
  164. ave_corr_matrix={}
  165. ave_corr_matrix['REM_active'] = np.corrcoef(sum_arr['REM_active'], rowvar=True)
  166. ave_corr_matrix['EM_active'] =np.corrcoef(sum_arr['EM_active'], rowvar=True)
  167. # ave_corr_matrix['REM_active'] = cosine_similarity(sum_arr['REM_active'])
  168. # ave_corr_matrix['EM_active'] =cosine_similarity(sum_arr['EM_active'])
  170. REM_active_index=np.arange(len(all_cell['cell_name']))[np.isin(sum_arr['REM_name'],REM_active_cellname)]
  171. EM_pos_index=np.arange(len(sum_arr['EM_name']))[np.isin(sum_arr['EM_name'],EM_positive)]
  172. EM_neg_index=np.arange(len(sum_arr['EM_name']))[np.isin(sum_arr['EM_name'],EM_negative)]
  173. EM_po_ne_index=np.arange(len(sum_arr['EM_name']))[np.isin(sum_arr['EM_name'],EM_po_ne_cellname)]
  174. index_cellname={'REM_active':REM_active_index,'EM_active':EM_pos_index}
  176. print (f'REM active index: {len(REM_active_index)}, {REM_active_index}')
  177. print (f'EM positive index: {len(EM_pos_index)}, {EM_pos_index}')
  178. print (f'EM negative index: {len(EM_neg_index)}, {EM_neg_index}')
  179. print (f'EM active index: {len(EM_po_ne_index)}, {EM_po_ne_index}')
  181. # %%
  182. np.save(new_path+'\\EM_hierarchy.dendrogram.npy',ave_corr_matrix['EM_active'])
  183. np.save(new_path+'\\REM_hierarchy.dendrogram.npy',ave_corr_matrix['REM_active'])
  185. # %%
  186. sns.clustermap(ave_corr_matrix['REM_active'],method='ward',metric='euclidean', figsize=(3,3), row_cluster=True,col_cluster=True,cmap="bwr")
  187. # plt.savefig(new_path+'\\EM_hierarchy.dendrogram.pdf')
  188. plt.show()
  190. # %%
  191. '''453
  192. INPUT:
  193. matrix: an array of correlation coefficient matrix
  194. start: the first order
  195. end: the last order
  196. RETURN:
  197. the cluster order which possess the maximal contrast
  198. the reordered matrix according to maximal contrast
  199. the cell index of the maximal contrast
  200. '''
  201. def contrast(matrix, start, end):
  202. correlation_matrix=matrix
  203. lin_clu=linkage(correlation_matrix,method='ward',metric='euclidean')
  205. contrast= {'order':[],'contrast':[]}
  207. # max_contrast,peak_index,peak_group_mat=0,np.arange(len(matrix)),matrix
  208. for numclu in range(start, end):
  209. c_node = fcluster(lin_clu, numclu, criterion='maxclust')
  210. order = np.argsort(c_node)
  211. grouped_id = c_node[order]
  213. ###按照新的cluster进行排序
  214. grouped_mat = correlation_matrix[order][:, order]
  216. Dall = np.sum(correlation_matrix)
  218. Din_sub = np.zeros(numclu)
  220. for iDmat in range(1, numclu+1):
  221. ind_subg = np.where(grouped_id == iDmat)[0]
  223. order_sub = order[ind_subg]
  225. ###计算每个cluster的CC的和
  226. matiD = correlation_matrix[order_sub][:, order_sub]
  227. Din_sub[iDmat-1] = np.sum(matiD)
  229. Din = np.sum(Din_sub)
  230. Dout = Dall - Din
  231. Contrast = (Din - Dout) / (Din + Dout)
  233. # if numclu == 2:
  234. # max_contrast = Contrast
  235. # peak_group_mat=grouped_mat
  236. # peak_group_index=order
  237. #
  238. # else:
  239. # if Contrast > max_contrast:
  240. # max_contrast = Contrast
  241. # peak_group_mat=grouped_mat
  242. # peak_group_index=order
  244. contrast['order'].append(numclu)
  245. contrast['contrast'].append(Contrast)
  246. peak_num_index=np.where(contrast['contrast']==max(contrast['contrast']))[0][0]
  248. return contrast['order'][peak_num_index],contrast['contrast'] ##,peak_group_mat,peak_group_index
  250. # %%
  251. ###提取W, NR, R统一的threshold
  252. def thre_extract(ave_corr_matrix):
  253. thre_list=[]
  254. per=95
  255. contrast_item=[]
  256. for i in ave_corr_matrix.keys():
  257. matrix_i=ave_corr_matrix[i]
  258. np.fill_diagonal(matrix_i, 0)
  260. ###获得最优的层次聚类的order
  261. peak_numclu_i,cc_contrast=contrast(matrix_i,1,11)
  263. ###用上述最优的层次聚类的order进行分析
  264. lin_clu=linkage(matrix_i,method='ward',metric='euclidean')
  265. c_node = fcluster(lin_clu, peak_numclu_i, criterion='maxclust')
  266. print(c_node)
  267. order = np.argsort(c_node)
  268. grouped_id = c_node[order]
  269. print(grouped_id)
  271. Din_sub_val=[]
  272. for iDmat in range(1, peak_numclu_i+1):
  273. ind_subg = np.where(grouped_id == iDmat)[0]
  274. order_sub = order[ind_subg]
  275. matiD = matrix_i[order_sub][:, order_sub]
  276. Din_sub_val.extend(matiD.flatten())
  277. thre_list.extend(Din_sub_val)
  278. contrast_item.append(cc_contrast)
  279. return np.percentile(thre_list,per),contrast_item
  280. thre,contrast_item=thre_extract(ave_corr_matrix)
  281. thre
  283. # %%
  284. arr_contrast_item=np.array(contrast_item)
  286. plt.figure(figsize=(6,4))
  287. for i,contrast_i in enumerate(arr_contrast_item):
  288. peak_x=np.where(contrast_i==max(contrast_i))[0][0]
  289. peak_y=contrast_i[peak_x]
  290. plt.plot(contrast_i,lw=1,label=i);plt.legend()
  291. plt.scatter(peak_x,peak_y,s=5,c='r')
  292. np.save(new_path+'\\contrast.npy', contrast_item)
  293. # plt.savefig(new_path+'\\contrast.pdf')
  294. plt.show()
  296. # %%
  297. warnings.filterwarnings("ignore")
  298. ###寻找具有最优contrast的层次聚类order，并用该order对数据进行ensemble分析
  299. ###寻找最优oorder->确定最优clusetr->阈筛选
  301. peak_numclu = []
  302. binarized_optimal_clusetr_matrix,cluster_index,all_binarized_optimal,all_cluster_index={},{},{},{}
  303. for i in ave_corr_matrix.keys():
  304. plt.figure(figsize=(6,8))
  305. matrix_i=ave_corr_matrix[i]
  306. np.fill_diagonal(matrix_i, 0)
  307. # matrix_i = np.where(matrix_i >thre[i], 1, 0)
  309. # matrix_i=(1-matrix_i)**2
  311. ###对原始的cc matrix进行作图
  312. plt.subplot(321)
  313. sns.heatmap(matrix_i,cmap='bwr');plt.title('Raw CC values')
  315. ###获得最优的层次聚类的order
  316. peak_numclu_i,cc_contrast=contrast(matrix_i,2,11)
  318. ###用上述最优的层次聚类的order进行分析
  319. lin_clu=linkage(matrix_i,method='ward',metric='euclidean')
  320. c_node = fcluster(lin_clu, peak_numclu_i, criterion='maxclust')
  321. order = np.argsort(c_node)
  322. grouped_id = c_node[order]
  323. grouped_mat = matrix_i[order][:, order]
  325. Din_sub = np.zeros(peak_numclu_i)
  327. Din_sub_val,Din_sub_val_matrix,Din_sub_val_index=[],[],[]
  328. for iDmat in range(1, peak_numclu_i+1):
  329. ind_subg = np.where(grouped_id == iDmat)[0]
  330. order_sub = order[ind_subg] ###order_sub对应的就是细胞的命名编号
  331. matiD = matrix_i[order_sub][:, order_sub]
  333. Din_sub_val.extend(matiD.flatten())
  334. Din_sub_val_matrix.append(matiD)
  335. Din_sub_val_index.append(order_sub)
  337. Din_sub[iDmat-1] = np.sum(matiD)
  339. per=90
  340. ###每个状态单独提取阈值
  341. thre=np.percentile(Din_sub_val,per)
  342. print(thre)
  343. ###用三个状态统一的阈值
  344. # thre=thre_extract(ave_corr_matrix)
  346. ###填充三个状态下层次聚类的最优order
  347. peak_numclu.append(peak_numclu_i)
  349. ###对具有最大contrast的的cc matrix进行重新作图
  350. plt.subplot(322)
  351. sns.heatmap(grouped_mat,cmap='bwr');plt.title('Ordered CC values')
  352. ###所有位于cluster中的cc值的分布
  353. plt.subplot(323)
  354. sns.distplot(Din_sub_val,color='tomato');plt.title('The distribution of CC values within clusters')
  355. plt.vlines(x=thre,ymin=0,ymax=5,colors='gray',lw=2,linestyle='--')
  357. print(f'The threshold of {per} percentile：{thre}')
  358. plt.subplot(324)
  359. binarized_connectivity_matrix = np.where(grouped_mat >thre, 1, 0)
  360. sns.heatmap(binarized_connectivity_matrix,cmap='bwr');plt.title('Binarized CC values')
  362. ###获取具有最大均值的cluster及其对应的细胞的index
  363. averaged_cluster=[np.mean(cluster_i) for cluster_i in Din_sub_val_matrix]
  364. optimal_cluster_index=np.where(averaged_cluster==max(averaged_cluster))[0][0]
  366. print(f'the cluster which possesses the maximal averaged contrast: {optimal_cluster_index+1}')
  367. optimal_cluster=Din_sub_val_matrix[optimal_cluster_index]
  368. cluster_index[i]=Din_sub_val_index[optimal_cluster_index]
  369. ###存储所有cell
  370. all_cluster_index[i]=order
  372. plt.subplot(325)
  373. sns.heatmap(optimal_cluster,cmap='bwr');plt.title('Cluster with maximal average')
  375. plt.subplot(326)
  376. binarized_optimal_cluster = np.where(optimal_cluster >thre, 1, 0)
  377. sns.heatmap(binarized_optimal_cluster,cmap='bwr');plt.title('Binarized cluster with maximal average')
  379. binarized_optimal_clusetr_matrix[i]=binarized_optimal_cluster
  380. all_binarized_optimal[i]=binarized_connectivity_matrix
  382. print(all_cluster_index[i].shape,all_binarized_optimal[i].shape)
  384. plt.tight_layout()
  385. if is_save=='no':
  386. plt.savefig(new_path+f'\\1_workflow_{i}.pdf')
  388. plt.show()
  391. # %%
  392. for stage_name,cluster_matrix in binarized_optimal_clusetr_matrix.items():
  393. plt.figure(figsize=(6,3))
  394. plt.subplot(121)
  395. sns.heatmap(cluster_matrix,cmap='bwr');plt.title('Selected cluster')
  396. print(cluster_index[stage_name])
  397. plt.subplot(122)
  398. sns.heatmap(all_binarized_optimal[stage_name],cmap='bwr');plt.title('All neurons')
  399. print(all_cluster_index[stage_name])
  400. plt.show()
  402. # %%
  403. ###绘制连接网络
  404. valid_cell_ind,all_cell_ind={},{}
  405. network_parameters,node_parameters={}, {}
  406. net_graph={}
  407. for stage_name,cluster_matrix in binarized_optimal_clusetr_matrix.items():
  408. plt.figure(figsize=(12,3))
  410. plt.subplot(131)
  411. # binarized_connectivity_matrix = np.where(stage_matrix >thre[stage_name], 1, 0) ###np.percentile(similarity_matrix,99)
  412. sns.heatmap(cluster_matrix,cmap='bwr');plt.title(f'Binarized cluster matrix {cluster_matrix.shape}')
  414. plt.subplot(132)
  415. ### remove the isolated cell
  416. nonzero_valid_nodes=[i for i,arr_i in enumerate(cluster_matrix) if np.sum(arr_i)>0]
  417. nonzero_nodes_matrix=cluster_matrix[nonzero_valid_nodes][:,nonzero_valid_nodes]
  418. sns.heatmap(nonzero_nodes_matrix,cmap='bwr');plt.title(f'Cluster matrix {nonzero_nodes_matrix.shape}')
  420. plt.subplot(133)
  421. ### remove the two-isolated cell
  422. nonisolated_valid_nodes=[i for i,arr_i in enumerate(nonzero_nodes_matrix) if np.sum(arr_i)>1 and np.sum(nonzero_nodes_matrix[:,i])>1]
  423. nonisolated_nodes_matrix=nonzero_nodes_matrix[nonisolated_valid_nodes][:,nonisolated_valid_nodes]
  424. valid_cell_ind[stage_name]=cluster_index[stage_name][nonzero_valid_nodes][nonisolated_valid_nodes]
  426. mismatch_cell_index=[i for i in valid_cell_ind[stage_name] if i not in all_cluster_index[stage_name]]
  427. print(f'{stage_name}中{len(mismatch_cell_index)}个细胞不在总体中')
  429. ### 提取所有有连接的细胞
  430. included_nodes=[i for i,arr_i in enumerate(all_binarized_optimal[stage_name]) if np.sum(arr_i)>=1 and np.sum(all_binarized_optimal[stage_name][:,i])>=1]
  431. print(f'selectively connected cell:{len(nonisolated_valid_nodes)}, all connected cell: {len(included_nodes)}')
  432. all_nodes_matrix=all_binarized_optimal[stage_name][included_nodes][:,included_nodes]
  433. all_cell_ind[stage_name]=all_cluster_index[stage_name][included_nodes]
  435. mismatch_cell_index=[i for i in valid_cell_ind[stage_name] if i not in all_cell_ind[stage_name]]
  436. print(f'{stage_name}中{len(mismatch_cell_index)}个细胞不在总体中')
  438. # # Assuming similarity_matrices is a list of nonzero similarity matrices
  439. # adjacency_matrices = csr_matrix(nonisolated_nodes_matrix)
  440. # # Calculate clustering coefficients for each adjacency matrix
  441. # graph = nx.from_scipy_sparse_matrix(adjacency_matrices)
  442. #
  443. # # Draw the graph with dotted lines for functional connectivity
  444. # pos = nx.spring_layout(graph)
  446. # Get the indices of the connected nodes in the binary connectivity matrix
  447. connected_indices = np.argwhere(nonisolated_nodes_matrix == 1)
  448. edges = [(valid_cell_ind[stage_name][i], valid_cell_ind[stage_name][j]) for (i, j) in connected_indices]
  449. nodes=[valid_cell_ind[stage_name][i] for i,arr_i in enumerate(nonisolated_nodes_matrix)] # if np.sum(arr_i)>0
  450. network_included_nodes=valid_cell_ind[stage_name][np.isin(valid_cell_ind[stage_name],index_cellname[stage_name])]# Get the indices of the connected nodes in the binary connectivity matrix
  451. # 获取所有细胞的网络节点
  452. all_connected_indices = np.argwhere(all_nodes_matrix == 1)
  453. all_edges = [(all_cell_ind[stage_name][i], all_cell_ind[stage_name][j]) for (i, j) in all_connected_indices]
  454. all_nodes=[all_cell_ind[stage_name][i] for i,arr_i in enumerate(all_nodes_matrix)] # if np.sum(arr_i)>0
  455. print(f'selectively connected cell:{len(edges)}, all connected cell: {len(all_edges)}')
  457. graph=nx.Graph()
  458. graph.add_edges_from(edges)
  459. pos = nx.spring_layout(graph)
  460. nx.draw_networkx_nodes(graph, pos, nodelist=nodes, node_size=10, node_color="green")
  461. nx.draw_networkx_nodes(graph, pos, nodelist=network_included_nodes,node_size=10, node_color="magenta")
  462. nx.draw_networkx_edges(graph, pos, edgelist=edges, style='dotted',edge_color='gray')
  464. plt.title(f'Connectivity n={len(network_included_nodes)}/{graph.number_of_nodes()}')
  466. net_graph[stage_name]={'edges': edges,'nodes': nodes,'shared':network_included_nodes,'all_nodes':all_nodes, 'all_edges':all_edges}
  468. # ###网络分析细胞中已经label的细胞
  469. network_included_cellname=np.array(sum_arr[f'{stage_name[:-6]}name'])[network_included_nodes]
  470. if stage_name=='EM_active':
  471. network_map_labeld_cellname=network_included_cellname[np.isin(network_included_cellname,EM_positive)]
  472. if stage_name=='REM_active':
  473. network_map_labeld_cellname=network_included_cellname[np.isin(network_included_cellname,REM_active_cellname)]
  474. if len(network_map_labeld_cellname)==len(network_included_cellname):
  475. print(f'{stage_name}网络匹配数据没有问题')
  476. else:
  477. print(f'{stage_name}网络匹配数据存在问题')
  478. all_cellname_in_network=np.array(sum_arr[f'{stage_name[:-6]}name'])[valid_cell_ind[stage_name]]
  479. print('all cellname in network {}'.format(all_cellname_in_network))
  480. np.save(new_path+f'\\network_incluede_{stage_name}.npy',all_cellname_in_network)
  483. ### the innate properties of network
  484. ### Compute the average clustering coefficient for the graph
  485. clustering_coefficient = nx.average_clustering(graph)
  486. ### Compute the clustering density
  487. clustering_density=nx.density(graph)
  488. ### Returns the average local efficiency of the graph
  489. clustering_efficiency=nx.global_efficiency(graph)
  490. # ### Returns the rich-club coefficient of the graph
  491. # rich_club_coefficient=nx.rich_club_coefficient(graph)
  493. print(f'cluster parameters: {clustering_coefficient}, density: {clustering_density}, efficiency: {clustering_efficiency}')
  495. ### Compute the clustering coefficient for nodes
  496. nodes_coefficient=nx.clustering(graph)
  497. ave_nodes_coefficient=np.mean(list(nodes_coefficient.values()))
  499. ### Compute the degree centrality for nodes
  500. Gdegree=nx.degree_centrality(graph) ###计算中心度-点度中心度
  501. ave_Gdegree=np.mean(list(Gdegree.values()))
  503. #中介中心性计算
  504. Bdegree=nx.betweenness_centrality(graph) #返回的值是一个字典
  505. ave_Bdegree=np.mean(list(Bdegree.values()))
  506. # Bdegree=pd.DataFrame({'name':list(Bdegree.keys()),'Bdegree':list(Bdegree.values())})
  508. if is_save=='no':
  509. plt.savefig(new_path+f'\\2_workflow_{stage_name}.pdf')
  511. # print(list(zip(list(nodes_coefficient.values()),list(Gdegree.values()),list(Bdegree.values()))))
  513. network_parameters[stage_name]=[clustering_coefficient,clustering_density,clustering_efficiency,
  514. ave_nodes_coefficient, ave_Gdegree, ave_Bdegree]
  516. plt.show()
  517. # break
  520. # %%
  521. import networkx as nx
  522. import matplotlib.pyplot as plt
  523. import numpy as np
  524. from matplotlib.patches import Ellipse
  526. def plot_ellipse(mean, cov, color, scale=2):
  527. eigenvalues, eigenvectors = np.linalg.eig(cov)
  528. max_variance_index = np.argmax(eigenvalues)
  529. max_eigenvector = eigenvectors[:, max_variance_index]
  530. angle = np.degrees(np.arctan2(max_eigenvector[1], max_eigenvector[0]))
  531. ellipse = Ellipse(xy=mean,
  532. width=scale * 2 * np.sqrt(eigenvalues[max_variance_index]),
  533. height=scale * 2 * np.sqrt(eigenvalues[1 - max_variance_index]),
  534. angle=angle,
  535. edgecolor=color,
  536. facecolor='None',
  537. linewidth=2)
  538. plt.gca().add_patch(ellipse)
  540. plt.figure(figsize=(12, 8))
  541. G = nx.Graph()
  543. G1 = nx.Graph()
  544. G1.add_edges_from(net_graph['REM_active']['all_edges'])
  546. G2 = nx.Graph()
  547. G2.add_edges_from(net_graph['EM_active']['all_edges'])
  549. k=0.45
  550. plt.subplot(231)
  551. ###调整K值，k 参数值越小，节点之间的距离越近，从而使布局更紧凑
  552. pos1 = nx.spring_layout(G1, k=k)
  553. nx.draw_networkx_nodes(G1, pos1, nodelist=G1.nodes(), node_size=10, node_color='gray')
  554. nx.draw_networkx_nodes(G1, pos1, nodelist=net_graph['REM_active']['nodes'], node_size=5, node_color='limegreen')
  555. nx.draw_networkx_edges(G1, pos1, edgelist=G1.edges(), style='dotted', edge_color='gray')
  556. plt.title('REM_active n={}/{}'.format(len(net_graph['REM_active']['nodes']), G1.number_of_nodes()))
  557. ## label each node
  558. # label1 = {node: node for node in G1.nodes()}
  559. # nx.draw_networkx_labels(G1, pos1, labels=label1, font_size=5, font_color='black', verticalalignment='bottom')
  561. ###连接强度包括：度数中心性（degree_centrality）；接近中心性（closeness_centrality）；中介中心性（betweenness_centrality）
  562. closeness_centrality1 = nx.closeness_centrality(G1)
  563. shared_clos_cen1 = [closeness for node, closeness in closeness_centrality1.items() if node in net_graph['REM_active']['nodes']]
  564. isolated_clos_cen1 = [closeness for node, closeness in closeness_centrality1.items() if node not in net_graph['REM_active']['nodes']]
  565. print(len(closeness_centrality1), np.mean(list(closeness_centrality1.values())), '\n',
  566. len(shared_clos_cen1), np.mean(shared_clos_cen1), '\n',
  567. len(isolated_clos_cen1), np.mean(isolated_clos_cen1))
  569. # 提取 REM_active 节点的位置
  570. rem_active_positions = np.array([pos1[node] for node in net_graph['REM_active']['nodes']])
  571. mean_pos_rem = np.mean(rem_active_positions, axis=0)
  572. cov_pos_rem = np.cov(rem_active_positions, rowvar=False)
  573. # 绘制 REM_active 节点区域的椭圆
  574. plot_ellipse(mean_pos_rem, cov_pos_rem, 'limegreen')
  576. plt.subplot(232)
  577. pos2 = nx.spring_layout(G2, k=k)
  578. nx.draw_networkx_nodes(G2, pos2, nodelist=G2.nodes(), node_size=10, node_color='gray')
  579. nx.draw_networkx_nodes(G2, pos2, nodelist=net_graph['EM_active']['nodes'], node_size=5, node_color='darkmagenta')
  580. nx.draw_networkx_edges(G2, pos2, edgelist=G2.edges(), style='dotted', edge_color='gray')
  581. plt.title('EM_active n={}/{}'.format(len(net_graph['EM_active']['nodes']), G2.number_of_nodes()))
  582. ## label each node
  583. # label2 = {node: node for node in G2.nodes()}
  584. # nx.draw_networkx_labels(G2, pos2, labels=label2, font_size=5, font_color='black', verticalalignment='bottom')
  586. ###连接强度包括：度数中心性（degree_centrality）；接近中心性（closeness_centrality）；中介中心性（betweenness_centrality）
  587. closeness_centrality2 = nx.closeness_centrality(G2)
  588. shared_clos_cen2 = [closeness for node, closeness in closeness_centrality2.items() if node in net_graph['EM_active']['nodes']]
  589. isolated_clos_cen2 = [closeness for node, closeness in closeness_centrality2.items() if node not in net_graph['EM_active']['nodes']]
  590. print(len(closeness_centrality2), np.mean(list(closeness_centrality2.values())), '\n',
  591. len(shared_clos_cen2), np.mean(shared_clos_cen2), '\n',
  592. len(isolated_clos_cen2), np.mean(isolated_clos_cen2))
  594. # 提取 EM_active 节点的位置
  595. em_active_positions = np.array([pos2[node] for node in net_graph['EM_active']['nodes']])
  596. mean_pos_em = np.mean(em_active_positions, axis=0)
  597. cov_pos_em = np.cov(em_active_positions, rowvar=False)
  599. # 绘制 EM_active 节点区域的椭圆
  600. plot_ellipse(mean_pos_em, cov_pos_em, 'darkmagenta')
  602. plt.subplot(233)
  603. # 创建整体图形
  604. G = nx.compose(G1, G2)
  605. # 绘制整体图形的布局
  606. pos = nx.spring_layout(G)
  608. # 绘制第一个网络的节点和边
  609. nx.draw_networkx_nodes(G1, pos, nodelist=G1.nodes(), node_size=12, node_color='#7e318e')
  610. # nx.draw_networkx_nodes(G1, pos, nodelist=net_graph['REM_active']['shared'], node_size=4, node_color='magenta') ###color_convert_plt('#ffaf74')
  611. nx.draw_networkx_edges(G1, pos, edgelist=G1.edges(), style='dotted', edge_color='#7e318e')
  613. # 绘制第二个网络的节点和边
  614. nx.draw_networkx_nodes(G2, pos, nodelist=G2.nodes(), node_size=12, node_color='#00ff00')
  615. # nx.draw_networkx_nodes(G2, pos, nodelist=net_graph['EM_active']['shared'], node_size=4, node_color='magenta')
  616. nx.draw_networkx_edges(G2, pos, edgelist=G2.edges(), style='dotted', edge_color='#00ff00')
  618. shared_node = np.intersect1d(net_graph['EM_active']['nodes'], net_graph['REM_active']['nodes'])
  619. nx.draw_networkx_nodes(G, pos, nodelist=shared_node, node_size=2, node_color='black')
  620. plt.title('co-local n={}'.format(G.number_of_nodes()))
  622. if is_save == 'yes':
  623. np.save(new_path + '\\All_net_data.npy', net_graph)
  624. plt.savefig(new_path + '\\All_neurons_network.pdf')
  626. plt.show()
  628. # %%
  629. from t_test import t_test
  631. plt.figure(figsize=(5,5))
  632. isolated_clos_cen2.remove(max(isolated_clos_cen2))
  634. net_REM=np.concatenate((shared_clos_cen1,isolated_clos_cen1))
  635. net_EM=np.concatenate((shared_clos_cen2,isolated_clos_cen2))
  637. _avg = [np.mean(net_REM),np.mean(shared_clos_cen1),np.mean(isolated_clos_cen1),np.mean(net_EM),np.mean(shared_clos_cen2),np.mean(isolated_clos_cen2)]
  639. _err1 = np.std(net_REM)/np.sqrt(len(net_REM))
  640. _err2 = np.std(shared_clos_cen1)/np.sqrt(len(shared_clos_cen1))
  641. _err3 = np.std(isolated_clos_cen1)/np.sqrt(len(isolated_clos_cen1))
  642. _err4 = np.std(net_EM)/np.sqrt(len(net_EM))
  643. _err5 = np.std(shared_clos_cen2)/np.sqrt(len(shared_clos_cen2))
  644. _err6 = np.std(isolated_clos_cen2)/np.sqrt(len(isolated_clos_cen2))
  645. _err=[_err1,_err2,_err3,_err4,_err5,_err6]
  647. _,p0=t_test(net_REM,net_EM)
  648. _,p1=t_test(shared_clos_cen1,isolated_clos_cen1)
  649. _,p2=t_test(shared_clos_cen2,isolated_clos_cen2)
  651. colors=['magenta','magenta','magenta','gray','gray','gray']
  652. plt.bar([1,2,3,4,5,6],_avg, width=0.5, color=colors, alpha=0.4)
  654. for item in net_REM:
  655. plt.plot(1, item, 'ro')
  656. for item in shared_clos_cen1:
  657. plt.plot(2, item, 'ro')
  658. for item in isolated_clos_cen1:
  659. plt.plot(3, item, 'ro')
  660. for item in net_EM:
  661. plt.plot(4, item, 'bo')
  662. for item in shared_clos_cen2:
  663. plt.plot(5, item, 'bo')
  664. for item in isolated_clos_cen2:
  665. plt.plot(6, item, 'bo')
  666. plt.errorbar([1,2,3,4,5,6][0],_avg[0], yerr=_err[0], capsize=10, color='k')
  667. plt.errorbar([1,2,3,4,5,6][1],_avg[1], yerr=_err[1], capsize=10, color='k')
  668. plt.errorbar([1,2,3,4,5,6][2],_avg[2], yerr=_err[2], capsize=10, color='k')
  669. plt.errorbar([1,2,3,4,5,6][3],_avg[3], yerr=_err[3], capsize=10, color='k')
  670. plt.errorbar([1,2,3,4,5,6][4],_avg[4], yerr=_err[4], capsize=10, color='k')
  671. plt.errorbar([1,2,3,4,5,6][5],_avg[5], yerr=_err[5], capsize=10, color='k')
  673. plt.hlines(0.75, 1, 2, 'k');plt.text(1, 0.8, f'P = {np.round(p0,4)}')
  674. plt.hlines(0.7, 3, 4, 'k');plt.text(3, 0.75, f'P = {np.round(p1,4)}')
  675. plt.hlines(0.7, 5, 6, 'k');plt.text(5, 0.75, f'P = {np.round(p2,4)}')
  677. plt.ylabel("Closeness centrality")
  678. plt.xticks([2,4],['REM','EMs']);plt.ylim(0,0.9)
  679. plt.tight_layout()
  681. para_network=pd.DataFrame([shared_clos_cen1,isolated_clos_cen1,shared_clos_cen2,isolated_clos_cen2]).T
  682. para_network.columns=['shared_clos_cen1','isolated_clos_cen1','shared_clos_cen2','isolated_clos_cen2']
  684. if is_save=='yes':
  685. para_network.to_excel(new_path+'\\closeness_centrality2.xlsx',index=None)
  686. plt.savefig(new_path+'\\connectivity strength2.pdf')
  688. plt.show()
  690. # %%
  691. venn2(subsets=(17,14, 11), set_labels=('REM active', 'EM active'),set_colors=(color_convert_plt('#bfeebe'),color_convert_plt('#d3d3d3')),alpha=0.6,normalize_to=1.0)
  692. plt.title('overlap_EM_REM')
  693. plt.savefig(new_path + '\\overlap_node.pdf')
  694. plt.show()
  696. # %%

Fig4. Network_analysis1.ipynb at commit e6ed22f, under MIT · at the source

Overview

Authors: Chengyong Jiang1, Yuanyuan Luo1, Xinrong Tan1, Hongtao Wang1, Qingshuo Meng1, Yanyu Xiong1, Er Chen1, Yuqing Chen1, Liyuan Cui1, Zhili Huang1,2, Biao Yan1, Jiayi Zhang1,3
ORCID iDs: Zhili Huang, Biao Yan, Jiayi Zhang
  1. Institutes of Brain Science, State Key Laboratory of Brain Function and Disorders, MOE Frontiers Center for Brain Science, Fudan University,Shanghai, PR China
  2. Department of Pharmacology, School of Basic Medical Sciences, Fudan University,Shanghai, PR China
  3. Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University,Shanghai, PR China
Institutions: Frontiers Center for Brain Science of the Ministry of Education (China); Fudan University (China)
Journal: Nature communications, volume 17, issue 1, article 7934
Dates: received 21 November 2024; accepted 12 June 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74768-5 · PMID 42342690 · PMCID PMC13448821 · OpenAlex W7165804481
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Brainstem, REM sleep
MeSH: Cholinergic Neurons*, Eye Movements*, Oculomotor Nuclear Complex*, Sleep, REM*, Animals, Female, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Oculomotor Muscles, Optogenetics, Periaqueductal Gray, Vesicular Glutamate Transport Protein 2, Vesicular Inhibitory Amino Acid Transport Proteins, Wakefulness (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (T2325008, 82021002, 82588301)
Citations: cited by 1 paper (Europe PMC); 76 references in the paper
Research resources: ChAT-Cre transgenic mice RRID:IMSR_JAX:006410, Ai14 reporter mice RRID:IMSR_JAX:007914

Abstract

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18810386233/Parallel-circuit-in-oculomotor-nucleus-to-control-eye-movements-and-REM-sleep

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Cite

This paper

Jiang, C., Luo, Y., Tan, X., Wang, H., Meng, Q., Xiong, Y., Chen, E., Chen, Y., Cui, L., Huang, Z., Yan, B., & Zhang, J. (2026). Parallel cholinergic circuit in oculomotor nucleus to control eye movements and REM sleep. Nature communications, 17(1), 7934. https://doi.org/10.1038/s41467-026-74768-5

BibTeX

@article{jiang2026parallel,
author = {Jiang, Chengyong and Luo, Yuanyuan and Tan, Xinrong and Wang, Hongtao and Meng, Qingshuo and Xiong, Yanyu and Chen, Er and Chen, Yuqing and Cui, Liyuan and Huang, Zhili and Yan, Biao and Zhang, Jiayi},
title = {{Parallel cholinergic circuit in oculomotor nucleus to control eye movements and REM sleep}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7934},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74768-5},
url = {https://doi.org/10.1038/s41467-026-74768-5},
pmid = {42342690},
pmcid = {PMC13448821}
}

RIS

TY - JOUR
AU - Jiang, Chengyong
AU - Luo, Yuanyuan
AU - Tan, Xinrong
AU - Wang, Hongtao
AU - Meng, Qingshuo
AU - Xiong, Yanyu
AU - Chen, Er
AU - Chen, Yuqing
AU - Cui, Liyuan
AU - Huang, Zhili
AU - Yan, Biao
AU - Zhang, Jiayi
TI - Parallel cholinergic circuit in oculomotor nucleus to control eye movements and REM sleep
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/24
VL - 17
IS - 1
SP - 7934
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74768-5
UR - https://doi.org/10.1038/s41467-026-74768-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-74768-5",
"type": "article-journal",
"title": "Parallel cholinergic circuit in oculomotor nucleus to control eye movements and REM sleep",
"container-title": "Nature communications",
"author": [
{
"family": "Jiang",
"given": "Chengyong"
},
{
"family": "Luo",
"given": "Yuanyuan"
},
{
"family": "Tan",
"given": "Xinrong"
},
{
"family": "Wang",
"given": "Hongtao"
},
{
"family": "Meng",
"given": "Qingshuo"
},
{
"family": "Xiong",
"given": "Yanyu"
},
{
"family": "Chen",
"given": "Er"
},
{
"family": "Chen",
"given": "Yuqing"
},
{
"family": "Cui",
"given": "Liyuan"
},
{
"family": "Huang",
"given": "Zhili"
},
{
"family": "Yan",
"given": "Biao"
},
{
"family": "Zhang",
"given": "Jiayi"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7934",
"DOI": "10.1038/s41467-026-74768-5",
"PMID": "42342690",
"PMCID": "PMC13448821",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74768-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
24
]
]
}
}

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