OSCR

An atlas of primate insular cortex reveals a signal-processing strategy in von Economo neurons.

Code ↔ Paper

10 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 10 matches
  1. [1] § Results › Transcriptomic landscape of the macaque AIC ↔ 10X/Clusters_Plotting.ipynb, lines 46–63 · score 0.86 · vascular leptomeningeal cells, C1QA, C1QL1, inhibitory neurons, BGN, VLMCs
  2. [2] § Results › Transcriptomic landscape of the macaque AIC ↔ 10X/Clusters_Plotting.ipynb, lines 46–63 · score 0.73 · C1QA, C1QL1, SLC17A7, inhibitory neuron, MBP, GAD2
  3. [3] § Results › Transcriptomic landscape of the macaque AIC ↔ 10X/Glia_Plot.ipynb, lines 73–77 · score 0.68 · C1QA, C1QL1, BGN, VLMCs, microglia, FLT1
  4. [4] § Methods › RNAscope mFISH ↔ 10X/Clusters_Plotting.ipynb, lines 74–93 · score 0.57 · SLC17A7, COL24A1, DSG2, HPCAL1, RORB, FEZF2
  5. [5] § Methods › Metabolic network analysis ↔ code/Fig3_humk_GABA_homology_matrix.py, lines 1–67 · score 0.56 · crab eating macaque, gene, cell
  6. [6] § Methods › Metabolic network analysis ↔ code/Fig2_humk_glut_homology_matrix.py, lines 27–32 · score 0.56 · crab eating macaque, gene
  7. [7] § Methods › RNAscope mFISH ↔ 10X/InsularCells_integrateHodgeMTG_heatmap/integrat_Ext_humanMTG_vs_mkAAI.ipynb, lines 173–206 · score 0.55 · SLC17A7, COL24A1, DSG2, HPCAL1, RORB, FEZF2
  8. [8] § Results › Multimodal profiling of two VEN subtypes ↔ 10X/InsularNeurons_MonkeyAIC_vs_HodgeHumanFI_heatmap/merge_MKinsular_HodgeFI_Ext.ipynb, lines 225–243 · score 0.52 · Exc FEZF2 DSG2, Exc FEZF2 COL24A1, neurons
  9. [9] § Results › Multimodal profiling of two VEN subtypes ↔ 10X/InsularCells_integrateHodgeMTG_heatmap/integrat_Ext_humanMTG_vs_mkAAI.ipynb, lines 173–206 · score 0.52 · Exc FEZF2 DSG2, Exc FEZF2 COL24A1, L5
  10. [10] § Results › Multimodal profiling of two VEN subtypes ↔ 10X/InsularNeurons_MonkeyAIC_vs_HodgeHumanFI_heatmap/merge_MKinsular_HodgeFI_Ext.ipynb, lines 225–243 · score 0.52 · Exc FEZF2 DSG2, Exc FEZF2 COL24A1, neurons, gene

Paper

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The authors' code

Jupyter notebook · 409 lines · 15 KB · no license · 3 matches

  1. # %%
  2. %pylab inline
  3. import scanpy as sc
  4. import pandas as pd
  5. import seaborn as sns
  6. import numpy as np
  7. import scanpy as sc
  8. import matplotlib.pyplot as plt
  10. rcParams['pdf.fonttype']=42 # in order to save fonttype in AI
  11. rcParams['ps.fonttype']=42
  13. import warnings
  14. warnings.filterwarnings("ignore")
  16. # %%
  17. adata = sc.read('./data_h5/Insular_sorted_clusters.h5ad')
  18. adata
  20. # %%
  21. adata.obs['cellType_labels_2'].cat.categories
  23. # %%
  24. figsize(10,10)
  25. sc.pl.umap(adata, color=['cell_subtypeID','cell_subtypeLabels'], legend_loc='on data', title='', frameon=False,use_raw=True,size=5)
  27. # %%
  28. figsize(10,10)
  29. sc.pl.umap(adata, color=['cellTypeID','cellTypeLabel'], legend_loc='on data', title='', frameon=False,use_raw=True,size=5)
  30. # sc.pl.umap(adata, color=['cluster_modified','sorted_ident'], legend_loc='on data', title='', frameon=False,use_raw=True,size=5,save='allCells_umap.pdf')
  32. # %%
  33. figsize(10,10)
  34. sc.pl.umap(adata, color=['cellTypeID_2','cellType_labels_2'], legend_loc='on data', title='', frameon=False,use_raw=True,size=5)
  36. # %%
  37. # adata.uns['cellType_labels_2_colors'] = adata.uns['cellTypeID_2_colors']
  38. adata.obs['cellTypeID_2'].cat.categories
  40. # %%
  41. adata.obs['cellType_labels_2'].cat.categories
  43. # %%
  44. adata.obs['cellType_labels_2']
  46. # %%
  47. marker_genes = ['PDGFRA', 'C1QL1', # markers for OPC
  48. 'AQP4', 'SLC1A3', # Astro
  49. 'AIF1', 'C1QA', # Micro
  50. 'MBP', 'MOG', # Oligo
  51. 'SNAP25', # Neuron
  52. 'TUBB3', # 'RGS4', # Neuron
  53. 'GAD1', 'GAD2', # Inhibitory Neurons
  54. 'SLC17A7', 'CAMK2A', # Ext Neurons
  55. 'MEIS2', # Meis2 Neurons (maybe Ext)
  56. 'FLT1', # Endothalial cells
  57. 'BGN', 'DCN' ] # Vascular leptomeningeal cells (VLMCs)
  59. figsize(10,10)
  60. sc.pl.stacked_violin(adata, groupby='cellTypeLabel', var_names=marker_genes,use_raw = True, swap_axes = False) #,save='stacked_vio_allCells.pdf')
  61. sc.pl.dotplot(adata, marker_genes, groupby='cellTypeLabel', dendrogram=True)
  62. # sc.pl.stacked_violin(adata, groupby='cluster_labels', var_names=marker_genes,use_raw = True, swap_axes = False,save='stacked_vio_allCells.pdf')
  63. # sc.pl.dotplot(adata, marker_genes, groupby='cluster_labels', dendrogram=True)
  65. # %%
  66. figsize(3,3)
  67. sc.pl.umap(adata, color=['C1QL1','AQP4','C1QA','MBP','GAD1','SLC17A7','MEIS2','FLT1','DCN'], legend_loc='on data', cmap = 'RdGy_r',vmax = 5,vmin = -1, ncols=5, frameon=False,use_raw=True,size=5)#,save='allCellMarkers_umap.pdf')
  69. # %%
  70. plot_genes = ['SNAP25','TUBB3','SLC17A7','GAD1','GAD2','FEZF2','FREM3','RORB','HPCAL1','LHX6','VIP','LAMP5','SST','PVALB','MEIS2','C1QL1','AQP4', 'MOG','C1QB','FLT1','DCN']
  71. sc.tl.dendrogram(adata,groupby='cellType_labels_2',var_names=plot_genes,n_pcs=20,use_rep='X_pca')
  72. sc.pl.dotplot(adata, var_names = plot_genes, groupby = 'cellType_labels_2', swap_axes = True, edgecolor='k', dendrogram = True)#, save = '/Fig_all_dot_NN.pdf')
  74. # %%
  75. plot_genes = ['SNAP25','TUBB3','SLC17A7','GAD1','GAD2','FEZF2','FREM3','RORB','HPCAL1','LHX6','VIP','LAMP5','SST','PVALB','MEIS2','C1QL1','AQP4', 'MOG','C1QB','FLT1','DCN']
  77. categories_order = ['Exc RORB DEPTOR', 'Exc FEZF2 RSPO3', 'Exc RORB CHI3L1', 'Exc RORB CYP26B1', 'Exc RORB FAP',
  78. 'Exc RORB CDK2AP1', 'Exc RORB SCN7A', 'Exc RORB TRDN ', 'Exc FEZF2 SMYD1', 'Exc HPCAL1 MEPE',
  79. 'Exc HPCAL1 PENK', 'Exc HPCAL1 FREM3', 'Exc HPCAL1 FRZB', 'Exc HPCAL1 KANK4',
  80. 'Exc FEZF2 DSG2', 'Exc FEZF2 COL24A1', 'Exc FEZF2 HTR2C', 'Exc RORB NRG1', 'Exc FEZF2 LTBP3', 'Exc FEZF2 MCUB',
  81. 'Inh PVALB COL5A2', 'Inh PVALB NKX2-1', 'Inh PVALB PVALB', 'Inh SST SP5', 'Inh SST CHODL',
  82. 'Inh SST MME', 'Inh SST PENK', 'Inh SST PTK2B', 'Inh SST ANXA2', 'Inh SST SLC30A3',
  83. 'Inh LAMP5 BMP2', 'Inh LAMP5 BCAM', 'Inh LAMP5 SERPINF1', 'Inh LAMP5 GSTM2', 'Inh LAMP5 RGCC',
  84. 'Inh LAMP5 IL13RA1',
  85. 'Inh SNCG IGFBP2', 'Inh SNCG ARHGAP18', 'Inh SNCG PIK3CG', 'Inh SNCG PDLIM5', 'Inh SNCG KLHL13',
  86. 'Inh SNCG S100A6', 'Inh SNCG ADRA1D', 'Inh SNCG CDH24', 'Inh SNCG C1QL2', 'Inh SNCG TPM2',
  88. 'Inh SERPINF1 DCN', 'Inh VIP SHISA8', 'Inh VIP SMOC1', 'Inh VIP RASL10A',
  89. 'Inh VIP CRISPLD1', 'Inh VIP SCNG', 'Inh VIP SLIT1',
  91. 'MEIS2 Neurons', 'OPC', 'Astrocytes', 'Oligocytes', 'Microglia', 'Endothalial cells', 'VLMCs']
  93. sc.pl.dotplot(adata, var_names = plot_genes, groupby = 'cellType_labels_2', categories_order=categories_order, swap_axes = True, edgecolor='k', dendrogram = False)#,save = 'Fig_allCellTypes_dotPlot.pdf')
  95. # %%
  96. adata
  98. # %% [markdown]
  99. # # Plot cell counts bar
  101. # %%
  102. cell_label_reorder = categories_order
  103. cell_label_reorder
  104. cell_counts = pd.DataFrame(adata.obs['cellType_labels_2'].value_counts())
  105. cell_counts = cell_counts.reindex(cell_label_reorder)
  107. cell_label = adata.obs['cellType_labels_2'].cat.categories
  108. cell_color = list(adata.uns['cellType_labels_2_colors'])
  110. # %%
  111. barWidth = 1
  112. cell_type_all = list(cell_counts.index)
  114. # %%
  115. heatmap_color = dict(zip(cell_label,cell_color))
  117. # %%
  118. heatmap_color_wk= pd.DataFrame.from_dict(heatmap_color, orient = 'index', columns = ['color'])
  119. heatmap_color_wk = heatmap_color_wk.reindex(cell_label_reorder)
  121. # %%
  122. cell_cat = cell_label_reorder
  123. cell_counts = pd.DataFrame(adata.obs['cellType_labels_2'].value_counts())
  124. cell_counts = cell_counts.reindex(cell_cat)
  126. # width of the bars
  127. barWidth = 1
  128. cell_type_all = list(cell_counts.index) # becase the sequence distinction between adata.obs['cellTypeID_2'].cat.categories and adata.obs['cellType_labels_2'].cat.categories. we have to reorder the cellTpe labels in cell_type_all same as that in cellTypeID_2.
  130. bar_color = dict(zip(cell_label,cell_color))
  131. bar_color= pd.DataFrame.from_dict(heatmap_color, orient = 'index', columns = ['color'])
  132. bar_color = heatmap_color_wk.reindex(cell_label_reorder)
  134. bars_original = np.array(cell_counts['cellType_labels_2'])
  135. bars1 = np.log10(bars_original)
  137. # The x position of bars
  138. r1 = np.arange(len(bars1))
  140. # %%
  141. fig = plt.figure(figsize = (20,0.8))
  142. plt.rcParams['font.size'] = '2'
  144. gs = GridSpec(1, 1, figure=fig)
  145. sns.set(font_scale=1)
  148. ax = fig.add_subplot(gs[0, 0])
  149. # Create blue bars
  150. ax.bar(r1, bars1, width = barWidth, color = bar_color['color'])
  152. # general layout
  153. ax.set_yticks([0,2,4])
  154. ax.set_xticklabels(cell_type_all,rotation=90,size=8)
  155. ax.set_xticks(np.arange(len(cell_type_all)))
  157. ax.set_xlim([-1,60])
  158. ax.set_facecolor('white')
  160. ax.spines['bottom'].set_color('#000000')
  161. ax.spines['left'].set_color('#000000')
  163. # plt.savefig("figures/Fig1_cell_counts.pdf", dpi = 600)
  165. # %% [markdown]
  166. # # Proportion of cell types present in individual sample.
  168. # %%
  169. adata
  171. # %%
  172. subCellTypeIDs = np.sort(adata.obs['cellTypeID_2'].cat.codes.unique())
  173. subCellTypeIDs
  174. nSample = len(np.sort(adata.obs['Sample'].cat.codes.unique()))
  175. nSample
  177. # %%
  178. n_cells = np.zeros([nSample,len(subCellTypeIDs)])
  179. for sample in range(nSample):
  180. sampleData = adata[adata.obs['Sample']=='batch-'+str(sample+1)]
  181. for subCellTypeID in subCellTypeIDs:
  182. n_cells[sample,subCellTypeID] = np.sum(sampleData.obs['cellTypeID_2']==subCellTypeID)
  184. # n_cells[sample] = n_cells[sample]/np.sum(n_cells[sample,:])
  185. nCell_percent = np.zeros([nSample,len(subCellTypeIDs)])
  186. for sample in range(nSample):
  187. nCell_percent[sample] = n_cells[sample]/np.sum(n_cells[sample,:])
  190. # %%
  191. fig, axs = plt.subplots(1,2, figsize=(16,6), sharey=True)
  192. width = 0.5
  193. for i in range(len(subCellTypeIDs)):
  194. axs[0].barh(range(22), nCell_percent[:,i], width, left = np.sum(nCell_percent[:,:i],axis=1), color=bar_color['color'][ adata.obs['cellType_labels_2'].cat.categories[i] ],linewidth=0.4)
  195. # ax.bar(range(22), nCell_percent[:,i], width,bottom = np.sum(nCell_percent[:,:i],axis=1) )
  197. axs[0].set_title("Cell type composition in individual sample")
  198. axs[0].spines['right'].set_visible(True)
  199. axs[0].spines['top'].set_visible(True)
  200. # axs[0].set_xlim([0,2])
  201. axs[0].legend(loc="best")
  202. axs[0].set_xlabel('Percentage')
  203. axs[0].set_ylabel('Sample ID')
  204. axs[0].set_facecolor('white')
  206. # plot the number of cells in each sample
  207. n_cells_sample = np.sum(n_cells,axis=1)
  208. n_cells_sample
  209. axs[1].barh(range(22),n_cells_sample, width, left=0)
  210. axs[1].set_title("# of Cells in individual sample")
  211. axs[1].spines['right'].set_visible(True)
  212. axs[1].spines['top'].set_visible(True)
  213. axs[1].set_xlabel('# of cells')
  214. axs[1].set_facecolor('white')
  216. # plt.savefig("figures/suppl_NrCellsStat.pdf", dpi = 600)
  218. # %%
  219. # Prepare the number of cells in each cell type.
  220. n_cellTypesAllSample = np.sum(n_cells,axis=0)
  221. n_cellTypes = np.zeros(9)
  222. n_cellTypes[0] = np.sum(n_cellTypesAllSample[0:32])
  223. n_cellTypes[1] = np.sum(n_cellTypesAllSample[33:53])
  224. n_cellTypes[2] = np.sum(n_cellTypesAllSample[53])
  225. n_cellTypes[3] = np.sum(n_cellTypesAllSample[54])
  226. n_cellTypes[4] = np.sum(n_cellTypesAllSample[55]) # np.sum(n_cellTypesAllSample[54:63])
  227. n_cellTypes[5] = np.sum(n_cellTypesAllSample[56]) # np.sum(n_cellTypesAllSample[63:68])
  228. n_cellTypes[6] = np.sum(n_cellTypesAllSample[57]) # np.sum(n_cellTypesAllSample[68:75])
  229. n_cellTypes[7] = np.sum(n_cellTypesAllSample[58]) # np.sum(n_cellTypesAllSample[75:80])
  230. n_cellTypes[8] = np.sum(n_cellTypesAllSample[59]) # np.sum(n_cellTypesAllSample[80:87])
  233. # %%
  234. # reorder the colors
  235. clusterOrder = adata.obs['cellType_labels_2'].cat.categories
  236. bar_color = bar_color.reindex(clusterOrder)
  238. # Pie plot of the cell type composition.
  239. size = 0.15
  240. cmap = plt.colormaps["tab20c"]
  241. outer_colors = ['coral','steelblue','#C0C0C0', 'darkgray','grey', '#D3D3D3', 'dimgray', 'darkgray','black',]
  243. figsize(15,10)
  244. fig, axs = plt.subplots(1,2)
  245. plt.figure(figsize=(20, 12))
  246. axs[0].pie(n_cellTypes, colors=outer_colors, )
  247. axs[0].set(aspect="equal", title='Cell type composition')
  249. axs[1].pie(n_cellTypesAllSample, colors=bar_color['color'], )
  250. axs[1].set(aspect="equal", title='Cell type composition')
  251. plt.show()
  253. # %%
  254. # Pie plot of the cell type composition.
  255. size = 0.2
  256. lineW = 0.3
  257. cmap = plt.colormaps["tab20c"]
  258. # outer_colors = cmap(np.arange(8)*4)
  259. inner_colors = cmap(range(87))
  261. figsize(5,5)
  262. fig, ax = plt.subplots()
  263. ax.pie(n_cellTypes, radius=1, colors=outer_colors,
  264. wedgeprops=dict(width=0.5, edgecolor='w',linewidth=lineW))
  265. ax.pie(n_cellTypesAllSample, radius=1-size, colors=bar_color['color'],
  266. wedgeprops=dict(width=0.5, edgecolor='w',linewidth=lineW))
  267. ax.set(aspect="equal", title='Cell type composition')
  269. # plt.show()
  270. # fig.savefig("figures/suppl_subclustersCountsPie.pdf", dpi = 600)
  272. # %%
  273. # plot the detected genes in each cell cluster.
  274. figsize(16,1.5)
  275. sc.pl.violin(adata,'n_genes',groupby='cellTypeID_2',log=True, jitter=False,color=bar_color['color'])#,save='suppl_detectedGenes_eachClass.pdf')
  277. # %%
  278. # Sample size in animals
  279. n_cells_sample
  281. animal1 = n_cells_sample[0:5]
  282. animal2 = n_cells_sample[5:7]
  283. animal3 = n_cells_sample[7:11]
  284. animal4 = n_cells_sample[11:13]
  285. animal5 = n_cells_sample[13:17]
  286. animal6 = n_cells_sample[17:21]
  287. animal7 = n_cells_sample[21:22]
  288. n_sample = np.array([len(animal1), len(animal2), len(animal3), len(animal4), len(animal5), len(animal6), len(animal7)])
  290. fig, ax = plt.subplots(1,7,figsize=(5,5), sharey=True)
  291. barWidth = 1.0
  292. for j in range(5):
  293. ax[0].bar(1,animal1[j],barWidth,bottom=np.sum(animal1[:j]),color='cornflowerblue',edgecolor='k' )
  294. ax[0].spines['right'].set_visible(False)
  295. ax[0].spines['top'].set_visible(False)
  296. for j in range(2):
  297. ax[1].bar(1,animal2[j],barWidth,bottom=np.sum(animal2[:j]),color='cornflowerblue',edgecolor='k' )
  298. ax[1].spines['right'].set_visible(False)
  299. ax[1].spines['top'].set_visible(False)
  300. ax[1].spines['left'].set_visible(False)
  301. ax[1].spines['bottom'].set_visible(False)
  302. for j in range(4):
  303. ax[2].bar(1,animal3[j],barWidth,bottom=np.sum(animal3[:j]),color='cornflowerblue',edgecolor='k' )
  304. ax[2].spines['right'].set_visible(False)
  305. ax[2].spines['top'].set_visible(False)
  306. ax[2].spines['left'].set_visible(False)
  307. ax[2].spines['bottom'].set_visible(False)
  308. for j in range(2):
  309. ax[3].bar(1,animal4[j],barWidth,bottom=np.sum(animal4[:j]),color='cornflowerblue',edgecolor='k' )
  310. ax[3].spines['right'].set_visible(False)
  311. ax[3].spines['top'].set_visible(False)
  312. ax[3].spines['left'].set_visible(False)
  313. ax[3].spines['bottom'].set_visible(False)
  314. for j in range(4):
  315. ax[4].bar(1,animal5[j],barWidth,bottom=np.sum(animal5[:j]),color='cornflowerblue',edgecolor='k' )
  316. ax[4].spines['right'].set_visible(False)
  317. ax[4].spines['top'].set_visible(False)
  318. ax[4].spines['left'].set_visible(False)
  319. ax[4].spines['bottom'].set_visible(False)
  320. for j in range(4):
  321. ax[5].bar(1,animal6[j],barWidth,bottom=np.sum(animal6[:j]),color='cornflowerblue',edgecolor='k' )
  322. ax[5].spines['right'].set_visible(False)
  323. ax[5].spines['top'].set_visible(False)
  324. ax[5].spines['left'].set_visible(False)
  325. ax[5].spines['bottom'].set_visible(False)
  326. for j in range(1):
  327. ax[6].bar(1,animal7[j],barWidth,bottom=np.sum(animal7[:j]),color='cornflowerblue',edgecolor='k' )
  328. ax[6].spines['right'].set_visible(False)
  329. ax[6].spines['top'].set_visible(False)
  330. ax[6].spines['left'].set_visible(False)
  331. ax[6].spines['bottom'].set_visible(False)
  333. # plt.savefig("figures/suppl_samplesFromEachAnimal.pdf", dpi = 600)
  335. # %%
  336. adata.obs['cellType_labels_2']
  338. # %%
  339. # plot the detected genes in each sample
  340. figsize(8,3)
  341. sc.pl.violin(adata,'n_genes',groupby='Sample',log=True, jitter=False,color='g')#,save='suppl_detectedGenes_eachSample.pdf')
  343. # %%
  344. # plot the total counts in each sample
  345. figsize(8,3)
  346. ax = sc.pl.violin(adata,'total_counts',groupby='Sample',log=True, jitter=False,color='g')#,save='suppl_totalCounts_eachSample.pdf')
  348. # %%
  349. # of cells relative to the UMI counts
  350. figsize(8,5)
  351. nCells_hist = hist(adata.obs['total_counts'],bins=range(0,30000,1000))
  352. meanCounts = np.mean(adata.obs['total_counts'])
  353. medianCount = np.median(adata.obs['total_counts'])
  354. # add figure properties.
  355. ax = plt.gca()
  356. ylim= ax.get_ylim()
  357. ax.plot([meanCounts,meanCounts],ylim,c='r')
  358. ax.plot([medianCount,medianCount],ylim,c='k')
  359. ax.text(15000,20000,'meanCounts: '+str(meanCounts))
  360. ax.text(15000,15000,'medianCount: '+str(medianCount))
  361. ax.spines['right'].set_visible(False)
  362. ax.spines['top'].set_visible(False)
  363. ax.legend(loc='upper right')
  365. # plt.savefig("figures/suppl_UMIcountsDistribution.pdf", dpi = 600)
  367. # %%
  368. # of cells relative to the detected genes
  369. figsize(8,5)
  370. nCells_hist = hist(adata.obs['n_genes'],bins=range(0,9000,500),color = 'darkgreen')
  371. mean_nGenes = np.mean(adata.obs['n_genes'])
  372. median_nGenes = np.median(adata.obs['n_genes'])
  373. # add figure properties.
  374. ax = plt.gca()
  375. ylim= ax.get_ylim()
  376. ax.plot([mean_nGenes,mean_nGenes],ylim,c='r')
  377. ax.plot([median_nGenes,median_nGenes],ylim,c='k')
  378. ax.text(4000,20000,'mean_nGenes: '+str(mean_nGenes))
  379. ax.text(4000,15000,'median_nGenes: '+str(median_nGenes))
  380. ax.spines['right'].set_visible(False)
  381. ax.spines['top'].set_visible(False)
  382. ax.legend(loc='upper right')
  384. # plt.savefig("figures/suppl_detectedGenesDistribution.pdf", dpi = 600)
  386. # %%
  387. figsize(8,7)
  388. sc.pl.umap(adata,color=['doublet_score'],vmin=0,vmax=1.0, size=12, cmap = 'summer')#,save='suppl_doubleletScore.pdf')# 'RdGy_r',)
  390. # %%
  391. figsize(8,8)
  392. sc.pl.umap(adata,color=['Sex'], size=10)#,save='suppl_sex.pdf')
  394. # %%
  395. figsize(8,7)
  396. sc.pl.umap(adata,color=['sampleLayers'],vmin=0,vmax=1.0, size=12)#, save='suppl_layersDistribution_allClusters.pdf')
  398. # %%
  399. figsize(8,7)
  400. sc.pl.umap(adata,color=['Sample'],vmin=0,vmax=1.0, size=3, frameon='', title='', cmap = 'RdGy_r')#,save='suppl_batchDistribution_allClusters.pdf')
  402. # %%
  405. # %%
  408. # %%

Clusters_Plotting.ipynb at commit 44cd8a6, no license · at the source

Overview

Authors: Rui-Feng Liu1, Mengyao Huang1, Yuhui Shen1, Mingting Shao1, Junzhan Jing2,3, Nana Xu1, Lei Tang1, Biaodi Liu1, Jianming Shi1, Fanrui Chen1, Zhao-Zhe Hao1, Xiaolong Jiang2,3, Sheng Liu1
ORCID iDs: Rui-Feng Liu, Junzhan Jing, Zhao-Zhe Hao, Xiaolong Jiang, Sheng Liu
  1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
  2. Department of Neuroscience, Baylor College of Medicine, Houston, TX USA
  3. Jan and Dan Duncan Neurological Research Institute, Texas Children’s Hospital, Houston, TX USA
Institutions: State Key Laboratory of Ophthalmology; Zhongshan Ophthalmic Center, Sun Yat-sen University; Baylor College of Medicine (United States); Texas Children's Hospital (United States); Sun Yat-sen University (China)
Journal: Nature cell biology, volume 28, issue 8, pages 1742-1757
Dates: received 4 May 2025; accepted 5 June 2026; published online 2 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41556-026-02009-4 · PMID 42393360 · PMCID PMC13457102 · OpenAlex W7167052611
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: RNA sequencing, Transcriptomics, Gene expression, Computer modelling
MeSH: Insular Cortex*, Neurons*, Action Potentials, Animals, Axons, Dendrites, Female, Male, Transcriptome (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (R01 MH120404, R01 MH122169); National Natural Science Foundation of China (National Science Foundation of China) (82425016, 81961128021); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (R01 MH120404, R01 MH122169)
Citations: not cited yet (Europe PMC); 113 references in the paper
Research resources: Neuroscience Multi-omics Archive RRID:SCR_016152

Abstract

The anterior insular cortex (AIC) is a critical hub integrating exteroceptive and interoceptive information into high-order cognition, yet its neural basis remains incompletely understood. Here, by combining whole-cell-based single-cell transcriptomics with Patch-seq recordings, we resolved and characterized 78 detailed cell types in the macaque AIC, revealing the diversity and specialization of this region in cell type, connectivity profile, signal-processing strategy and metabolic characteristics. Among these, we identified two transcriptomically and morphoelectrically defined von Economo neuron (VEN) subtypes, DSG2-expressing VEN-L and POC5-expressing VEN-S, transcriptomically relating to extratelencephalic and corticothalamic projection neurons, respectively. We also uncovered a previously underappreciated signal-processing strategy by VENs, whereby the geometry of the dendrite-originating axon reshapes action potential dynamics and enhances somatic responsiveness to deep-layer synaptic inputs. Our multimodal atlas establishes a molecular and functional framework for investigating the circuit principles underlying cognitive processes in the primate AIC.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

haozhaozhe/FM_V1

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 360f770e4cae8b6cebcf0748f3f9d2b8c9daa2aa, 8 November 2022
Languages: Python (6)
Size: 19 files, 6 scripts
Software Heritage: not archived
Found in: the text, “Wei et al.”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Scanpy (6 files), Matplotlib (4 files), pandas (3 files), NumPy (2 files), seaborn (2 files), Harmony (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
8 files
At the source: github.com/haozhaozhe/FM_V1

RFLiu2021/AIC_proj

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 44cd8a6dfbb1f1ce363d73742e4f3652086b0f9d, 5 July 2026
Languages: MATLAB (44), Jupyter (31), Python (3)
Size: 159 files, 78 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 31 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (10 files), Scanpy (10 files), seaborn (9 files), Matplotlib (7 files), NumPy (7 files), Harmony (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/RFLiu2021/AIC_proj

Zenodo 19995355

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

Zenodo 17799559

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 6 files
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source:

Code availability

The custom-made software PatchAnaLab was developed and deposited at Zenodo112. The code used for data analysis, computational modelling and figure generation is available on GitHub at https://github.com/RFLiu2021/AIC_proj, with direct links to the processed data and meta data deposited at Zenodo113.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

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  • 16 scripts, each with its path and the digest of its content;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

Datasets cited

Data Availability Statement

Sequencing data that support the findings of this study, including 10x scRNA-seq data and Patch-seq data (both in FASTQ format), have been deposited in the Gene Expression Omnibus (GEO) under accession numbers GSE319557 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE319557) and GSE319369 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE319369), respectively. Previously published sequencing data that were reanalysed here are available at EMBL-EBI under accession code E-MTAB-10459 (http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-10459/) (ref. 2), Neuroscience Multi-omics Archive (NeMO, RRID:SCR_016152 (https://scicrunch.org/resolver/SCR_016152/))41, and GEO under accession numbers GSE115746 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115746) (ref. 47), GSE127898 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE127898) and GSE127774 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE127774) (ref. 46). Metabolomics data that support the findings of this study have been deposited in MetaboLights under accession code MTBLS13927 (http://www.ebi.ac.uk/metabolights/MTBLS13927). Electrophysiological data that supporting the findings have been deposited in the DANDI archive at https://dandiarchive.org/dandiset/001746 (main dataset, Patch-seq), https://dandiarchive.org/dandiset/001750 (https://dandiarchive.org/dandiset/001746) (sodium channel currents), https://dandiarchive.org/dandiset/001751 (https://dandiarchive.org/dandiset/001746) (multi-Patch recording) and https://dandiarchive.org/dandiset/001752 (https://dandiarchive.org/dandiset/001746) (postsynaptic currents). Neuronal morphology data supporting the findings have been deposited at NeuroMorpho.org ref. 111 in ASC format. All other data supporting the findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

The custom-made software PatchAnaLab was developed and deposited at Zenodo112. The code used for data analysis, computational modelling and figure generation is available on GitHub at https://github.com/RFLiu2021/AIC_proj, with direct links to the processed data and meta data deposited at Zenodo113.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 4 keywords, 9 MeSH terms, 3 funders, 110 references, 1 RRID.

Cite

This paper

Liu, R.-F., Huang, M., Shen, Y., Shao, M., Jing, J., Xu, N., Tang, L., Liu, B., Shi, J., Chen, F., Hao, Z.-Z., Jiang, X., & Liu, S. (2026). An atlas of primate insular cortex reveals a signal-processing strategy in von Economo neurons. Nature cell biology, 28(8), 1742-1757. https://doi.org/10.1038/s41556-026-02009-4

BibTeX

@article{liu2026atlas,
author = {Liu, Rui-Feng and Huang, Mengyao and Shen, Yuhui and Shao, Mingting and Jing, Junzhan and Xu, Nana and Tang, Lei and Liu, Biaodi and Shi, Jianming and Chen, Fanrui and Hao, Zhao-Zhe and Jiang, Xiaolong and Liu, Sheng},
title = {{An atlas of primate insular cortex reveals a signal-processing strategy in von Economo neurons}},
journal = {Nature cell biology},
year = {2026},
month = jul,
volume = {28},
number = {8},
pages = {1742--1757},
publisher = {Nature Portfolio},
issn = {1465-7392},
doi = {10.1038/s41556-026-02009-4},
url = {https://doi.org/10.1038/s41556-026-02009-4},
pmid = {42393360},
pmcid = {PMC13457102}
}

RIS

TY - JOUR
AU - Liu, Rui-Feng
AU - Huang, Mengyao
AU - Shen, Yuhui
AU - Shao, Mingting
AU - Jing, Junzhan
AU - Xu, Nana
AU - Tang, Lei
AU - Liu, Biaodi
AU - Shi, Jianming
AU - Chen, Fanrui
AU - Hao, Zhao-Zhe
AU - Jiang, Xiaolong
AU - Liu, Sheng
TI - An atlas of primate insular cortex reveals a signal-processing strategy in von Economo neurons
T2 - Nature cell biology
J2 - Nat Cell Biol
PY - 2026
DA - 2026/07/02
VL - 28
IS - 8
SP - 1742
EP - 1757
SN - 1465-7392
PB - Nature Portfolio
DO - 10.1038/s41556-026-02009-4
UR - https://doi.org/10.1038/s41556-026-02009-4
LA - en
ER -

CSL-JSON

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