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A multimodal spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain.

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  1. [1] § Methods › Alignment of transcriptomic cell types with morphology and electrophysiology › Alignment of mRNA to established neuronal t-types. ↔ densities_app/Validation_compute_m_type_gene_expressions.ipynb, lines 89 to 190 · score 0.94 · log2 nonzero expression, gene selection, near zero, selects genes, gene expression, decay · Report a mismatch
  2. [2] § Methods › Atlas construction: whole-brain, voxel-scale cell-type density atlas › Estimation of cutting angles and coronal region volumes for MERFISH sections. ↔ t-types/notebooks/density1.1_missing_regions-Copy1.ipynb, lines 48 to 64 · score 0.79 · Golgi cells, Bergman glia, Purkinje cells, Purkinje layers, neurotransmitter, GABA · Report a mismatch
  3. [3] § Methods › Atlas construction: whole-brain, voxel-scale cell-type density atlas › Estimation of cutting angles and coronal region volumes for MERFISH sections. ↔ t-types/density1.1_missing_regions.py, lines 47 to 80 · score 0.76 · Golgi cells, Bergman glia, Purkinje cells, neurotransmitter, GABA, Glut · Report a mismatch
  4. [4] § Methods › Atlas construction: whole-brain, voxel-scale cell-type density atlas › Cell Density Estimation and Scaling Across Brain Regions. ↔ t-types/density1_estimation_all_region_53slices_average_reconstcords_voxvol.py, lines 74 to 115 · score 0.73 · voxelized region, parcellation substructures, region volumes, neurotransmitter, division, intersecting · Report a mismatch
  5. [5] § Methods › Atlas construction: whole-brain, voxel-scale cell-type density atlas › Cell Density Estimation and Scaling Across Brain Regions. ↔ t-types/notebooks/density1_estimation_all_region_53slices_average_reconstcords_voxvol-Copy1.ipynb, lines 29 to 104 · score 0.72 · parcellation substructures, MERFISH cell, region volumes, neurotransmitter, division, intersecting · Report a mismatch
  6. [6] § Methods › Alignment of transcriptomic cell types with morphology and electrophysiology › Elbow method to determine the number of clusters. ↔ densities_app/Validation_compute_m_type_gene_expressions.ipynb, lines 580 to 674 · score 0.72 · optimal cluster, UMAP projected, WCSS, Elbow, compositions, model · Report a mismatch
  7. [7] § Methods › Atlas construction: whole-brain, voxel-scale cell-type density atlas › Estimation of cutting angles and coronal region volumes for MERFISH sections. ↔ t-types/notebooks/density1_estimation_all_region_53slices_average_reconstcords_voxvol-Copy1.ipynb, lines 29 to 104 · score 0.72 · x_ccf, y_ccf, region ID, reconstructed, template, voxel · Report a mismatch
  8. [8] § Methods › Alignment of transcriptomic cell types with morphology and electrophysiology › Alignment of mRNA to established neuronal t-types. ↔ t-types/scripts/calc_av_jobarray_classesonly_writegpfs.py, the whole file · a weak match · score 0.71 · gene identifiers, gene expression, AnnData, libraries, trimmed, log2 · Report a mismatch
  9. [9] § Methods › Alignment of transcriptomic cell types with morphology and electrophysiology › Elbow method to determine the number of clusters. ↔ densities_app/transcribe_t_types_to_me_types.ipynb, lines 191 to 321 · score 0.69 · optimal cluster, UMAP projected, WCSS, Elbow, model, density · Report a mismatch
  10. [10] § Results › Data quality, technical considerations, and validation of density estimates ↔ t-types/notebooks/density5_calculate_total_cells_newjson-Copy1.ipynb, lines 1036 to 1095 · score 0.59 · fiber tracts, cerebral nuclei, olfactory areas, thresholds, cell · Report a mismatch
  11. [11] § Results › Data quality, technical considerations, and validation of density estimates ↔ t-types/notebooks/density1.3_print_info_unscaled-Copy1.ipynb, lines 164 to 202 · score 0.57 · fiber tracts, cerebral nuclei, olfactory areas, literature, validation · Report a mismatch
  12. [12] § Methods › Atlas construction: whole-brain, voxel-scale cell-type density atlas › Extending CCFv3’s hierarchical annotations and parcellations for comprehensive dataset integration. ↔ t-types/density0.1_update_parcellation_annotation_with_CRB_MOB.py, lines 18 to 68 · score 0.54 · Purkinje layers, glomerular layer, olfactory bulb, parcellation term, lobules, ontology · Report a mismatch
  13. [13] § Methods › Alignment of transcriptomic cell types with morphology and electrophysiology › Elbow method to determine the number of clusters. ↔ densities_app/Validation_compute_m_type_gene_expressions.ipynb, lines 410 to 495 · score 0.53 · mRNA, gene expression, Semilogarithmic, log10, brain regions, summed · Report a mismatch
  14. [14] § Methods › Alignment of transcriptomic cell types with morphology and electrophysiology › Elbow method to determine the number of clusters. ↔ densities_app/Validation_compute_m_type_gene_expressions.ipynb, lines 580 to 674 · score 0.52 · UMAP projection, UMAP1, UMAP2, composition, Elbow, cluster · Report a mismatch

Paper

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The authors' code

Jupyter notebook · 816 lines · 29 KB · Apache-2.0 · 4 matches

  1. # %%
  2. !pip freeze
  4. # %%
  5. import pandas as pd
  6. import numpy as np
  8. # %%
  9. import pandas as pd
  10. import numpy as np
  12. extended_p_me_t = pd.read_csv("../probabilistic_mapping_extention/extended_p_me_t.csv", index_col=0)
  14. common_t_types = extended_p_me_t.index
  15. msk_t_types = np.asarray([("IMN" not in x) & ("NN" not in x) for x in common_t_types])
  16. common_t_types = common_t_types[msk_t_types]
  18. extended_p_me_t = extended_p_me_t.reindex(common_t_types, axis=0)
  19. extended_p_me_t = extended_p_me_t.div(np.sum(extended_p_me_t, axis=1), axis=0)
  21. np.sum(extended_p_me_t,axis=1)
  23. # %%
  24. # mt_labels = pd.read_csv("../probabilistic_mapping_extention/t_and_m_types.csv", index_col=0)
  26. # dict_mclass = {0.0: "PC", 1.0: "IN"}
  27. # dict_dendclass = {x: "dend_" + str(int(x)) for x in np.unique(mt_labels["dendrites"])}
  28. # dict_axclass = {x: "ax_" + str(int(x)) for x in np.unique(mt_labels["axon"])}
  29. # dict_mlabels = {"class": dict_mclass, "dendrites": dict_dendclass, "axon": dict_axclass}
  31. # m_labels = ["_".join([dict_mlabels["class"][mt_labels["class"][x]],
  32. # dict_mlabels["dendrites"][mt_labels["dendrites"][x]],
  33. # dict_mlabels["axon"][mt_labels["axon"][x]]
  34. # ]) for x in mt_labels.index]
  35. # t_labels = mt_labels["AIBS lvl 0"]
  37. # %%
  38. neur_list = ["IN_", "PC_"]
  39. dend_list = [f"dend_{x}_" for x in range(10)]
  40. ax_list = [f"ax_{x}" for x in range(10)]
  42. m_types_list = []
  44. for neur in neur_list:
  45. for dend in dend_list:
  46. for ax in ax_list:
  47. m_types_list.append(neur+dend+ax)
  49. pd.DataFrame(m_types_list, columns=["canonical m-type"]).to_csv("/Users/yroussel/Documents/projects/probabilistic_mapping_wholebrain/lida_m_types/m_types_list.csv")
  51. # %%
  52. import mygene
  54. def convert_gene_tags_to_labels(dataframe):
  55. """
  56. Convert gene tags to gene labels using MyGeneInfo API.
  58. :param dataframe: Pandas DataFrame containing gene tags as column names.
  59. :return: Pandas DataFrame with gene labels replacing gene tags.
  60. """
  61. mg = mygene.MyGeneInfo()
  62. ens = dataframe.columns.to_list()
  63. ginfo = mg.querymany(ens, scopes='ensembl.gene')
  65. dict_id_to_symbol = {}
  67. for g in ginfo:
  68. try:
  69. dict_id_to_symbol[g["query"]] = g["symbol"]
  70. except:
  71. dict_id_to_symbol[g["query"]] = g["query"]
  73. return dataframe.rename(dict_id_to_symbol, axis=1)
  75. yao = pd.read_csv("../experimental_data/yao/all_clusters_trim_mean25_g32285ct5322.csv", index_col="gene_identifier").T
  76. # yao = convert_gene_tags_to_labels(yao)
  78. # %%
  79. # df = pd.concat([yao.T[t_labels].T,
  80. # pd.DataFrame(m_labels, index=t_labels, columns=["m_type"])],
  81. # axis=1)
  82. # df.head()
  84. # %%
  85. # mean_df = df.groupby("m_type").mean()
  86. # std_df = df.groupby("m_type").std()
  87. # sum_df = df.groupby("m_type").sum()
  89. # %%
  90. from scipy import sparse
  91. import seaborn as sns
  92. import matplotlib.pyplot as plt
  94. def geneSelection(data, threshold=0, atleast=10,
  95. yoffset=.02, xoffset=5, decay=1.5, n=None,
  96. plot=True, markers=None, genes=None, figsize=(6,3.5),
  97. markeroffsets=None, labelsize=10, alpha=1, verbose=1):
  99. if sparse.issparse(data):
  100. zeroRate = 1 - np.squeeze(np.array((data>threshold).mean(axis=0)))
  101. A = data.multiply(data>threshold)
  102. # A.data = np.log2(A.data)
  103. A.data = A.data
  104. meanExpr = np.zeros_like(zeroRate) * np.nan
  105. detected = zeroRate < 1
  106. meanExpr[detected] = np.squeeze(np.array(A[:,detected].mean(axis=0))) / (1-zeroRate[detected])
  107. else:
  108. zeroRate = 1 - np.mean(data>threshold, axis=0)
  109. meanExpr = np.zeros_like(zeroRate) * np.nan
  110. detected = zeroRate < 1
  111. mask = data[:,detected]>threshold
  112. logs = np.zeros_like(data[:,detected]) * np.nan
  113. # logs[mask] = np.log2(data[:,detected][mask])
  114. logs[mask] = data[:,detected][mask]
  115. meanExpr[detected] = np.nanmean(logs, axis=0)
  117. lowDetection = np.array(np.sum(data>threshold, axis=0)).squeeze() < atleast
  118. zeroRate[lowDetection] = np.nan
  119. meanExpr[lowDetection] = np.nan
  121. if n is not None:
  122. up = 10
  123. low = 0
  124. for t in range(100):
  125. nonan = ~np.isnan(zeroRate)
  126. selected = np.zeros_like(zeroRate).astype(bool)
  127. selected[nonan] = zeroRate[nonan] > np.exp(-decay*(meanExpr[nonan] - xoffset)) + yoffset
  128. if np.sum(selected) == n:
  129. break
  130. elif np.sum(selected) < n:
  131. up = xoffset
  132. xoffset = (xoffset + low)/2
  133. else:
  134. low = xoffset
  135. xoffset = (xoffset + up)/2
  136. if verbose>0:
  137. print('Chosen offset: {:.2f}'.format(xoffset))
  138. else:
  139. nonan = ~np.isnan(zeroRate)
  140. selected = np.zeros_like(zeroRate).astype(bool)
  141. selected[nonan] = zeroRate[nonan] > np.exp(-decay*(meanExpr[nonan] - xoffset)) + yoffset
  143. if plot:
  144. if figsize is not None:
  145. plt.figure(figsize=figsize)
  146. plt.ylim([0, 1])
  147. if threshold>0:
  148. plt.xlim([np.log2(threshold), np.ceil(np.nanmax(meanExpr))])
  149. # plt.xlim([threshold, np.ceil(np.nanmax(meanExpr))])
  150. else:
  151. plt.xlim([0, np.ceil(np.nanmax(meanExpr))])
  152. x = np.arange(plt.xlim()[0], plt.xlim()[1]+.1,.1)
  153. y = np.exp(-decay*(x - xoffset)) + yoffset
  154. if decay==1:
  155. plt.text(.4, 0.2, '{} genes selected\ny = exp(-x+{:.2f})+{:.2f}'.format(np.sum(selected),xoffset, yoffset),
  156. color='k', fontsize=labelsize, transform=plt.gca().transAxes)
  157. else:
  158. plt.text(.4, 0.2, '{} genes selected\ny = exp(-{:.1f}*(x-{:.2f}))+{:.2f}'.format(np.sum(selected),decay,xoffset, yoffset),
  159. color='k', fontsize=labelsize, transform=plt.gca().transAxes)
  161. plt.plot(x, y, color=sns.color_palette()[1], linewidth=2)
  162. xy = np.concatenate((np.concatenate((x[:,None],y[:,None]),axis=1), np.array([[plt.xlim()[1], 1]])))
  163. t = plt.matplotlib.patches.Polygon(xy, color=sns.color_palette()[1], alpha=.4)
  164. plt.gca().add_patch(t)
  166. plt.scatter(meanExpr, zeroRate, s=1, alpha=alpha, rasterized=True)
  167. if threshold==0:
  168. plt.xlabel('Mean log2 nonzero expression')
  169. plt.ylabel('Frequency of zero expression')
  170. else:
  171. plt.xlabel('Mean log2 nonzero expression')
  172. plt.ylabel('Frequency of near-zero expression')
  173. plt.tight_layout()
  175. if markers is not None and genes is not None:
  176. if markeroffsets is None:
  177. markeroffsets = [(0, 0) for g in markers]
  178. for num,g in enumerate(markers):
  179. i = np.where(genes==g)[0]
  180. plt.scatter(meanExpr[i], zeroRate[i], s=10, color='k')
  181. dx, dy = markeroffsets[num]
  182. plt.text(meanExpr[i]+dx+.1, zeroRate[i]+dy, g, color='k', fontsize=labelsize)
  184. return selected
  187. selected_genes_level0 = geneSelection(yao.values, threshold=0, atleast=10,
  188. yoffset=.02, xoffset=5, decay=1.5)
  190. selected_genes_level0 = yao.columns[selected_genes_level0]
  192. # %%
  193. # x = std_df[selected_genes_level0].mean(axis=0)
  194. # y = std_df[selected_genes_level0].std(axis=0)
  196. # plt.plot(x, y, "o")
  198. # selected_genes_level1 = (x < 2) & (y > 1.5)
  199. # plt.plot(x[selected_genes_level1], y[selected_genes_level1], "o")
  201. # selected_genes_level1 = selected_genes_level0[selected_genes_level1]
  203. # plt.xlabel('in m-type gene variability (std)')
  204. # plt.ylabel('cross m-type gene variability (std)')
  205. # plt.show()
  207. # %%
  208. # m_type_gene_expression = convert_gene_tags_to_labels(mean_df[selected_genes_level1])
  209. # m_type_gene_expression.to_csv('./m_type_gene_expression.csv')
  211. # %%
  212. # m_type_gene_expression.head(3)
  214. # %%
  215. # m_type_gene_expression_sum = convert_gene_tags_to_labels(sum_df[selected_genes_level1])
  216. # m_type_gene_expression_sum.to_csv('./m_type_gene_expression_sum.csv')
  218. # %%
  219. # t_type_gene_expression = convert_gene_tags_to_labels(df[selected_genes_level1].drop_duplicates())
  220. # t_type_gene_expression.to_csv('./t_type_gene_expression.csv')
  222. # %%
  223. # t_type_gene_expression = t_type_gene_expression.rename({x : "_".join(x.split(" ")) for x in t_type_gene_expression.index}, axis=0)
  225. # %%
  228. # %%
  229. file = './Brain_regions_me_types_composition.csv'
  230. me_types_densities = pd.read_csv(file, index_col=0, header=[0, 1])
  233. # Sum the columns within each "m-type" group
  234. m_types_densities = me_types_densities.T.groupby(level='m-type').sum().T
  236. me_types_densities.columns = [f"{col[0]}|{col[1]}" for col in me_types_densities.columns]
  237. m_types_densities.head()
  239. # %%
  240. import pickle
  241. import pandas as pd
  243. def concatenate_dataframes(dataframes_dict):
  244. """
  245. Concatenates a dictionary of DataFrames into a single DataFrame.
  247. Parameters:
  248. - dataframes_dict (dict): Dictionary where keys are strings and values are DataFrames with a single column named 'density'.
  250. Returns:
  251. - pd.DataFrame: A single concatenated DataFrame with columns renamed to the keys of the input dictionary.
  252. """
  253. # Create an empty list to hold the renamed DataFrames
  254. renamed_dfs = []
  256. # Iterate over the dictionary
  257. for key, df in dataframes_dict.items():
  258. # Rename the 'density' column to the key
  259. renamed_df = df.rename(columns={'density_mm3': key})
  260. # Append the renamed DataFrame to the list
  261. renamed_dfs.append(renamed_df)
  263. # Concatenate all DataFrames along the columns
  264. concatenated_df = pd.concat(renamed_dfs, axis=1)
  266. return concatenated_df
  268. # Open the pickle file in binary read mode
  269. with open('./scaled_densities_t_types_as_keys.pickle', 'rb') as file:
  270. # Load the data from the file
  271. data = pickle.load(file)
  273. # Now you can use the data variable which contains the deserialized object
  274. # print(data)
  276. sorted_data = {k: data[k] for k in sorted(data)}
  278. t_densities_df = concatenate_dataframes(sorted_data)
  279. t_tpes_dict = {x: x.replace(" ", "_") for x in t_densities_df.columns}
  280. t_densities_df = t_densities_df.rename(t_tpes_dict, axis=1)
  281. t_densities_df.head()
  283. # %%
  286. # %%
  287. # t_densities_df.columns
  288. # len(common_ttypes)
  289. # t_densities_df[common_ttypes]
  290. # t_type_gene_expression.T[common_ttypes].T
  292. # %%
  293. # common_ttypes = t_type_gene_expression.index.intersection(t_densities_df.columns)
  295. # ish_true = t_densities_df[common_ttypes].fillna(0) @ t_type_gene_expression.T[common_ttypes].T.fillna(0)
  298. # %%
  299. # common_mtypes = m_type_gene_expression.index.intersection(m_types_densities.columns)
  301. # ish_pred = m_types_densities[common_mtypes].fillna(0) @ m_type_gene_expression.T[common_mtypes].T.fillna(0)
  302. # ish_pred_sum = m_types_densities[common_mtypes].fillna(0) @ m_type_gene_expression_sum.T[common_mtypes].T.fillna(0)
  304. # %%
  305. extended_p_me_t
  307. # %%
  308. yao_df = yao.rename({x : "_".join(x.split(" ")) for x in yao.index}, axis=0)
  309. comon_yaotypes = yao_df.index.intersection(extended_p_me_t.index)
  310. comon_yaotypes = comon_yaotypes[extended_p_me_t.T[comon_yaotypes].fillna(0).sum() > 0]
  311. common_ttypes = comon_yaotypes.intersection(t_densities_df.columns)
  313. ttype_gene_expression = convert_gene_tags_to_labels(yao_df[selected_genes_level0][selected_genes_level0].T[common_ttypes].fillna(0).T)
  315. me_type_gene_expression = ttype_gene_expression.T @ extended_p_me_t.T[common_ttypes].T.fillna(0)
  316. me_type_gene_expression = me_type_gene_expression.T
  318. common_brain_regions = t_densities_df.index.intersection(me_types_densities.index)
  319. me_types_densities = t_densities_df.reindex(common_brain_regions)[common_ttypes].fillna(0) @ extended_p_me_t.T[common_ttypes].T.fillna(0)
  321. # common_metypes = me_type_gene_expression.index.intersection(me_types_densities.columns)
  325. me_types_used = extended_p_me_t.columns[extended_p_me_t.T[comon_yaotypes].fillna(0).sum(axis=1) > 0]
  326. correction = (len(comon_yaotypes) / len(me_types_used))
  328. ish_me = me_types_densities.reindex(common_brain_regions).fillna(0) / correction @ me_type_gene_expression / correction
  330. ish_true = t_densities_df.reindex(common_brain_regions)[common_ttypes].fillna(0) @ ttype_gene_expression
  333. # %%
  334. print(len(me_types_used))
  335. print(len(comon_yaotypes))
  336. print((len(me_types_used)/ len(comon_yaotypes))**2)
  338. # %%
  339. print(me_type_used)
  341. # %%
  342. print(
  343. np.mean(t_densities_df.reindex(common_brain_regions)[common_ttypes].fillna(0), axis=1).mean(),
  344. np.mean(me_types_densities.reindex(common_brain_regions).fillna(0), axis=1).mean()
  345. )
  347. print(
  348. ttype_gene_expression.mean().mean(),
  349. me_type_gene_expression.mean().mean()
  350. )
  352. (len(common_ttypes) / len(me_types_densities.columns)) **2
  354. # %%
  355. plt.figure(figsize=(20,4))
  356. plt.semilogy(np.sum(t_densities_df.reindex(common_brain_regions)[common_ttypes].fillna(0), axis=1), 'o')
  357. plt.semilogy(np.sum(me_types_densities.reindex(common_brain_regions).fillna(0), axis=1), 'o', alpha=.5)
  358. plt.xticks(rotation=90)
  359. plt.show()
  361. plt.figure(figsize=(20,4))
  362. plt.semilogy(np.abs(
  363. np.sum(t_densities_df.reindex(common_brain_regions)[common_ttypes].fillna(0), axis=1) - np.sum(me_types_densities.reindex(common_brain_regions).fillna(0), axis=1)
  364. ),
  365. 'o')
  366. plt.xticks(rotation=90)
  367. plt.show()
  369. # %%
  370. plt.figure(figsize=(20,4))
  371. plt.semilogy(np.sum(ttype_gene_expression, axis=0), 'o')
  372. plt.semilogy(np.sum(me_type_gene_expression, axis=0), 'o', alpha=.5)
  373. plt.xticks(rotation=90)
  374. plt.show()
  376. # %%
  379. # %%
  380. from sklearn.preprocessing import StandardScaler
  381. # ish_pred = StandardScaler().fit_transform(ish_pred)
  382. # ish_true = StandardScaler().fit_transform(ish_true)
  384. # err = np.abs(ish_pred - ish_true) / np.abs(ish_true)
  385. # err_sum = np.abs(ish_pred_sum - ish_true) / np.abs(ish_true)
  386. err_me = np.abs(ish_me - ish_true + 1e-1) / np.abs(ish_true + 1e-1)
  388. err_me
  390. # %%
  391. me_types_used = extended_p_me_t.columns[extended_p_me_t.T[comon_yaotypes].fillna(0).sum(axis=1) > 0]
  393. (len(comon_yaotypes) / len(me_types_used))**2
  395. # %%
  396. plt.figure(figsize=(12,4))
  397. plt.plot(extended_p_me_t.T[comon_yaotypes].fillna(0).sum(), 'o')
  398. plt.xticks(rotation=90)
  399. plt.show()
  401. plt.figure(figsize=(12,4))
  402. plt.plot(extended_p_me_t.T[comon_yaotypes].fillna(0).sum(axis=1), 'o')
  403. plt.xticks(rotation=90)
  404. plt.show()
  407. # %%
  408. showed_brain_regions
  410. # %%
  411. # Defining the global font properties
  412. plt.rcParams.update({
  413. 'font.size': 16, # Default font size for text
  414. 'font.family': 'Helvetica Neue', # Font family (e.g., 'serif', 'sans-serif')
  415. 'axes.titleweight': 'bold', # Title font weight (e.g., 'bold', 'normal')
  416. 'axes.labelsize': 12, # Font size for axis labels
  417. 'xtick.labelsize': 8, # Font size for x-tick labels
  418. 'ytick.labelsize': 8, # Font size for y-tick labels
  419. 'figure.titlesize': 16, # Font size for figure title
  420. 'figure.titleweight': 'bold',# Font weight for figure title
  421. 'axes.titlepad': 20, # Padding between title and plot
  422. 'figure.figsize': (10, 6), # Default figure size (width, height)
  423. 'axes.titlelocation': 'center' # Center-align the title
  424. })
  426. showed_brain_regions = common_brain_regions[::20].drop('y')
  427. showed_genes = ish_true.columns[::10]
  429. plt.figure(figsize=(11.8,4))
  431. plt.subplot(221)
  432. plt.semilogy(err_me.sum()[showed_genes], 'o')
  433. plt.xticks([], [], rotation=90)
  434. plt.ylabel(r'$\sum_{regions}^{} \frac{|mRNA_{t} - mRNA_{me}|}{mRNA_{t}}$')
  436. plt.subplot(222)
  437. plt.semilogy(err_me.T.sum()[showed_brain_regions], 'o')
  438. plt.xticks([], [], rotation=90)
  439. plt.ylabel(r'$\sum_{genes}^{} \frac{|mRNA_{t} - mRNA_{me}|}{mRNA_{t}}$')
  441. plt.subplot(223)
  442. plt.semilogy(ish_true.sum()[showed_genes], 'o')
  443. plt.semilogy((ish_me).sum()[showed_genes], 'o') #/70
  444. plt.ylabel(r'$\sum_{regions}^{} mRNA$')
  445. plt.xticks(rotation=90)
  446. plt.legend([r'$mRNA_{t}$', r'$mRNA_{me}$'])
  448. plt.subplot(224)
  449. plt.semilogy(ish_true.T.sum()[showed_brain_regions], 'o')
  450. plt.semilogy(ish_me.T.sum()[showed_brain_regions], 'o') #/70
  451. plt.ylabel(r'$\sum_{genes}^{} mRNA$')
  452. plt.xticks(rotation=90)
  453. plt.legend([r'$mRNA_{t}$', r'$mRNA_{me}$'])
  455. plt.tight_layout()
  456. plt.show()
  459. plt.figure(figsize=(11.8,11.8/3))
  461. plt.subplot(131)
  462. plt.imshow(np.log10(ish_true.reindex(showed_brain_regions)[showed_genes] + 1), vmin=0, vmax=7)
  463. plt.title(r'$log_{10}(mRNA_{t})$')
  464. plt.xticks(np.arange(len(showed_genes)), showed_genes, rotation=90)
  465. plt.yticks(np.arange(len(showed_brain_regions)), showed_brain_regions)
  466. # plt.colorbar()
  468. plt.subplot(132)
  469. plt.imshow(np.log10(ish_me.reindex(showed_brain_regions)[showed_genes] + 1), vmin=0, vmax=7)
  470. plt.title(r'$log_{10}(mRNA_{me})$')
  471. plt.xticks(np.arange(len(showed_genes)), showed_genes, rotation=90)
  472. plt.yticks(np.arange(len(showed_brain_regions)), showed_brain_regions)
  473. plt.yticks([], [])
  474. # plt.colorbar()
  476. plt.subplot(133)
  477. plt.imshow(np.log10(err_me.reindex(showed_brain_regions)[showed_genes] + 1), vmin=0, vmax=7)
  478. plt.title(r'$log_{10}(\frac{|mRNA_{t} - mRNA_{me}|}{mRNA_{t}})$')
  479. plt.xticks(np.arange(len(showed_genes)), showed_genes, rotation=90)
  480. plt.yticks(np.arange(len(showed_brain_regions)), showed_brain_regions)
  481. plt.yticks([], [])
  482. # plt.colorbar()
  484. plt.tight_layout()
  485. plt.show()
  487. plt.figure(figsize=(11.8/3,11.8/3))
  488. plt.imshow(np.log10(err_me.reindex(showed_brain_regions)[showed_genes] + 1), vmin=0, vmax=7)
  489. plt.title(r'$log_{10}(\frac{|mRNA_{t} - mRNA_{me}|}{mRNA_{t}})$')
  490. plt.xticks(np.arange(len(showed_genes)), showed_genes, rotation=90)
  491. plt.xticks([], [])
  492. plt.yticks(np.arange(len(showed_brain_regions)), showed_brain_regions)
  493. plt.yticks([], [])
  494. plt.colorbar(label=r'$density \ log_{10}$', location='bottom', orientation='horizontal')
  495. plt.show()
  497. # %%
  498. print(
  499. ish_true.sum().sum(),
  500. (ish_me).sum().sum(),
  501. (ish_true.sum().sum() - (ish_me).sum().sum() )/ ish_true.sum().sum()
  502. )
  504. # %%
  505. ish_true
  507. # %%
  508. ish_me/26.5
  510. # %%
  511. import matplotlib.pyplot as plt
  513. def plot_map(data, title, filename):
  514. """Plot and save heatmap of data."""
  515. plt.figure(figsize=(5, 3))
  516. heatmap = plt.pcolor(data.values, cmap="jet")
  518. # for y in range(data.shape[0]):
  519. # for x in range(data.shape[1]):
  520. # if 0 < data.values[y, x] < 1.:
  521. # plt.text(x + 0.5, y + 0.5, int(100 * data.values[y, x]),
  522. # horizontalalignment='center',
  523. # verticalalignment='center')
  525. plt.colorbar(heatmap, label =r"$log_{10}(mm^{3})$")
  526. plt.xticks(np.arange(len(data.columns)) + .5, data.columns, rotation=90)
  527. plt.yticks(np.arange(len(data.index)) + .5, data.index)
  528. plt.title(title)
  529. # plt.tight_layout()
  530. plt.savefig(filename)
  532. # %%
  533. # showed_brain_regions_heatmap = common_brain_regions[::20].drop('y')
  534. me_types_used_heatmap = me_types_used[::12]
  535. plot_map(np.log10(me_types_densities.reindex(showed_brain_regions)[me_types_used_heatmap] + 1.),
  536. r"$me-types \ densities$",
  537. "me_types_densities_.png")
  538. plt.show()
  540. # %%
  541. import pandas as pd
  542. import umap
  543. import matplotlib.pyplot as plt
  545. def plot_umap(df, types, showed_reg):
  546. """
  547. Plot a UMAP of the dataset and print the sample names next to the datapoints.
  549. Parameters:
  550. df (pd.DataFrame): Input dataframe with sample names as the index and feature values as the values.
  551. """
  552. # Initialize UMAP model
  553. reducer = umap.UMAP()
  555. # Fit and transform the data
  556. embedding = reducer.fit_transform(df.values)
  558. # Create a scatter plot
  559. plt.figure(figsize=(5, 5))
  560. plt.scatter(embedding[:, 0], embedding[:, 1], s=100, alpha=0.5)
  562. # Annotate points with sample names
  563. for i, sample_name in enumerate(df.index):
  564. if sample_name in showed_reg:
  565. plt.annotate(sample_name, (embedding[i, 0], embedding[i, 1]), fontsize=9, alpha=0.75)
  567. # Set plot title and labels
  568. plt.title(f'UMAP of the {types} compositon')
  569. plt.xlabel('UMAP1')
  570. plt.ylabel('UMAP2')
  572. # Show the plot
  573. plt.show()
  575. # %%
  576. showed_brain_regions_umap = common_brain_regions[::10].drop('y')
  577. plot_umap(np.log10(me_types_densities + 1), "me-types", showed_brain_regions_umap)
  578. plot_umap(np.log10(t_densities_df[common_ttypes].fillna(0) + 1), "t-types", showed_brain_regions_umap)
  580. # %%
  581. import pandas as pd
  582. import umap
  583. import matplotlib.pyplot as plt
  584. from sklearn.cluster import KMeans
  585. from scipy.cluster.hierarchy import dendrogram, linkage
  586. import seaborn as sns
  588. def plot_umap_and_clusters(df, types, showed_reg, max_clusters=10):
  589. """
  590. Plot a UMAP projection and perform clustering.
  591. Use the Elbow Method to determine the optimal number of clusters.
  592. Plot a dendrogram and a bar plot of e-type distribution within clusters.
  594. Parameters:
  595. df (pd.DataFrame): Input dataframe with sample names as the index and feature values as the values.
  596. max_clusters (int): Maximum number of clusters to test for the Elbow Method.
  597. """
  598. # Initialize UMAP model
  599. reducer = umap.UMAP()
  601. # Fit and transform the data
  602. embedding = reducer.fit_transform(df.values)
  604. # Determine the optimal number of clusters using the Elbow Method
  605. wcss = []
  606. for n in range(1, max_clusters + 1):
  607. kmeans = KMeans(n_clusters=n, random_state=42)
  608. # kmeans.fit(embedding)
  609. kmeans.fit_predict(df.values)
  610. wcss.append(kmeans.inertia_)
  612. # Plot the Elbow Method graph
  613. plt.figure(figsize=(5, 2.5))
  614. plt.plot(range(1, max_clusters + 1), wcss, marker='o')
  615. plt.title('Elbow Method for Optimal Number of Clusters')
  616. plt.xlabel('Number of clusters')
  617. plt.ylabel('WCSS')
  618. plt.show()
  620. # Ask the user to input the optimal number of clusters based on the Elbow plot
  621. optimal_clusters = int(input("Enter the optimal number of clusters based on the Elbow plot: "))
  623. # Perform K-Means clustering with the selected number of clusters
  624. kmeans = KMeans(n_clusters=optimal_clusters, random_state=42)
  625. df['cluster'] = kmeans.fit_predict(embedding)
  626. # df['cluster'] = kmeans.fit_predict(df.values)
  627. df['cluster'] = ['cluster_' + str(x) for x in df['cluster']]
  629. # Plot UMAP projection with cluster labels
  630. plt.figure(figsize=(11.8 / 3, 4))
  631. scatter = plt.scatter(embedding[:, 0], embedding[:, 1],
  632. c=[int(x.split("_")[1]) for x in df['cluster']],
  633. cmap='viridis', s=100, alpha=0.5)
  634. # plt.title('UMAP projection with K-Means clusters')
  635. plt.title(f'{types} compositon UMAP')
  636. plt.xlabel('UMAP1')
  637. plt.ylabel('UMAP2')
  638. plt.legend(*scatter.legend_elements(), title="Clusters", fontsize=8)
  640. # Annotate points with sample names
  641. for i, sample_name in enumerate(df.index):
  642. if sample_name in showed_reg:
  643. plt.annotate(sample_name, (embedding[i, 0], embedding[i, 1]), fontsize=9, alpha=0.75)
  644. plt.show()
  646. # try:
  647. # # Hierarchical Clustering and Dendrogram
  648. # Z = linkage(df.drop('cluster', axis=1), method='ward')
  649. # plt.figure(figsize=(30, 8))
  650. # dendrogram(Z, labels=df.index, leaf_rotation=90, leaf_font_size=8, color_threshold=0)
  651. # plt.title('Hierarchical Clustering Dendrogram')
  652. # plt.xlabel('Sample Index')
  653. # plt.ylabel('Distance')
  654. # plt.show()
  655. # except:
  656. # print("HCA error")
  657. # try:
  658. # # Bar plot of e-type distribution within clusters
  659. # cluster_counts = df.groupby('cluster').size()
  660. # type_distribution = df.groupby(['cluster'])[df.columns[:-1]].sum()
  661. # # e_type_distribution_percent = e_type_distribution.div(cluster_counts, axis=0) * 100
  662. # type_distribution_percent = type_distribution.div(np.sum(type_distribution, axis=1), axis=0) * 100
  665. # type_distribution_percent.plot(kind='bar', stacked=True, figsize=(5, 5), colormap='jet')
  666. # plt.title('Type Distribution within Clusters')
  667. # plt.xlabel('Cluster')
  668. # plt.ylabel('Percentage')
  669. # # plt.legend(title='Type', bbox_to_anchor=(1.05, 1), loc='upper left')
  670. # plt.show()
  671. # except:
  672. # print("bar plot error")
  674. return df
  677. # %%
  678. showed_brain_regions_umap = common_brain_regions[::10].drop('y')
  679. me_types_densities_clusters = plot_umap_and_clusters(np.log10(me_types_densities + 1.),
  680. "me-types",
  681. showed_brain_regions_umap,
  682. max_clusters=20)
  684. # %%
  685. t_types_densities_clusters = plot_umap_and_clusters(np.log10(t_densities_df[common_ttypes].fillna(0) + 1),
  686. "t-types",
  687. showed_brain_regions_umap,
  688. max_clusters=20)
  690. # %%
  691. import seaborn as sns
  693. # Merge the two dataframes on index and keep only the cluster columns
  694. df_merged = pd.DataFrame({'cluster_t': t_types_densities_clusters['cluster'], 'cluster_me': me_types_densities_clusters['cluster']})
  696. # Create the contingency table using `pd.crosstab`
  697. contingency_table = pd.crosstab(df_merged['cluster_me'], df_merged['cluster_t'])
  699. # Plot the contingency table as a heatmap
  700. plt.figure(figsize=(11.8 / 3, 4))
  701. sns.heatmap(contingency_table, annot=True, cmap="YlGnBu", cbar=True)
  702. plt.xlabel("t-types clusters")
  703. plt.ylabel("me-types clusters")
  704. plt.title("Shared Regions Between Clusters")
  705. plt.show()
  707. # %%
  708. def get_example_indices(df_me):
  709. # Group by the cluster column and get the first index from each cluster
  710. example_indices = df_me.groupby('cluster').apply(lambda x: x.index[0])
  711. return example_indices
  713. def create_pie_charts(df, df_c, example_indices, threshold=1):
  714. # Determine the layout of subplots based on the number of clusters
  715. n_clusters = len(example_indices)
  716. n_cols = 3 # Define how many columns per row
  717. n_rows = (n_clusters + n_cols - 1) // n_cols # Calculate rows needed
  719. fig, axes = plt.subplots(n_rows, n_cols, figsize=(11.8, (11.8 / n_cols) * n_rows))
  720. axes = axes.flatten() # Flatten to make indexing easier
  722. # Loop over each region and create a pie chart in a subplot
  723. for i, reg in enumerate(example_indices):
  724. data = df.T[reg].T
  725. values = data / data.sum() # Normalize to get percentages
  726. labels = data.index
  728. # Filter out labels with less than threshold percentage
  729. filtered_labels = [label if pct > threshold else '' for label, pct in zip(labels, values * 100)]
  730. filtered_pct = [f'{pct:.1f}%' if pct > threshold else '' for pct in values * 100]
  732. # Plot pie chart in the corresponding subplot
  733. axes[i].pie(values, labels=filtered_labels, autopct=lambda pct: f'{pct:.1f}%' if pct > threshold else '', startangle=140)
  734. axes[i].set_title(f'{reg} ({df_c["cluster"][reg]})')
  736. # Remove any unused subplots
  737. for j in range(i + 1, len(axes)):
  738. fig.delaxes(axes[j])
  740. # plt.tight_layout()
  741. plt.show()
  743. # %%
  744. import matplotlib.pyplot as plt
  745. import matplotlib.colors as mcolors
  746. import numpy as np
  748. def get_example_indices(df_me):
  749. # Group by the cluster column and get the first index from each cluster
  750. example_indices = df_me.groupby('cluster').apply(lambda x: x.index[0])
  751. return example_indices
  753. def create_pie_charts(df, df_c, example_indices, threshold=1):
  754. # Determine the layout of subplots based on the number of clusters
  755. n_clusters = len(example_indices)
  756. n_cols = 3 # Define how many columns per row
  757. n_rows = (n_clusters + n_cols - 1) // n_cols # Calculate rows needed
  759. fig, axes = plt.subplots(n_rows, n_cols, figsize=(11.8, (11.8 / n_cols) * n_rows))
  760. axes = axes.flatten() # Flatten to make indexing easier
  762. # Define a perceptually uniform colormap
  763. cmap = colormaps["jet"]
  764. colors = cmap(np.linspace(0, 1, len(df.index))) # Generate distinct colors for all labels
  766. # Loop over each region and create a pie chart in a subplot
  767. for i, reg in enumerate(example_indices):
  768. data = df.T[reg].T
  769. values = data / data.sum() # Normalize to get percentages
  770. labels = data.index
  772. # Filter out labels with less than threshold percentage
  773. filtered_labels = [label if pct > threshold else '' for label, pct in zip(labels, values * 100)]
  774. filtered_pct = [f'{pct:.1f}%' if pct > threshold else '' for pct in values * 100]
  776. # Select corresponding colors
  777. chart_colors = [colors[j] for j in range(len(labels))]
  779. # Plot pie chart in the corresponding subplot
  780. axes[i].pie(
  781. values,
  782. labels=filtered_labels,
  783. autopct=lambda pct: f'{pct:.1f}%' if pct > threshold else '',
  784. startangle=140,
  785. colors=chart_colors
  786. )
  787. axes[i].set_title(f"{reg} ({df_c['cluster'][reg]})")
  789. # Remove any unused subplots
  790. for j in range(i + 1, len(axes)):
  791. fig.delaxes(axes[j])
  793. plt.tight_layout()
  794. plt.show()
  797. # %%
  798. showed_brain_regions
  800. # %%
  801. example_indices
  803. # %%
  804. from matplotlib import colormaps
  806. cmap = colormaps["jet"]
  808. example_indices = get_example_indices(me_types_densities_clusters.reindex(showed_brain_regions))
  809. create_pie_charts(me_types_densities, me_types_densities_clusters, example_indices, threshold=4.9)
  811. # %%
  812. example_indices = get_example_indices(t_types_densities_clusters.reindex(showed_brain_regions))
  813. create_pie_charts(t_densities_df[common_ttypes].fillna(0), t_types_densities_clusters, example_indices, threshold=4.9)
  815. # %%

Validation_compute_m_type_gene_expressions.ipynb at commit b55ee2e, under Apache-2.0 · at the source

Overview

Authors: Csaba Verasztó1, Yann Roussel1, Lida Kanari1,2, Sébastien Piluso1, Henry Markram1,3, Daniel Keller1,3
ORCID iDs: Csaba Verasztó, Yann Roussel, Daniel Keller
  1. Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland
  2. Department of Mathematics, University of Oxford, Oxford, United Kingdom
  3. Open Brain Institute, Lausanne, Switzerland
Institutions: École Polytechnique Fédérale de Lausanne (Switzerland); University of Oxford (United Kingdom); Open Brain Institute (Switzerland)
Journal: PLoS computational biology, volume 22, issue 3, article e1014106
Dates: received 15 July 2025; accepted 7 March 2026; published online 24 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1014106 · PMID 41875149 · PMCID PMC13120702 · OpenAlex W7140225840
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Statistics
MeSH: Brain*, Neurons*, Transcriptome*, Animals, Cell Count, Computational Biology, Electrophysiological Phenomena, Gene Expression Profiling, Mice, Spatial Transcriptomics (* major topic)
Journal subjects: Biology and Life Sciences, Computational Biology, Genome Analysis, Transcriptome Analysis, Genetics, Genomics, Cell Biology, Cellular Types, Animal Cells, Neurons, Neuroscience, Cellular Neuroscience, Physiology, Electrophysiology, Anatomy, Nervous System, Central Nervous System, Medicine and Health Sciences, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Neurophysiology, Earth Sciences, Geography, Cartography, Neuronal Morphology, Neuronal Dendrites
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Board of the Swiss Federal Institutes of Technology
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Brain cells can be classified according to their transcriptomic, morphological, and electrophysiological features. Comprehensive data on the spatial density of cell types that integrate all three properties are critical for improving models of how neuronal diversity contributes to brain function, yet existing atlases lack this information. To address this gap, we created a quantitative, three-dimensional atlas of cell type density distributions in the mouse brain. We began by generating a transcriptomic cell type atlas, scaling regional density estimates from brain slices using cell counts and anatomical dimensions. For densely populated regions like the cerebellum, we further refined these estimates by applying voxel-wise corrections based on average Nissl staining intensity. To connect transcriptomic identities with functional characteristics, we leveraged patch-sequencing datasets that combine single-neuron mRNA profiles, morphological reconstructions, and electrophysiological recordings from cortical neurons. Transcriptomic types were determined from gene expression data, morphological types were assigned based on structural reconstructions, and electrophysiological types were identified using K-means clustering. The resulting whole-brain atlas (consisting of 5274 transcriptomic clusters and 458 functional morphological-electrophysiological types) and computational tools offer a high-resolution (25 μm3 voxel size), integrative resource compatible with a broad range of neuroscience applications and enable the parsing of individual cell types to reveal previously unrecognized features.

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

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BlueBrain/Molsys-transcriptomic-atlas

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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source: zenodo.org/records/15176439

YannRoussel/probabilistic_mapping_extension

License: Apache-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: b55ee2e8d8bf3f654630511b4240283d60164249, 10 March 2026
Languages: Jupyter (19), Python (15), Shell (2)
Size: 1,156 files, 36 scripts
Software Heritage: not archived
Found in: the text, “Alignment of mRNA to established neuronal t-type”
Holds: README, license file, environment (pyproject.toml, requirements.txt), 15 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (9 files), pandas (9 files), Matplotlib (5 files), scikit-learn (4 files), SciPy (4 files), seaborn (4 files), UMAP (4 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
11 files
At the source: github.com/YannRoussel/probabilistic_mapping_extension

The paper's code and data availability statement is in the Data section.

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:

  • 7 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 89 scripts, each with its path and the digest of its content;
  • 14 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability

The code and data necessary to reproduce our results are publicly available at the following repositories: https://github.com/BlueBrain/Molsys-transcriptomic-atlas, https://github.com/cveraszto/TME-types, https://github.com/YannRoussel/probabilistic_mapping_extention, https://github.com/BlueBrain/ccfv3a-extended-atlas, https://registry.opendata.aws/bluebrain_opendata/, https://zenodo.org/records/15176439.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 10 MeSH terms, 1 funder, 61 references.

Cite

This paper

Verasztó, C., Roussel, Y., Kanari, L., Piluso, S., Markram, H., & Keller, D. (2026). A multimodal spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain. PLoS computational biology, 22(3), e1014106. https://doi.org/10.1371/journal.pcbi.1014106

BibTeX

@article{veraszto2026multimodal,
author = {Verasztó, Csaba and Roussel, Yann and Kanari, Lida and Piluso, Sébastien and Markram, Henry and Keller, Daniel},
title = {{A multimodal spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain}},
journal = {PLoS computational biology},
year = {2026},
month = mar,
volume = {22},
number = {3},
pages = {e1014106},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1014106},
url = {https://doi.org/10.1371/journal.pcbi.1014106},
pmid = {41875149},
pmcid = {PMC13120702}
}

RIS

TY - JOUR
AU - Verasztó, Csaba
AU - Roussel, Yann
AU - Kanari, Lida
AU - Piluso, Sébastien
AU - Markram, Henry
AU - Keller, Daniel
TI - A multimodal spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/03/24
VL - 22
IS - 3
SP - e1014106
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1014106
UR - https://doi.org/10.1371/journal.pcbi.1014106
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pcbi.1014106",
"type": "article-journal",
"title": "A multimodal spatial atlas of transcriptomic, morphological, and electrophysiological cell type densities in the mouse brain",
"container-title": "PLoS computational biology",
"author": [
{
"family": "Verasztó",
"given": "Csaba"
},
{
"family": "Roussel",
"given": "Yann"
},
{
"family": "Kanari",
"given": "Lida"
},
{
"family": "Piluso",
"given": "Sébastien"
},
{
"family": "Markram",
"given": "Henry"
},
{
"family": "Keller",
"given": "Daniel"
}
],
"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "3",
"page": "e1014106",
"DOI": "10.1371/journal.pcbi.1014106",
"PMID": "41875149",
"PMCID": "PMC13120702",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1014106",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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