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Temporal Topology Provides an Interpretable Framework for Neuronal Morphogenesis.

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  1. [1] § Methods › Classification Process ↔ Code.zip/Code/Figure_3.ipynb, lines 186–196 · score 0.72 · DecisionTreeClassifier, LinearDiscriminantAnalysis, scikit-learn, QuadraticDiscriminant, classification, classes
  2. [2] § Methods › Classification Process ↔ Code.zip/Code/Figure_5.ipynb, lines 105–122 · score 0.71 · DecisionTreeClassifier, LinearDiscriminantAnalysis, scikit-learn, QuadraticDiscriminant, classification, classes

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The authors' code

Jupyter notebook · 340 lines · 14 KB · CC-BY-4.0 · 1 match

  1. # %% [markdown]
  2. # This notebook gathers the code used to produce the Figure 3 of "Quantifying neuronal differentiation using temporal topological persistence", from Ferreira Castro 2020 dataset for class I neurons and from Stuerner 2022 for class III neurons.
  4. # %%
  5. # One first imports all the necessary packages
  6. import os
  7. import tmd
  8. import numpy as np
  9. import matplotlib.pyplot as plt
  10. import pandas as pd
  11. import sys
  12. from tmd.view import plot
  13. from tmd.view import view
  14. from tmd.view import common
  15. from tmd.Population import Population
  16. from matplotlib import cm
  18. # Import methods defined in time_series_tmd.py using sys
  19. from time_series_tmd import vineyards, get_persistence_diagram_timelapse
  20. from advanced_plot import plot_persistent_homology_video, multiple_trees_plot
  21. from classification import *
  23. from NeuronAnalysis import *
  25. from classifier import classify_cell_in_groups
  27. # %% [markdown]
  28. # After ensuring one is working with the correct files, that have "R" entries fixed to 3 and not 1 (except for the first node), one loads class I neurons.
  30. # %%
  31. FOLDER = "Neuron_dataset/Class I/Curated"
  32. March = ["March1_14_1h", "March1_15_1h", "March2_15_1h", "March3_14_1h", "March3_15_1h", "March4_15_1h"]
  33. sa = ["sa1", "sa2", "sa3", "sa4"]
  34. sat = ["sat1", "sat2", "sat3", "sat4"]
  35. t_30 = ["t1_30", "t2_30", "t3_30", "t4_30", "t7_30"] # "t5_30" and "t6_30" are not considered as their dendrite morphologies have not differentiated yet
  36. tl_lothar_1b = ["tl_lothar_1b_2l", "tl_lothar_1b_3l", "tl_lothar_1b_4r", "tl_lothar_1b_5l", "tl_lothar_1b_6r"]
  37. tl_lothar_2B = ["tl_lothar_2B_3r", "tl_lothar_2B_4r", "tl_lothar_2B_5r"]
  38. tl_lothar_4A = ["tl_lothar_4A_2r", "tl_lothar_4A_3r", "tl_lothar_4A_5r", "tl_lothar_4A_6r", "tl_lothar_4A_7r"]
  39. tl_lothar_5A = ["tl_lothar_5A_4r", "tl_lothar_5A_5r", "tl_lothar_5A_6r"]
  40. tl_lothar_a = ["tl_lothar_a3", "tl_lothar_a4", "tl_lothar_a5", "tl_lothar_a6"]
  41. tl_nov_friday = ["tl_nov_friday1", "tl_nov_friday2", "tl_nov_friday3", "tl_nov_friday4", "tl_nov_friday5", "tl_nov_friday6"]
  42. tl_nov_thursday = ["tl_nov_thursday1", "tl_nov_thursday2", "tl_nov_thursday3", "tl_nov_thursday4", "tl_nov_thursday5"]
  43. tl_nov_tuesday = ["tl_nov_tuesday_late_1"]
  45. larval_series = March + sa + sat + t_30 + tl_nov_thursday + tl_nov_tuesday # tl_nov_friday is not considered as its dendrite morphologies have not differentiated yet
  46. embryonic_series = tl_lothar_1b + tl_lothar_2B + tl_lothar_4A + tl_lothar_5A + tl_lothar_a
  47. class_I_series = [larval_series, embryonic_series]
  49. # %% [markdown]
  50. # Class I neurons are formatted into a pandas data frame for each neuron type, that contains the path to data and the time of the measurement
  52. # %%
  53. larval_df = pd.DataFrame()
  54. embryonic_df = pd.DataFrame()
  56. for category in class_I_series:
  57. category_list = []
  58. for family in category:
  59. path_to_neuron = FOLDER +"/"+ family
  60. family_files = sorted(os.listdir(path_to_neuron))
  61. family_files = [file for file in family_files if file != ".DS_Store"] # to take out the DS store
  63. paths_to_neurons = []
  64. neurons_time = []
  65. neurons_indices = []
  67. for neuron in family_files:
  68. full_path = path_to_neuron + "/" + neuron
  69. endtime = full_path.rfind(".swc") # find the end of file name
  70. time = full_path.rfind("_") # find the last occurence of _ , that distinguishes the time
  71. index = full_path.rfind("_", 0, time) # find the one before last occurence of _ , that distinguishes the index
  72. paths_to_neurons.append(full_path)
  73. neurons_time.append(full_path[time+1:endtime])
  74. neurons_indices.append(full_path[index+1:time])
  76. category_list.append(pd.DataFrame(data={"Time": neurons_time, "Path": paths_to_neurons}, index=neurons_indices))
  78. if family in larval_series:
  79. larval_df = pd.concat(category_list, axis=1, keys=category, names=["Type", "Quantity"])
  80. else:
  81. embryonic_df = pd.concat(category_list, axis=1, keys=category, names=["Type", "Quantity"])
  83. class_I_df = pd.concat([larval_df, embryonic_df], axis=1, keys=["larval", "embryonic"])
  84. class_I_df.head()
  86. # %% [markdown]
  87. # One now loads class III neurons.
  89. # %%
  90. FOLDER = "Neuron_dataset/Class III/Curated"
  91. DAC161 = ["capu", "control", "singed", "spire"]
  92. DAC161_Paths = []
  93. DAMARCM_40A = ["arp", "control"]
  94. DAMARCM_40A_Paths = []
  95. DAMARCM_G13 = ["control", "ena", "twinstar"]
  96. DAMARCM_G13_Paths = []
  98. for neuron_class in DAC161:
  99. DAC161_Paths.append(FOLDER+"/DA_C161_"+neuron_class+"/timelapse_C161_"+neuron_class+"_data")
  101. for neuron_class in DAMARCM_40A:
  102. if neuron_class == "arp":
  103. DAMARCM_40A_Paths.append(FOLDER+"/DAMARCM_40A_"+neuron_class+"/timelapse_MARCM_"+neuron_class+"_data")
  104. else:
  105. DAMARCM_40A_Paths.append(FOLDER+"/DAMARCM_40A_"+neuron_class+"/timelapse_MARCM_"+neuron_class+"_40A_data")
  107. for neuron_class in DAMARCM_G13:
  108. if neuron_class == "control":
  109. DAMARCM_G13_Paths.append(FOLDER+"/DAMARCM_G13_"+neuron_class+"/timelapse_MARCM_"+neuron_class+"_G18_data")
  110. else:
  111. DAMARCM_G13_Paths.append(FOLDER+"/DAMARCM_G13_"+neuron_class+"/timelapse_MARCM_"+neuron_class+"_data")
  113. class_III_paths = DAC161_Paths + DAMARCM_40A_Paths + DAMARCM_G13_Paths
  115. class_III_paths_clean = [path+"_clean" for path in class_III_paths]
  116. class_III_folders = sorted(os.listdir(class_III_paths_clean[0])) # since all files are the same
  117. class_III_folders.pop(0) # to take out the DS store
  119. # %% [markdown]
  120. # A pandas dataframe is also created with class III neurons.
  122. # %%
  123. DAC161_df = pd.DataFrame()
  124. DAMARCM_40A_df = pd.DataFrame()
  125. DAMARCM_G13_df = pd.DataFrame()
  127. for path in class_III_paths_clean:
  128. category_list = []
  129. for folder in class_III_folders:
  130. path_to_neuron = path+"/"+folder
  131. family_files = sorted(os.listdir(path_to_neuron))
  132. family_files = [file for file in family_files if file != ".DS_Store"] # to take out the DS store
  134. paths_to_neurons = []
  135. neurons_indices = []
  136. neurons_time = []
  138. for neuron in family_files:
  139. full_path = path_to_neuron + "/" + neuron
  140. endtime = full_path.rfind(".swc") # find the end of file name
  141. time = full_path.rfind("/") # find the last occurence of / , that distinguishes the time
  142. paths_to_neurons.append(full_path)
  143. neurons_time.append(str(5*(float(full_path[time+1:endtime]) - 1))) # conversion according to the paper
  144. neurons_indices.append(full_path[time+1:endtime])
  146. category_list.append(pd.DataFrame(data={"Time": neurons_time, "Path": paths_to_neurons}, index=neurons_indices))
  148. class_name = path.split("/")[4].split("_")[2]
  149. class_keys = [class_name + "_" + f for f in class_III_folders]
  150. category_df = pd.concat(category_list, axis=1, keys=class_keys, names=["Type", "Quantity"])
  152. if "DA_C161" in path:
  153. DAC161_df = pd.concat([DAC161_df, category_df], axis=1)
  154. elif "DAMARCM_40A" in path:
  155. DAMARCM_40A_df = pd.concat([DAMARCM_40A_df, category_df], axis=1)
  156. else:
  157. DAMARCM_G13_df = pd.concat([DAMARCM_G13_df, category_df], axis=1)
  159. class_III_df = pd.concat([DAC161_df, DAMARCM_40A_df, DAMARCM_G13_df], axis=1, keys=["DA_C161", "DAMARCM_40A", "DAMARCM_G13"])
  160. class_III_df.head()
  162. # %% [markdown]
  163. # Both class I and class III dataframes are combined to a global dataframe.
  165. # %%
  166. total_df = pd.concat([class_I_df, class_III_df], axis=1, keys=["class_I", "class_III"])
  167. total_df.head()
  169. # %% [markdown]
  170. # The reconstructed dendritic trees, from example the one of a class I neuron, are represented as follows with the methods of the tmd package.
  172. # %%
  173. filename = total_df.class_I.larval.March1_14_1h["Path"][0]
  174. neu = tmd.io.load_neuron_from_morphio(filename)
  175. view.neuron(neu, title="");
  177. # %% [markdown]
  178. # The tmd package also computes the persistent image of the neuron.
  180. # %%
  181. tree = neu.neurites[0]
  182. ph = tmd.methods.get_persistence_diagram(tree)
  183. ph_apical = tmd.methods.get_ph_neuron(neu, neurite_type="apical_dendrite")
  184. plot.persistence_image(ph);
  186. # %% [markdown]
  187. # Classification of class I from class III neurons is performed with the methods implemented in Scikit-learn package. In what follows, one compares three classifiers: linear, quadratic and decision tree.
  189. # %%
  190. # The separation is the confusion matrix, so one plots it with the metrics package of sklearn
  191. from sklearn import metrics
  193. neurite_type = "apical_dendrite"
  194. # Possible modules and classifiers one can use:
  195. list_of_modules = ["discriminant_analysis", "discriminant_analysis", "tree"]
  196. list_of_classifiers = ["LinearDiscriminantAnalysis", "QuadraticDiscriminantAnalysis","DecisionTreeClassifier"]
  198. # %% [markdown]
  199. # In each classification task, labels are first assigned to neurons with the make_data_labels method, in order to define the desired classification. A model, called by leave_one_out_mixing method, is then trained on the dataset. The quality of classification is evaluated with the balanced accuracy and the confusion matrix.
  201. # %% [markdown]
  202. # ### Linear classification
  204. # %%
  205. chosen_module = list_of_modules[0]
  206. chosen_classifier = list_of_classifiers[0]
  207. n_trials = 5
  208. class_labels = ["class_I", "class_III"]
  209. printed_labels = ["class I", "class III"]
  210. FontSize = 16
  212. neuron_id = 0
  213. full_dataset = np.array([])
  214. full_labels = np.array([])
  216. for index,column in total_df.items():
  218. if index[3] == "Path": # one only keeps path and not time columns
  219. path_list = []
  221. for i, cat in enumerate(class_labels):
  222. if cat in index[0]: # one defines here the two categories
  223. neuron_id = i
  225. for path in column.values:
  226. if str(type(path)) != "<class 'float'>": # check if not nan and that all entries have less than two elements to have tables of the same size
  227. path_list.append(path)
  229. # Check that one adds well a non empty array
  230. if len(path_list) != 0:
  231. dataset, labels = make_data_labels(path_list, neurite_type, neuron_id)
  232. if len(full_dataset) == 0:
  233. full_dataset = dataset
  234. full_labels = labels
  235. else:
  236. full_dataset = np.concatenate((full_dataset, dataset), axis=0)
  237. full_labels = np.concatenate((full_labels, labels), axis=0)
  239. accuracy, cf_matrix = leave_one_out_mixing(chosen_module, chosen_classifier, full_dataset, full_labels)
  240. balanced_accuracy = np.mean(np.diag(cf_matrix)/np.sum(cf_matrix, axis = 1))
  241. print("The accuracy is ",accuracy)
  242. print("The balanced accuracy is ",balanced_accuracy)
  244. cm_display_lin = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix, display_labels=printed_labels)
  245. cm_display_lin.plot(cmap="Blues", values_format=".2f")
  246. cm_display_lin.ax_.set_title("Linear classifier")
  247. plt.savefig('Figures/FiguresIntermediaires/CI_C3_Lin.eps', format='eps')
  248. plt.show()
  250. # %% [markdown]
  251. # ### Quadratic classifier
  253. # %%
  254. chosen_module = list_of_modules[1]
  255. chosen_classifier = list_of_classifiers[1]
  257. neuron_id = 0
  258. full_dataset = np.array([])
  259. full_labels = np.array([])
  261. for index,column in total_df.items():
  263. if index[3] == "Path": # one only keeps path and not time columns
  264. path_list = []
  266. if index[0] == class_labels[0]: # one defines here the two categories
  267. neuron_id = 0
  268. elif index[0] == class_labels[1]:
  269. neuron_id = 1
  271. for path in column.values:
  272. if str(type(path)) != "<class 'float'>": # check if not nan and that all entries have less than two elements to have tables of the same size
  273. path_list.append(path)
  275. # Check that one adds well a non empty array
  276. if len(path_list) != 0:
  277. dataset, labels = make_data_labels(path_list, neurite_type, neuron_id)
  278. if len(full_dataset) == 0:
  279. full_dataset = dataset
  280. full_labels = labels
  281. else:
  282. full_dataset = np.concatenate((full_dataset, dataset), axis=0)
  283. full_labels = np.concatenate((full_labels, labels), axis=0)
  285. accuracy, cf_matrix = leave_one_out_mixing(chosen_module, chosen_classifier, full_dataset, full_labels)
  286. balanced_accuracy = np.mean(np.diag(cf_matrix)/np.sum(cf_matrix, axis = 1))
  287. print("The accuracy is ",accuracy)
  288. print("The balanced accuracy is ",balanced_accuracy)
  290. cm_display_quad = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix, display_labels=printed_labels)
  291. cm_display_quad.plot(cmap="Blues", values_format=".2f")
  292. cm_display_quad.ax_.set_title("Quadratic classifier")
  293. plt.savefig('Figures/FiguresIntermediaires/C1_C3_Quad.eps', format='eps')
  294. plt.show()
  296. # %% [markdown]
  297. # ### Tree classifier
  299. # %%
  300. chosen_module = list_of_modules[2]
  301. chosen_classifier = list_of_classifiers[2]
  303. neuron_id = 0
  304. full_dataset = np.array([])
  305. full_labels = np.array([])
  307. for index,column in total_df.items():
  309. if index[3] == "Path": # one only keeps path and not time columns
  310. path_list = []
  312. if index[0] == class_labels[0]: # one defines here the two categories
  313. neuron_id = 0
  314. elif index[0] == class_labels[1]:
  315. neuron_id = 1
  317. for path in column.values:
  318. if str(type(path)) != "<class 'float'>": # check if not nan and that all entries have less than two elements to have tables of the same size
  319. path_list.append(path)
  321. # Check that one adds well a non empty array
  322. if len(path_list) != 0:
  323. dataset, labels = make_data_labels(path_list, neurite_type, neuron_id)
  324. if len(full_dataset) == 0:
  325. full_dataset = dataset
  326. full_labels = labels
  327. else:
  328. full_dataset = np.concatenate((full_dataset, dataset), axis=0)
  329. full_labels = np.concatenate((full_labels, labels), axis=0)
  331. accuracy, cf_matrix = leave_one_out_mixing(chosen_module, chosen_classifier, full_dataset, full_labels)
  332. balanced_accuracy = np.mean(np.diag(cf_matrix)/np.sum(cf_matrix, axis = 1))
  333. print("The accuracy is ",accuracy)
  334. print("The balanced accuracy is ",balanced_accuracy)
  336. cm_display_tree = metrics.ConfusionMatrixDisplay(confusion_matrix = cf_matrix, display_labels=printed_labels)
  337. cm_display_tree.plot(cmap="Blues", values_format=".2f")
  338. cm_display_tree.ax_.set_title("Tree classifier")
  339. plt.savefig('Figures/FiguresIntermediaires/C1_C3_Tree.eps', format='eps')
  340. plt.show()

Figure_3.ipynb, under CC-BY-4.0 · at the source

Overview

Authors: Killian Rigaux1, André Ferreira Castro2, Lida Kanari3
  1. The Quantum Plumbing Lab, École Polytechnique Fédérale de Lausanne (EPFL),1015 Lausanne, Switzerland
  2. School of Life Sciences, Technical University of Munich,Freising, Germany
  3. Mathematical Institute, University of Oxford,Oxford, UK
Institutions: École Polytechnique Fédérale de Lausanne (Switzerland); Technical University of Munich (Germany); University of Oxford (United Kingdom)
Journal: Neuroinformatics, volume 24, issue 3, article 48
Dates: received 15 September 2025; accepted 15 June 2026; published online 24 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12021-026-09795-0 · PMID 42496825 · PMCID PMC13400593 · OpenAlex W7170276590
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: drosophila (organism), developmental (subfield)
Methods: Machine learning, Evoked potentials
Keywords: Neuron morphology, Topological data analysis, Morphological comparison, Drosophila neurons, Neuronal development
MeSH: Morphogenesis*, Neurons*, Sensory Receptor Cells*, Animals, Drosophila, Neurodevelopment, Time Factors, Time-Lapse Imaging (* major topic)
Topic: Topological and Geometric Data Analysis (Computational Theory and Mathematics, Computer Science), according to OpenAlex
Funding: Daimler Benz foundation; UK Research and Innovation Future (MR/Z504804/1)
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Neuronal morphogenesis arises through coordinated neurite dynamics that generate cell-type specific dendritic branching during development. Recent advances in high-throughput time-lapse imaging techniques have transformed our ability to track such growth dynamics, yielding comprehensive anatomical datasets of neuronal morphologies. However, quantifying these structural trajectories during neuronal development remains a major challenge. We introduce the Temporal Topological Morphology Descriptor (TTMD), a framework that combines persistent topology with time-resolved neuronal imaging to quantify developmental trajectories of neuronal architecture. Applying TTMD to datasets of Drosophila sensory neurons, we show that developmental stages, branching processes, and mutant-specific growth dynamics can be decoded directly from topology without manual feature selection. Topological Morphology Descriptor (TMD) accurately resolves major developmental transitions in Class I Drosophila neurons, whereas temporal topology (TTMD) is required to uncover subtle alterations in branch dynamics caused by mutations in actin regulatory pathways in Class III Drosophila neurons. TTMD further enables automated topological tracking of branch emergence and retraction across development, overcoming the limitations of labor-intensive manual annotation. These results demonstrate that temporal topology provides a unified and interpretable language for neuronal morphogenesis, enabling the automated analysis of developmental neuroanatomy, and identifying pathological alterations in neuronal structure.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12021-026-09795-0.

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

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Zenodo 20813267

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (10 files), NumPy (10 files), pandas (10 files), scikit-learn (8 files), SciPy (6 files), seaborn (4 files), UMAP (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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All data that were analysed and the respective codes are available in Zenodo 10.5281/zenodo.20813267.

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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 8 MeSH terms, 2 funders, 48 references.

Cite

This paper

Rigaux, K., Castro, A. F., & Kanari, L. (2026). Temporal Topology Provides an Interpretable Framework for Neuronal Morphogenesis. Neuroinformatics, 24(3), 48. https://doi.org/10.1007/s12021-026-09795-0

BibTeX

@article{rigaux2026temporal,
author = {Rigaux, Killian and Castro, André Ferreira and Kanari, Lida},
title = {{Temporal Topology Provides an Interpretable Framework for Neuronal Morphogenesis}},
journal = {Neuroinformatics},
year = {2026},
month = jul,
volume = {24},
number = {3},
pages = {48},
publisher = {Springer Science+Business Media},
issn = {1539-2791},
doi = {10.1007/s12021-026-09795-0},
url = {https://doi.org/10.1007/s12021-026-09795-0},
pmid = {42496825},
pmcid = {PMC13400593}
}

RIS

TY - JOUR
AU - Rigaux, Killian
AU - Castro, André Ferreira
AU - Kanari, Lida
TI - Temporal Topology Provides an Interpretable Framework for Neuronal Morphogenesis
T2 - Neuroinformatics
J2 - Neuroinformatics
PY - 2026
DA - 2026/07/24
VL - 24
IS - 3
SP - 48
SN - 1539-2791
PB - Springer Science+Business Media
DO - 10.1007/s12021-026-09795-0
UR - https://doi.org/10.1007/s12021-026-09795-0
LA - en
ER -

CSL-JSON

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The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41593-026-02376-z [code]
A framework for comparative analysis of human and mouse cortical neuron dendrites in corresponding brain regions.
Journal: Nature neuroscience
In common: UMAP, seaborn, scikit-learn, 4 other tools, 4 references
[2] doi:10.1126/sciadv.adz6517 [code]
Corticosterone-linked microglial activity underpins sexually dimorphic neuroplasticity after ketamine anesthesia.
Journal: Science advances
In common: UMAP, seaborn, scikit-learn, 4 other tools, 2 references
[3] doi:10.1038/s41467-026-72935-2 [code]
Spindle neurons in human cortex possess distinctive firing properties and transcriptomic signatures.
Journal: Nature communications
In common: UMAP, seaborn, scikit-learn, 4 other tools, 2 references
[4] doi:10.1073/pnas.2533168123 [code]
Dendritic morphology and synaptic nonlinearities enhance functional complexity in human cortical neurons.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: scikit-learn, pandas, SciPy, 2 other tools, 3 references
[5] doi:10.1038/s41467-026-72710-3 [code]
A modular multi-color fluorescence microscope for simultaneous tracking of cellular activity and behavior.
Journal: Nature communications
In common: UMAP, scikit-learn, pandas, 3 other tools, drosophila, 1 reference
[6] doi:10.1038/s41593-026-02267-3 [code]
Spatial proteomic analysis in human Alzheimer's disease brains enables identification of microenvironment-dependent microglial cell states.
Journal: Nature neuroscience
In common: UMAP, seaborn, scikit-learn, 4 other tools, 1 reference
[7] doi:10.1038/s41467-026-71331-0 [code]
A multimodal approach for visualizing and identifying electrophysiological cell types in vivo.
Journal: Nature communications
In common: UMAP, seaborn, scikit-learn, 4 other tools, 1 reference
[8] doi:10.1186/s13073-026-01704-z [code]
Gene expression profiling enables refined parcellation of cortical layers in the heterogeneous human cerebral cortex.
Journal: Genome medicine
In common: UMAP, seaborn, scikit-learn, 4 other tools, 1 reference
[9] doi:10.1186/s13059-026-04177-w [code]
Genomic sequence evolution underlying human neocortical interareal diversification.
Journal: Genome biology
In common: UMAP, seaborn, scikit-learn, 4 other tools, 1 reference
[10] doi:10.1371/journal.pbio.3003959 [code]
Recurrent synapses between CO2-sensitive olfactory sensory neurons enable robust CO2 detection in Aedes aegypti mosquitoes.
Journal: PLoS biology
In common: seaborn, scikit-learn, pandas, 3 other tools, drosophila, 1 reference

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