OSCR

ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories.

Code ↔ Paper

14 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 14 matches
  1. [1] § Results › Trajectories in CD8 T cells responding to viral infection ↔ CD8_T/1_Joint_clustering.ipynb, lines 98–105 · score 0.84 · Top2a, Il7r, Pcna, Cxcr5, Slamf6, Gzmb
  2. [2] § Results › Trajectories in CD8 T cells responding to viral infection ↔ CD8_T/1_Joint_clustering.ipynb, lines 131–156 · score 0.76 · Inter exh, Inter eff, G2M.1, G2M.2, Il7r, Gzm
  3. [3] § Methods › CD8 T cell data analysis › RNA modality ↔ src/ArchVelo/preprocessing.py, lines 23–30 · score 0.63 · n_top_genes, min_shared_counts, Scanpy, preprocessing, PCs, filtered
  4. [4] § Methods › CD8 T cell data analysis › RNA modality ↔ CD8_T/1_Joint_clustering.ipynb, lines 58–63 · score 0.61 · n_top_genes, min_shared_counts, jointly, filtered, clustering, CD8
  5. [5] § Methods › Single-cell ATAC-seq data preprocessing ↔ src/ArchVelo/preprocessing.py, lines 14–21 · score 0.60 · Poisson correction, Pearson residual, Scanpy, preprocessing, filtered, raw
  6. [6] § Methods › Benchmarking metrics and details ↔ src/ArchVelo/metrics.py, lines 219–259 · score 0.58 · cross_boundary_correctness, cross boundary direction, edge, metrics, transitions, cluster
  7. [7] § Methods › Benchmarking metrics and details ↔ src/ArchVelo/metrics.py, lines 219–259 · score 0.58 · cross_boundary_correctness, cross boundary direction, edge, metrics, transitions, cluster
  8. [8] § Methods › Linear model to predict RNA from ATAC modality ↔ src/ArchVelo/archetypal_regression/archetypes_regression.py, lines 287–350 · score 0.56 · cross validation, regression, ridge, optimize, training, Pearson
  9. [9] § Methods › Single-cell ATAC-seq data preprocessing ↔ CD8_T/2_Create_archetypes_2_clones.ipynb, lines 56–60 · score 0.54 · Poisson correction, Pearson residual, raw, ATAC, archetypal
  10. [10] § Methods › Archetypal velocity components ↔ src/ArchVelo/__init__.py, lines 89–157 · score 0.54 · velocity embedding, velocity graph, norm, connectivity, smoothed, chromatin
  11. [11] § Methods › Archetypal velocity components ↔ src/ArchVelo/__init__.py, lines 85–149 · score 0.53 · velocity embedding, velocity graph, norm, connectivity, smoothed, chromatin
  12. [12] § Results › Trajectories in CD8 T cells responding to viral infection ↔ CD8_T/6_Trajectory_components_arm.ipynb, lines 137–138 · score 0.52 · Il7r, Pola1, Gzma, Ccl5, Pdcd1, Tox
  13. [13] § Methods › Archetypal analysis on ATAC modality ↔ src/ArchVelo/archetypal_regression/My_PCHA/_pcha/__init__.py, lines 4–19 · score 0.51 · convex hull, pcha, Python, archetypal
  14. [14] § Methods › ArchVelo expectation-maximization details ↔ src/ArchVelo/optimization.py, lines 280–284 · score 0.51 · dual annealing, optimize, scipy, chromatin

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 334 lines · 11 KB · BSD-3-Clause · 3 matches

  1. # %% [markdown]
  2. # # Joint clustering of CD8+ T cells
  4. # %% [markdown]
  5. # These notebooks contain the code to annotate the data in our CD8 T cells dataset (the CD8 T cell compartment of the multi-omic scATAC+RNA-seq dataset collected at day 7 post-infection with the Armstrong (acute) or clone 13 (chronic) strains of lymphocytic choriomeningitis virus (LCMV)).
  6. #
  7. # The RNA matrix for both clones filtered for CD8 T cells (rna_CD8.h5ad), corresponding ATAC matrix (atac_CD8.h5ad) and peaks-to-genes map (nearest_genes_to_summits.distances.csv) can be downloaded at https://doi.org/10.6084/m9.figshare.31843234
  8. #
  9. # The preprocessed adata_rna and adata_atac_raw anndata objects used for ArchVelo analysis of each clone should be downloaded into the processed_data/arm/ and processed_data/cl13/ folders respectively.
  11. # %%
  12. import numpy as np
  13. import pandas as pd
  15. # %%
  16. import os
  18. # %%
  19. import matplotlib.pyplot as plt
  20. import seaborn as sns
  22. # %%
  23. import scanpy as sc
  24. import anndata
  26. # %%
  27. from scipy.sparse import csr_matrix
  29. # %%
  30. data_outdir = 'processed_data/2_clones/'
  31. os.makedirs(data_outdir, exist_ok = True)
  33. # %%
  34. num_comps = 9
  36. # %%
  37. import os
  38. fig_outdir = 'figures/2_clones/'
  39. os.makedirs(fig_outdir, exist_ok = True)
  41. # %% [markdown]
  42. # # Preprocess data
  44. # %%
  45. adata_rna = sc.read_h5ad('data/2_clones/rna_CD8.h5ad')
  47. # %%
  48. adata_rna_full = adata_rna.copy()
  50. # %%
  51. import scvelo as scv
  52. adata_rna.X = adata_rna.layers['raw_counts'].copy()
  53. scv.pp.filter_and_normalize(adata_rna, min_shared_counts = 30)
  55. # %%
  56. adata_rna.layers['log1p'] = adata_rna.X.copy()
  58. # %%
  59. # for visualization
  60. adat_copy = adata_rna_full.copy()
  61. adat_copy.X = adat_copy.layers['raw_counts'].copy()
  62. scv.pp.filter_and_normalize(adat_copy, min_shared_counts = 0)#, n_top_genes = 1500)
  63. adat_copy.layers['log1p'] = adat_copy.X.copy()
  65. # %%
  66. adata_rna_arm = sc.read_h5ad('processed_data/arm/adata_rna.h5ad')
  67. adata_rna_cl13 = sc.read_h5ad('processed_data/cl13/adata_rna.h5ad')
  69. # %%
  70. both_genes = np.unique(list(adata_rna_arm.var_names)+list(adata_rna_cl13.var_names))
  71. len(both_genes)
  73. # %%
  74. # load 2 clones and filter only to genes in at least 1 clones
  75. adata_rna = adata_rna[:, both_genes]
  77. # %%
  78. adata_rna.X = adata_rna.layers['theta_10'].copy()
  80. # %%
  81. n_neigh = 30
  82. n_pcs = 50
  83. met = 'cosine'
  84. res = 1.5
  85. rand_st = 3
  86. np.random.seed(rand_st)
  88. # %%
  89. sc.pp.pca(adata_rna, n_pcs, random_state = rand_st)
  90. sc.pp.neighbors(adata_rna, n_neighbors=n_neigh, n_pcs=n_pcs,
  91. metric = met, random_state = rand_st)
  94. # %%
  95. sc.tl.umap(adata_rna, random_state = rand_st)
  98. # %%
  99. marks_full = pd.Index(['Pola1','Pcna', 'Top2a', 'Mki67', 'Cdc20',
  100. 'Klrg1', 'Gzma', 'Gzmk','Gzmb',
  101. 'Ccl5','Ccr2',
  102. 'Il7r','Sell', 'Ccr7',
  103. 'Nt5e','Cxcr5','Bcl6', 'Id3','Tcf7', 'Slamf6', 'Ccr6', 'Nfat5', #'Ctla4',
  104. #'Prf1',
  105. 'Tox','Pdcd1', 'Lag3', 'Cd160','Prf1','Havcr2','Il2ra','Xcl1','Ccl3','Ccl4','Ifng',])
  107. # %%
  108. marks = marks_full.intersection(adata_rna.var_names)
  110. # %%
  111. sc.tl.leiden(adata_rna, resolution = 2, random_state = rand_st)
  113. # %%
  114. np.random.seed(rand_st)
  115. num_clusts = len(adata_rna.obs['leiden'].cat.categories)
  116. pal = dict(zip(adata_rna.obs['leiden'].cat.categories,
  117. list(np.array(sns.color_palette('husl', num_clusts))[np.random.choice(num_clusts,num_clusts, replace = False)])))
  119. # %%
  120. sc.pl.umap(adata_rna,
  121. color = ['leiden'],
  122. palette = pal,
  123. legend_loc = 'on data')
  125. # %%
  126. sc.tl.dendrogram(adata_rna,groupby = 'leiden')
  127. sc.pl.dotplot(adata_rna, marks, groupby = 'leiden',layer = 'log1p',
  128. standard_scale = 'var', figsize = (10,5),
  129. dendrogram = True)
  131. # %%
  132. def coarse(cl):
  133. if cl in ['13', '0', ] :
  134. return 'Inter exh'
  135. elif cl == '5':
  136. return 'Prog'
  137. elif cl in ['21', '6', '2']:
  138. return 'Il7r+ eff'
  139. elif cl in ['11', '15', '20', '19', '9']:
  140. return 'Inter eff'
  141. # elif cl == '5':
  142. # return 'Il7r+Klrg1+ Eff'
  143. elif cl in [ '18']:
  144. return 'Klrg1+ eff'
  145. elif cl in [ '16']:
  146. return 'Gzm+ eff'
  147. elif cl in ['12', '17', '3', '7']:
  148. return 'S'
  149. elif cl in ['22', '8', '4', '14']:
  150. return 'G2M.1'
  151. elif cl == '1':
  152. return 'G2M.2'
  153. elif cl == '10':
  154. return 'Exh'
  155. else:
  156. return 'Other'
  158. # %%
  159. adata_rna.obs['coarse'] = adata_rna.obs['leiden'].map(coarse)
  162. # %%
  163. sc.pl.umap(adata_rna, color = ['coarse', ], layer = 'log1p'
  164. )
  166. # %%
  167. sns.set(style = 'ticks', font_scale = 1.)
  169. # %%
  170. ct_order = [ 'S', 'G2M.1', 'G2M.2', 'Inter eff', 'Klrg1+ eff', 'Gzm+ eff', 'Il7r+ eff', 'Inter exh', 'Exh','Prog']
  173. # %%
  174. cur_dat = adat_copy[adata_rna.obs.index,:][adata_rna.obs['type'] == 'cl13',:].copy()
  175. cur_dat.X = cur_dat.layers['theta_10']
  176. cur_dat.obs['coarse'] = adata_rna[cur_dat.obs.index,:].obs['coarse']
  177. adata_rna_cl13_full = cur_dat.copy()
  178. sc.tl.dendrogram(cur_dat,groupby = 'coarse')
  179. dp = sc.pl.dotplot(cur_dat, marks_full, groupby = 'coarse',#layer = 'log1p',
  180. standard_scale = 'var', figsize = (10,3),
  181. title = 'LCMV clone 13', return_fig=True, show = False,
  182. #title_fontsize = 25,
  183. categories_order = ct_order,
  184. dendrogram = False)
  185. dp.add_totals()#.style(dot_edge_color='black', dot_edge_lw=0.5, cmap = 'Reds')#.show()
  186. #plt.tight_layout()
  187. dp.savefig(fig_outdir+'clone_13_coarse.png', dpi = 500)
  188. dp.savefig(fig_outdir+'clone_13_coarse.svg', dpi = 500)
  190. # %%
  191. cur_dat = adat_copy[adata_rna.obs.index,:][adata_rna.obs['type'] == 'arm',:].copy()
  192. cur_dat.X = cur_dat.layers['theta_10']
  193. cur_dat.obs['coarse'] = adata_rna[cur_dat.obs.index,:].obs['coarse']
  194. adata_rna_arm_full = cur_dat.copy()
  195. sc.tl.dendrogram(cur_dat,groupby = 'coarse')
  196. dp = sc.pl.dotplot(cur_dat, marks_full, groupby = 'coarse',#layer = 'log1p',
  197. standard_scale = 'var', figsize = (10,3),
  198. title = 'LCMV Armstrong', return_fig=True, show = False,
  199. categories_order = ct_order,
  200. dendrogram = False)
  201. dp.add_totals()#.style(dot_edge_color='black', dot_edge_lw=0.5).show()
  202. #plt.tight_layout()
  203. dp.savefig(fig_outdir+'arm_coarse.png', dpi = 500)
  204. dp.savefig(fig_outdir+'arm_coarse.svg', dpi = 500)
  206. # %%
  207. cop = adata_rna[adata_rna.obs['coarse'] == 'Prog',:].copy()
  208. sc.tl.leiden(cop, resolution = 0.4, random_state = rand_st)
  210. # %%
  211. leidens = np.array(adata_rna.obs['coarse'])
  212. leidens[leidens == 'Prog'] = ['Ccr6+ prog' if x == '0' else 'Ccr6- prog' for x in cop.obs['leiden'].values]
  213. adata_rna.obs['small_coarse'] = pd.Series(leidens, index = adata_rna.obs_names).astype('category')
  215. # %%
  216. sns.set(style = 'ticks', font_scale = 1.3)
  218. # %%
  219. adata_rna_no_cycle = adata_rna[~adata_rna.obs['coarse'].isin(['S', 'G2M.1', 'G2M.2']),:].copy()
  220. total_cells = {}
  221. for cl in ['arm', 'cl13']:
  222. total_cells[cl] = adata_rna_no_cycle[adata_rna_no_cycle.obs['type'] ==cl,:].shape[0]
  223. total_cells_prop = total_cells['arm']/(total_cells['arm']+total_cells['cl13'])
  224. num_cells_no_cycle = {}
  225. for cl in ['arm', 'cl13']:
  226. num_cells_no_cycle[cl] = adata_rna_no_cycle[adata_rna_no_cycle.obs['type'] ==cl,:].obs['coarse'].value_counts()
  227. num_cells_no_cycle = pd.DataFrame(num_cells_no_cycle)
  228. norm_to_plot = num_cells_no_cycle.div(num_cells_no_cycle.sum(1),0)
  230. # %%
  231. norm_to_plot.plot.barh(stacked = True, figsize = (3,5), width = 1, color = ['#d8b365', '#5ab4ac'])
  232. plt.axvline(total_cells_prop, linestyle = 'dashed', lw = 2, color = 'black')
  233. plt.ylabel('')
  234. plt.xlabel('Proportion')
  235. plt.savefig(fig_outdir+'props_no_cycle.png', dpi = 500)
  236. plt.savefig(fig_outdir+'props_no_cycle.svg', dpi = 500)
  238. # %% [markdown]
  239. # ## Progenitor subtypes
  241. # %%
  242. only_prog = adata_rna[adata_rna.obs['coarse'] == 'Prog',:].copy()
  243. sc.tl.rank_genes_groups(only_prog,
  244. groupby = 'small_coarse',
  245. use_raw = False,
  246. layer = 'log1p',
  247. method = 'wilcoxon')
  248. sc.tl.dendrogram(only_prog,
  249. groupby = 'small_coarse')
  250. sc.pl.rank_genes_groups_dotplot(only_prog,
  251. layer = 'log1p',
  252. groupby = 'small_coarse',
  253. n_genes = 30,
  254. values_to_plot = 'logfoldchanges',
  255. vmin = -4, vmax = 4,
  256. cmap = 'RdBu_r')
  259. # %%
  260. ct_order = [ 'Ccr6- prog', 'Ccr6+ prog']
  262. # %%
  263. def map_to_other(x):
  264. if x in ct_order:
  265. return x
  266. else:
  267. return 'Other'
  268. adata_rna.obs['prog_other'] = adata_rna.obs['small_coarse'].map(map_to_other).astype('category')
  270. # %%
  271. adat_copy.obs['prog_other'] = adata_rna.obs['prog_other']
  273. # %%
  274. prog_marks = pd.Index(['Tcf7', 'Slamf6', 'Id3','Nt5e', 'Cxcr5', 'Sell', 'Il7r'])
  276. # %%
  277. prog_de_marks = pd.Index(['Bach2','Myb','Dapl1', 'Ccr7','Sell', 'Lef1', 'Satb1']+
  278. ['Klf3', 'Arl4c','Klrd1', 'Ifngr1' ,'Ccl5', 'Id3','Tox', 'Itgb1', 'Nfat5','Cd160','Batf', 'Pdcd1', 'Lag3',
  279. 'Xcl1', 'Ccr6' ])#'Ccr7','Xcl1',
  280. #'Klrd1', 'Ifngr1', 'Satb1','Il7r','Id2','Prf1', 'Nfat5', 'Nkg7',
  283. # %%
  284. prog_marks_all = pd.Index(list(prog_marks)+list(prog_de_marks)).unique()
  286. # %%
  287. cur_dat = adat_copy[adat_copy.obs['type'] == 'arm',:].copy()
  288. cur_dat.X = cur_dat.layers['theta_10']
  289. cur_dat.obs['small_coarse'] = adata_rna[cur_dat.obs.index,:].obs['small_coarse']
  290. cur_dat.obs['prog_other'] = adata_rna[cur_dat.obs.index,:].obs['prog_other']
  293. # %%
  294. sns.set(style = 'ticks', font_scale = 1.3)
  296. # %%
  297. sc.tl.dendrogram(cur_dat,groupby = 'prog_other')
  298. dp = sc.pl.dotplot(cur_dat, prog_marks_all,
  299. groupby = 'prog_other',#layer = 'log1p',
  300. standard_scale = 'var',
  301. figsize = (10,1.5),
  302. title = 'LCMV Armstrong',
  303. return_fig=True,
  304. show = False,
  305. categories_order = ct_order+['Other'],
  306. dendrogram = False,
  307. ).add_totals()
  308. dp.savefig(fig_outdir+'prog_marks_all_arm_new.png', dpi = 500)
  309. dp.savefig(fig_outdir+'prog_marks_all_arm_new.svg', dpi = 500)
  311. # %%
  312. cur_dat = adat_copy[adat_copy.obs['type'] == 'cl13',:].copy()
  313. cur_dat.X = cur_dat.layers['theta_10']
  314. cur_dat.obs['small_coarse'] = adata_rna[cur_dat.obs.index,:].obs['small_coarse']
  315. cur_dat.obs['prog_other'] = adata_rna[cur_dat.obs.index,:].obs['prog_other']
  318. # %%
  319. sc.tl.dendrogram(cur_dat,groupby = 'prog_other')
  320. dp = sc.pl.dotplot(cur_dat, prog_marks_all,
  321. groupby = 'prog_other',#layer = 'log1p',
  322. standard_scale = 'var',
  323. figsize = (10,1.5),
  324. title = 'LCMV clone 13',
  325. return_fig=True,
  326. show = False,
  327. categories_order = ct_order+['Other'],
  328. dendrogram = False,
  329. ).add_totals()#.style(dot_edge_color='black', dot_edge_lw=0.5).show()
  330. dp.savefig(fig_outdir+'prog_marks_all_cl13_new.png', dpi = 500)
  331. dp.savefig(fig_outdir+'prog_marks_all_cl13_new.svg', dpi = 500)
  333. # %%
  334. adata_rna.write('processed_data/2_clones/adata_rna.h5ad')

1_Joint_clustering.ipynb at commit 7f78765, under BSD-3-Clause · at the source

Overview

Authors: Maria Avdeeva1, Sarah K Walker2, Joris van der Veeken3, Alexander Y Rudensky4, Yuri Pritykin2,5
ORCID iDs: Sarah K Walker, Joris van der Veeken, Alexander Y Rudensky, Yuri Pritykin
  1. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, NY USA
  2. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ USA
  3. Research Institute of Molecular Pathology, Vienna, Austria
  4. Howard Hughes Medical Institute, Immunology Program, and Ludwig Center, Memorial Sloan Kettering Cancer Center, New York, NY USA
  5. Department of Computer Science, Princeton, NJ USA
Institutions: Simons Foundation (United States); Flatiron Institute (United States); Princeton University (United States); Research Institute of Molecular Pathology (Austria); Memorial Sloan Kettering Cancer Center (United States); Howard Hughes Medical Institute (United States)
Journal: Nature communications, volume 17, issue 1, article 7228
Dates: received 30 September 2025; accepted 26 May 2026; published online 6 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74000-4 · PMID 42248920 · PMCID PMC13396182 · OpenAlex W4414370743
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.14.676182
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: Computational models, Genomics, Adaptive immunity, Developmental biology
MeSH: Computational Biology*, Single-Cell Analysis*, Animals, Brain, CD8-Positive T-Lymphocytes, Cell Differentiation, Chromatin, Gene Expression Regulation, Genomics, Hematopoiesis, Humans, Mice, Multiomics, Single-Cell Gene Expression Analysis, T-Cell Exhaustion, Transcriptome (* major topic)
Topic: Gene Regulatory Network Analysis (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NCI NIH HHS (P30 CA008748); European Research Council (101116251); NIAID NIH HHS (R01 AI034206, DP2 AI171161)
Citations: cited by 1 paper (Europe PMC); 70 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

pritykinlab/ArchVelo

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f3835d37943df5efe436861a7a235bd4bd87e65b, 18 September 2026
Languages: Python (18), Jupyter (1)
Size: 29 files, 19 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (pyproject.toml, requirements.txt, src/ArchVelo/archetypal_regression/My_PCHA/setup.cfg, src/ArchVelo/archetypal_regression/My_PCHA/setup.py), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (13 files), pandas (11 files), SciPy (9 files), Scanpy (6 files), anndata (4 files), Matplotlib (3 files), scikit-learn (3 files), scVelo (3 files), seaborn (2 files), Numba (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
21 files
At the source: github.com/pritykinlab/ArchVelo

pritykinlab/ArchVelo_notebooks

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7f78765858d1d2888931270982cf8fcec5beab31, 8 May 2026
Languages: Jupyter (29), Python (9), Shell (3)
Size: 44 files, 41 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 29 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (9 files), pandas (9 files), Scanpy (9 files), Matplotlib (7 files), scVelo (7 files), seaborn (7 files), anndata (6 files), SciPy (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/pritykinlab/ArchVelo_notebooks

Zenodo 20085695

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (13 files), pandas (11 files), SciPy (9 files), Scanpy (6 files), anndata (4 files), Matplotlib (3 files), scikit-learn (3 files), scVelo (2 files), seaborn (2 files), Numba (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
21 files
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-74000-4.

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 47 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

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-74000-4.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 16 MeSH terms, 3 funders, 64 references.

Cite

This paper

Avdeeva, M., Walker, S. K., van der Veeken, J., Rudensky, A. Y., & Pritykin, Y. (2026). ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories. Nature communications, 17(1), 7228. https://doi.org/10.1038/s41467-026-74000-4

BibTeX

@article{avdeeva2026archvelo,
author = {Avdeeva, Maria and Walker, Sarah K and van der Veeken, Joris and Rudensky, Alexander Y and Pritykin, Yuri},
title = {{ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7228},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74000-4},
url = {https://doi.org/10.1038/s41467-026-74000-4},
pmid = {42248920},
pmcid = {PMC13396182}
}

RIS

TY - JOUR
AU - Avdeeva, Maria
AU - Walker, Sarah K
AU - van der Veeken, Joris
AU - Rudensky, Alexander Y
AU - Pritykin, Yuri
TI - ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/06
VL - 17
IS - 1
SP - 7228
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74000-4
UR - https://doi.org/10.1038/s41467-026-74000-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74000-4",
"type": "article-journal",
"title": "ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories",
"container-title": "Nature communications",
"author": [
{
"family": "Avdeeva",
"given": "Maria"
},
{
"family": "Walker",
"given": "Sarah K"
},
{
"family": "van der Veeken",
"given": "Joris"
},
{
"family": "Rudensky",
"given": "Alexander Y"
},
{
"family": "Pritykin",
"given": "Yuri"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7228",
"DOI": "10.1038/s41467-026-74000-4",
"PMID": "42248920",
"PMCID": "PMC13396182",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74000-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
6
]
]
}
}

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

Similar papers

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:10.7554/elife.108950 [code]
Comprehensive RNA velocity by modeling the cascade of gene regulation, transcription, and splicing from single-cell RNA sequencing data with TSvelo.
Journal: eLife
In common: scVelo, anndata, Scanpy, 5 other tools, genetics / omics, mouse, cellular / molecular, 10 references
[2] doi:10.1093/bioinformatics/btag652 [code]
mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.
Journal: Bioinformatics (Oxford, England)
In common: scVelo, anndata, Scanpy, 7 other tools, genetics / omics, mouse, 8 references
[3] doi:10.1016/j.crmeth.2026.101342 [code]
Interpretable learning of temporal cellular dynamics from single-cell data.
Journal: Cell reports methods
In common: scVelo, anndata, Numba, 7 other tools, genetics / omics, cellular / molecular, 7 references
[4] doi:10.1038/s41592-026-03057-2 [code]
CREsted: modeling genomic and synthetic cell-type-specific enhancers across tissues and species.
Journal: Nature methods
In common: anndata, Numba, Scanpy, 7 other tools, genetics / omics, mouse, 4 references
[5] doi:10.1038/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: scVelo, anndata, Numba, 8 other tools, genetics / omics, mouse, cellular / molecular, 2 references
[6] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: scVelo, anndata, Numba, 7 other tools, genetics / omics, cellular / molecular, 3 references
[7] doi:10.1016/j.isci.2026.116055 [code]
Mapping the transcriptional diversity of calcium signaling in the mouse and human brain.
Journal: iScience
In common: anndata, Numba, Scanpy, 7 other tools, genetics / omics, mouse, 3 references
[8] doi:10.1016/j.stemcr.2026.103015 [code]
Brain injury reactivates a developmental program driving genesis and integration of transient LGE-class interneurons.
Journal: Stem cell reports
In common: scVelo, anndata, Numba, 8 other tools, mouse, 1 reference
[9] doi:10.64898/2026.03.30.714220 [code]
An integrated single cell and spatial omics atlas of human prenatal development
Journal: bioRxiv (preprint)
In common: scVelo, anndata, Scanpy, 7 other tools, 2 references
[10] doi:10.1038/s41586-026-10490-y [code]
Lineage and organ signals sequentially build organ intrinsic nervous systems.
Journal: Nature
In common: scVelo, anndata, Scanpy, 6 other tools, mouse, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.