OSCR

Lineage and organ signals sequentially build organ intrinsic nervous systems.

Code ↔ Paper

20 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 20 matches
  1. [1] § Methods › ENS co-cultures with cardiac or intestinal cells for scRNA-seq › scRNA-seq analysis of co-cultured ENS cells ↔ Transcriptomic Analysis/Coculture integration analysis/03_Transcriptomic_changes_between_coculture_conditions.R, lines 48–84 · score 0.96 · logfc.threshold, min.pct, FindMarkers, genes upregulated, ICNS relative, neuron state
  2. [2] § Methods › Spatial dynamics of ICNS cells › Geometric transformation of 3D heart images ↔ Anatomical Analysis/ICNS spatial distribution/02_ICNS spatial parameters.m, lines 3–24 · score 0.93 · inner region enclosed, enclosed area, inner area, ICNS contours, ICNS ring, ICNS spatial distribution
  3. [3] § Methods › Spatial dynamics of ICNS cells › Geometric transformation of 3D heart images ↔ Anatomical Analysis/ICNS spatial distribution/01_ICNS transformation.m, lines 64–86 · score 0.88 · atrial dome, atrial landmarks, left junctions, posterior edge, anterior edge, appendages
  4. [4] § Methods › Image analysis of OINS spatial distribution › OINS distribution patterns during development ↔ Anatomical Analysis/ICNS spatial distribution/01_ICNS transformation.m, lines 64–86 · score 0.86 · atrial dome, atrial landmarks, left junctions, posterior edge, anterior edge, appendages
  5. [5] § Methods › scRNA-seq and data analysis › Basic scRNA-seq data processing and quality control ↔ Transcriptomic Analysis/Coculture integration analysis/03_Transcriptomic_changes_between_coculture_conditions.R, lines 1–45 · score 0.81 · Cell Ranger, FindMarkers, E18.5, E16.5, scRNA, DEG
  6. [6] § Methods › Integration of maturing ENS and ICNS neurons ↔ Transcriptomic Analysis/Coculture integration analysis/03_Transcriptomic_changes_between_coculture_conditions.R, lines 1–45 · score 0.80 · E15.5, neuron populations, E18.5, E16.5, scRNA, seq
  7. [7] § Methods › Integration of maturing ENS and ICNS neurons ↔ Transcriptomic Analysis/Coculture integration analysis/01_Coculture_QC.R, lines 286–345 · score 0.78 · nCount_RNA, nFeature_RNA, percent.mito, stringent, Phox2b, Mki67
  8. [8] § Methods › Statistics and reproducibility ↔ Anatomical Analysis/ICNS spatial distribution/03_Contourmap.m, lines 5–10 · score 0.75 · E13.5, E18.5, E12.5, E16.5, E14.5, age
  9. [9] § Methods › Cross-organ similarity analysis along the Slingshot-inferred trajectory ↔ Transcriptomic Analysis/OINS cosine similarity/OINS_cosine_similarity.ipynb, lines 84–118 · score 0.75 · sc.pp.scale, sc.pp.log1p, Scanpy, components, cosine, fitted
  10. [10] § Methods › Statistics and reproducibility ↔ Anatomical Analysis/ICNS spatial distribution/02_ICNS spatial parameters.m, lines 127–129 · score 0.73 · E13.5, E18.5, E12.5, E16.5, E14.5, animal
  11. [11] § Distinct progenitor pools underlie OINS architectures ↔ Transcriptomic Analysis/ICNS Monocle3/01_create_trajectory.R, lines 2–47 · score 0.70 · E12.5, E16.5, scRNA, Phox2b, progenitor, populations
  12. [12] § Methods › scRNA-seq and data analysis › Monocle trajectory inference ↔ Transcriptomic Analysis/ICNS Monocle3/01_create_trajectory.R, lines 49–93 · score 0.70 · choose_cells, neuroblast branch, Monocle, graph, trajectory, pseudotime
  13. [13] § ECM underpins the architectural precision of OINSs ↔ Anatomical Analysis/ICNS spatial distribution/02_ICNS spatial parameters.m, lines 127–129 · score 0.69 · E13.5, E18.5, E12.5, E16.5, E14.5, animal
  14. [14] § ECM underpins the architectural precision of OINSs ↔ Anatomical Analysis/ICNS spatial distribution/03_Contourmap.m, lines 5–10 · score 0.68 · E13.5, E18.5, E12.5, E16.5, E14.5, animal
  15. [15] § Methods › scRNA-seq and data analysis › RNA velocity inference ↔ Transcriptomic Analysis/ICNS veloVI/plot_ICNS_veloVI.ipynb, lines 23–29 · score 0.67 · velocity embedding, RNA velocity, VeloVI, pl, ICNS
  16. [16] § Methods › scRNA-seq and data analysis › Basic scRNA-seq data processing and quality control ↔ Transcriptomic Analysis/Coculture integration analysis/01_Coculture_QC.R, lines 286–345 · score 0.65 · nFeature_RNA, percent.mito, Phox2b, UMAP, clustering, ENS
  17. [17] § Methods › scRNA-seq and data analysis › Trajectory and pseudotime inference using Slingshot ↔ Transcriptomic Analysis/OINS Slingshot/OIN_Slingshot.R, lines 19–58 · score 0.64 · FindNeighbors, FindClusters, Seurat clusters, Slingshot, Portal, cell
  18. [18] § Organ signals drive OINS identity and differentiation ↔ Transcriptomic Analysis/Coculture integration analysis/03_Transcriptomic_changes_between_coculture_conditions.R, lines 48–84 · score 0.62 · genes upregulated, E18.5, ENS neurons, score, intestine, neuroblasts
  19. [19] § Methods › scRNA-seq and data analysis › RNA velocity inference ↔ Transcriptomic Analysis/ICNS veloVI/run_ICNS_veloVI.ipynb, lines 21–41 · score 0.58 · RNA velocity, CellRanger, velocyto, cell state, e14, ICNS
  20. [20] § Methods › Spatial transcriptomics with 10x Visium ↔ Transcriptomic Analysis/OINS cosine similarity/OINS_cosine_similarity.ipynb, lines 84–118 · score 0.51 · log1p, Scanpy, transformed, Gene, Hearts, ICNS

Paper

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The authors' code

R · 173 lines · 6.8 KB · no license · 4 matches

  1. </> R
  2. ## R version 4.1.2 (2021-11-01)
  3. ## SeuratObject_4.1.3 sp_2.2-0
  5. library(Seurat)
  6. library(dplyr)
  7. library(ggplot2)
  8. library(cowplot)
  9. library(RColorBrewer)
  11. ## Compare ICNS vs ENS neurons to define system-specific marker genes. Use that to capture changes induced by ENS-heart
  13. #ENS_ICNS.rds (ENS_ICNS) is the integration of the E16.5, E18.5 ICNS scRNA-seq dataset of this study with
  14. #the E15.5 and E18.5 ENS scRNA-seq dataset from GSE149524, Morarach, K., et al., Nat Neurosci, 2021.
  15. # "ICN.neuron" and "ENS.neuron" include only the neuron populations.
  16. # "ICN.precursors" and "ENS.precursors" include only the precursor populations.
  17. # "ICN.neuroblast" and "ENS.neuroblast" include only the neuroblast populations.
  18. # "common_genes" includes genes that are commonly present in our scRNA-seq datasets and GSE149524 datasets, since a different
  19. # version of CellRanger processed GSE149524 datasets.
  21. # 1. Acquire DEGs in the precursor or neuroblast state using E15.5 ENS and E16.5 ICNS
  22. sub <- subset(ENS_ICNS, orig.ident %in% c("ENS.E15.5","ICNS.E16.5"))
  23. DefaultAssay(sub) <- "RNA"
  24. neuroblast_d <- FindMarkers(sub, ident.1 = 'ICN.neuroblast', ident.2 = 'ENS.neuroblast', only.pos = FALSE, min.pct = 0.2, logfc.threshold = 0.25, test.use = 'wilcox', features = common_genes)
  25. neuroblast_d$DE_group <- ifelse(neuroblast_d$avg_log2FC > 0,
  26. "ICN",
  27. "ENS")
  28. neuroblast_d <- tibble::rownames_to_column(neuroblast_d, var = "gene")
  29. write.table(neuroblast_d, "ENS_E15_ICN_E16_neuroblasts.xls", sep = "\t")
  31. precursor_d <- FindMarkers(sub, ident.1 = 'ICN.precursors', ident.2 = "ENS.precursors", only.pos = FALSE, min.pct = 0.2, logfc.threshold = 0.25, test.use = 'wilcox', features = common_genes)
  32. precursor_d$DE_group <- ifelse(precursor_d$avg_log2FC > 0,
  33. "ICN",
  34. "ENS")
  35. precursor_d <- tibble::rownames_to_column(precursor_d, var = "gene")
  36. write.table(precursor_d, "ENS_E15_ICN_E16_precursors.xls", sep = "\t")
  38. # Acquire DEGs in the precursor or neuroblast state using E18.5 ENS and E18.5 ICNS
  39. sub <- subset(ENS_ICNS, orig.ident %in% c("ENS.E18.5","ICNS.E18.5"))
  40. neuron_d <- FindMarkers(sub, ident.1 = 'ICN.neuron', ident.2 = "ENS.neuron", only.pos = FALSE, min.pct = 0.2, logfc.threshold = 0.25, test.use = 'wilcox', features = common_genes)
  41. neuron_d$DE_group <- ifelse(neuron_d$avg_log2FC > 0,
  42. "ICN",
  43. "ENS")
  44. neuron_d <- tibble::rownames_to_column(neuron_d, var = "gene")
  45. write.table(neuron_d, "ENS_E18_ICN_E18_neuron.xls", sep = "\t")
  48. ## Compare neurons of ENS-heart and ENS-intestine for DEGs
  49. n <- subset(coculture, state %in% "Neurons")
  50. DefaultAssay(n) <- "RNA"
  51. d <- FindMarkers(n, ident.1 = 'ENS_Heart', ident.2 = 'ENS_Gut', only.pos = FALSE, min.pct = 0.2, logfc.threshold = 0.25, test.use = 'wilcox', features = common_genes)
  52. d$DE_group <- ifelse(d$avg_log2FC > 0,
  53. "ENS_Heart",
  54. "ENS_Gut")
  55. d <- tibble::rownames_to_column(d, var = "gene")
  56. write.table(d, "coculture_Neurons_deg.xls", sep = "\t")
  58. neuron_heart <- neuron_d %>% filter(DE_group == "ICN",
  59. p_val_adj < 0.05)
  60. neuron_gut <- neuron_d %>% filter(DE_group == "ENS",
  61. p_val_adj < 0.05)
  62. cc_neuron_heart <- d %>% filter(DE_group == "ENS_Heart",
  63. p_val_adj < 0.05)
  64. cc_neuron_gut <- d %>% filter(DE_group == "ENS_Gut",
  65. p_val_adj < 0.05)
  66. # Use neuron_heart and cc_neuron_heart, we found that of the 663 genes upregulated in ENS-heart relative to ENS-intestine at the neuron state,
  67. # 372 (56%) were also upregulated in ICNS relative to ENS neurons at E18.5.
  69. #2. Compute ICNS-ENS similarity scores at the precursor, neuronblast or neuron state.
  71. neuron_heart <- neuron_d %>% filter(DE_group == "ICN",
  72. p_val_adj < 0.05)
  73. neuron_gut <- neuron_d %>% filter(DE_group == "ENS",
  74. p_val_adj < 0.05)
  76. neuroblast_heart <- neuroblast_d %>% filter(DE_group == "ICN",
  77. p_val_adj < 0.05)
  78. neuroblast_gut <- neuroblast_d %>% filter(DE_group == "ENS",
  79. p_val_adj < 0.05)
  81. precursor_heart <- precursor_d %>% filter(DE_group == "ICN",
  82. p_val_adj < 0.05)
  83. precursor_gut <- precursor_d %>% filter(DE_group == "ENS",
  84. p_val_adj < 0.05)
  87. #### Prepare the gene and cells to test ####
  88. Idents(coculture) <- "state"
  89. sub <- subset(coculture, state %in% c("Neuroblast"))
  90. DefaultAssay(sub) <- "RNA"
  91. Idents(sub) <- "ID"
  93. gene <- neuroblast_gut$gene #change this to "neuroblast_heart" for "icn_neuroblast"
  95. set.seed(1)
  96. test <- AddModuleScore(
  97. sub,
  98. features = gene,
  99. name = "ens_neuroblast", #change this to "icn_neuroblast" for "icn_neuroblast"
  100. ctrl = 200,
  101. assay = "RNA")
  103. meta <- [email hidden]
  104. meta <- meta[,10:825] #change accroding to gene number
  105. meta <- rowMeans(meta) %>%
  106. as.data.frame()
  107. sub[["ens_neuroblast"]] <- meta[,1]
  108. FeaturePlot(sub,"ens_neuroblast", split.by = "ID")
  110. sub$icn_ens_similarity <- sub$icn_neuroblast - sub$ens_neuroblast
  112. FeaturePlot(sub, "icn_ens_similarity", split.by = "ID",
  113. cols = rev(brewer.pal(n = 11, name = "RdBu")))
  114. saveRDS(sub, "neuroblast_score.rds")
  117. sub <- subset(coculture, state %in% c("Neurons"))
  118. DefaultAssay(sub) <- "RNA"
  119. Idents(sub) <- "ID"
  121. gene <- neuron_gut$gene #change this to "neuron_heart" for "icns_neuron"
  123. set.seed(1)
  124. test <- AddModuleScore(
  125. sub,
  126. features = gene,
  127. name = "ens_neuron", #change this to "icns_neuron" for "icns_neuron"
  128. ctrl = 200,
  129. assay = "RNA")
  131. meta <- [email hidden]
  132. meta <- meta[,10:842] #change accroding to gene number
  133. meta <- rowMeans(meta) %>%
  134. as.data.frame()
  135. sub[["ens_neuron"]] <- meta[,1]
  136. FeaturePlot(sub,"ens_neuron", split.by = "ID")
  138. sub$icn_ens_similarity <- sub$icn_neuron - sub$ens_neuron
  140. FeaturePlot(sub, "icn_ens_similarity", split.by = "ID",
  141. cols = rev(brewer.pal(n = 11, name = "RdBu")))
  142. saveRDS(sub, "neuron_score.rds")
  145. sub <- subset(coculture, state %in% c("Precursors"))
  146. DefaultAssay(sub) <- "RNA"
  147. Idents(sub) <- "ID"
  149. gene <- precursor_gut$gene #change this to "precursor_heart" for "icns_precursor"
  151. set.seed(1)
  152. test <- AddModuleScore(
  153. sub,
  154. features = gene,
  155. name = "ens_precursor", #change this to "icns_precursor" for "icns_precursor"
  156. ctrl = 200,
  157. assay = "RNA")
  159. meta <- [email hidden]
  160. meta <- meta[,10:952] #change accroding to gene number
  161. meta <- rowMeans(meta) %>%
  162. as.data.frame()
  163. sub[["ens_precursor"]] <- meta[,1]
  164. FeaturePlot(sub,"ens_precursor", split.by = "ID")
  166. sub$icn_ens_similarity <- sub$icn_precursor - sub$ens_precursor
  167. FeaturePlot(sub, "icn_ens_similarity", split.by = "ID",
  168. cols = rev(brewer.pal(n = 11, name = "RdBu")))
  169. saveRDS(sub, "precursor_score.rds")

03_Transcriptomic_changes_between_coculture_conditions.R at commit b106405, no license · at the source

Overview

Authors: I-Uen Yvonne Hsu1,2, Jia Zhao3, Yingxin Lin3, Yunshan Guo3, Qian J Xu1,2,4, Yuancheng Shao1,2, Ruiqi L Wang1,2,4, Dominic Yin5, Kakali Ghoshal6, Rida Mourad7, Ambra Pozzi6,8, Carmen M Halabi7, Lawrence H Young2,9, Hongyu Zhao3, Le Zhang1,4,5, Rui B Chang1,2,4
ORCID iDs: I-Uen Yvonne Hsu, Jia Zhao, Yingxin Lin, Ruiqi L Wang, Dominic Yin, Carmen M Halabi, Hongyu Zhao, Le Zhang, Rui B Chang
  1. Department of Neuroscience, Yale University School of Medicine, New Haven, CT USA
  2. Department of Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT USA
  3. Department of Biostatistics, Yale School of Public Health, New Haven, CT USA
  4. Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT USA
  5. Department of Neurology, Yale University School of Medicine, New Haven, CT USA
  6. Department of Medicine, Division of Nephrology and Hypertension, Vanderbilt University School of Medicine, Nashville, TN USA
  7. Department of Pediatrics, Division of Nephrology, Washington University School of Medicine, St Louis, MO USA
  8. Department of Veterans Affairs, Nashville, TN USA
  9. Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT USA
Institutions: Yale University (United States); Vanderbilt University (United States); Washington University in St. Louis (United States); United States Department of Veterans Affairs (United States)
Journal: Nature, volume 655, issue 8122, pages 429-437
Dates: received 12 December 2023; accepted 3 April 2026; published online 13 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41586-026-10490-y · PMID 42129551 · PMCID PMC13345970 · OpenAlex W7161057575
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: Peripheral nervous system, Neurophysiology
MeSH: Cell Lineage*, Nervous System*, Organ Specificity*, Signal Transduction*, Animals, Cell Differentiation, Cell Movement, Coculture Techniques, Extracellular Matrix, Heart, Integrins, Intestines, Lung, Mice, Neural Crest, Neurogenesis, Neurons, Pancreas, Single-Cell Analysis (* major topic)
Topic: Congenital heart defects research (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NHLBI NIH HHS (R01 HL150449, DP2 HL151354); NCCIH NIH HHS (R01 AT012041)
Citations: cited by 2 papers (Europe PMC); 71 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 20 matches between paragraphs and lines of code.

lezhanglab/Organ-Intrinsic-Nervous-Systems

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b1064054286046de2acecfe4b5a464247a17a933, 22 April 2026
Languages: R (12), Jupyter (10), Python (4), MATLAB (3)
Size: 38 files, 29 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 10 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (10 files), Seurat (8 files), Matplotlib (7 files), NumPy (7 files), pandas (7 files), Scanpy (7 files), tidyverse (6 files), anndata (5 files), Monocle 3 (4 files), NetworkX (4 files), cowplot (3 files), Statistics and Machine Learning Toolbox (2 files), patchwork (2 files), PyTorch (2 files), scVelo (2 files), seaborn (2 files), ComplexHeatmap (1 file), Image Processing Toolbox (1 file), reshape2 (1 file), scikit-learn (1 file), SciPy (1 file), UMAP (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
22 files
At the source: github.com/lezhanglab/Organ-Intrinsic-Nervous-Systems

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/s41586-026-10490-y.

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 21 scripts, each with its path and the digest of its content;
  • 20 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 statement

The paper has a 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/s41586-026-10490-y.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 16 authors, 2 keywords, 19 MeSH terms, 2 funders, 71 references.

Cite

This paper

Hsu, I.-U. Y., Zhao, J., Lin, Y., Guo, Y., Xu, Q. J., Shao, Y., Wang, R. L., Yin, D., Ghoshal, K., Mourad, R., Pozzi, A., Halabi, C. M., Young, L. H., Zhao, H., Zhang, L., & Chang, R. B. (2026). Lineage and organ signals sequentially build organ intrinsic nervous systems. Nature, 655(8122), 429-437. https://doi.org/10.1038/s41586-026-10490-y

BibTeX

@article{hsu2026lineage,
author = {Hsu, I-Uen Yvonne and Zhao, Jia and Lin, Yingxin and Guo, Yunshan and Xu, Qian J and Shao, Yuancheng and Wang, Ruiqi L and Yin, Dominic and Ghoshal, Kakali and Mourad, Rida and Pozzi, Ambra and Halabi, Carmen M and Young, Lawrence H and Zhao, Hongyu and Zhang, Le and Chang, Rui B},
title = {{Lineage and organ signals sequentially build organ intrinsic nervous systems}},
journal = {Nature},
year = {2026},
month = may,
volume = {655},
number = {8122},
pages = {429--437},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10490-y},
url = {https://doi.org/10.1038/s41586-026-10490-y},
pmid = {42129551},
pmcid = {PMC13345970}
}

RIS

TY - JOUR
AU - Hsu, I-Uen Yvonne
AU - Zhao, Jia
AU - Lin, Yingxin
AU - Guo, Yunshan
AU - Xu, Qian J
AU - Shao, Yuancheng
AU - Wang, Ruiqi L
AU - Yin, Dominic
AU - Ghoshal, Kakali
AU - Mourad, Rida
AU - Pozzi, Ambra
AU - Halabi, Carmen M
AU - Young, Lawrence H
AU - Zhao, Hongyu
AU - Zhang, Le
AU - Chang, Rui B
TI - Lineage and organ signals sequentially build organ intrinsic nervous systems
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/05/13
VL - 655
IS - 8122
SP - 429
EP - 437
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10490-y
UR - https://doi.org/10.1038/s41586-026-10490-y
LA - en
ER -

CSL-JSON

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{
"family": "Yin",
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{
"family": "Ghoshal",
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Genomic sequence evolution underlying human neocortical interareal diversification.
Journal: Genome biology
In common: Monocle 3, UMAP, anndata, 12 other tools, mouse, 3 references
[6] doi:10.1038/s41467-026-68596-w [code]
Spatial cartography of human thymus enables the geopositioning of lineage transcription factors in rare mimetic thymic epithelial cells.
Journal: Nature communications
In common: anndata, Scanpy, NetworkX, 12 other tools, 4 references
[7] doi:10.1038/s41467-026-76675-1 [code]
Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.
Journal: Nature communications
In common: Monocle 3, UMAP, anndata, 13 other tools, developmental, 1 reference
[8] doi:10.1016/j.xcrm.2026.102651 [code]
Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease.
Journal: Cell reports. Medicine
In common: UMAP, anndata, Scanpy, 14 other tools
[9] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: Monocle 3, UMAP, ComplexHeatmap, 14 other tools, mouse
[10] 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, anndata, Scanpy, 14 other tools, mouse

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