OSCR

FOXP1 is differentially active during development of murine vasopressin and oxytocin magnocellular neurons.

Code ↔ Paper

17 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 17 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › scRNA-seq data collection and preprocessing ↔ Scripts/Romanov2020/1-AlignFastQ.py, lines 118–152 · score 1.00 · CB_UMI_Simple, CellRanger4, EmptyDrops_CR, MultiGeneUMI_CR, clipAdapterType, multi_Nbase_pseudocounts
  2. [2] § STAR★Methods › Method details › scRNA-seq data integration ↔ Scripts/Romanov2020/settings.py, lines 1–19 · score 0.99 · early_stopping_monitor, early_stopping_patience, lr_factor, lr_patience, lr_scheduler_metric, elbo_validation
  3. [3] § STAR★Methods › Method details › Cell fate probability inference ↔ Scripts/Romanov2020/_5-CellRank.py, lines 113–131 · score 0.98 · TemporalProblem, age_num, conn_weight, scale_cost, self_transitions, n_neighbors
  4. [4] § STAR★Methods › Method details › Cell fate probability inference ↔ Scripts/Romanov2020/_5-CellRank.py, lines 23–47 · score 0.98 · cluster_key, n_cells, n_states, predict_initial_states, predict_terminal_states, top_n
  5. [5] § STAR★Methods › Method details › Transcription factor binding site prediction ↔ Scripts/Figures/Fig2.R, lines 1–26 · score 0.91 · MA0071.1, MA0481.4, MA0593.2, MA1637.2, MA1989.2, BCL11B
  6. [6] § STAR★Methods › Method details › scRNA-seq data integration ↔ Scripts/Romanov2020/settings.py, lines 88–104 · score 0.86 · classification_ratio, labels_key, unlabeled_category, max_epochs, scANVI, trained
  7. [7] § STAR★Methods › Method details › Gene regulatory network inference ↔ Scripts/Romanov2020/settings.py, lines 106–125 · score 0.85 · auc_threshold, nes_threshold, rank_threshold, adjacencies, pruning, modules
  8. [8] § Results › Publicly available single-cell RNAseq reveals all developmental stages of MCNs ↔ Scripts/Figures/Fig1.R, lines 223–250 · score 0.84 · A830018L16Rik, pou3f2, Angpt1, Erbb4, Galnt14, Galntl6
  9. [9] § STAR★Methods › Method details › scRNA-seq data integration ↔ Scripts/Romanov2020/3-Integrate.R, the whole file · a weak match · score 0.82 · FindClusters, FindNeighbors, RunUMAP, dim, resolution, neighborhoods
  10. [10] § STAR★Methods › Method details › scRNA-seq data integration ↔ Scripts/Romanov2020/_3-Integrate.py, lines 30–57 · score 0.64 · scVI model, latent representation, scANVI, trained, subset, clustering
  11. [11] § STAR★Methods › Method details › Gene regulatory network inference ↔ Scripts/Romanov2020/_4-pyScenic.py, lines 119–168 · score 0.61 · arboreto.algo.grnboost2, adjacencies, modules, regulatory, activating, pySCENIC
  12. [12] § Results › In silico and in vitro analyses further substantiate a role for FOXP1 and FOXP2 ↔ Scripts/Figures/Fig3.R, lines 248–351 · score 0.59 · Poisson GLM, Poisson distribution, lambda, background, cross, fitted
  13. [13] § STAR★Methods › Method details › Cell fate probability inference ↔ Scripts/Romanov2020/5-CellRank.R, the whole file · a weak match · score 0.58 · CellRank, fate probabilities, pseudotime, matrices, cells
  14. [14] § Results › RORA, EBF3, FOXP1, FOXP2, and BCL11B are candidate TFs for diverging differentiation ↔ Scripts/Figures/Fig2.R, lines 254–338 · score 0.56 · Bcl11b, Avp MCNs, Oxt MCNs, fate, pseudotime, probabilities
  15. [15] § Results › RORA, EBF3, FOXP1, FOXP2, and BCL11B are candidate TFs for diverging differentiation ↔ Scripts/Romanov2020/_4-pyScenic.py, lines 62–95 · score 0.56 · co expression, pyScenic, GRNs, TF, regulons, activity
  16. [16] § Results › RORA, EBF3, FOXP1, FOXP2, and BCL11B are candidate TFs for diverging differentiation ↔ Scripts/Romanov2020/_5-CellRank.py, lines 23–47 · score 0.55 · CellRank2, fate probabilities, velocity, pseudotime, clusters
  17. [17] § Results › In silico and in vitro analyses further substantiate a role for FOXP1 and FOXP2 ↔ Scripts/Figures/Fig3.R, lines 248–351 · score 0.50 · Poisson GLM, background, cross, seq, RNA, DEGs

Paper

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The authors' code

Python · 157 lines · 4.1 KB · no license · 3 matches

  1. class RunSettings:
  2. """
  3. Base settings for neural network training.
  4. """
  5. def __init__(self) -> None:
  6. self.plan_kwargs = {
  7. "lr": 1e-2,
  8. "lr_min": 1e-4,
  9. "lr_patience": 33,
  10. "lr_factor": 0.1**(1/3),
  11. "reduce_lr_on_plateau": True,
  12. "lr_scheduler_metric": "elbo_validation",
  13. }
  14. self.train_kwargs = {
  15. "check_val_every_n_epoch": 1,
  16. "early_stopping": True,
  17. "early_stopping_patience": 200,
  18. "early_stopping_monitor": "elbo_validation",
  19. }
  21. class SCARSettings:
  22. def __init__(self) -> None:
  23. run = RunSettings()
  24. run.plan_kwargs["lr"] = 4.7e-3
  25. run.plan_kwargs["lr_min"] = 1e-5
  27. self.setup = {
  28. "layer": None,
  29. }
  30. self.profile = {
  31. "prob": 0.9,
  32. "n_batch": 2,
  33. "sample": 5e4,
  34. }
  35. self.init = {
  36. # scAR Settings
  37. "sparsity": 0.95,
  38. "ambient_profile": "ambient_profile",
  40. # Model parameters
  41. "n_layers": 2, # Default
  42. "n_latent": 15, # Default
  43. "n_hidden": 150, # Default
  44. "dropout_rate": 0, # Default
  45. "gene_likelihood": "b", # Default
  46. }
  47. self.train = {
  48. "max_epochs": 400,
  49. "plan_kwargs": {
  50. "n_epochs_kl_warmup": 100,
  51. **run.plan_kwargs
  52. },
  53. **run.train_kwargs,
  54. }
  56. class SCVISettings:
  57. def __init__(self) -> None:
  58. run = RunSettings()
  60. self.hvg = {
  61. "n_top_genes": 1250,
  62. "batch_key": "sample",
  63. #"flavor": "seurat_v3_paper",
  64. #"subset": True
  65. }
  66. self.setup = {
  67. "layer": "counts",
  68. "batch_key": "sample",
  69. "categorical_covariate_keys": [],
  70. "continuous_covariate_keys": [],
  71. }
  72. self.init = {
  73. "n_layers": 1,
  74. "n_latent": 10,
  75. "dropout_rate": 0.1,
  76. "dispersion": "gene",
  77. "gene_likelihood": "nb",
  78. }
  79. self.train = {
  80. "max_epochs": 400,
  81. "plan_kwargs": {
  82. "n_epochs_kl_warmup": 200,
  83. **run.plan_kwargs
  84. },
  85. **run.train_kwargs,
  86. }
  88. class SCANVISettings():
  89. def __init__(self) -> None:
  90. run = RunSettings()
  92. self.setup = {
  93. "labels_key": "Age",
  94. "unlabeled_category": "nan",
  95. }
  96. self.train = {
  97. "max_epochs": 500,
  98. "plan_kwargs": {
  99. "classification_ratio": 1.67,
  100. "n_epochs_kl_warmup": 200,
  101. **run.plan_kwargs
  102. },
  103. **run.train_kwargs,
  104. }
  106. class PYSCENICSettings:
  107. def __init__(self) -> None:
  108. self.run = {
  109. "n_workers": 16,
  110. "threads_per_worker": 1,
  111. "memory_limit": "3GB",
  112. "dashboard_address": '0.0.0.0:38888'
  113. }
  114. self.adjacenies = {
  115. "seed": 0,
  116. "verbose": True,
  117. }
  118. self.modules = {
  119. "keep_only_activating": False
  120. }
  121. self.prune = {
  122. "rank_threshold": 1500,
  123. "auc_threshold": 0.05,
  124. "nes_threshold": 2.0,
  125. }
  128. class VELOVISettings:
  129. def __init__(self) -> None:
  130. run = RunSettings()
  132. self.setup = {
  133. "spliced_layer": "spliced",
  134. "unspliced_layer": "unspliced",
  135. }
  136. self.init = {
  137. "n_layers": 1, # Default
  138. "n_latent": 10, # Default
  139. "n_hidden": 256, # Default
  140. "dropout_rate": 0.1, # Default
  141. }
  142. self.train = {
  143. "max_epochs": 400,
  144. "plan_kwargs": {
  145. "n_epochs_kl_warmup": 200,
  146. **run.plan_kwargs
  147. },
  148. **run.train_kwargs,
  149. }
  151. class Settings:
  152. _run_defaults = RunSettings()
  153. scar = SCARSettings()
  154. scvi = SCVISettings()
  155. scanvi = SCANVISettings()
  156. pyscenic = PYSCENICSettings()
  157. velovi = VELOVISettings()

settings.py at commit f6f10b6, no license · at the source

Overview

Authors: Jari B Berkhout1,2, Sophie Trender3, Quirin Krabichler4, Yuval Podpecan3, Felix Franke3, Tim Schubert3, Peter Burbach5, Valery Grinevich4, Roger Adan5, Henning Fröhlich3, Ferdinand Althammer3, Onno C Meijer1, Ahmed Mahfouz2,6
ORCID iDs: Jari B Berkhout, Quirin Krabichler, Felix Franke, Tim Schubert, Peter Burbach, Valery Grinevich, Roger Adan, Ferdinand Althammer, Onno C Meijer, Ahmed Mahfouz
  1. Department of Internal Medicine, Division Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
  2. Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
  3. Institute of Human Genetics, Heidelberg University Hospital, Heidelberg, Germany
  4. Department of Neuropeptide Research in Psychiatry, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
  5. Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
  6. Department of Intelligent Systems, Division Pattern Recognition and Bioinformatics, Technical University Delft, Delft, the Netherlands
Institutions: Leiden University Medical Center (Netherlands); Heidelberg University (Germany); University Hospital Heidelberg (Germany); Central Institute of Mental Health (Germany); Utrecht University (Netherlands); University Medical Center Utrecht (Netherlands); Delft University of Technology (Netherlands)
Journal: iScience, volume 29, issue 5, article 115604
Dates: received 17 June 2025; accepted 1 April 2026; published online 4 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.115604 · PMID 42088346 · PMCID PMC13138059 · OpenAlex W7149536030
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Spectral & time-frequency, Statistics, fMRI & imaging
Keywords: Molecular physiology, Neuroscience, Developmental biology
Topic: Neuroendocrine regulation and behavior (Social Psychology, Psychology), according to OpenAlex
Funding: Netherlands Organisation for Health Research and Development (09120012010051); ZonMw (09120012010051); European Research Council (101071777)
Citations: not cited yet (Europe PMC); 70 references in the paper
Research resources: Goat anti-guinea pig Alexa Fluor 488 RRID:AB_2534117, Guinea pig anti-Oxytocin RRID:AB_2725768, Donkey anti-mouse Alexa Fluor 594 RRID:AB_2732073, Mouse anti-Neurophysin 2 RRID:AB_3741583

Abstract

Hypothalamic arginine vasopressin (AVP) and oxytocin (OXT) magnocellular neurons (MCNs), share a developmental lineage. The transcription factors driving specification are yet unknown. Using gene regulatory network analysis on published single-cell RNA-sequencing data of the developing mouse hypothalamus, we identified RORA, EBF3, FOXP1, FOXP2, and BCL11B as candidate transcription factors for differential MCN specification. We modeled developmental gene expression dynamics using computational cell fate mapping, revealing enrichment of EBF3 and BCL11B in the Avp lineage, and FOXP1 and FOXP2 in the Oxt lineage. In silico analysis of Avp and Oxt promoters predicted a binding site for FOXP1 and FOXP2, and an in vitro reporter assay identified regulation on both Avp and Oxt genomic promoters. Finally, heterozygous FOXP1 knockout mice exhibited a significant reduction in AVP and OXT neuron abundance, with OXT neurons disproportionally affected. We conclude that FOXP1 participates in MCN development, while being differentially active in OXT MCNs relative to AVP MCNs.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

jberkh/2025_Avp_Oxt_Diff

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f6f10b69539fa5ec5265582fa0aab6c737050a37, 16 March 2026
Languages: R (12), Python (6)
Size: 36 files, 18 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, environment (pixi.lock, pixi.toml, renv.lock)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: tidyverse (12 files), Seurat (8 files), reticulate (4 files), Scanpy (4 files), pandas (3 files), patchwork (3 files), NumPy (2 files), PyTorch (2 files), circlize (1 file), ComplexHeatmap (1 file), cowplot (1 file), SciPy (1 file), STAR (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
19 files
At the source: github.com/jberkh/2025_Avp_Oxt_Diff

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 scripts, each with its path and the digest of its content;
  • 17 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

No dataset and no data link were found in the paper.

Data and code availability

scRNA-seq data: The raw data were previously deposited by Romanov et al. (2020).17 The processed and annotated developmental mouse MCN dataset has been deposited at Zenodo and is publicly available as of the date of publication. DOIs are listed in the key resources table.

Code: All original code has been deposited at GitHub (links in KRT) and is publicly available. An archived version of the code has also been deposited to Zenodo. DOIs are listed in the key resources table.

Any additional information required to reanalyze the data reported in this paper is available from the corresponding contact upon request.

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

Versions

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

Version 2, 28 September 2026

  • Authors: added Tim Schubert (0009-0003-1696-4402); Peter Burbach (0000-0002-8619-6168); Roger Adan (0000-0001-8994-0661); Ferdinand Althammer (0000-0001-6018-8939); Onno C Meijer (0000-0002-8394-6859); Ahmed Mahfouz (0000-0001-8601-2149); removed Tim Schubert; Peter Burbach; Roger Adan; Ferdinand Althammer; Onno C Meijer; Ahmed Mahfouz

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 3 keywords, 3 funders, 68 references, 4 RRIDs.

Cite

This paper

Berkhout, J. B., Trender, S., Krabichler, Q., Podpecan, Y., Franke, F., Schubert, T., Burbach, P., Grinevich, V., Adan, R., Fröhlich, H., Althammer, F., Meijer, O. C., & Mahfouz, A. (2026). FOXP1 is differentially active during development of murine vasopressin and oxytocin magnocellular neurons. iScience, 29(5), 115604. https://doi.org/10.1016/j.isci.2026.115604

BibTeX

@article{berkhout2026foxp1,
author = {Berkhout, Jari B and Trender, Sophie and Krabichler, Quirin and Podpecan, Yuval and Franke, Felix and Schubert, Tim and Burbach, Peter and Grinevich, Valery and Adan, Roger and Fröhlich, Henning and Althammer, Ferdinand and Meijer, Onno C and Mahfouz, Ahmed},
title = {{FOXP1 is differentially active during development of murine vasopressin and oxytocin magnocellular neurons}},
journal = {iScience},
year = {2026},
month = apr,
volume = {29},
number = {5},
pages = {115604},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115604},
url = {https://doi.org/10.1016/j.isci.2026.115604},
pmid = {42088346},
pmcid = {PMC13138059}
}

RIS

TY - JOUR
AU - Berkhout, Jari B
AU - Trender, Sophie
AU - Krabichler, Quirin
AU - Podpecan, Yuval
AU - Franke, Felix
AU - Schubert, Tim
AU - Burbach, Peter
AU - Grinevich, Valery
AU - Adan, Roger
AU - Fröhlich, Henning
AU - Althammer, Ferdinand
AU - Meijer, Onno C
AU - Mahfouz, Ahmed
TI - FOXP1 is differentially active during development of murine vasopressin and oxytocin magnocellular neurons
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/04/04
VL - 29
IS - 5
SP - 115604
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115604
UR - https://doi.org/10.1016/j.isci.2026.115604
LA - en
ER -

CSL-JSON

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