OSCR

A Hierarchical Multimodal Knowledge Graph for Neural Cell-Type-Specific Regulation Integrating Single-Cell Transcriptomics and Literature Evidence.

Code ↔ Paper

16 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 16 matches
  1. [1] § 4. Materials and Methods › 4.5. Knowledge Graph Embedding and Link Prediction ↔ scripts/kge_training/train_pykeen_model.py, lines 113–244 · score 0.96 · adversarial temperature, NSSALoss, negative sampler, random seed, Validation MRR, RotatE
  2. [2] § 4. Materials and Methods › 4.5. Knowledge Graph Embedding and Link Prediction ↔ scripts/kge_training/evaluate_regulates_lp.py, lines 1–50 · score 0.80 · full entity vocabulary, filtered evaluation, tail prediction, head prediction, metrics, Hits
  3. [3] § 4. Materials and Methods › 4.6. In Silico Knockout Validation ↔ scripts/virtual_knockout/run_virtual_knockout.py, lines 1–21 · score 0.78 · virtual knockout, silico knockout, microglia regulator candidates, KGE, Validation
  4. [4] § 4. Materials and Methods › 4.6. In Silico Knockout Validation ↔ scripts/virtual_knockout/run_virtual_knockout.py, lines 88–175 · score 0.76 · virtual knockout, scTenifoldKnk, expression matrix, distance, networks, gene
  5. [5] § 4. Materials and Methods › 4.2. Single-Cell Transcriptomic Marker Gene Identification and Validation ↔ scripts/kg_construction/import_marker_genes.py, lines 37–111 · score 0.75 · gene biotype, gene symbols, gene records, Ensembl, pct, protein
  6. [6] § 2. Results › 2.1. A Hierarchical Neural Cell Type Taxonomy for Knowledge Graph Construction ↔ scripts/virtual_knockout/run_virtual_knockout.py, lines 23–40 · score 0.67 · excitatory neurons, inhibitory neurons, hippocampal CA1, Glial cell, astrocytes
  7. [7] § 4. Materials and Methods › 4.5. Knowledge Graph Embedding and Link Prediction ↔ scripts/kg_construction/export_triples.py, lines 1–15 · score 0.65 · Neo4j, Paper nodes, KGE training, exporting, tail, head
  8. [8] § 4. Materials and Methods › 4.5. Knowledge Graph Embedding and Link Prediction ↔ scripts/kge_training/evaluate_marker_retrieval.py, lines 1–69 · score 0.63 · reciprocal rank, filtered evaluation, metrics, vocabulary, candidate, scoring
  9. [9] § 4. Materials and Methods › 4.5. Knowledge Graph Embedding and Link Prediction ↔ scripts/kge_training/train_pykeen_model.py, lines 1–48 · score 0.60 · link prediction evaluation, REGULATES triples, absent, splitting, validation, Embedding
  10. [10] § 4. Materials and Methods › 4.2. Single-Cell Transcriptomic Marker Gene Identification and Validation ↔ scripts/kg_construction/import_marker_genes.py, lines 30–34 · score 0.58 · CA3 pyramidal neurons, hippocampal CA1, node, Gene
  11. [11] § 4. Materials and Methods › 4.7. Statistical Analysis and Reproducibility ↔ scripts/kge_training/evaluate_marker_retrieval.py, lines 1–69 · score 0.57 · model training, KGE train, reproducing, held, metrics, splits
  12. [12] § 4. Materials and Methods › 4.6. In Silico Knockout Validation ↔ scripts/virtual_knockout/run_virtual_knockout.py, lines 1–21 · score 0.56 · virtual knockout, prioritization, Silico, validation, regulated
  13. [13] § 2. Results › 2.4. Mapping Molecular Fingerprints and Literature Evidence onto the Hierarchical Backbone ↔ scripts/kg_construction/export_triples.py, lines 1–15 · score 0.56 · Neo4j, Paper nodes, KGE training, exported, triple, entities
  14. [14] § 2. Results › 2.1. A Hierarchical Neural Cell Type Taxonomy for Knowledge Graph Construction ↔ scripts/kg_construction/import_marker_genes.py, lines 30–34 · score 0.55 · CA3 pyramidal neurons, hippocampal CA1, CA2, nodes
  15. [15] § 4. Materials and Methods › 4.4. Multi-Source Knowledge Graph Integration and Neo4j Implementation ↔ scripts/kg_construction/import_hierarchy.py, lines 1–16 · score 0.55 · Level3 nodes, Neo4j, Level1, Level2
  16. [16] § 4. Materials and Methods › 4.5. Knowledge Graph Embedding and Link Prediction ↔ scripts/kge_training/split_triples.py, lines 1–68 · score 0.52 · transductive safe, ratio, splitting, validation, interacts, training

Paper

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The authors' code

Python · 179 lines · 6.7 KB · MIT · 4 matches

  1. #!/usr/bin/env python3
  2. """run_virtual_knockout.py
  4. In silico knockout validation for KGE-prioritized Microglia regulator candidates
  5. using the scTenifoldKnk framework (Cabezas-Bratesco et al., Patterns, 2022).
  7. Usage:
  8. python run_virtual_knockout.py
  9. """
  11. import io
  12. import time
  13. import warnings
  14. import numpy as np
  15. import pandas as pd
  16. from scipy.io import mmread
  17. from pathlib import Path
  19. from scTenifold import scTenifoldKnk
  21. warnings.filterwarnings("ignore")
  23. # ──────────────────────────────────────────────
  24. # Configuration
  25. # ──────────────────────────────────────────────
  26. BASE_DIR = Path(__file__).parent
  27. GENES_TO_KO = ["FMR1", "PTEN", "FKBP5"]
  28. N_SAMP_CELLS = 600 # cells sampled per network
  29. MAX_GENES = 3000 # gene cap after preprocessing
  31. KGE_INFO = {
  32. "FMR1": {"novel_rank": 6, "global_rank": 763, "score": -5.755,
  33. "cells": "Astrocyte; Bergmann glial cell; Excitatory neuron; "
  34. "Hippocampal CA1 PN; LAMP5+ interneuron; NSC; Radial glial cell"},
  35. "PTEN": {"novel_rank": 26, "global_rank": 787, "score": -6.113,
  36. "cells": "Cerebellar inhibitory neuron; Hippocampal DG granule cell; "
  37. "LAMP5+ interneuron; NSC; Pericyte"},
  38. "FKBP5": {"novel_rank": 38, "global_rank": 801, "score": -6.197,
  39. "cells": "Astrocyte; Fibroblast"},
  40. }
  43. # ──────────────────────────────────────────────
  44. # Data loading and preprocessing
  45. # ──────────────────────────────────────────────
  47. def load_expression_matrix():
  48. """Load Microglia expression matrix and downsample to 500 cells."""
  49. mtx_file = BASE_DIR / "data" / "microglia_matrix.mtx"
  50. gene_file = BASE_DIR / "data" / "microglia_gene_names.txt"
  52. print("\nLoading expression matrix...")
  53. with open(mtx_file, "rb") as f:
  54. mat = mmread(io.BytesIO(f.read())).tocsr()
  55. gene_names = open(gene_file, encoding="utf-8").read().strip().split("\n")
  56. if mat.shape[0] != len(gene_names):
  57. mat = mat.T.tocsr()
  59. # Downsample to 500 cells for input loading
  60. n_total = mat.shape[1]
  61. n_qc = min(500, n_total)
  62. np.random.seed(42)
  63. sel_cols = np.random.choice(n_total, n_qc, replace=False)
  64. mat_sub = mat[:, sel_cols]
  66. df = pd.DataFrame(mat_sub.toarray(), index=gene_names)
  67. print(f" Loaded: {df.shape[0]} genes x {df.shape[1]} cells (downsampled from {n_total})")
  68. return df
  71. def preprocess_genes(df, max_genes=3000):
  72. """Filter low-expression genes and cap gene count for memory."""
  73. gene_mask = (df.mean(axis=1) >= 0.05) & (df.sum(axis=1) >= 25)
  74. df = df.loc[gene_mask]
  75. if df.shape[0] > max_genes:
  76. top_genes = df.sum(axis=1).nlargest(max_genes).index
  77. ko_genes = [g for g in GENES_TO_KO if g in df.index]
  78. keep = set(top_genes) | set(ko_genes)
  79. df = df.loc[sorted(keep, key=lambda x: df.index.get_loc(x))]
  80. print(f" After gene filtering: {df.shape[0]} genes")
  81. return df
  84. # ──────────────────────────────────────────────
  85. # Main pipeline using scTenifoldKnk
  86. # ──────────────────────────────────────────────
  88. def main():
  89. print("=" * 60)
  90. print("Virtual Knockout - scTenifoldKnk")
  91. print(f"Genes: {', '.join(GENES_TO_KO)}")
  92. print(f"Sampled cells per network: {N_SAMP_CELLS}")
  93. print("=" * 60)
  95. df = load_expression_matrix()
  96. df = preprocess_genes(df, max_genes=MAX_GENES)
  98. # Check genes
  99. for gene in GENES_TO_KO:
  100. if gene in df.index:
  101. pct = (df.loc[gene] > 0).sum() / df.shape[1] * 100
  102. print(f" {gene}: OK ({pct:.0f}% cells)")
  103. else:
  104. print(f" {gene}: MISSING!")
  106. output_dir = BASE_DIR / "results"
  107. output_dir.mkdir(exist_ok=True)
  108. summary = []
  109. total_t0 = time.time()
  111. for gene in GENES_TO_KO:
  112. print(f"\n{'='*60}")
  113. print(f"[KO] {gene} (KGE Novel Rank: {KGE_INFO[gene]['novel_rank']})")
  114. print(f"{'='*60}")
  116. t0 = time.time()
  117. try:
  118. knk = scTenifoldKnk(
  119. data=df,
  120. ko_genes=gene,
  121. ko_method="default",
  122. qc_kws={"min_exp_avg": 0.05, "min_exp_sum": 25},
  123. nc_kws={"n_nets": 10, "n_samp_cells": N_SAMP_CELLS,
  124. "n_comp": 3, "q": 0.95},
  125. ma_kws={"d": 30},
  126. dr_kws={"n_ko_genes": 1},
  127. )
  128. dr_df = knk.build()
  129. elapsed = time.time() - t0
  131. # Save results
  132. dr_df.to_csv(output_dir / f"{gene}_vk_results.csv", index=False)
  133. sig = dr_df[dr_df["adjusted p-value"] < 0.05]
  134. sig.to_csv(output_dir / f"{gene}_dr_genes.csv", index=False)
  135. n_sig = len(sig)
  137. summary.append({
  138. "Gene": gene,
  139. "KGE_Novel_Rank": KGE_INFO[gene]["novel_rank"],
  140. "KGE_Global_Rank": KGE_INFO[gene]["global_rank"],
  141. "KGE_Score": KGE_INFO[gene]["score"],
  142. "Known_Regulated_Cells": KGE_INFO[gene]["cells"],
  143. "Total_DR_genes": len(dr_df),
  144. "Significant_DR_genes": n_sig,
  145. "Top_DR_gene": dr_df.iloc[0]["Gene"] if len(dr_df) > 0 else "N/A",
  146. "Status": "OK",
  147. })
  149. print(f" Done in {elapsed:.1f}s | Total: {len(dr_df)} genes, "
  150. f"Significant (adj.p<0.05): {n_sig}")
  151. if len(dr_df) > 0:
  152. print(" Top 5 DR genes:")
  153. for _, row in dr_df.head(5).iterrows():
  154. print(f" {row['Gene']}: dist={row['Distance']:.3f}, "
  155. f"adj.p={row['adjusted p-value']:.4e}")
  157. except Exception as e:
  158. print(f" FAILED: {e}")
  159. summary.append({
  160. "Gene": gene, "Status": "FAILED",
  161. "KGE_Novel_Rank": KGE_INFO[gene]["novel_rank"],
  162. "KGE_Global_Rank": KGE_INFO[gene]["global_rank"],
  163. "Total_DR_genes": 0, "Significant_DR_genes": 0,
  164. })
  166. # Summary
  167. total_elapsed = time.time() - total_t0
  168. summary_df = pd.DataFrame(summary)
  169. summary_df.to_csv(output_dir / "knockout_summary.csv", index=False)
  171. print(f"\n{'='*60}")
  172. print(f"ANALYSIS COMPLETE ({total_elapsed:.1f}s total)")
  173. print(f"{'='*60}")
  174. print(summary_df.to_string(index=False))
  175. print(f"\nResults saved to: {output_dir}")
  178. if __name__ == "__main__":
  179. main()

run_virtual_knockout.py at commit ba99138, under MIT · at the source

Overview

Authors: Chuangyu Chen1,2, Xiaomin Ni1, Yang Min1, Zhen Wang1, Zhilan Xu1, Yang Zhang3, Hao Yu1,4
ORCID iDs: Hao Yu
  1. Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (C.C.); (X.N.); (Y.M.); (Z.W.); (Z.X.)
  2. School of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
  3. Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China
  4. The Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, Shenzhen 518055, China
Institutions: Chinese Academy of Sciences (China); Shenzhen Institutes of Advanced Technology (China); University of Chinese Academy of Sciences (China); Shenzhen Bay Laboratory (China)
Journal: International journal of molecular sciences, volume 27, issue 15, article 6842
Dates: received 4 June 2026; accepted 23 July 2026; published online 30 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/ijms27156842 · PMID 42589508 · PMCID PMC13466513 · OpenAlex W7171942209
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: neural cell types, knowledge graph, single-cell transcriptomics, literature mining, knowledge graph embedding, link prediction
MeSH: Neurons*, Single-Cell Analysis*, Transcriptome*, Computational Biology, Data Mining, Gene Expression Profiling, Humans, Single-Cell Gene Expression Analysis (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Shenzhen Fundamental Research Program (JCYJ20220530154407017, 20240813155824032); Guangdong Basic and Applied Basic Research Foundation (2025A1515011714); Science and Technology special fund of Hainan Province (ZDYF2024SHFZ045); National Natural Science Foundation of China (NSFC82303769)
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

The nervous system comprises highly diverse cell types governed by cell-type-specific molecular regulatory programs. However, regulatory evidence is scattered across unstructured literature and described using inconsistent cell-type nomenclature and granularity, hindering systematic integration and cross-study comparison. Here, we construct a neural-cell-centric multimodal knowledge graph that transforms fragmented regulatory evidence into a standardized, computable substrate. We establish a three-level hierarchical cell-type taxonomy anchored to the Cell Ontology (79 nodes), integrate two large-scale human brain single-cell transcriptomic datasets (over 4 million cells) to derive molecular fingerprints, and use a large language model to retain 25,812 curated regulatory evidence records from PubMed abstracts. The resulting Neo4j graph contains 41,532 directed relationships. For knowledge graph embedding, we export a deduplicated non-paper training subgraph containing 19,819 triples over 10,660 entities, supporting cell-type-specific link prediction that prioritizes candidate regulators and markers, illustrated here for microglia. This framework provides a structured basis for cross-study comparison, hypothesis generation and knowledge-guided reasoning in neural cell-type-specific regulation.

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 16 matches between paragraphs and lines of code.

SiatBioInf/NeuroCellKG

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ba99138a38aed3d52a8b97826460ff5430ba467e, 29 May 2026
Languages: Python (10)
Size: 62 files, 10 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, CITATION.cff, environment (requirements.txt)
Not found: tests, continuous integration, documentation
Tools: pandas (7 files), NumPy (4 files), PyTorch (4 files), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/SiatBioInf/NeuroCellKG

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;
  • 10 scripts, each with its path and the digest of its content;
  • 16 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 Availability Statement

All source code, data files, and Supplementary Materials are publicly available at https://github.com/SiatBioInf/NeuroCellKG (accessed on 22 July 2026). The repository includes knowledge graph construction pipelines, KGE model training scripts, LLM extraction prompt templates, expert validation samples, and all data files described in the manuscript. Single-cell transcriptomic datasets were obtained from published studies (Braun et al., Science 2023; Siletti et al., Science 2023) [11,12]. Cell Ontology definitions are available at http://www.obofoundry.org/ontology/cl.html (accessed on 22 July 2026). STRING protein–protein interaction data are available at https://string-db.org/ (accessed on 22 July 2026).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 8 MeSH terms, 4 funders, 33 references.

Cite

This paper

Chen, C., Ni, X., Min, Y., Wang, Z., Xu, Z., Zhang, Y., & Yu, H. (2026). A Hierarchical Multimodal Knowledge Graph for Neural Cell-Type-Specific Regulation Integrating Single-Cell Transcriptomics and Literature Evidence. International journal of molecular sciences, 27(15), 6842. https://doi.org/10.3390/ijms27156842

BibTeX

@article{chen2026hierarchical,
author = {Chen, Chuangyu and Ni, Xiaomin and Min, Yang and Wang, Zhen and Xu, Zhilan and Zhang, Yang and Yu, Hao},
title = {{A Hierarchical Multimodal Knowledge Graph for Neural Cell-Type-Specific Regulation Integrating Single-Cell Transcriptomics and Literature Evidence}},
journal = {International journal of molecular sciences},
year = {2026},
month = jul,
volume = {27},
number = {15},
pages = {6842},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1422-0067},
doi = {10.3390/ijms27156842},
url = {https://doi.org/10.3390/ijms27156842},
pmid = {42589508},
pmcid = {PMC13466513}
}

RIS

TY - JOUR
AU - Chen, Chuangyu
AU - Ni, Xiaomin
AU - Min, Yang
AU - Wang, Zhen
AU - Xu, Zhilan
AU - Zhang, Yang
AU - Yu, Hao
TI - A Hierarchical Multimodal Knowledge Graph for Neural Cell-Type-Specific Regulation Integrating Single-Cell Transcriptomics and Literature Evidence
T2 - International journal of molecular sciences
J2 - Int J Mol Sci
PY - 2026
DA - 2026/07/30
VL - 27
IS - 15
SP - 6842
SN - 1422-0067
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/ijms27156842
UR - https://doi.org/10.3390/ijms27156842
LA - en
ER -

CSL-JSON

{
"id": "10.3390/ijms27156842",
"type": "article-journal",
"title": "A Hierarchical Multimodal Knowledge Graph for Neural Cell-Type-Specific Regulation Integrating Single-Cell Transcriptomics and Literature Evidence",
"container-title": "International journal of molecular sciences",
"author": [
{
"family": "Chen",
"given": "Chuangyu"
},
{
"family": "Ni",
"given": "Xiaomin"
},
{
"family": "Min",
"given": "Yang"
},
{
"family": "Wang",
"given": "Zhen"
},
{
"family": "Xu",
"given": "Zhilan"
},
{
"family": "Zhang",
"given": "Yang"
},
{
"family": "Yu",
"given": "Hao"
}
],
"container-title-short": "Int J Mol Sci",
"volume": "27",
"issue": "15",
"page": "6842",
"DOI": "10.3390/ijms27156842",
"PMID": "42589508",
"PMCID": "PMC13466513",
"ISSN": "1422-0067",
"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
"URL": "https://doi.org/10.3390/ijms27156842",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
30
]
]
}
}

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