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A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory.

Code ↔ Paper

2 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 2 matches
  1. [1] § Results › Reconstructing Connectome Structure Using Wiring PI. ↔ src/classes/ReconstructionResults.m, lines 1–20 · score 0.65 · ROC curve, binary connection matrices, FPR, TPR, predictive, thresholds
  2. [2] § Results › Comparison of Globally and Locally Randomized Neural Connection Patterns. ↔ src/classes/RandomConnectomeTestVisualizer.m, lines 80–122 · score 0.56 · globally randomized model, locally randomized model, correlation coefficients, components, connection

Paper

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The authors' code

MATLAB · 229 lines · 8.8 KB · MIT · 1 match

  1. classdef ReconstructionResults
  2. % ReconstructionResults
  3. % Computes connection reconstruction accuracy using wiring PI.
  5. properties
  6. Parameters ReconstructionParameters
  7. PIDistanceMatrix % Matrix of |PIs(xs) - PIt(xt)| differences
  8. Thresholds % Threshold values used
  9. TPR % True positive rate per threshold
  10. FPR % False positive rate per threshold
  11. Precision % Precision per threshold
  12. Recall % Recall per threshold (same as TPR)
  13. AUC_ROC % Area under ROC curve
  14. AUC_PR % Area under PR curve
  15. ROCPoints % [FPR, TPR] pairs
  16. PRPoints % [Recall, Precision] pairs
  17. PredictedMatrices % Cell array of predicted binary connection matrices per threshold (optional)
  18. end
  20. methods
  21. function obj = ReconstructionResults(reconstructionParameters)
  22. arguments
  23. reconstructionParameters ReconstructionParameters
  24. end
  25. obj.Parameters = reconstructionParameters;
  26. end
  28. function obj = compute(obj,wiringPIPairs,trueConnMat,domainMask,options)
  29. arguments
  30. obj ReconstructionResults
  31. wiringPIPairs WiringPIPairs
  32. trueConnMat (:,:) logical
  33. domainMask (:,:) logical
  34. options.StoreMatrixTag (1,1) logical = 0;
  35. options.ArbitralPIDistanceMatrix = [];
  36. end
  37. dimPI = obj.Parameters.DimPI;
  38. thresholds = obj.Parameters.getThresholds();
  39. if ~isempty(options.ArbitralPIDistanceMatrix)
  40. obj.PIDistanceMatrix = options.ArbitralPIDistanceMatrix;
  41. else
  42. sourcePI = wiringPIPairs.WiringPISource;
  43. targetPI = wiringPIPairs.WiringPITarget;
  44. obj.PIDistanceMatrix = obj.computePIDistanceMatrix(sourcePI, targetPI, dimPI);
  45. end
  46. [TPRs, FPRs, Precisions, Recalls, predMats] = obj.computeROC(obj.PIDistanceMatrix, thresholds, trueConnMat, domainMask);
  48. obj.TPR = TPRs;
  49. obj.FPR = FPRs;
  50. obj.Precision = Precisions;
  51. obj.Recall = Recalls;
  52. obj.Thresholds = thresholds;
  53. % ROC
  54. [auc_ROC, FPRSorted, TPRSorted] = obj.computeAUC(FPRs,TPRs);
  55. obj.AUC_ROC = auc_ROC;
  56. obj.ROCPoints = [FPRSorted, TPRSorted];
  57. % Precision-Recall
  58. [auc_PR, RecallSorted, PrecisionSorted] = obj.computeAUC(Recalls,Precisions);
  59. obj.AUC_PR = auc_PR;
  60. obj.PRPoints = [RecallSorted, PrecisionSorted];
  61. % optional: save predicted matrix
  62. if options.StoreMatrixTag
  63. obj.PredictedMatrices = predMats;
  64. else
  65. obj.PredictedMatrices = {};
  66. end
  67. end
  69. function hAx = plotROC(obj, options)
  70. arguments
  71. obj ReconstructionResults
  72. options.ParentAxes = [];
  73. options.PlotThresholds = [];
  74. options.PlotColor = [0 0.4470 0.7410];
  75. options.LineWidth = 1;
  76. options.LineStyle = "-";
  77. options.ScatterMarker = ".";
  78. options.ScatterColor = [0.8500 0.3250 0.0980];
  79. options.ScatterSize = 24;
  80. options.TitleOff = false;
  81. options.ReturnAx = 0;
  82. end
  83. % Get axes
  84. hAx = getOrCreateAxes(options.ParentAxes);
  85. % plot ROC
  86. ax = plot(hAx,obj.FPR, obj.TPR, 'Color',options.PlotColor,'LineStyle',options.LineStyle, ...
  87. 'LineWidth',options.LineWidth,'Marker',"none");
  88. xlabel(hAx, 'FPR');
  89. ylabel(hAx, 'TPR');
  90. if options.TitleOff == false
  91. title(hAx, sprintf('ROC Curve (AUC = %.3f)', obj.AUC_ROC));
  92. end
  93. box on
  94. axis square
  95. xlim([0,1])
  96. ylim([0,1])
  97. xticks([0,0.5,1])
  98. yticks([0,0.5,1])
  99. if ~isempty(options.PlotThresholds)
  100. thresholdIndices = obj.getThresholdIndices(options.PlotThresholds);
  101. plotFPR = obj.FPR(thresholdIndices);
  102. plotTPR = obj.TPR(thresholdIndices);
  103. hold on
  104. scatter(hAx,plotFPR,plotTPR,options.ScatterSize,options.ScatterColor,'filled');
  105. end
  106. if options.ReturnAx == 1
  107. hAx = ax;
  108. end
  109. end
  111. function plotPR(obj, options)
  112. arguments
  113. obj ReconstructionResults
  114. options.ParentAxes = [];
  115. options.PlotThresholds = [];
  116. options.PlotColor = [0 0.4470 0.7410];
  117. options.LineWidth = 1;
  118. options.LineStyle = "-";
  119. options.ScatterMarker = ".";
  120. options.ScatterColor = [0.8500 0.3250 0.0980];
  121. options.ScatterSize = 24;
  122. end
  123. % Get axes
  124. hAx = getOrCreateAxes(options.ParentAxes);
  125. % plot ROC
  126. plot(hAx,obj.Recall, obj.Precision, 'Color',options.PlotColor,'LineStyle',options.LineStyle, ...
  127. 'LineWidth',options.LineWidth,'Marker',"none");
  128. xlabel(hAx, 'Recall');
  129. ylabel(hAx, 'Precision');
  130. title(hAx, sprintf('ROC Curve (AUC = %.3f)', obj.AUC_ROC));
  131. box on
  132. axis square
  133. xlim([0,1])
  134. ylim([0,1])
  135. xticks([0,0.5,1])
  136. yticks([0,0.5,1])
  137. if ~isempty(options.PlotThresholds)
  138. thresholdIndices = obj.getThresholdIndices(options.PlotThresholds);
  139. plotRecall = obj.Recall(thresholdIndices);
  140. plotPrecision = obj.Precision(thresholdIndices);
  141. hold on
  142. scatter(hAx,plotRecall,plotPrecision,options.ScatterSize,options.ScatterColor,'filled');
  143. end
  144. end
  146. function threshholdIndices = getThresholdIndices(obj,threshholdsList)
  147. arguments
  148. obj ReconstructionResults
  149. threshholdsList
  150. end
  151. nThreshold = numel(threshholdsList);
  152. threshholdIndices = zeros(size(threshholdsList));
  153. for n = 1:nThreshold
  154. id = find(obj.Thresholds == threshholdsList(n));
  155. if isempty(id)
  156. error("the selected threshold was not found")
  157. end
  158. threshholdIndices(n) = id;
  159. end
  160. end
  161. end
  163. methods (Static)
  164. function diffMatrix = computePIDistanceMatrix(sourcePI, targetPI, D)
  165. diffMatrix = zeros(height(sourcePI),height(targetPI));
  166. for d = 1:D
  167. diffMatrix = diffMatrix + abs(sourcePI(:, d) - targetPI(:, d)');
  168. end
  169. diffMatrix = diffMatrix / max(diffMatrix,[],'all');
  170. end
  172. function [TPRs, FPRs, Precisions, Recalls, predMats] = computeROC(piDiffMatrix, thresholds, trueConnMatrix, domainMask)
  173. arguments
  174. piDiffMatrix (:,:) double
  175. thresholds (:,1) double
  176. trueConnMatrix (:,:) logical
  177. domainMask (:,:) logical
  178. end
  179. trueFlat = trueConnMatrix(domainMask);
  180. numThresholds = numel(thresholds);
  181. TPRs = zeros(1, numThresholds);
  182. FPRs = zeros(1, numThresholds);
  183. Precisions = zeros(1, numThresholds);
  184. Recalls = zeros(1, numThresholds);
  185. predMats = cell(1, numThresholds);
  187. for i = 1:numThresholds
  188. th = thresholds(i);
  189. predMatrix = piDiffMatrix <= th;
  190. predFlat = predMatrix(domainMask);
  192. TP = sum(predFlat & trueFlat);
  193. FP = sum(predFlat & ~trueFlat);
  194. FN = sum(~predFlat & trueFlat);
  195. TN = sum(~predFlat & ~trueFlat);
  197. TPRs(i) = TP / (TP + FN);
  198. FPRs(i) = FP / (FP + TN);
  199. if TP + FP == 0
  200. eps = 0.001;
  201. else
  202. eps = 0;
  203. end
  204. Precisions(i) = TP / (TP + FP + eps); % eps to avoid division by zero
  205. Recalls(i) = TPRs(i);
  206. predMats{i} = predMatrix .* domainMask;
  207. end
  208. end
  210. function [auc, xSorted, ySorted] = computeAUC(x, y)
  211. [xSorted, idx] = sort(x);
  212. ySorted = y(idx);
  213. % Ensure left end (0)
  214. if xSorted(1) > 0
  215. xSorted = [0, xSorted];
  216. ySorted = [ySorted(1), ySorted];
  217. end
  218. % Ensure right end (1)
  219. if xSorted(end) < 1
  220. xSorted = [xSorted, 1];
  221. ySorted = [ySorted, ySorted(end)];
  222. end
  223. auc = trapz(xSorted, ySorted);
  224. end
  228. end
  229. end

ReconstructionResults.m at commit 3337b9b, under MIT · at the source

Overview

Authors: Jigen Koike1,2, Ken Nakae3,4, Riichiro Hira5, Yuichiro Yada2, Honda Naoki1,2,4,6
ORCID iDs: Jigen Koike, Ken Nakae, Yuichiro Yada
  1. Laboratory of Data-driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashihiroshima 739-8526, Japan
  2. Laboratory of Data-driven Biology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan
  3. Digital Twin Lab, Graduate School of Engineering, University of Fukui, Fukui 910-8507, Japan
  4. The Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki 444-8787, Japan
  5. Department of Physiology and Cell Biology, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo 113-8519, Japan
  6. Center for One Medicine Innovative Translational Research (COMIT), Nagoya University, Nagoya 466-8550, Japan
Institutions: Hiroshima University (Japan); Nagoya University (Japan); University of Fukui (Japan); The Exploratory Research Center on Life and Living Systems (Japan)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 10, article e2516572123
Dates: received 24 June 2025; accepted 13 January 2026; published online 3 March 2026; in print 10 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1073/pnas.2516572123 · PMID 41774789 · PMCID PMC12974521 · OpenAlex W7133307816
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), computational (subfield)
Keywords: connectome, transcriptome, chemoaffinity theory, neural wiring, canonical correlation analysis (CCA)
MeSH: Brain*, Connectome*, Animals, Axons, Mice, Spatial Transcriptomics, Transcriptome (* major topic)
Journal subjects: Biological Sciences, Neuroscience
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: MEXT | Japan Science and Technology Agency (JST) (JPMJFS2129, JPMJSP2132); MEXT | JST | Moonshot Research and Development Program (Moonshot) (JPMJMS2024-9); MEXT | NINS | Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences (ExCELLS) (19-102); Japan Society for the Promotion of Science (JP22H05163)
Citations: not cited yet (Europe PMC); 96 references in the paper

Abstract

Understanding how brain-wide neural circuits are genetically wired remains a fundamental question in neuroscience. While Sperry’s chemoaffinity theory [Sperry, Proc. Natl. Acad. Sci. U.S.A. 50, 703–710 (1963)] posits that molecular gradients provide positional cues for axonal projections, its application has been largely limited to localized sensory systems. Here, we present SPERRFY (Spatial Positional Encoding for Reconstructing Rules of axonal Fiber connectivitY), a data-driven framework that operationalizes Sperry’s theory at the whole-brain scale. By integrating connectomic data with spatial transcriptomic profiles from the Allen Mouse Brain Atlas, SPERRFY infers latent positional gradients that underlie axonal wiring. Using canonical correlation analysis (CCA), we extract top gradient pairs that align with observed neural connectivity patterns, capturing both global (interregional) and local (intraregional) organizational principles. Connectivity reconstruction based on these gradients shows strong predictive performance, and permutation-based null models confirm the biological relevance of the inferred structures. Furthermore, SPERRFY can screen for candidate genes that may contribute to positional wiring information, providing molecular insight into the developmental logic of brain-wide circuitry. Our results extend Sperry’s foundational theory beyond the sensory domain, offering a unified, data-driven framework for understanding genetically encoded connectivity across the entire brain.

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 2 matches between paragraphs and lines of code.

JigenKoike/SPERRFY

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 3337b9b9aedf68476f906cefdd7e8ea7479b93b5, 18 November 2025
Languages: MATLAB (70), Jupyter (3)
Size: 857 files, 73 scripts
Software Heritage: not archived
Found in: the text, “Materials and Methods”
Holds: README, license file, 8 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), BrainSMASH (2 files), NumPy (2 files), SciPy (2 files), fdr_bh (Benjamini-Hochberg FDR) (1 file), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
75 files
At the source: github.com/JigenKoike/SPERRFY

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;
  • 73 scripts, each with its path and the digest of its content;
  • 2 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, Materials, and Software Availability

Source code for analysis data have been deposited in GitHub (https://github.com/JigenKoike/SPERRFY). Study data are included in the article and/or SI Appendix (http://www.pnas.org/lookup/doi/10.1073/pnas.2516572123#supplementary-materials). Previously published data were used for this work (24, 32, 33).

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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 7 MeSH terms, 4 funders, 94 references.

Cite

This paper

Koike, J., Nakae, K., Hira, R., Yada, Y., & Naoki, H. (2026). A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory. Proceedings of the National Academy of Sciences of the United States of America, 123(10), e2516572123. https://doi.org/10.1073/pnas.2516572123

BibTeX

@article{koike2026data,
author = {Koike, Jigen and Nakae, Ken and Hira, Riichiro and Yada, Yuichiro and Naoki, Honda},
title = {{A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = mar,
volume = {123},
number = {10},
pages = {e2516572123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2516572123},
url = {https://doi.org/10.1073/pnas.2516572123},
pmid = {41774789},
pmcid = {PMC12974521}
}

RIS

TY - JOUR
AU - Koike, Jigen
AU - Nakae, Ken
AU - Hira, Riichiro
AU - Yada, Yuichiro
AU - Naoki, Honda
TI - A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/03/03
VL - 123
IS - 10
SP - e2516572123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2516572123
UR - https://doi.org/10.1073/pnas.2516572123
LA - en
ER -

CSL-JSON

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"title": "A data-driven framework linking the connectome to spatial gene expression gradients inspired by chemoaffinity theory",
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