OSCR

Retinal waves shape starburst amacrine cell dendrite development through a direction-selective dendritic computation.

Code ↔ Paper

6 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 6 matches
  1. [1] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Statistics ↔ compareQuadPathLength.m, lines 247–295 · score 0.72 · post hoc, nasal temporal, temporal quadrant, interaction, ANOVA, mixed
  2. [2] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Statistics ↔ compareQuadPathLength.m, lines 247–295 · score 0.70 · post hoc, nasal temporal, temporal quadrants, interaction, ANOVA, mixed
  3. [3] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Statistics ↔ plotWTB2byQuad.m, lines 283–316 · score 0.69 · Anderson Darling, Mann Whitney
  4. [4] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Statistics ↔ plotWTB2byQuad.m, lines 80–144 · score 0.62 · post hoc, Quadrant interaction, ANOVA, genotypes, WT, temporal
  5. [5] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › SAC quadrant tuning ↔ makeDendTraces.m, lines 19–31 · score 0.55 · correlation coefficient, light stimulus, pixels, trace
  6. [6] § STAR★METHODS › QUANTIFICATION AND STATISTICAL ANALYSIS › Statistics ↔ plotWTB2byQuad.m, lines 80–144 · score 0.54 · post hoc, interaction, ANOVA, ventral, dorsal, genotypes

Paper

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The authors' code

MATLAB · 320 lines · 8.6 KB · no license · 3 matches

  1. data = readtable('summaryData_251210.csv');
  3. % Normalize 'litter' field
  4. data.litter = string(data.litter);
  5. wtData = data(strcmp(data.geno,'wt'),:);
  6. koData = data(strcmp(data.geno,'ko'),:);
  8. % ============================================================
  9. % PATH LENGTH
  10. % ============================================================
  12. wt = wtData.pathLength;
  13. ko = koData.pathLength;
  15. % ---- Normality + automatic statistical test ----
  16. [testName_path, p_path] = autoTest(wt, ko);
  18. % ---- Plot ----
  19. figure('Position',[100, 100, 600, 400]); hold on
  20. sz = 7;
  21. markers = {'o','s','^','d','v','>','<','p','h'};
  22. uniqueLitters = unique(data.litter);
  24. legHandles = gobjects(0);
  25. legLabels = strings(0);
  27. % WT
  28. for i = 1:numel(uniqueLitters)
  29. L = uniqueLitters(i);
  30. m = markers{mod(i-1,numel(markers))+1};
  32. maskWT = wtData.litter == L;
  33. if any(maskWT)
  34. h = plot(ones(sum(maskWT),1), wt(maskWT), 'k', ...
  35. 'LineStyle','none','Marker',m,'MarkerSize',sz, ...
  36. 'MarkerFaceColor','w','MarkerEdgeColor','k');
  37. legHandles(end+1) = h;
  38. legLabels(end+1) = "Litter " + L;
  39. end
  40. end
  42. % KO
  43. for i = 1:numel(uniqueLitters)
  44. L = uniqueLitters(i);
  45. m = markers{mod(i-1,numel(markers))+1};
  46. maskKO = koData.litter == L;
  47. if any(maskKO)
  48. plot(2*ones(sum(maskKO),1), ko(maskKO), 'k', ...
  49. 'LineStyle','none','Marker',m,'MarkerSize',sz, ...
  50. 'MarkerFaceColor','w','MarkerEdgeColor','k');
  51. end
  52. end
  54. xlim([0 3]);
  55. ylim([0 3500]);
  56. xticks([1 2]); xticklabels({'WT','KO'});
  57. ylabel('Path Length (\mum)');
  59. x_offset = 0.2;
  60. means = [mean(wt,'omitnan'), mean(ko,'omitnan')];
  61. stdevs = [std(wt,'omitnan'), std(ko,'omitnan')];
  63. errorbar([1-x_offset, 2+x_offset], means, stdevs, ...
  64. 'r','LineStyle','none','LineWidth',2, ...
  65. 'CapSize',10,'Marker','o','MarkerFaceColor','r');
  67. legend(legHandles, legLabels, 'Location','bestoutside');
  68. box on;
  71. %% CELL AREA
  74. wt = wtData.cellArea;
  75. ko = koData.cellArea;
  77. [testName_area, p_area] = autoTest(wt, ko);
  80. %% by quadrant: Split-plot ANOVA (Genotype x Quadrant) on Path Length
  82. % ---- 1) Specify the quadrant columns in *data* (both genotypes) ----
  83. quadVars = {'nasal','temporal','dorsal','ventral'}; % EDIT if needed
  85. missingVars = setdiff(quadVars, data.Properties.VariableNames);
  86. if ~isempty(missingVars)
  87. error("These quadrant columns are missing from data: %s", strjoin(missingVars, ", "));
  88. end
  90. % ---- 2) Keep only WT/KO rows and build wide matrix ----
  91. subData = data( strcmp(data.geno,'wt') | strcmp(data.geno,'ko'), : );
  93. Y = subData{:, quadVars}; % N x 4
  94. geno = categorical(subData.geno); % between-subject factor
  96. % Drop rows with any NaNs in quadrant measures
  97. rowOK = all(~isnan(Y), 2);
  98. Y = Y(rowOK,:);
  99. geno = geno(rowOK);
  101. % ---- 3) Build table for fitrm (wide format + between factor) ----
  102. rmTbl = array2table(Y, 'VariableNames', {'Nasal','Temporal','Dorsal','Ventral'});
  103. rmTbl.Genotype = geno;
  105. within = table(categorical({'Nasal';'Temporal';'Dorsal';'Ventral'}), ...
  106. 'VariableNames', {'Quadrant'});
  108. % ---- 4) Fit split-plot RM model and run ANOVA ----
  109. rm = fitrm(rmTbl, 'Nasal-Ventral ~ Genotype', 'WithinDesign', within);
  111. % This table includes:
  112. % - Genotype (between-subject main effect)
  113. % - Quadrant (within-subject main effect)
  114. % - Genotype:Quadrant interaction <-- your main question
  115. ranovatbl = ranova(rm, 'WithinModel', 'Quadrant');
  117. disp("Split-plot ANOVA: Path length ~ Genotype x Quadrant");
  118. disp(ranovatbl);
  120. % (Optional) Between-subjects table for Genotype main effect
  121. betweenTbl = anova(rm);
  122. disp("Between-subjects effects:");
  123. disp(betweenTbl);
  125. % ---- 5) Optional: post-hoc simple effects (Quadrant comparisons within each Genotype) ----
  126. mc_byG = multcompare(rm, 'Quadrant', 'By', 'Genotype');
  128. % Holm correction within each genotype separately
  129. mc_byG.pHolm = nan(height(mc_byG),1);
  130. G = categories(mc_byG.Genotype);
  131. for g = 1:numel(G)
  132. idxG = mc_byG.Genotype == G{g};
  133. p = mc_byG.pValue(idxG);
  134. [~, ord] = sort(p);
  135. m = numel(p);
  136. pHolm = nan(m,1);
  137. for k = 1:m
  138. pHolm(ord(k)) = min(1, (m-k+1) * p(ord(k)));
  139. end
  140. mc_byG.pHolm(idxG) = pHolm;
  141. end
  143. disp("Post-hoc Quadrant pairwise within each Genotype (uncorrected p + Holm):");
  144. disp(mc_byG(:, {'Genotype','Quadrant_1','Quadrant_2','Difference','StdErr','pValue','pHolm'}));
  147. %% visualize quads
  148. % ---- 2) Keep only WT/KO rows and build wide matrix ----
  149. subData = data( strcmp(data.geno,'wt') | strcmp(data.geno,'ko'), : );
  151. Y = subData{:, quadVars}; % N x 4
  152. geno = categorical(subData.geno); % between-subject factor
  154. % Drop rows with any NaNs in quadrant measures
  155. rowOK = all(~isnan(Y), 2);
  156. Y = Y(rowOK,:);
  157. geno = geno(rowOK);
  160. %% visualize quads WT then KO (grouped by genotype)
  161. % ============================================================
  162. % VISUALIZATION: Path Length by Quadrant, grouped by Genotype
  163. % (WT quadrants first, then KO quadrants)
  164. % ============================================================
  166. quadNames = {'Nasal','Temporal','Dorsal','Ventral'};
  167. nQ = numel(quadNames);
  169. % Quadrant color scheme
  170. quadColors = [
  171. 0.2 0.4 0.9; % Nasal (blue)
  172. 0.9 0.2 0.2; % Temporal (red)
  173. 0.2 0.7 0.3; % Dorsal (green)
  174. 0.6 0.3 0.8 % Ventral (purple)
  175. ];
  177. % Split by genotype
  178. isWT = geno == 'wt';
  179. isKO = geno == 'ko';
  181. Y_wt = Y(isWT,:);
  182. Y_ko = Y(isKO,:);
  184. % Means and SDs
  185. mu_wt = mean(Y_wt, 1, 'omitnan');
  186. sd_wt = std(Y_wt, 0, 1, 'omitnan');
  188. mu_ko = mean(Y_ko, 1, 'omitnan');
  189. sd_ko = std(Y_ko, 0, 1, 'omitnan');
  191. figure('Position',[100 100 600 450]); hold on
  193. % X positions: WT (1–4), KO (6–9)
  194. x_wt = 1:nQ;
  195. gap = 1;
  196. x_ko = (nQ + gap + 1) : (nQ + gap + nQ);
  198. % ---- Bars (solid fill) ----
  199. for q = 1:nQ
  200. % WT
  201. bar(x_wt(q), mu_wt(q), ...
  202. 'FaceColor', quadColors(q,:), ...
  203. 'EdgeColor', quadColors(q,:), ...
  204. 'LineWidth',1.5, ...
  205. 'BarWidth',0.6);
  207. % KO
  208. bar(x_ko(q), mu_ko(q), ...
  209. 'FaceColor', quadColors(q,:), ...
  210. 'EdgeColor', quadColors(q,:), ...
  211. 'LineWidth',1.5, ...
  212. 'BarWidth',0.6);
  213. end
  215. % ---- SD error bars ----
  216. for q = 1:nQ
  217. errorbar(x_wt(q), mu_wt(q), sd_wt(q), ...
  218. 'Color', quadColors(q,:), ...
  219. 'LineStyle','none', ...
  220. 'LineWidth',1.5, ...
  221. 'CapSize',10);
  223. errorbar(x_ko(q), mu_ko(q), sd_ko(q), ...
  224. 'Color', quadColors(q,:), ...
  225. 'LineStyle','none', ...
  226. 'LineWidth',1.5, ...
  227. 'CapSize',10);
  228. end
  230. % ---- Plot paired data (within genotype) ----
  231. nWT = size(Y_wt,1);
  232. for i = 1:nWT
  233. plot(x_wt, Y_wt(i,:), '-', ...
  234. 'Color',[0.75 0.75 0.75], ...
  235. 'LineWidth',0.75);
  236. end
  238. nKO = size(Y_ko,1);
  239. for i = 1:nKO
  240. plot(x_ko, Y_ko(i,:), '-', ...
  241. 'Color',[0.75 0.75 0.75], ...
  242. 'LineWidth',0.75);
  243. end
  245. % ---- Overlay dots (filled, quadrant-colored) ----
  246. sz = 28;
  247. for q = 1:nQ
  248. scatter( ...
  249. x_wt(q)*ones(nWT,1), Y_wt(:,q), sz, ...
  250. 'MarkerFaceColor', quadColors(q,:), ...
  251. 'MarkerEdgeColor', quadColors(q,:), ...
  252. 'LineWidth',1.0);
  254. scatter( ...
  255. x_ko(q)*ones(nKO,1), Y_ko(:,q), sz, ...
  256. 'MarkerFaceColor', quadColors(q,:), ...
  257. 'MarkerEdgeColor', quadColors(q,:), ...
  258. 'LineWidth',1.0);
  259. end
  261. % ---- Formatting ----
  262. allX = [x_wt, x_ko];
  263. xticks(allX);
  264. xticklabels([quadNames, quadNames]);
  265. ylabel('Path Length (\mum)');
  266. title('WT then KO Path Length by Quadrant');
  267. ylim([0 1100]);
  269. % Genotype labels
  270. midWT = mean(x_wt);
  271. midKO = mean(x_ko);
  272. yl = ylim;
  273. text(midWT, yl(1) - 0.08*range(yl), 'WT', ...
  274. 'HorizontalAlignment','center','FontSize',12);
  275. text(midKO, yl(1) - 0.08*range(yl), 'KO', ...
  276. 'HorizontalAlignment','center','FontSize',12);
  278. xlim([0.5 max(allX)+0.5]);
  279. box on
  280. set(gca,'TickDir','out','FontSize',12);
  282. %% fuctions
  283. function [testName, pVal] = autoTest(wt, ko)
  284. % Remove NaNs
  285. wt = wt(~isnan(wt));
  286. ko = ko(~isnan(ko));
  288. % ---------- Normality tests ----------
  289. try
  290. [h_wt, p_wt] = swtest(wt); % Shapiro–Wilk
  291. [h_ko, p_ko] = swtest(ko);
  292. method = 'Shapiro–Wilk';
  293. catch
  294. % Fallback: Anderson–Darling (built-in)
  295. h_wt = adtest(wt); p_wt = NaN;
  296. h_ko = adtest(ko); p_ko = NaN;
  297. method = 'Anderson–Darling';
  298. end
  300. fprintf('\nNormality test (%s):\n', method);
  301. fprintf(' WT: h=%d, p=%.4f\n', h_wt, p_wt);
  302. fprintf(' KO: h=%d, p=%.4f\n\n', h_ko, p_ko);
  304. % ---------- Choose appropriate statistical test ----------
  305. if h_wt == 0 && h_ko == 0
  306. % Both normal → parametric
  307. [~, pVal] = ttest2(wt, ko);
  308. testName = 'Two-sample t-test';
  309. else
  310. % Non-normal → non-parametric
  311. pVal = ranksum(wt, ko);
  312. testName = 'Mann–Whitney U (ranksum)';
  313. end
  315. fprintf('Selected test: %s\np = %.4g\n', testName, pVal);
  316. end

plotWTB2byQuad.m at commit 5cfbc76, no license · at the source

Overview

Authors: Miah N. Pitcher1, Aanica S.B. Gonzales1, Raul Habib1, Marla B. Feller1,2,3
ORCID iDs: Marla B. Feller
  1. Department of Neuroscience, University of California, Berkeley, Berkeley, CA 94720, USA
  2. Senior author
  3. Lead contact
Institutions: University of California, Berkeley (United States)
Journal: Cell reports, volume 45, issue 6, article 117476
Dates: published online 1 June 2026; in print 23 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.celrep.2026.117476 · PMID 42224079 · PMCID PMC13374506 · OpenAlex W7163034843
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Statistics, Evoked potentials, Graphs, Single-unit activity, calcium imaging
Keywords: Spontaneous activity, Optic flow, Spatiotemporal Integration, Motion Computation, Subcellular Signaling, Visual System Development, Cp: Neuroscience, Dendritic Compartmentalization, Circuit Maturation
MeSH: Amacrine Cells*, Dendrites*, Retina*, Animals, Mice (* major topic)
Topic: Retinal Development and Disorders (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Eye Institute (P30EY003176, R01EY013528, 1F31EY035571, R01EY019498); NEI NIH HHS (R01 EY013528, F31 EY035571, P30 EY003176, R01 EY019498); National Institutes of Health
Citations: not cited yet (Europe PMC); 59 references in the paper
Research resources: Mouse: Frmd7tm1a(KOMP)Wtsi (FRMD7tm) RRID:MMRRC_047759-UCD, MATLAB RRID:SCR_001622, FIJI RRID:SCR_003070, Python RRID:SCR_008394, ScanImage RRID:SCR_014307

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 6 matches between paragraphs and lines of code.

FellerLabCodeShare/waves-shape-SAC-development-

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5cfbc7642fde4d4b4a2ad455efbf9c8af6dbd236, 30 March 2026
Languages: MATLAB (17), Jupyter (1)
Size: 20 files, 18 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (12 files), Matplotlib (1 file), NumPy (1 file), pandas (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/FellerLabCodeShare/waves-shape-SAC-development-

The paper's code and data availability statement is in the Data section.

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Read it in the paper: doi.org/10.1016/j.celrep.2026.117476.

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Version 2, 28 September 2026

  • Publisher: n/a → Cell Press
  • Authors: added Marla B. Feller (0000-0002-9137-5849); removed Marla B. Feller

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 9 keywords, 5 MeSH terms, 3 funders, 58 references, 5 RRIDs.

Cite

This paper

Pitcher, M. N., Gonzales, A. S., Habib, R., & Feller, M. B. (2026). Retinal waves shape starburst amacrine cell dendrite development through a direction-selective dendritic computation. Cell reports, 45(6), 117476. https://doi.org/10.1016/j.celrep.2026.117476

BibTeX

@article{pitcher2026retinal,
author = {Pitcher, Miah N. and Gonzales, Aanica S.B. and Habib, Raul and Feller, Marla B.},
title = {{Retinal waves shape starburst amacrine cell dendrite development through a direction-selective dendritic computation}},
journal = {Cell reports},
year = {2026},
month = jun,
volume = {45},
number = {6},
pages = {117476},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117476},
url = {https://doi.org/10.1016/j.celrep.2026.117476},
pmid = {42224079},
pmcid = {PMC13374506}
}

RIS

TY - JOUR
AU - Pitcher, Miah N.
AU - Gonzales, Aanica S.B.
AU - Habib, Raul
AU - Feller, Marla B.
TI - Retinal waves shape starburst amacrine cell dendrite development through a direction-selective dendritic computation
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/06/01
VL - 45
IS - 6
SP - 117476
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117476
UR - https://doi.org/10.1016/j.celrep.2026.117476
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.celrep.2026.117476",
"type": "article-journal",
"title": "Retinal waves shape starburst amacrine cell dendrite development through a direction-selective dendritic computation",
"container-title": "Cell reports",
"author": [
{
"family": "Pitcher",
"given": "Miah N."
},
{
"family": "Gonzales",
"given": "Aanica S.B."
},
{
"family": "Habib",
"given": "Raul"
},
{
"family": "Feller",
"given": "Marla B."
}
],
"container-title-short": "Cell Rep",
"volume": "45",
"issue": "6",
"page": "117476",
"DOI": "10.1016/j.celrep.2026.117476",
"PMID": "42224079",
"PMCID": "PMC13374506",
"ISSN": "2211-1247",
"publisher": "Cell Press",
"URL": "https://doi.org/10.1016/j.celrep.2026.117476",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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