OSCR

Blocking apoptosis promotes survival and alters developmental dynamics of human retinal ganglion cells in retinal organoids.

Code ↔ Paper

4 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 4 matches
  1. [1] § STAR★METHODS › METHOD DETAILS › Cell type annotation and transfer learning ↔ SZ_reproject_rename.py, lines 486–542 · score 0.89 · random forest classifier, max_features, n_estimators, probability, log, trained
  2. [2] § STAR★METHODS › METHOD DETAILS › Cell type annotation and transfer learning ↔ SZ_percabun_annotated.py, lines 201–255 · score 0.66 · chi squared, sz percabun annotated, cell
  3. [3] § STAR★METHODS › METHOD DETAILS › Library preparation and preprocessing ↔ SZ_PearsonNMF_Annotated.py, lines 1–30 · score 0.63 · Leiden clusters, regress, log, UMAP, matrixes, preprocessing
  4. [4] § STAR★METHODS › METHOD DETAILS › Pseudotime analysis of RGCs ↔ SZ_Plotter_Annotated.py, lines 66–68 · score 0.55 · score_genes, maturity score, Scanpy

Paper

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The authors' code

Python · 576 lines · 13 KB · no license · 1 match

  1. #!/usr/bin/env python3
  3. import pandas as pd
  4. import seaborn as sns
  5. import numpy as np
  6. import scipy
  7. import anndata as ad
  8. import scanpy as sc
  9. from scipy.stats import chisquare
  10. from sklearn.decomposition import NMF
  11. import umap
  12. import statistics as stat
  13. import scipy.stats
  14. import scProject as scP
  15. import hdf5plugin
  17. # Modelling
  18. from sklearn.ensemble import RandomForestClassifier
  19. from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, ConfusionMatrixDisplay
  20. from sklearn.model_selection import RandomizedSearchCV, train_test_split
  21. from scipy.stats import randint
  23. print('importing done')
  26. #basic settings, from scanpy tutorial
  27. sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
  28. sc.logging.print_header()
  29. sc.settings.set_figure_params(dpi=80, facecolor="white")
  31. #%%
  33. #first we wrangle the chen data
  34. path ='Chen.h5ad'
  35. c_data = sc.read_h5ad(path)
  36. c_names = list(c_data.var_names)
  37. c_data.var['gene_id'] = c_names
  39. #%%
  41. path= "SZ_NMF_UMAP.h5ad"
  42. bg_data = sc.read_h5ad(path)
  43. b_names = list(bg_data.var_names)
  44. bg_data.var['gene_id'] = b_names
  47. #%%
  48. sc.pp.normalize_per_cell(bg_data)
  49. sc.pp.log1p(bg_data)
  52. print('beep boop')
  54. #%%
  55. #making my own matcher because scProject's is giving me a hard time
  57. matched = []
  58. unmatched = []
  59. c_match_bool = []
  60. count = 0
  61. for i in c_names:
  62. if i in b_names:
  63. matched.append( i )
  64. c_match_bool.append(True)
  65. else:
  66. unmatched.append( i )
  67. c_match_bool.append(False)
  69. c_data.var['Matched'] = c_match_bool
  71. print('before match')
  72. print(c_data)
  73. print('')
  75. matched = []
  76. unmatched = []
  77. b_match_bool = []
  78. count = 0
  79. for i in b_names:
  80. if i in c_names:
  81. matched.append( i )
  82. b_match_bool.append(True)
  83. else:
  84. unmatched.append( i )
  85. b_match_bool.append(False)
  87. bg_data.var['Matched'] = b_match_bool
  90. c_data = c_data[:, c_data.var["Matched"]]
  91. bg_data = bg_data[:,bg_data.var['Matched']]
  94. #%%
  95. ##########
  96. #processing bg data
  97. #########
  98. #basic filtering
  101. # define outliers and further filtering. We have excluded the suggested MT filtering.
  103. filtered_bgdata = bg_data.copy()
  104. print('here is bg filtered')
  105. print(filtered_bgdata)
  107. sc.pp.scale(bg_data)
  109. mins = bg_data.X.min()
  110. minval = abs(mins.min())
  111. bg_data.X = bg_data.X + minval
  114. #%%
  115. #Basic filtering of chen data
  118. #applying Pearson residuals
  119. sc.experimental.pp.highly_variable_genes(
  120. c_data, flavor="pearson_residuals", n_top_genes=3000)
  123. filtered_cdata = c_data.copy()
  125. #applying gene selection, maintaining just hvgs
  126. c_data = c_data[:, c_data.var["highly_variable"]]
  127. hvgs = c_data.var["highly_variable"]
  129. sc.pp.scale(c_data)
  131. #%%
  132. #now we start the NMF
  134. print('starting NMF')
  136. #Adjust if doing Pearson normalized, since NMF doesn't like negative numbers. whodathunkit.
  137. mins = c_data.X.min()
  138. minval = abs(mins.min())
  139. c_data.X = c_data.X + minval
  141. #convert X to DF, i dont think I do NMF within anndata
  142. prDF = c_data.to_df()
  143. genenames = list(prDF.columns.values)
  145. #number of components we will use for NMF
  146. comp = 40
  148. #naming outputDF columns
  149. colnam = []
  150. count = 1
  151. for i in range(0,comp):
  152. name = "NMF"
  153. name += str(count)
  154. colnam.append(name)
  155. count+= 1
  157. #actual NMF
  158. model = NMF(n_components=comp,
  159. init='random',
  160. random_state=0,
  161. verbose= 1,
  162. max_iter=10000)
  163. W = model.fit_transform(prDF)
  164. H = model.components_
  165. #%%
  166. #below returns pattern weights. This lets you know what genes are driving your NMF patterns (NRL could be a rod pattern driver, opsins for cones, etc)
  167. weightsDF = pd.DataFrame(H, columns = genenames)
  168. nmfDF= pd.DataFrame(data = W, columns = colnam )
  169. weightsAD = ad.AnnData(weightsDF)
  171. #weightsAD.write_h5ad('FetalPatterns.h5ad')
  172. weightsAD.write_h5ad('Fetal_BG_Patterns.h5ad')
  174. print('NMF is done')
  176. #%%
  177. ###########
  178. #actual transfer learning
  179. ###########
  180. print('')
  181. print('Transfer Learning started')
  183. matched_bg, patterns_filtered = scP.matcher.filterAnnDatas(bg_data, weightsAD, 'gene_id')
  184. scP.rg.NNLR_ElasticNet(matched_bg, patterns_filtered, 'fetalProject', alpha=.005, L1=.005)
  186. matched_chen, patterns_filtered = scP.matcher.filterAnnDatas(c_data, weightsAD, 'gene_id')
  187. scP.rg.NNLR_ElasticNet(matched_chen, patterns_filtered, 'fetalProject', alpha=.005, L1=.005)
  189. print('')
  190. print('Transfer Learning End')
  192. #%%
  193. print('')
  194. print('reassembling data')
  195. #assembling BG pattern space
  196. bg_pspace = matched_bg.obsm['fetalProject']
  198. nmfs = []
  199. for i in range(1,41):
  200. name = 'NMF'
  201. newname = name + str(i)
  202. nmfs.append(newname)
  204. cellname = []
  205. for i in range(0,len(bg_pspace)):
  206. name = 'bg_'
  207. newname = name + str(i)
  208. cellname.append(newname)
  211. bpat_df = pd.DataFrame(bg_pspace,columns = nmfs, index = cellname)
  213. #%%
  215. #assembling RC pattern space
  216. chen_pspace = matched_chen.obsm['fetalProject']
  218. cellname = []
  219. for i in range(0,len(chen_pspace)):
  220. name = 'chen_'
  221. newname = name + str(i)
  222. cellname.append(newname)
  224. cpat_df = pd.DataFrame(chen_pspace,columns = nmfs, index=cellname)
  227. for i in bpat_df.columns:
  228. bg_data.obs[i] = list(bpat_df[i].values)
  230. for i in cpat_df.columns:
  231. c_data.obs[i] = list(cpat_df[i].values)
  234. #%%
  236. bg_data.obs['Data_Source'] = 'BG_RA+/-'
  237. c_data.obs['Data_Source']= 'Chen_Fetal'
  239. #Ill make dummy columns so i can be more conservative with my concatenation
  240. c_data.obs['Day'] = 'N/A'
  241. c_data.obs['Genotype'] = 'Fetal'
  242. c_data.obs['Day/Genotype'] = 'N/A'
  243. c_data.obs['Manu_CT']= c_data.obs['majorclass']
  245. bg_data.obs['author_cell_type']='Organoid'
  246. bg_data.obs['majorclass'] = 'Organoid'
  247. bg_data.obs['subclass'] = 'Organoid'
  248. bg_data.obs['development_stage'] = 'N/A'
  250. cobs = c_data.obs
  251. bobs = bg_data.obs
  253. #%%
  255. print(cobs)
  257. #%%
  258. """
  259. cobs.to_csv('/Volumes/BGSEQFISH/Projecting_in_full/Partial_data/ChenObs_withPatterns.csv')
  260. bobs.to_csv('/Volumes/BGSEQFISH/Projecting_in_full/Partial_data/BG_Full_Obs_withPatterns.csv')
  261. """
  262. #%%
  264. #bringing back original count info. unfortunately the full dataset is too much for my machine to handle so im going to limit the count matrix to the top 15k hvgs from MY dataset.
  266. #applying Pearson residuals
  267. sc.experimental.pp.highly_variable_genes(
  268. filtered_bgdata, flavor="pearson_residuals", n_top_genes=15000)
  269. filtered_bgdata = filtered_bgdata[:,filtered_bgdata.var["highly_variable"]]
  271. #making my own matcher because scProject's is giving me a hard time
  273. c_names = list(filtered_cdata.var_names)
  274. b_names = list(filtered_bgdata.var_names)
  276. matched = []
  277. unmatched = []
  278. c_match_bool = []
  279. count = 0
  280. for i in c_names:
  281. if i in b_names:
  282. matched.append( i )
  283. c_match_bool.append(True)
  284. else:
  285. unmatched.append( i )
  286. c_match_bool.append(False)
  288. filtered_cdata.var['Matched'] = c_match_bool
  290. print('before match')
  291. print(c_data)
  292. print('')
  294. matched = []
  295. unmatched = []
  296. b_match_bool = []
  297. count = 0
  298. for i in b_names:
  299. if i in c_names:
  300. matched.append( i )
  301. b_match_bool.append(True)
  302. else:
  303. unmatched.append( i )
  304. b_match_bool.append(False)
  306. filtered_bgdata.var['Matched'] = b_match_bool
  308. filtered_cdata = filtered_cdata[:,filtered_cdata.var["Matched"]]
  309. filtered_bgdata = filtered_bgdata[:,filtered_bgdata.var['Matched']]
  312. #%%
  314. c_counts = filtered_cdata.X
  315. bg_counts = filtered_bgdata.X
  317. #%%
  318. fin_cdata = ad.AnnData( X = c_counts, obs = cobs)
  319. fin_cdata.var_names = filtered_cdata.var_names
  321. fin_bgdata = ad.AnnData(X = bg_counts, obs = bobs )
  322. fin_bgdata.var_names = filtered_bgdata.var_names
  325. #%%
  327. # %%
  329. #projecting my data into chen umap
  331. trans = umap.UMAP(n_neighbors=30, n_components=2, min_dist= 0.5, random_state=42).fit(cpat_df)
  332. test_embedding = trans.transform(bpat_df)
  334. fin_cdata.obsm['X_umap'] = trans.embedding_
  335. fin_bgdata.obsm['X_umap'] = test_embedding
  336. print('2D done')
  338. #%%
  339. #alrighty here is the randomforest classifier. first, for subclass
  341. adatas = [fin_cdata,fin_bgdata]
  342. data_big= ad.concat(adatas, join = 'inner')
  344. data_subclass = data_big.copy()
  346. sc.tl.rank_genes_groups(fin_cdata,
  347. groupby='subclass',
  348. n_genes=50,
  349. method= 'wilcoxon',
  350. )
  352. treatmentstow = fin_cdata.uns['rank_genes_groups']['names']
  354. print('markers found')
  356. genes = []
  357. for i in treatmentstow:
  358. for k in i:
  359. if k not in genes:
  360. genes.append(k)
  363. print('subsetting')
  365. class_gene_subset = []
  366. for i in data_subclass.var_names:
  367. if i in genes:
  368. class_gene_subset.append(True)
  369. else:
  370. class_gene_subset.append(False)
  372. data_subclass.var['Used_In_Classifier'] = class_gene_subset
  373. data_subclass = data_subclass[:,data_subclass.var['Used_In_Classifier']]
  375. print('here is the classifier data')
  376. print(data_subclass)
  378. ######
  381. rfc_chen= data_subclass[data_subclass.obs['Data_Source'] == 'Chen_Fetal']
  382. rfc_bg = data_subclass[data_subclass.obs['Data_Source'] == 'BG_RA+/-']
  384. rfc_chendf = rfc_chen.to_df()
  385. rfc_bgdf = rfc_bg.to_df()
  387. rfc_chendf['subclass'] = rfc_chen.obs['subclass']
  389. print('dfs made, training classifier')
  390. # Split the data into training and test sets
  391. X = rfc_chendf.drop('subclass', axis = 1)
  392. y = rfc_chendf['subclass']
  393. #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
  395. #training the model
  396. rf = RandomForestClassifier(verbose= 3,n_jobs=16,n_estimators=200, random_state=13,max_features= None)
  397. rf.fit(X,y)
  399. print('predicting...')
  400. class_pred = rf.predict(rfc_bgdf)
  401. log_pred = rf.predict_log_proba(rfc_bgdf)
  402. prob_pred = rf.predict_proba(rfc_bgdf)
  404. max_logs = []
  406. for i in log_pred:
  407. realvals = []
  408. for k in i:
  409. if np.isneginf(k) == False:
  410. realvals.append(round(k,2))
  412. arr = np.array(realvals)
  413. maxi = arr.max()
  414. max_logs.append(maxi)
  417. maxprobs = []
  419. for i in prob_pred:
  420. maxi = i.max()
  421. maxprobs.append(maxi)
  424. print('outputting...')
  425. fin_bgdata.obs['RF_SubClass'] = class_pred
  426. fin_bgdata.obs['RF_SubClass_log']= max_logs
  427. fin_bgdata.obs['RF_SubClass_prob']= maxprobs
  429. fin_cdata.obs['RF_SubClass'] = list(fin_cdata.obs['subclass'].values)
  430. fin_cdata.obs['RF_SubClass_log']= 0
  431. fin_cdata.obs['RF_SubClass_prob'] = 1
  434. #perf. subclasses done. for ease of access, we are also gonna do a classifier on the majorclass
  436. data_majorclass = data_big.copy()
  438. sc.tl.rank_genes_groups(fin_cdata,
  439. groupby='majorclass',
  440. n_genes=50,
  441. method= 'wilcoxon',
  442. )
  444. treatmentstow = fin_cdata.uns['rank_genes_groups']['names']
  446. print('markers found')
  448. genes = []
  449. for i in treatmentstow:
  450. for k in i:
  451. if k not in genes:
  452. genes.append(k)
  455. print('subsetting')
  457. class_gene_subset = []
  458. for i in data_majorclass.var_names:
  459. if i in genes:
  460. class_gene_subset.append(True)
  461. else:
  462. class_gene_subset.append(False)
  464. data_majorclass.var['Used_In_Classifier'] = class_gene_subset
  465. data_majorclass = data_majorclass[:,data_majorclass.var['Used_In_Classifier']]
  467. print('here is the classifier data')
  468. print(data_majorclass)
  470. ######
  473. rfc_chen= data_majorclass[data_majorclass.obs['Data_Source'] == 'Chen_Fetal']
  474. rfc_bg = data_majorclass[data_majorclass.obs['Data_Source'] == 'BG_RA+/-']
  476. rfc_chendf = rfc_chen.to_df()
  477. rfc_bgdf = rfc_bg.to_df()
  479. rfc_chendf['majorclass'] = rfc_chen.obs['majorclass']
  481. print('dfs made, training classifier')
  482. # Split the data into training and test sets
  483. X = rfc_chendf.drop('majorclass', axis = 1)
  484. y = rfc_chendf['majorclass']
  486. #training the model
  487. rf = RandomForestClassifier(verbose= 3,n_jobs=16,n_estimators=200, random_state=13,max_features= None)
  488. rf.fit(X,y)
  490. print('predicting...')
  491. class_pred = rf.predict(rfc_bgdf)
  492. log_pred = rf.predict_log_proba(rfc_bgdf)
  493. prob_pred = rf.predict_proba(rfc_bgdf)
  495. max_logs = []
  497. for i in log_pred:
  498. realvals = []
  499. for k in i:
  500. if np.isneginf(k) == False:
  501. realvals.append(round(k,2))
  503. arr = np.array(realvals)
  504. maxi = arr.max()
  505. max_logs.append(maxi)
  508. maxprobs = []
  510. for i in prob_pred:
  511. maxi = i.max()
  512. maxprobs.append(maxi)
  515. print('outputting...')
  516. fin_bgdata.obs['RF_MajorClass'] = class_pred
  517. fin_bgdata.obs['RF_MajorClass_log']= max_logs
  518. fin_bgdata.obs['RF_MajorClass_prob']= maxprobs
  520. fin_cdata.obs['RF_MajorClass'] = list(fin_cdata.obs['majorclass'].values)
  521. fin_cdata.obs['RF_MajorClass_log']= 0
  522. fin_cdata.obs['RF_MajorClass_prob'] = 1
  525. fin_cdata.obs['Cell_Type'] = list(fin_cdata.obs['majorclass'].values)
  527. ct_id = []
  528. manu_calls= ['BSLC','RPE','Lens']
  530. manus = list(fin_bgdata.obs['Manu_CT'].values)
  531. rf_calls = list(fin_bgdata.obs['RF_MajorClass'].values)
  533. count = 0
  534. for i in manus:
  535. if i in manu_calls:
  536. ct_id.append(i)
  537. count += 1
  538. else:
  539. ct_id.append(rf_calls[count])
  540. count += 1
  542. fin_bgdata.obs['Cell_Type'] = ct_id
  544. #slapping these dudes together
  546. adatas = [fin_cdata,fin_bgdata]
  547. merged = ad.concat(adatas, join = 'inner')
  549. nmfDF = pd.DataFrame()
  551. for i in merged.obs.columns:
  552. if 'NMF' in i:
  553. nmfDF[i] = list(merged.obs[i].values)
  556. dr_adata = ad.AnnData(X = nmfDF)
  558. sc.pp.neighbors(dr_adata, n_pcs = 0)
  559. #sc.tl.umap(dr_adata)
  560. sc.tl.leiden(dr_adata,
  561. key_added= 'Leiden')
  564. merged.obs['Leiden'] = list(dr_adata.obs['Leiden'].values)
  566. print(merged)
  567. print(merged.obs)
  570. merged.write_h5ad('merged_SZ_Chen.h5ad')
  572. #%%
  576. #%%

SZ_reproject_rename.py at commit 53c99f4, no license · at the source

Overview

Authors: Jingliang Simon Zhang1, Brian Guy1, Clayton P Santiago2, Caterina Tiozzo3, Meghana Sreenath4, Ya-Wen Chen4,5,6,7, Seth Blackshaw2, Robert J Johnston Jr1,2,8
ORCID iDs: Jingliang Simon Zhang, Robert J Johnston Jr
  1. Department of Biology, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
  2. The Solomon H. Snyder Department of Neuroscience, Johns Hopkins Medical Institute, Baltimore, MD 21218, USA
  3. Division of Neonatology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
  4. Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
  5. Department of Cell, Developmental, and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
  6. Institute for Airway Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
  7. Institute for Regenerative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
  8. Lead contact
Institutions: Johns Hopkins University (United States); Johns Hopkins Medicine (United States); Icahn School of Medicine at Mount Sinai (United States)
Journal: Cell reports, volume 45, issue 4, article 117270
Dates: published online 16 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.celrep.2026.117270 · PMID 41996242 · PMCID PMC13181862 · OpenAlex W4414583886
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.27.678991
Status: code verified
Categories: genetics / omics (modality), human (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials
Keywords: Human, Retina, Apoptosis, Bax, Retinal ganglion cell, bak, necrosis, Transcriptomics, Rgc, Organoid, Cp: Developmental Biology, Cp: Stem Cell Research
Topic: Retinal Development and Disorders (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NEI NIH HHS (R01 EY030872); National Institutes of Health (2T32EY007143, F31EY033656, R01EY030872, R01EY031685, R01EY036173); Icahn School of Medicine at Mount Sinai; Technology Development (2022-MSCRFD-5895); BrightFocus Foundation (G2019300)
Citations: not cited yet (Europe PMC); 130 references in the paper
Research resources: RRID:AB_10374882, Rabbit polyclonal anti-TFAP2A, 1:200 RRID:AB_10861200, RRID:AB_141607, RRID:AB_162542, RRID:AB_162543, Rabbit polyclonal anti-OPN1LW, 1:200 RRID:AB_177456, Rabbit polyclonal anti-PROX1, 1:1000 RRID:AB_177485, Mouse monoclonal anti-CRX, 1:100 RRID:AB_1840990, Goat polyclonal anti-CHAT, 1:100 RRID:AB_2313845, Sheep polyclonal anti-VSX2/CHX10, 1:300 RRID:AB_2314191, Mouse monoclonal anti-ISL1/2, 1:100 RRID:AB_2314683, Rat polyclonal anti-RFP, 1:800 RRID:AB_2336064, RRID:AB_2341188, Rabbit polyclonal anti-RBPMS, 1:400 RRID:AB_2492225, RRID:AB_2534082, RRID:AB_2534096, RRID:AB_2534102, RRID:AB_253572, RRID:AB_2535855, RRID:AB_2535867, RRID:AB_2536183, Mouse monoclonal anti-PAX6, 1:200 RRID:AB_2536820, Guinea Pig polyclonal anti-RBPMS, 1:500 RRID:AB_2913249, Mouse monoclonal anti-Ki67, 1:500 RRID:AB_393778, Mouse monoclonal anti-SNCG, 1:500 RRID:AB_464249, Sheep polyclonal anti-GFP, 1:500 RRID:AB_619712, RRID:AB_626765

Abstract

Retinal ganglion cells (RGCs) are the projection neurons connecting the retina to the brain. In many species, a substantial proportion of RGCs are eliminated by programmed cell death during development to regulate their final number, but how cell death impacts human RGC development remains poorly understood. Here, we characterized cell death in human fetal retinas and retinal organoids. Both retinas and organoids exhibited two waves of apoptosis: an early wave targeting neurogenic retinal progenitor cells and neuronal precursors and a late wave affecting RGCs and other neurons. Additionally, organoids displayed a distinct wave of necrosis. Blocking apoptosis in organoids via BAX/BAK double knockout improved RGC survival but delayed RGC neurogenesis and maturation. Our results highlight the roles of apoptosis in human RGC development and the challenges in retinal organoid design. Addressing these limitations will improve the utility of organoids for studying human retinal development and modeling optic neuropathies such as glaucoma.

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 4 matches between paragraphs and lines of code.

BGuy2/SZ_BAX-BAKdKO

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 53c99f4a5f940fd43dae14fa7588dec27225d7bb, 16 September 2025
Languages: Python (9)
Size: 10 files, 9 scripts
Software Heritage: not archived
Found in: the text, “Footnotes”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: anndata (9 files), pandas (9 files), Scanpy (9 files), seaborn (9 files), Matplotlib (8 files), NumPy (8 files), scikit-learn (6 files), UMAP (6 files), SciPy (5 files), statsmodels (3 files), Plotly (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
10 files
At the source: github.com/BGuy2/SZ_BAX-BAKdKO/

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;
  • 9 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 12 keywords, 5 funders, 128 references, 27 RRIDs.

Cite

This paper

Zhang, J. S., Guy, B., Santiago, C. P., Tiozzo, C., Sreenath, M., Chen, Y.-W., Blackshaw, S., & Johnston, R. J. (2026). Blocking apoptosis promotes survival and alters developmental dynamics of human retinal ganglion cells in retinal organoids. Cell reports, 45(4), 117270. https://doi.org/10.1016/j.celrep.2026.117270

BibTeX

@article{zhang2026blocking,
author = {Zhang, Jingliang Simon and Guy, Brian and Santiago, Clayton P and Tiozzo, Caterina and Sreenath, Meghana and Chen, Ya-Wen and Blackshaw, Seth and Johnston, Robert J},
title = {{Blocking apoptosis promotes survival and alters developmental dynamics of human retinal ganglion cells in retinal organoids}},
journal = {Cell reports},
year = {2026},
month = apr,
volume = {45},
number = {4},
pages = {117270},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117270},
url = {https://doi.org/10.1016/j.celrep.2026.117270},
pmid = {41996242},
pmcid = {PMC13181862}
}

RIS

TY - JOUR
AU - Zhang, Jingliang Simon
AU - Guy, Brian
AU - Santiago, Clayton P
AU - Tiozzo, Caterina
AU - Sreenath, Meghana
AU - Chen, Ya-Wen
AU - Blackshaw, Seth
AU - Johnston, Robert J
TI - Blocking apoptosis promotes survival and alters developmental dynamics of human retinal ganglion cells in retinal organoids
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/04/16
VL - 45
IS - 4
SP - 117270
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117270
UR - https://doi.org/10.1016/j.celrep.2026.117270
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.celrep.2026.117270",
"type": "article-journal",
"title": "Blocking apoptosis promotes survival and alters developmental dynamics of human retinal ganglion cells in retinal organoids",
"container-title": "Cell reports",
"author": [
{
"family": "Zhang",
"given": "Jingliang Simon"
},
{
"family": "Guy",
"given": "Brian"
},
{
"family": "Santiago",
"given": "Clayton P"
},
{
"family": "Tiozzo",
"given": "Caterina"
},
{
"family": "Sreenath",
"given": "Meghana"
},
{
"family": "Chen",
"given": "Ya-Wen"
},
{
"family": "Blackshaw",
"given": "Seth"
},
{
"family": "Johnston",
"given": "Robert J"
}
],
"container-title-short": "Cell Rep",
"volume": "45",
"issue": "4",
"page": "117270",
"DOI": "10.1016/j.celrep.2026.117270",
"PMID": "41996242",
"PMCID": "PMC13181862",
"ISSN": "2211-1247",
"publisher": "Cell Press",
"URL": "https://doi.org/10.1016/j.celrep.2026.117270",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
16
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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