OSCR

NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Joint decomposition ↔ code/SJDcode_Figure1n2n3.R, lines 85 to 124 · score 0.65 · ortholog mapping, biomaRt, variation, matched, species, mouse · Report a mismatch
  2. [2] § Methods › Cell-type reannotation and CellOracle analysis ↔ NeMO_Analytics/h5ad_NeMO/h5ad_NeMO_VariableMemory.py, lines 17 to 61 · score 0.54 · expression matrix, variable, accessibility, selection, co, filtered · Report a mismatch
  3. [3] § Methods › Gene set enrichment ↔ code/GSEAcode_SonthaliaEtAl.R, lines 1 to 32 · score 0.54 · disease gene, KEGG, GO · Report a mismatch
  4. [4] § Methods › Projection analyses ↔ code/SJDcode_Figure4n5n6.R, lines 116 to 147 · score 0.52 · jointNMF, NeMO, variation, upload, algorithm, Analytics · Report a mismatch
  5. [5] § Methods › jointNMF decomposition of fetal scRNA-seq data from mouse, macaque, and human neocortex ↔ code/SJDcode_Figure4n5n6.R, lines 116 to 147 · score 0.51 · jointNMF, NeMO, variation, Analytics, assembled, dimensions · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

R · 150 lines · 7 KB · no license · 2 matches

  1. #####################################################################################################################################################################
  2. # # wgets from nemo for fig 4/5/6: from unix command line
  3. #
  4. cd /path/
  5. # jorstad 2023:
  6. wget -O Jorstad.h5ad 'https://nemoanalytics.org/cgi/download_source_file.cgi?type=h5ad&share_id=bcfb48b1'
  7. # bakken 2021:
  8. wget -O Bakken.h5ad 'https://nemoanalytics.org/cgi/download_source_file.cgi?type=h5ad&share_id=0b665d58'
  10. ##############################################################################################################################################################
  11. # # begin R
  12. library(anndata)
  13. jor <- read_h5ad("/path/Jorstad.h5ad")
  14. bak <- read_h5ad("/path/Bakken.h5ad")
  16. #indices to later split into individal donor matrices
  17. str(jor$obs)
  18. indxJOR1=jor$obs$external_donor_name_label=='H200.1025';length(indxJOR1);sum(indxJOR1)#31229, 6658
  19. indxJOR2=jor$obs$external_donor_name_label=='H200.1030';length(indxJOR2);sum(indxJOR2)#31229, 12354
  20. indxJOR3=jor$obs$external_donor_name_label=='H200.1023';length(indxJOR3);sum(indxJOR3)#31229, 12217
  21. str(bak$obs)
  22. indxBAK1=bak$obs$donor_id=='H18.30.001';length(indxBAK1);sum(indxBAK1)#7805, 4576
  23. indxBAK2=bak$obs$donor_id=='H18.30.002';length(indxBAK2);sum(indxBAK2)#7805, 3229
  25. ###############################################################################
  26. # assemble data for joint decomposition
  27. # library(devtools)
  28. # install_github("CHuanSite/SJD")
  29. library(SJD)
  31. baseDIR="/path/"
  32. lbb1="AdultNeoctxNeuron.snRNAseq.JorBak.TEST"
  33. META.list=list()
  34. EXPRS.list=list()
  36. META.list$jor1=cbind(colnames(t(as.matrix(jor$X[indxJOR1,]))),jor$obs[indxJOR1,])
  37. META.list$jor2=cbind(colnames(t(as.matrix(jor$X[indxJOR2,]))),jor$obs[indxJOR2,])
  38. META.list$jor3=cbind(colnames(t(as.matrix(jor$X[indxJOR3,]))),jor$obs[indxJOR3,])
  39. META.list$bak1=cbind(colnames(t(as.matrix(bak$X[indxBAK1,]))),bak$obs[indxBAK1,])
  40. META.list$bak2=cbind(colnames(t(as.matrix(bak$X[indxBAK2,]))),bak$obs[indxBAK2,])
  42. EXPRS.list$jor1=t(as.matrix(jor$X[indxJOR1,]))
  43. EXPRS.list$jor2=t(as.matrix(jor$X[indxJOR2,]))
  44. EXPRS.list$jor3=t(as.matrix(jor$X[indxJOR3,]))
  45. EXPRS.list$bak1=t(as.matrix(bak$X[indxBAK1,]))
  46. EXPRS.list$bak2=t(as.matrix(bak$X[indxBAK2,]))
  48. rm(jor)
  49. rm(bak)
  50. gc()
  52. # str(META.list)
  53. names(META.list)
  54. # [1] "jor1" "jor2" "jor3" "bak1" "bak2"
  56. str(EXPRS.list)
  57. # List of 5
  58. # $ jor1: num [1:50281, 1:6658] 0 0 0 0 0 ...
  59. # ..- attr(*, "dimnames")=List of 2
  60. # .. ..$ : chr [1:50281] "NOensemblIDmapped.1" "NOensemblIDmapped.2" "NOensemblIDmapped.3" "NOensemblIDmapped.4" ...
  61. # .. ..$ : chr [1:6658] "F2S4_160113_028_B01" "F2S4_160113_028_C01" "F2S4_160113_029_D01" "F2S4_160113_030_A01" ...
  62. # $ jor2: num [1:50281, 1:12354] 0 0 0 0 0 ...
  63. # ..- attr(*, "dimnames")=List of 2
  64. # .. ..$ : chr [1:50281] "NOensemblIDmapped.1" "NOensemblIDmapped.2" "NOensemblIDmapped.3" "NOensemblIDmapped.4" ...
  65. # .. ..$ : chr [1:12354] "F1S4_160106_001_D01" "F1S4_160106_001_E01" "F1S4_160106_001_G01" "F1S4_160106_001_H01" ...
  66. # $ jor3: num [1:50281, 1:12217] 0 0 0 0 0 0 0 0 0 0 ...
  67. # ..- attr(*, "dimnames")=List of 2
  68. # .. ..$ : chr [1:50281] "NOensemblIDmapped.1" "NOensemblIDmapped.2" "NOensemblIDmapped.3" "NOensemblIDmapped.4" ...
  69. # .. ..$ : chr [1:12217] "F1S4_161026_001_B01" "F1S4_161026_001_F01" "F1S4_161026_002_C01" "F1S4_161026_076_A01" ...
  70. # $ bak1: num [1:14544, 1:4576] 0.94 0 0 0 0 ...
  71. # ..- attr(*, "dimnames")=List of 2
  72. # .. ..$ : chr [1:14544] "ENSG00000188976" "ENSG00000187583" "ENSG00000187642" "ENSG00000187608" ...
  73. # .. ..$ : chr [1:4576] "AAACCCAAGTATGGCG.21L8TX_180927_001_A01" "AAACGAAGTATGAAGT.21L8TX_180927_001_A01" "AAACGCTAGTGCACCC.21L8TX_180927_001_A01" "AAAGGATAGAATTCAG.21L8TX_180927_001_A01" ...
  74. # $ bak2: num [1:14544, 1:3229] 0 0 0 0 0 ...
  75. # ..- attr(*, "dimnames")=List of 2
  76. # .. ..$ : chr [1:14544] "ENSG00000188976" "ENSG00000187583" "ENSG00000187642" "ENSG00000187608" ...
  77. # .. ..$ : chr [1:3229] "AAACCCACAGCTGTAT.25L8TX_180927_003_A01" "AAACCCACATTCAGCA.25L8TX_180927_003_A01" "AAACCCATCTAACGGT.25L8TX_180927_003_A01" "AAACGAAAGGAAGTCC.25L8TX_180927_003_A01" ...
  79. # SampleMetaNamesTable=data.frame(row.names = names(EXPRS.list),
  80. # Type=c('Yaxis','Yaxis','Yaxis','Yaxis','Yaxis'),#'2Dscatter'
  81. # XaxisColumn=c("cortical_layer_label","cortical_layer_label","cortical_layer_label","cross_species_cluster_label","cross_species_cluster_label"),
  82. # YaxisColumn=c("SJDscores","SJDscores","SJDscores","SJDscores","SJDscores"),
  83. # PCHColumn=c("","","","",""),
  84. # COLaxisColumn=c("cortical_layer_color","cortical_layer_color","cortical_layer_color","cross_species_cluster_color","cross_species_cluster_color"),
  85. # cexx=c(1,1,1,1,1))
  87. SampleMetaNamesTable=data.frame(row.names = names(EXPRS.list),
  88. Type=c('2Dscatter','2Dscatter','2Dscatter','2Dscatter','2Dscatter'),#'Yaxis'
  89. XaxisColumn=c("tsne_1","tsne_1","tsne_1","tSNE_1","tSNE_1"),
  90. YaxisColumn=c("tsne_2","tsne_2","tsne_2","tSNE_2","tSNE_2"),
  91. PCHColumn=c("","","","",""),
  92. COLaxisColumn=c("SJDscores","SJDscores","SJDscores","SJDscores","SJDscores"),
  93. cexx=c(1,1,1,1,1))
  95. SJDdataIN=sjdWrap(
  96. data.list=EXPRS.list,
  97. species.vector=c("human","human","human","human","human"),
  98. geneType.vector=c("ensembl","ensembl","ensembl","ensembl","ensembl"),
  99. geneType.out="ensembl",species.out="human")
  101. str(SJDdataIN)
  102. lapply(SJDdataIN,dim)
  103. # $jor1
  104. # [1] 14088 6658
  105. # $jor2
  106. # [1] 14088 12354
  107. # $jor3
  108. # [1] 14088 12217
  109. # $bak1
  110. # [1] 14088 4576
  111. # $bak2
  112. # [1] 14088 3229
  114. rm(EXPRS.list)
  116. ####################################################################################
  117. # searhcing for common elements of variation across all 5 matrices
  118. grp=list(shared_all=c(1:5))
  120. # 20 dimension decomposition
  121. dims=c(20) # must have same length as "grp"
  122. lbb2=paste(lbb1,".p",dims[1],sep="")
  124. print(Sys.time())
  125. jointNMF=jointNMF(dataset=SJDdataIN,group=grp,comp_num=dims)
  126. print(Sys.time())
  127. save(SampleMetaNamesTable,grp,dims,META.list,jointNMF,file=paste(baseDIR,"SJDdataOUT_",lbb2,"_jointNMF_fullShareONLY.RData",sep=""))
  128. source("/path/GeneCart2idCols.R")
  129. tbl2=GeneCart2idCols(tbl=data.frame(ensembl=rownames(jointNMF$linked_component_list$Shared.Mammal),jointNMF$linked_component_list$Shared.Mammal),IDcol="ensembl",IDtype="ensembl",IDspec="human",useNewestVersion=TRUE)
  130. str(tbl2)
  131. write.table(tbl2,row.names=FALSE,col.names=TRUE,sep="\t",file=paste0(baseDIR,lbb2,"_genelist.tab"))# this file can be uploaded to NeMO Analytics to create a gene list for projection
  132. #################
  133. # SJD plotting
  134. library(ggplot2)
  135. library(gridExtra)
  136. alg="jointNMF"
  137. scrs=jointNMF$score_list;grpp=names(scrs[[1]])[1];kt=dim(scrs[[1]][[grpp]])[1];kk=1:kt;Nrows=length(kk)
  138. Ncols=length(scrs)
  139. SJDScorePlotter.obj=SJDScorePlotter(SJDalg=alg,scores=scrs,lbb=lbb2,info=META.list,SampleMetaNamesTable=SampleMetaNamesTable)
  140. assemble.byComponent=assemble.byComponent(SJDScorePlotter.obj=SJDScorePlotter.obj,component=kk,SJD_algorithm=alg,group=grpp)
  141. adj=5
  142. pdf(width=Ncols*adj,height=Nrows*adj,paste0(baseDIR,"SJDdataOUT_",lbb2,"_",alg,"_",grpp,"_k",kk[1],".to.",kk[length(kk)],"of",kt,"new.pdf"))
  143. g = grid.arrange(grobs=assemble.byComponent,nrow=Nrows,ncol=Ncols)
  144. dev.off()
  146. #####################
  147. quit(save='no')

SJDcode_Figure4n5n6.R at commit 49d8bb9, no license · at the source

Overview

Authors: Shreyash Sonthalia1,2,3, Brian Herb4, Ricky S Adkins4, Joshua Orvis4, Guangyan Li1,2,5, Xiangyu Liao1,6, Qingjie Yu1,7, Xoel Mato Blanco8,9, Alex Casella4,10, Jinrui Liu1, Genevieve Stein-O’Brien1, Brian Caffo5, Ronna Hertzano11, Anup Mahurkar4, Jin-Chong Xu1, Jesse Gillis12, Jonathan Werner4,12, Shaojie Ma13,14, Suel-Kee Kim13, Nicola Micali13, Nenad Sestan13, Pasko Rakic13, Gabriel Santpere9, Seth A Ament4,15,16, Carlo Colantuoni1,4
ORCID iDs: Brian Herb, Ricky S Adkins, Qingjie Yu, Alex Casella, Jinrui Liu, Genevieve Stein-O’Brien, Ronna Hertzano, Anup Mahurkar, Jesse Gillis, Jonathan Werner, Shaojie Ma, Suel-Kee Kim, Nicola Micali, Nenad Sestan, Pasko Rakic, Gabriel Santpere, Seth A Ament, Carlo Colantuoni
16 affiliations
  1. Departments of Neurology and Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD USA
  2. Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD USA
  3. Present Address: Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
  4. Institute for Genome Sciences, University of Maryland, Baltimore, MD USA
  5. Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD USA
  6. Johns Hopkins Whiting School of Engineering, Johns Hopkins University, Baltimore, MD USA
  7. Program in Bioinformatics, Johns Hopkins University, Baltimore, MD USA
  8. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
  9. Hospital del Mar Research Institute, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain
  10. Medical Scientist Training Program, University of Maryland School of Medicine, Baltimore, MD USA
  11. Neurotology Branch, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD USA
  12. University of Toronto, Toronto, Ontario Canada
  13. Department of Neuroscience, Yale University, New Haven, CT USA
  14. Present Address: Department of Genome Sciences, University of Washington, Seatle, WA USA
  15. Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD USA
  16. UM-MIND Institute for Neuroscience Discovery, University of Maryland School of Medicine, Baltimore, MD USA
Institutions: Johns Hopkins University (United States); Chinese Academy of Sciences (China); Johns Hopkins Medicine (United States); Center for Excellence in Brain Science and Intelligence Technology (China); University of Maryland, Baltimore (United States); Universitat Pompeu Fabra (Spain); Barcelona Biomedical Research Park (Spain); Hospital del Mar Research Institute (Spain); National Institutes of Health (United States); National Institute on Deafness and Other Communication Disorders (United States); University of Toronto (Canada); University of Washington (United States); Yale University (United States)
Journal: Nature neuroscience, volume 29, issue 4, pages 978-991
Dates: received 8 August 2024; accepted 9 January 2026; published online 25 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02204-4 · PMID 41882195 · PMCID PMC13061640 · OpenAlex W7140327033
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), mouse (organism), non-human primate (organism)
Methods: Smoothing, state filtering, decompositions, Statistics, Preprocessing, Machine learning
Keywords: Developmental neurogenesis, Neural progenitors, Data integration, Gene expression, Neuronal development
MeSH: Neocortex*, Neurogenesis*, Transcriptome*, Animals, Gene Expression Regulation, Developmental, Humans, Macaca, Mice, Neurodevelopment, Neurons (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (U01 MH124619, UM1 MH130991, R24 MH114815, R24 MH114788); NHGRI NIH HHS (R01 HG010898); NIDCD NIH HHS (F32 DC000094, R01 DC013817, R01 DC019370); NIDA NIH HHS (R01 DA023999, R37 DA023999); NINDS NIH HHS (R00 NS122085)
Citations: cited by 2 papers (Europe PMC); 111 references in the paper

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.

Repositories

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carlocolantuoni/NeocortexDevelopment_Sonthalia2024

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 49d8bb93e61e963018bcd3ab221a52bd680ac584, 17 September 2026
Languages: R (7), Python (5)
Size: 21 files, 12 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), h5py (2 files), NumPy (2 files), limma (1 file), reticulate (1 file), Scanpy (1 file), scikit-learn (1 file), SciPy (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
13 files
At the source: github.com/carlocolantuoni/NeocortexDevelopment_Sonthalia2024/

CHuanSite/SJD

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 4d37790a5651023dbe07dbac04473808193524bb, 23 September 2026
Languages: R (44), JavaScript (4), Jupyter (1)
Size: 279 files, 49 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (DESCRIPTION), continuous integration, documentation, 4 notebooks
Not found: license file, CITATION.cff, tests
Tools: tidyverse (5 files), ggplot2 (1 file), NumPy (1 file), rpy2 (1 file), UMAP (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
50 files
At the source: github.com/CHuanSite/SJD

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41593-026-02204-4.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 61 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

Datasets cited

Data availability statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41593-026-02204-4.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 25 authors, 5 keywords, 10 MeSH terms, 5 funders, 109 references.

Cite

This paper

Sonthalia, S., Herb, B., Adkins, R. S., Orvis, J., Li, G., Liao, X., Yu, Q., Blanco, X. M., Casella, A., Liu, J., Stein-O’Brien, G., Caffo, B., Hertzano, R., Mahurkar, A., Xu, J.-C., Gillis, J., Werner, J., Ma, S., Kim, S.-K., . . . Colantuoni, C. (2026). NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development. Nature neuroscience, 29(4), 978-991. https://doi.org/10.1038/s41593-026-02204-4

BibTeX

@article{sonthalia2026nemo,
author = {Sonthalia, Shreyash and Herb, Brian and Adkins, Ricky S and Orvis, Joshua and Li, Guangyan and Liao, Xiangyu and Yu, Qingjie and Blanco, Xoel Mato and Casella, Alex and Liu, Jinrui and Stein-O’Brien, Genevieve and Caffo, Brian and Hertzano, Ronna and Mahurkar, Anup and Xu, Jin-Chong and Gillis, Jesse and Werner, Jonathan and Ma, Shaojie and Kim, Suel-Kee and Micali, Nicola and Sestan, Nenad and Rakic, Pasko and Santpere, Gabriel and Ament, Seth A and Colantuoni, Carlo},
title = {{NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development}},
journal = {Nature neuroscience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {978--991},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02204-4},
url = {https://doi.org/10.1038/s41593-026-02204-4},
pmid = {41882195},
pmcid = {PMC13061640}
}

RIS

TY - JOUR
AU - Sonthalia, Shreyash
AU - Herb, Brian
AU - Adkins, Ricky S
AU - Orvis, Joshua
AU - Li, Guangyan
AU - Liao, Xiangyu
AU - Yu, Qingjie
AU - Blanco, Xoel Mato
AU - Casella, Alex
AU - Liu, Jinrui
AU - Stein-O’Brien, Genevieve
AU - Caffo, Brian
AU - Hertzano, Ronna
AU - Mahurkar, Anup
AU - Xu, Jin-Chong
AU - Gillis, Jesse
AU - Werner, Jonathan
AU - Ma, Shaojie
AU - Kim, Suel-Kee
AU - Micali, Nicola
AU - Sestan, Nenad
AU - Rakic, Pasko
AU - Santpere, Gabriel
AU - Ament, Seth A
AU - Colantuoni, Carlo
TI - NeMO Analytics: a compendium of transcriptomic data for the exploration of neocortical development
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/03/25
VL - 29
IS - 4
SP - 978
EP - 991
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02204-4
UR - https://doi.org/10.1038/s41593-026-02204-4
LA - en
ER -

CSL-JSON

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"issued": {
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[
2026,
3,
25
]
]
}
}

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