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CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity.

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Paper

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The authors' code

R · 94 lines · 2.3 KB · no license

  1. #' @export
  3. CellCover = function(J = 3, alpha = 0.05, genes_per_class = 6000, te=0.1,
  4. mat, CellTypeNames, CellTypeLabels,prev = NULL)
  5. {
  6. selectedtrain = Find_CTGroups(CellTypeLabels, CellTypeNames)
  7. trainfreq = CompPropMatrix(selectedtrain, mat, paral = FALSE)
  9. nct = length(selectedtrain)
  10. ng = nrow(mat)
  12. library(slam)
  13. library(gurobi)
  15. marginmatrix=matrix(NA,ng,nct)
  16. for (currenttype in 1:nct)
  17. {
  18. for (i in 1:ng)
  19. {
  20. temp=mean(trainfreq[i,-currenttype])
  21. marginmatrix[i,currenttype]=trainfreq[i,currenttype]/temp
  22. }
  23. }
  25. currentmarker=list()
  26. for (currenttype in 1:nct)
  27. {
  28. td = sort(marginmatrix[,currenttype], decreasing = TRUE)[genes_per_class]
  29. if (td < 1){
  30. td = 1
  31. }
  32. keepgeneindex=intersect(which(marginmatrix[,currenttype]>td),which(trainfreq[,currenttype]>=te))
  33. G=length(keepgeneindex)
  34. group=selectedtrain[[currenttype]]
  35. tempA=mat[keepgeneindex,group]
  36. N=ncol(tempA)
  38. A=matrix(0,G,N)
  39. for (i in 1:G)
  40. {
  41. A[i,which(tempA[i,]>0)]=1
  42. }
  44. if (is.null(prev) == FALSE)
  45. {
  46. z0 = prev[[currenttype]] #only the index
  47. holder1 = rep(0,ng)
  48. for (i in 1:length(z0))
  49. {
  50. holder1[z0[i]] = 1
  51. }
  52. holder2 = rep(0,G)
  53. for (i in 1:G)
  54. {
  55. holder2[i] = holder1[keepgeneindex[i]]
  56. }
  57. AA = matrix(0, N+1+G,G+N)
  58. AA[1:N,1:G]=matrix(t(A),N,G)
  59. AA[1:N,(G+1):(G+N)]=diag(J,N,N)
  60. AA[N+1,]=c(rep(0,G),rep(1,N))
  61. AA[(N+2):(N+1+G),1:G] = diag(1,G,G)
  62. rhs=c(rep(J,N),alpha*N,holder2)
  63. sense= c(rep('>=',N),'<=',rep('>=',G))
  64. }
  66. else{
  67. AA=matrix(NA,N+1,G+N)
  68. AA[1:N,1:G]=matrix(t(A),N,G)
  69. AA[1:N,(G+1):(G+N)]=diag(J,N,N)
  70. AA[N+1,]=c(rep(0,G),rep(1,N))
  71. rhs = c(rep(J,N),alpha*N)
  72. sense = c(rep('>=',N),'<=')
  73. }
  75. model = list()
  76. model$A = AA
  77. model$obj = c(1/(marginmatrix[keepgeneindex,currenttype]-td+0.00001),rep(0,N))
  78. model$modelsense = 'min'
  79. model$modelname = 'poolserch'
  80. model$rhs = rhs
  81. model$sense = sense
  82. model$vtype = 'B'
  83. params = list()
  85. params$MIPFocus = 0
  87. params$PoolSearchMode = 0
  88. #params$PoolSolutions = 2
  89. solutions = gurobi(model, params = params)
  90. markerlist=keepgeneindex[which(solutions$x[1:G]>0.9)]
  92. currentmarker[[currenttype]]=markerlist
  93. }
  94. return(currentmarker)}

CellCover.R at commit 9c3d331, no license · at the source

Overview

Authors: Lanlan Ji1, An Wang1, Shreyash Sonthalia2, Seungmae Seo3, Daniel Q Naiman1, Laurent Younes1,4, Carlo Colantuoni2,5, Donald Geman1,4
ORCID iDs: An Wang, Seungmae Seo, Carlo Colantuoni
  1. Department of Applied Mathematics and Statistics, Johns Hopkins University Baltimore United States
  2. Departments of Neurology and Neuroscience, Johns Hopkins University Baltimore United States
  3. Department of Natural Sciences, University of Maryland Eastern Shore Princess Anne United States
  4. Center for Imaging Science, Johns Hopkins University Baltimore United States
  5. Institute for Genome Sciences, University of Maryland School of Medicine Baltimore United States
Institutions: Johns Hopkins University (United States); University of Maryland Eastern Shore (United States); University of Maryland, Baltimore (United States)
Journal: eLife, volume 14, article RP107531
Dates: published online 28 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.107531 · PMID 42517345 · PMCID PMC13412324 · OpenAlex W4415402955
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: human, primate, mouse, single-cell RNA-seq, marker genes, neocortex
MeSH: Neocortex*, Neural Stem Cells*, Neurogenesis*, Animals, Biomarkers, Gene Expression Regulation, Developmental, Humans, Mice, Neurodevelopment, Sequence Analysis, RNA, Single-Cell Analysis, Single-Cell Gene Expression Analysis (* major topic)
Journal subjects: Neuroscience
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Cancer Institute (R01CA200859); National Science Foundation (2124230); Data sharing and visualization via NeMO Analytics (R24MH114815, R01DC019370)
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Defining cell classes is central to the analysis of growing single-cell RNA sequencing (scRNA-seq) atlases. Marker genes are most often identified by differential expression (DE) methods that assess genes one at a time, ignoring the redundancy and complementarity revealed when genes are considered jointly. Working with binarized expression data, we instead seek discriminating panels of genes that together are specific to a cell type, framing marker-panel selection as a variant of the minimal set-covering problem in combinatorial optimization. This formulation efficiently searches the vast space of candidate panels, exploits the large cell numbers typical of scRNA-seq, and is robust to zero-inflation. Using blood and brain data, we show that our method, CellCover, reduces gene redundancy and captures cell-class-specific signals distinct from those found by DE. Transfer-learning experiments across mouse, primate, and human data demonstrate that CellCover identifies conserved cell classes in neocortical neurogenesis and tracks developmental progression in progenitors and neurons. Examining outer radial glia markers across mammals, we find that transcriptomic elements of this key cell type likely arose in rodent gliogenic precursors before the full program emerged in the primate lineage.

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

Repository

Its files are read in the Code ↔ Paper reader above.

lanlanji/CoveringPackage

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9c3d331e65f0d9fe5860d13abbec219f3dab4059, 12 April 2023
Languages: R (4)
Size: 11 files, 4 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (DESCRIPTION)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/lanlanji/CoveringPackage

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;
  • 4 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

While we have not generated any new data in this report, all the public RNA-seq data used in the analyses is freely explorable at NeMO Analytics (https://nemoanalytics.org/expression.html?gene_symbol=SOX2&gene_symbol_exact_match=1&is_multigene=0&layout_id=CellCover). Links to the data we used and original data sources are available at NeMO Analytics. We invite researchers to interrogate all the public data resources we examine here in the NeMO Analytics multi-omics data exploration environment that is designed to provide biologists with no programming expertise the ability to perform powerful analyses across collections of data in brain development (Sonthalia et al., 2026). Users can visualize individual genes (NeMO: Individual genes in cortex (https://nemoanalytics.org/p?&l=CellCover&g=EOMES) and NeMO: Individual genes in blood (https://nemoanalytics.org/p?&l=immune&g=CSF1R)) or groups of genes simultaneously across multiple datasets, including automated transfer of the CellCover gene marker panels we have identified across cell types and developmental time (NeMO: Telley 3 CellCover Panels (https://nemoanalytics.org/p?p=p&l=CellCover&c=TelleyCellCover3sets&algo=binary), NeMO: Telley 12 CellCover Panels (https://nemoanalytics.org/p?p=p&l=CellCover&c=TelleyCellCover12sets&algo=binary), NeMO: Sorted Brain Cell CellCover Panels (https://nemoanalytics.org/p?p=p&l=CellCover&c=LiuCellCoverSorted&algo=binary), and NeMO: Blood 34 CellCover Panels (https://nemoanalytics.org/p?p=p&l=immune&c=CellCoverHao34&algo=binary)). CellCover is available in CellCover R (https://github.com/lanlanji/CoveringPackage) and CellCover Python (https://pypi.org/project/CellCover/).

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, pages, dates, 8 authors, 6 keywords, 12 MeSH terms, 3 funders, 50 references.

Cite

This paper

Ji, L., Wang, A., Sonthalia, S., Seo, S., Naiman, D. Q., Younes, L., Colantuoni, C., & Geman, D. (2026). CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity. eLife, 14, RP107531. https://doi.org/10.7554/elife.107531

BibTeX

@article{ji2026cellcover,
author = {Ji, Lanlan and Wang, An and Sonthalia, Shreyash and Seo, Seungmae and Naiman, Daniel Q and Younes, Laurent and Colantuoni, Carlo and Geman, Donald},
title = {{CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity}},
journal = {eLife},
year = {2026},
month = jul,
volume = {14},
pages = {RP107531},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.107531},
url = {https://doi.org/10.7554/elife.107531},
pmid = {42517345},
pmcid = {PMC13412324}
}

RIS

TY - JOUR
AU - Ji, Lanlan
AU - Wang, An
AU - Sonthalia, Shreyash
AU - Seo, Seungmae
AU - Naiman, Daniel Q
AU - Younes, Laurent
AU - Colantuoni, Carlo
AU - Geman, Donald
TI - CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/07/28
VL - 14
SP - RP107531
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.107531
UR - https://doi.org/10.7554/elife.107531
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.107531",
"type": "article-journal",
"title": "CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity",
"container-title": "eLife",
"author": [
{
"family": "Ji",
"given": "Lanlan"
},
{
"family": "Wang",
"given": "An"
},
{
"family": "Sonthalia",
"given": "Shreyash"
},
{
"family": "Seo",
"given": "Seungmae"
},
{
"family": "Naiman",
"given": "Daniel Q"
},
{
"family": "Younes",
"given": "Laurent"
},
{
"family": "Colantuoni",
"given": "Carlo"
},
{
"family": "Geman",
"given": "Donald"
}
],
"container-title-short": "eLife",
"volume": "14",
"page": "RP107531",
"DOI": "10.7554/elife.107531",
"PMID": "42517345",
"PMCID": "PMC13412324",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.107531",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
28
]
]
}
}

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