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Gene expression profiling enables refined parcellation of cortical layers in the heterogeneous human cerebral cortex.

Code ↔ Paper

19 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 19 matches
  1. [1] § Results › Accurate distribution of cortical cell types through GD-Ls ↔ Cellbin/11_cellbin.snRNA.cor.ipynb, lines 32–56 · score 0.92 · L6 CT neurons, Car3 neurons, PVALB neurons, VIP neurons, RELN neurons, L6b neurons
  2. [2] § Results › Accurate distribution of cortical cell types through GD-Ls ↔ Cellbin/01_snRNA.UMAP.ipynb, lines 52–74 · score 0.92 · L6 CT neurons, Car3 neurons, PVALB neurons, VIP neurons, RELN neurons, L6b neurons
  3. [3] § Methods › Unsupervised clustering and benchmarking for cortical parcellation ↔ Cellbin/07_Uniport-Spann.ipynb, lines 12–105 · score 0.88 · AnnData, T_names, UMAP visualization, Scanpy, Leiden, neighbor
  4. [4] § Methods › Stereo-seq cell type annotation ↔ Cellbin/07_Uniport-Spann.ipynb, lines 12–105 · score 0.87 · lambda_cd, lambda_nb, lambda_spa, SPANN, maxiter, torch
  5. [5] § Methods › Stereo-seq cell type annotation ↔ Cellbin/04_Cell2location.ipynb, lines 37–95 · score 0.80 · train_size, batch_size, Cell2location, max_epochs, Model
  6. [6] § Methods › Stereo-seq cell type annotation ↔ Cellbin/08_Tangram.ipynb, lines 38–81 · score 0.79 · density_prior, num_epochs, map cells, Tangram, space, rna
  7. [7] § Methods › Stereo-seq cell type annotation ↔ Cellbin/02_Spotlight.ipynb, lines 10–75 · score 0.77 · SPOTlight, getTopHVGs, weight_id, auc, seq, genes
  8. [8] § Results › Gene-expression-defined parcellation of human cortical layers ↔ Cellbin/11_cellbin.snRNA.cor.ipynb, lines 32–56 · score 0.74 · L6b neurons, C1QL2, COL5A2, RORB, CCN2, KRT17
  9. [9] § Results › Gene-expression-defined parcellation of human cortical layers ↔ Binsize/05.BayesVisualization.ipynb, lines 104–115 · score 0.73 · C1QL2, COL5A2, blood, MBP, RORB, PCP4
  10. [10] § Methods › XGBoost-based label transfer ↔ Binsize/07.xgboost.ARI.ipynb, lines 252–385 · score 0.71 · confusion matrices, XGBoost classifier, trained, ARI, predict
  11. [11] § Methods › Stereo-seq cell type annotation ↔ Cellbin/03_Seurat.ipynb, lines 25–48 · score 0.71 · FindTransferAnchors, reference.reduction, Seurat, pca, mapping, dims
  12. [12] § Methods › Quantification of laminar disorganization ↔ Binsize/10.Degree_of_GroupIII_Disturbance.ipynb, lines 329–348 · score 0.69 · Benjamini Hochberg FDR, Wilcoxon, entropy, weighted, Bayes20
  13. [13] § Methods › Sensitivity analysis for bin-size selection ↔ Binsize/01.SensitivityAnalysis_BinsizeSelection.ipynb, lines 153–173 · score 0.68 · spatial coherence, adjusted Rand, manual layers, NMI, bin, purity
  14. [14] § Methods › Stereo-seq cell type annotation ↔ Cellbin/06_RCTD.ipynb, lines 16–34 · score 0.68 · min_UMI, n_max_cells, RCTD, Stereo
  15. [15] § Methods › Stereo-seq cell type annotation ↔ Cellbin/05_Destvi.ipynb, lines 50–95 · score 0.66 · DestVI, max_epochs, scvi, model, training, cell
  16. [16] § Methods › Cell subtype identification ↔ Binsize/02.merge.debach.bayes.ipynb, lines 83–88 · score 0.66 · FindClusters, FindNeighbors, Harmony, resolutions, dims
  17. [17] § Results › GD-Ls accurately delineate cortical layers across species ↔ Binsize/06.Anno.Layer.ipynb, lines 64–72 · score 0.63 · C1QL2, COL5A2, Fabp7, LAMP5, KRT17, RELN
  18. [18] § Methods › Unsupervised clustering and benchmarking for cortical parcellation ↔ Cellbin/04_Cell2location.ipynb, lines 37–95 · score 0.63 · T_names, hyperparameter, variation, detection, AnnData, Batch
  19. [19] § Methods › Visualization of Allen Brain Atlas ISH data ↔ Binsize/example_data/00.Bin1gef2Binsize_file.ipynb, lines 40–57 · score 0.56 · Human Brain Atlas, quality, tissue, cortex, genes

Paper

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The authors' code

Jupyter notebook · 161 lines · 5.7 KB · MIT · 2 matches

  1. # %%
  2. ###
  3. library(dplyr)
  4. library(Matrix)
  5. library(data.table)
  6. library(Seurat)
  7. library(ggplot2)
  8. library(RColorBrewer)
  9. library(Seurat)
  10. library(ggplot2)
  11. library (tidyverse)
  12. library(RColorBrewer)
  13. library(cowplot)
  14. library(pheatmap)
  15. library(deldir)
  16. cols = c(brewer.pal(9, "Set1"),brewer.pal(8,"Set2")[1:8],brewer.pal(12,"Paired")[1:12],brewer.pal(8,"Dark2")[1:8],brewer.pal(8,"Accent"),brewer.pal(12, "Set3"),brewer.pal(9,"Pastel1"),brewer.pal(8,"Pastel2"))
  17. best_color = c("#EA5514", "#D23918", "#FFB400", "#99F880", "#da32e9",
  18. "#00e2ff", "#832aff", "#008396", "#01fac9", "#940202",
  19. "#01fa65", "#00adff", "#058d32", "#c10265", "#bea013",
  20. "#bcbe13", "#FF0000", "#ebb076", "#fa5a03", "#e06565",
  21. "#7E9853", "#6565bc", "#b5a48d", "#90734e", "#88C6CE")
  25. # %%
  26. seuobj = readRDS(paste0('/data/Celbin.seurat.rds'))
  28. # %%
  29. head(seuobj)
  30. table(seuobj$CellType_Spatialid)
  32. # %%
  33. ###cellbin celltype marker gene dotplot
  34. data = NormalizeData(seuobj)
  35. celltype.levels = c("L2 IT neurons", "L2/3 IT neurons", "L3 IT neurons", "L3-6 IT neurons", "L3/4 IT neurons",
  36. "L4 IT neurons", "L4/5 IT neurons", "L5 ET neurons", "L5/6 CAR3 neurons", "L5/6 NP neurons",
  37. "L6 CT neurons", "L6 IT neurons", "L6b neurons",
  38. 'SST neurons','SST CHODL neurons','VIP neurons','PVALB neurons','PVALB Chandelier neurons','LAMP5 neurons','RELN neurons',
  39. 'Oligodendrocytes','Oligodendrocyte precursor cells','Astrocytes','Microglia',
  40. 'Vascular cells')
  41. names(best_color) = celltype
  42. marker=unique(c("SLC17A7","KCNIP4","C1QL2","CUX2","HS6ST3" ,'COL5A2',"RORB",'PDYN','POU3F1','GRM8','SCN4B',"SCN4B" ,
  43. 'NTNG2',"HTR2C" ,"SEMA3E" ,"SYT6" ,"CCN2","KRT17",'LRP1B',
  44. "GAD1","GAD2",'LHX6', "SST",'VIP','MEPE','PVALB','LAMP5','RELN',
  45. 'CNTN5','UNC5B','PLP1',"MOBP" ,"OPALIN" ,
  46. "PDGFRA" ,"AQP4" ,"C1QB" ,"C3" ,"CLDN5"))
  47. data$CellType_Spatialid = factor(data$CellType_Spatialid,levels = celltype.levels)
  48. Idents(data)="CellType_Spatialid"
  49. DefaultAssay(data)="RNA"
  50. p = DotPlot(data, features = marker)+coord_flip()+
  51. theme_bw()+
  52. theme(panel.grid = element_blank(), axis.text.x=element_text(hjust = 1,angle = 60))+
  53. labs(x=NULL,y=NULL)+guides(size=guide_legend(order=3))+
  54. scale_color_gradientn(values = seq(0,1,0.2),colours = c('#330066','#336699','#66CC66','#FFCC33'))
  55. options(repr.plot.width=10, repr.plot.height=10)
  56. p
  58. # %%
  59. #Chart Beautification:
  60. data$CellType_RCTD = factor(data$CellType_Spatialid,levels = wei)
  61. Idents(object =data) <- "CellType_Spatialid"
  63. p <- DotPlot(data,assay ='RNA',
  64. features = marker) +
  65. # scale_color_continuous_c4a_seq('linear_yl_mg_bu',reverse = T) +
  66. theme(axis.text.x = element_text(angle = 60, hjust = 1))
  68. mycolors <- c("#556aa6","#5298ab","#f6f4b9","#f3af72","#aa2c52")
  70. p$data %>%
  71. ggplot(aes(x = features.plot,
  72. y = id)) +
  73. geom_point(aes(size = pct.exp,
  74. color = avg.exp.scaled)) +
  75. theme_classic() +
  76. theme(axis.text.x = element_text(angle = 90,
  77. hjust = 0.5,
  78. vjust = 0.3,
  79. color = "black"),
  80. axis.title = element_blank(),
  81. strip.background = element_rect(color = "white"),
  82. axis.text.y = element_blank()) +
  83. scale_color_gradientn(colours = mycolors) -> p
  84. options(repr.plot.width=13, repr.plot.height=7)
  86. df <- data.frame(x = 0, y = levels(data), stringsAsFactors = F )
  87. df$y <- factor(df$y, levels = df$y )
  89. pl <- ggplot(df, aes(x, y, color = factor(y))) +
  90. geom_point(size = 6, show.legend = F) +
  91. scale_color_manual(values = best_color) +
  92. theme_classic() +
  93. scale_x_continuous(expand = c(0,0)) +
  94. theme(
  95. plot.margin = margin(r=0),
  96. axis.title = element_blank(),
  97. axis.text.x = element_blank(),
  98. axis.text.y = element_text(size = 9),
  99. axis.ticks = element_blank(),
  100. axis.line = element_blank()
  101. )+theme_bw()
  102. plot_grid(pl, p, align = "h", axis="bt", rel_widths = c(2, 9))
  103. ggsave('/data/cellbin.marker_dotplot.pdf',width = 15,height = 7)
  105. # %%
  106. ###Correlation Analysis with snRNA-seq
  108. # %%
  109. SC = readRDS('/data/snRNA_downsample.rds')
  110. ST = data
  112. # %%
  113. Genelist = fread('/data/snRNA.subclass.Marker..tsv')
  114. library(data.table)
  115. setDT(Genelist)
  116. # Extract the top 100 genes from each cluster, sorted by avg_log2FC.
  117. top_genes <- Genelist[, .SD[order(-avg_log2FC)][1:50], by = cluster]
  118. top_genes = unique(top_genes$gene)
  120. # %%
  121. av1 <-AverageExpression(ST,group.by = "CellType_Spatialid",assays = "Spatial")
  122. av2 <-AverageExpression(SC,group.by = "subclass.v4",assays = "RNA")
  123. gene = rownames(ST)
  124. gene1 = rownames(SC)
  125. GENE1 = intersect(gene,gene1)
  126. GENE = intersect(GENE1,top_genes)
  127. ST_order = celltype.levels
  128. av1$RNA <- av1$RNA[, match(ST_order, colnames(av1$RNA))]
  129. av2$RNA <- av2$RNA [, match(ST_order, colnames(av2$RNA))]
  131. # %%
  132. av1$RNA <- av1$RNA[, match(celltype, colnames(av1$RNA))]
  133. av2$RNA <- av2$RNA [, match(celltype, colnames(av2$RNA))]
  135. # %%
  136. mycolors =c("#14448C" ,"#5076C1", "#92A6DE", "#CED7F2", "#F6F6F6" ,"#DE9494", "#B85A5B" ,"#7D2828")
  138. options(repr.plot.width=11, repr.plot.height=11)
  139. p = pheatmap(cor(av1$RNA[GENE,], av2$RNA[GENE,], method = 'spearman'),
  140. cluster_rows = FALSE,
  141. cluster_cols = FALSE,
  142. border = FALSE,
  143. main = paste0(" scRNAanno vs Spatial-id","\n"),
  144. scale = "column",
  145. fontsize = 10,
  146. color = colorRampPalette(mycolors)(100),
  147. # color = colorRampPalette(c("#fafafa", "white", "#005cab"))(50),
  148. # border_color = "white",
  149. angle_col = 315)
  150. save_pheatmap_pdf <- function(x, filename, width=8, height=9) {
  151. stopifnot(!missing(x))
  152. stopifnot(!missing(filename))
  153. pdf(filename, width=width, height=height)
  154. grid::grid.newpage()
  155. grid::grid.draw(x$gtable)
  156. dev.off()
  157. }
  158. save_pheatmap_pdf(p, paste0("/data/snRNA-cellbin_corrlation.pdf"))
  160. # %%

11_cellbin.snRNA.cor.ipynb at commit 256d5b5, under MIT · at the source

Overview

Authors: Yanrong Wei1,2, Youzhe He1,2, Yuyang Liu1,2, Langjian Zhu2, Tiannan Feng1,2, Zhiming Shen3,4, Wu Wei5,6, Longqi Liu1,2,7, Lei Han2,7, Lifang Wang2,8
ORCID iDs: Youzhe He
  1. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049 China
  2. State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Hangzhou, 310030 China
  3. Institute of Neuroscience, State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
  4. Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, 201602 China
  5. Lingang Laboratory, Shanghai, China
  6. CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031 China
  7. Key Laboratory of Brain Cell Mapping of Zhejiang Province, BGI Research, Hangzhou, 310030 China
  8. Key Laboratory of Spatial Omics of Zhejiang Province, BGI Research, Hangzhou, 310030 China
Institutions: Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China); BGI Research (China); Center for Excellence in Brain Science and Intelligence Technology (China); Shanghai Center for Brain Science and Brain-Inspired Technology (China); Shanghai Institute of Nutrition and Health (China)
Journal: Genome medicine, volume 18, issue 1, article 104
Dates: received 24 November 2025; accepted 22 June 2026; published online 8 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13073-026-01704-z · PMID 42421091 · PMCID PMC13360882 · OpenAlex W7167672330
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Human cortex, Gene expression-defined cortical layers, Stereo-seq, Layer parcellation, Cross-species, Laminar disorganization
MeSH: Cerebral Cortex*, Gene Expression Profiling*, Algorithms, Animals, Female, Humans, Mice, Spatial Transcriptomics, Transcriptome (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Key Program of the National Natural Science Foundation of China (32530027); National Science and Technology Innovation 2030 Major Program (Grant No. 2021ZD0204400)
Citations: cited by 1 paper (Europe PMC); 62 references in the paper

Abstract

Background: Precise delineation of cortical layers is fundamental for understanding human brain organization, cell-type architecture, and disease-related tissue alterations. However, traditional anatomy-based methods often lack molecular resolution and suffer from inter-observer subjectivity.

Methods: Here, we present gene expression-defined cortical layers (GD-Ls) using the BayesSpace algorithm, a high-resolution framework for cortical parcellation based on spatial transcriptomics.

Results: Compared with traditional anatomy-based approaches, GD-Ls more accurately resolve laminar boundaries and capture fine-scale laminar heterogeneity, including sublayer-like domains within L1, L3, and L6, as well as a molecularly distinct transition zone at the gray-white matter interface. Validation across diverse cortical lobes, multiple spatial platforms, and independent healthy postmortem datasets demonstrates that GD-Ls capture the intrinsic molecular architecture of the cortex irrespective of tissue source. Furthermore, cross-species analyses show that this framework is extensible to macaque and mouse cortices. Crucially, GD-Ls successfully identify subtle laminar disorganization and aberrant cellular and molecular signatures in pathologically altered tissues, which are often missed by conventional histology.

Conclusions: Together, GD-Ls provide an objective and reproducible tool for standardized cortical mapping and for identifying early pathological signatures in the human brain. The source code is available on GitHub (https://github.com/YanrongWei/GD-Ls).

Supplementary Information: The online version contains supplementary material available at 10.1186/s13073-026-01704-z.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

YanrongWei/GD-Ls

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 256d5b5b5c203060dd35b4ef320f15aa818bd930, 19 May 2026
Languages: Jupyter (23)
Size: 27 files, 23 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file, 23 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (16 files), Seurat (16 files), tidyverse (15 files), cowplot (10 files), data.table (10 files), pheatmap (7 files), NumPy (6 files), pandas (6 files), Scanpy (6 files), anndata (4 files), ComplexHeatmap (4 files), Matplotlib (4 files), PyTorch (4 files), scikit-image (3 files), SciPy (3 files), seaborn (3 files), UMAP (3 files), Harmony (2 files), SingleCellExperiment (2 files), ggpubr (1 file), patchwork (1 file), reshape2 (1 file), scikit-learn (1 file), XGBoost (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
25 files
At the source: github.com/YanrongWei/GD-Ls

Zenodo 20199715

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (16 files), Seurat (16 files), tidyverse (15 files), cowplot (10 files), data.table (10 files), pheatmap (7 files), NumPy (6 files), pandas (6 files), Scanpy (6 files), anndata (4 files), ComplexHeatmap (4 files), Matplotlib (4 files), PyTorch (4 files), scikit-image (3 files), SciPy (3 files), seaborn (3 files), UMAP (3 files), Harmony (2 files), SingleCellExperiment (2 files), ggpubr (1 file), patchwork (1 file), reshape2 (1 file), scikit-learn (1 file), XGBoost (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
24 files
At the source:

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

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;
  • 45 scripts, each with its path and the digest of its content;
  • 19 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

Datasets cited

Data availability

The spatial transcriptomics sequencing data generated from the 15 human cortex sections analyzed in this study have been deposited in the China National GeneBank Sequence Archive (CNSA) under accession code CNP0009546 and are available at https://db.cngb.org/data_resources/project/CNP0009546 [10, 60]. Previously published datasets used in this study are available from the Gene Expression Omnibus (GEO) under accession codes GSE269906 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE269906) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE269906) [26] and GSE307403 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE307403) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE307403) [22], and from STOmicsDB under accession code STDS0000242 (https://db.cngb.org/stomics/datasets/STDS0000242/summary). The source code implementing the GD-Ls pipeline, including scripts for data preprocessing, parameter selection, clustering, GD-Ls annotation, and validation analyses, is publicly available on GitHub at https://github.com/YanrongWei/GD-Ls [61, 62]. The released version of the source code has also been archived in Zenodo and can be cited using the DOI: 10.5281/zenodo.20199715.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 6 keywords, 9 MeSH terms, 2 funders, 60 references.

Cite

This paper

Wei, Y., He, Y., Liu, Y., Zhu, L., Feng, T., Shen, Z., Wei, W., Liu, L., Han, L., & Wang, L. (2026). Gene expression profiling enables refined parcellation of cortical layers in the heterogeneous human cerebral cortex. Genome medicine, 18(1), 104. https://doi.org/10.1186/s13073-026-01704-z

BibTeX

@article{wei2026gene,
author = {Wei, Yanrong and He, Youzhe and Liu, Yuyang and Zhu, Langjian and Feng, Tiannan and Shen, Zhiming and Wei, Wu and Liu, Longqi and Han, Lei and Wang, Lifang},
title = {{Gene expression profiling enables refined parcellation of cortical layers in the heterogeneous human cerebral cortex}},
journal = {Genome medicine},
year = {2026},
month = jul,
volume = {18},
number = {1},
pages = {104},
publisher = {BMC},
issn = {1756-994X},
doi = {10.1186/s13073-026-01704-z},
url = {https://doi.org/10.1186/s13073-026-01704-z},
pmid = {42421091},
pmcid = {PMC13360882}
}

RIS

TY - JOUR
AU - Wei, Yanrong
AU - He, Youzhe
AU - Liu, Yuyang
AU - Zhu, Langjian
AU - Feng, Tiannan
AU - Shen, Zhiming
AU - Wei, Wu
AU - Liu, Longqi
AU - Han, Lei
AU - Wang, Lifang
TI - Gene expression profiling enables refined parcellation of cortical layers in the heterogeneous human cerebral cortex
T2 - Genome medicine
J2 - Genome Med
PY - 2026
DA - 2026/07/08
VL - 18
IS - 1
SP - 104
SN - 1756-994X
PB - BMC
DO - 10.1186/s13073-026-01704-z
UR - https://doi.org/10.1186/s13073-026-01704-z
LA - en
ER -

CSL-JSON

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In common: Harmony, SingleCellExperiment, anndata, 18 other tools, genetics / omics, mouse, cellular / molecular, 1 reference
[5] doi:10.1016/j.xcrm.2026.102651 [code]
Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease.
Journal: Cell reports. Medicine
In common: Harmony, SingleCellExperiment, UMAP, 18 other tools, genetics / omics, cellular / molecular
[6] doi:10.1038/s41586-026-10214-2 [code]
Multidimensional profiling of heterogeneity in supratentorial ependymomas.
Journal: Nature
In common: Harmony, SingleCellExperiment, anndata, 17 other tools, genetics / omics, mouse, 1 reference
[7] doi:10.1038/s41593-026-02300-5 [code]
Integrated single-cell and spatial transcriptomic profiling in ALS uncovers peripheral-to-central immune infiltration and reprogramming.
Journal: Nature neuroscience
In common: Harmony, anndata, Scanpy, 16 other tools, genetics / omics, cellular / molecular, 2 references
[8] doi:10.21203/rs.3.rs-9676637/v1 [code]
A Comprehensive Benchmarking of Spatial Deconvolution and Domain Detection Methods across Diverse Tissues and Spatial Transcriptomic Technologies
Journal: Research Square (preprint)
In common: SingleCellExperiment, UMAP, anndata, 14 other tools, genetics / omics, 4 references
[9] doi:10.1038/s42003-026-10034-0 [code]
Region- and cell type-specific changes in gene expression in the cerebellum after classical fear conditioning.
Journal: Communications biology
In common: SingleCellExperiment, anndata, Scanpy, 14 other tools, genetics / omics, mouse, cellular / molecular, 3 references
[10] doi:10.1016/j.cpblue.2026.100007 [code]
An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.
Journal: Cell press blue
In common: SingleCellExperiment, UMAP, anndata, 17 other tools

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