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Developing mouse inhibitory neuron single-cell transcriptomes reveal distinct modes of cell-type diversification.

Code ↔ Paper

23 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 23 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Data integration and annotation (module 3) ↔ R_code/Module_3_part_1.R, lines 135–214 · score 0.98 · mitochondrial gene expression, IntegrateData, anchor.features, k.filter, k.score, FindIntegrationAnchors
  2. [2] § Methods › Sample processing and filtering of Sst+ cells (module 1) ↔ R_code/Module_3_part_1.R, lines 135–214 · score 0.96 · mitochondrial gene expression, RunUMAP, principal component, FindClusters, FindNeighbors, FindVariableFeatures
  3. [3] § Methods › Sample processing and filtering of Sst+ cells (module 1) ↔ R_code/Module_1.R, lines 42–81 · score 0.95 · Cell cycle phase, CellCycleScoring, mitochondrial gene expression, FindVariableFeatures, NormalizeData, ScaleData
  4. [4] § Methods › Trajectory and pseudotime analysis ↔ R_code/Trajectory_analysis.R, lines 97–141 · score 0.94 · diffusion components, root cell, diffusion map, early sample cells, gene modules, DPT
  5. [5] § Methods › WOT analysis ↔ R_code/WaddingtonOT_analysis.R, lines 195–254 · score 0.90 · median fate probabilities, growth_iters, optimal_transport, possible cluster identities, growth rates, command
  6. [6] § Methods › Cell annotation to adult counterparts ↔ R_code/MapMyCell.R, lines 1–39 · score 0.78 · reference taxonomy, hierarchical mapping, mouse brain, MapMyCells, CCN20230722, algorithm
  7. [7] § Methods › Saturation analysis ↔ R_code/Saturation_analysis.R, lines 174–246 · score 0.77 · MetaNeighborUS, cluster centroids, inflection point, AUROC, saturation, 95 %
  8. [8] § Methods › Integrability testing of datasets (module 2) › Neighborhood composition › k-NN computation ↔ R_code/Module_2.R, lines 117–168 · score 0.76 · evaluate neighborhood composition, global fraction, reference atlas, NN, graph, cells
  9. [9] § Methods › Iterative clustering of Dev-SST-v0, Dev-SST-v1, Dev-SST-v2 ↔ R_code/Module_3_part_2_mergeClusters.R, lines 80–130 · score 0.76 · cophenetic distance, DEscore, cluster pairs, logfc.threshold, dendrogram, genes
  10. [10] § Methods › Iterative clustering of Dev-SST-v0, Dev-SST-v1, Dev-SST-v2 ↔ R_code/Module_3_part_2_mergeClusters.R, lines 80–130 · score 0.75 · FindMarkers, DEscore, merging clusters, logfc.threshold, log10, cutoff
  11. [11] § Methods › Integrability testing of datasets (module 2) › Anchor points ↔ R_code/Module_2_number_of_anchors_threshold_definition.R, lines 1–51 · score 0.74 · acceptance criterion, FindIntegrationAnchors, Atlas v0, cca, Seurat, subset
  12. [12] § Methods › Random forest classification and atlas comparison ↔ R_code/Cluster_validation.R, lines 557–636 · score 0.72 · diagonal accuracy, v2 v1, classification accuracy, Cross talk, predicted, matrices
  13. [13] § Methods › Iterative clustering of Dev-SST-v0, Dev-SST-v1, Dev-SST-v2 ↔ R_code/Iterative_Clustering_pipeline_functions.R, lines 206–228 · score 0.70 · FindMarkers, DEscore, logfc.threshold, pos, log10, adj
  14. [14] § Methods › Cell annotation to adult counterparts ↔ Figures/MapMyCell/mapmycell_Base_atlas.rmd, lines 65–89 · score 0.68 · hierarchical mapping, reference taxonomy, mouse brain, MapMyCells, CCN20230722, algorithm
  15. [15] § Methods › Cortical and striatal integration ↔ R_code/Module_3_part_1.R, lines 251–316 · score 0.66 · FindTransferAnchors, TransferData, major clusters, UMAP, v0, module
  16. [16] § Results › Dev-SST-v2 reveals 26 clusters of cortical SST+ inhibitory neurons ↔ R_code/Cluster_validation.R, lines 557–636 · score 0.63 · diagonal accuracy, v2 v1, cross talk, bars, Martinotti, Seurat
  17. [17] § Methods › Cell annotation validation ↔ R_code/Cluster_validation.R, lines 268–334 · score 0.62 · min.pct, logfc.threshold, ranked, validate, cross, predict
  18. [18] § Methods › Integrability testing of datasets (module 2) › Neighborhood composition ↔ R_code/Module_2.R, lines 53–114 · score 0.60 · IntegrateData, RunPCA, ScaleData, PCs, anchors, SST
  19. [19] § Results › LRP1 and LRP2.1 are distinct cell types with divergent trajectories ↔ R_code/Trajectory_analysis.R, lines 25–95 · score 0.59 · synapse assembly, LRP2.1, axon, ANTLER, trajectory, LRP1
  20. [20] § Results › Generalized computational pipeline to assess integrability of different datasets ↔ R_code/Module_2.R, lines 117–168 · score 0.58 · nearest neighbors, rejection rate, BET, NN, fraction, batch
  21. [21] § Methods › Cortical and striatal integration ↔ R_code/Merfish_annotation.R, lines 45–94 · score 0.53 · FindTransferAnchors, TransferData, UMAP, Seurat, v2, genes
  22. [22] § Methods › Iterative clustering of Dev-SST-v0, Dev-SST-v1, Dev-SST-v2 ↔ R_code/Module_3_part_2_IterativeClustering.R, the whole file · a weak match · score 0.51 · Clustering stability evaluation, iteration, Jaccard, resolutions, subsamplings, v2
  23. [23] § Results › Dev-SST-v2 reveals 26 clusters of cortical SST+ inhibitory neurons ↔ R_code/Saturation_analysis.R, lines 142–171 · score 0.50 · robust Hausdorff distance, Inflection points, Saturation, subsampled, Dev, v2

Paper

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The authors' code

R · 317 lines · 19 KB · no license · 3 matches

  1. ## Installation needed
  2. if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")
  4. pacman::p_load(
  5. Seurat, dittoSeq, ggplot2, gtools, dplyr, R.utils, colorspace, tidyr,
  6. purrr, Matrix, patchwork, BiocManager
  7. )
  8. devtools::install_github("crazyhottommy/scclusteval")
  9. BiocManager::install("scDblFinder")
  10. BiocManager::install("MAST")
  11. BiocManager::install("dittoSeq")
  13. ########################
  14. # Load required packages
  15. ########################
  16. library(Seurat)
  17. library(dittoSeq)
  18. library(ggplot2)
  19. library(gtools)
  20. library(dplyr)
  21. library(dittoSeq)
  22. library(scDblFinder)
  23. library(R.utils)
  24. library(colorspace)
  25. library(scclusteval)
  26. library(tidyr)
  27. library(purrr)
  28. library(Matrix)
  29. library(patchwork)
  30. library(MAST)
  31. source("Seurat_Utils.R")
  32. colors_ditto<-dittoColors()
  33. names(colors_ditto)<-as.character(c(0:(length(colors_ditto)-1)))
  35. ###########
  36. # Load data
  37. ###########
  38. # Load previously Sst+ filtered cells from samples passing integrability tests
  39. base_atlas <- readRDS("integrated_all_Sst_filtered.rds")
  40. base_atlas$batch <- paste0("BaseAtlas_",base_atlas$orig.ident)
  41. E16_Lim4 <- readRDS("lim4_E16_Sst_filtered.rds")
  42. E16_Lim4$batch <- "E16_Lim4"
  43. P1_Lim3 <- readRDS('lim3_P1_Sst_filtered.rds')
  44. P1_Lim3$batch <- "P1_Lim3"
  45. P1_Lim5 <- readRDS("lim5_P1_Sst_filtered.rds")
  46. P1_Lim5$batch <- "P1_Lim5"
  47. test_dataset_filtered_sst<-readRDS("P5_WT1_WT23_Sst_filtered.rds")
  48. P5_WT1_Lim1 <- subset(test_dataset_filtered_sst, subset= orig.ident %in% "lim_P5_sorted_sept23_WT1")
  49. P5_WT1_Lim1$batch <- "P5_WT1_Lim1"
  50. P5_WT23_Lim2 <- subset(test_dataset_filtered_sst, subset= orig.ident %in% "lim_P5_sorted_sept23_WT23")
  51. P5_WT23_Lim2$batch <- "P5_WT23_Lim2"
  52. E18_Lippi<-readRDS("Lippi_lab_e18.5_Sst_filtered.rds")
  53. E18_Lippi$batch <- "E18_Lippi"
  54. P1_EMI014_Lim<-readRDS("lim_P1_EMI014_Sst_filtered.rds")
  55. P1_EMI014_Lim$batch <- "P1_EMI014_Lim"
  56. Wu_data <- readRDS("P2_P7_GSE272706_Sst_filtered.RDS")
  57. list_Wu <-SplitObject(Wu_data, split.by = "orig.ident")
  58. Wu_p2 <- list_Wu[["GSM8409508_P2MGE_Fezf2HET_BaxcHET"]]
  59. Wu_p2$batch <- "P2_Wu"
  60. E16_EMI018 <-readRDS("lim_E16.5_EMI018_Sst_filtered.rds")
  61. E16_EMI018$batch <- 'E16_EMI018'
  62. # Set the default assay to RNA for analysis
  63. DefaultAssay(base_atlas) <- "RNA"
  64. DefaultAssay(E16_Lim4) <- "RNA"
  65. DefaultAssay(P1_Lim3) <- "RNA"
  66. DefaultAssay(P1_Lim5) <- "RNA"
  67. DefaultAssay(P5_WT1_Lim1) <- "RNA"
  68. DefaultAssay(P5_WT23_Lim2) <- "RNA"
  69. DefaultAssay(E18_Lippi) <- "RNA"
  70. DefaultAssay(P1_EMI014_Lim) <- "RNA"
  71. DefaultAssay(Wu_p2)<-"RNA"
  72. DefaultAssay(E16_EMI018)<-"RNA"
  73. # Split the base atlas into separate objects based on the sampleID
  74. integrated_all_list <- SplitObject(base_atlas, split.by = "batch")
  75. # Add the cleaned dataset to the list of samples
  76. integrated_all_list$E16_Lim4 <- E16_Lim4
  77. integrated_all_list$P1_Lim3 <- P1_Lim3
  78. integrated_all_list$P1_Lim5 <- P1_Lim5
  79. integrated_all_list$P5_WT1_Lim1 <- P5_WT1_Lim1
  80. integrated_all_list$P5_WT23_Lim2 <- P5_WT23_Lim2
  81. integrated_all_list$E18_Lippi <- E18_Lippi
  82. integrated_all_list$P1_EMI014_Lim <- P1_EMI014_Lim
  83. integrated_all_list$Wu_p2 <- Wu_p2
  84. integrated_all_list$E16_EMI018 <- E16_EMI018
  86. #########################
  87. # Doublets identification
  88. #########################
  89. # First, we annotate cells according to Atlas-v0 clusters using label transfer.
  90. # The predicted clusters will be used to identify potential doublets.
  91. # Normalize the data, identify variable features, and scale the Atlas-v0 dataset.
  92. base_atlas <- NormalizeData(base_atlas)
  93. base_atlas <- FindVariableFeatures(base_atlas)
  94. base_atlas <- ScaleData(base_atlas, vars.to.regress = c("nFeature_RNA",'percent.mt','ccDiff'))
  95. base_atlas <- RunPCA(base_atlas,npcs = 50)
  96. # Initialize empty vector to store cluster annotation
  97. minor_label <- c()
  98. # Perform label transfer
  99. for (sample in c("E16_Lim4","P1_Lim3","P1_Lim5","P5_WT1_Lim1","P5_WT23_Lim2","E18_Lippi","P1_EMI014_Lim","Wu_p2","E16_EMI018")) {
  100. integrated_all_list[[sample]] <- NormalizeData(integrated_all_list[[sample]])
  101. integrated_all_list[[sample]] <- FindVariableFeatures(integrated_all_list[[sample]])
  102. integrated_all_list[[sample]] <- ScaleData(integrated_all_list[[sample]], vars.to.regress = c("nFeature_RNA",'percent.mt','ccDiff'))
  103. integrated_all_list[[sample]] <- RunPCA(integrated_all_list[[sample]],npcs = 50)
  104. # Find transfer anchors between the Atlas-v0 (reference) and the samples
  105. transfer.anchors <- FindTransferAnchors(reference = base_atlas, query = integrated_all_list[[sample]], dims = 1:40, reference.reduction = "pca", features=intersect(rownames(base_atlas), rownames(integrated_all_list[[sample]])))
  106. # Transfer labels based on the reference
  107. predictions <- TransferData(anchorset = transfer.anchors, refdata = base_atlas$cluster_label_trained_with_all, dims = 1:40)
  108. # Add the predicted cluster annotation to the sample object
  109. integrated_all_list[[sample]]$minor_label_transferAnchors_BaseAtlas <- predictions[colnames(integrated_all_list[[sample]]),'predicted.id']
  110. # Append the predicted cluster annotation to the overall list
  111. minor_pred <- predictions$predicted.id
  112. names(minor_pred) <- rownames(predictions)
  113. minor_label <- c(minor_label,minor_pred)
  114. }
  115. for (sample in c("BaseAtlas_E16","BaseAtlas_lim_P5_fixed_sorted","BaseAtlas_P1","BaseAtlas_P5") ) {
  116. integrated_all_list[[sample]]$minor_label_transferAnchors_BaseAtlas <- integrated_all_list[[sample]]$cluster_label_trained_with_all
  117. }
  118. # Doublets Prediction Using predicted clusters
  119. # Initialize an empty vector to store doublet annotations
  120. doublets_sceDblF <- c()
  121. # Loop through all datasets in the integrated list and predict doublets
  122. for (i in 1:length(integrated_all_list)){
  123. sceDblF <- scDblFinder(integrated_all_list[[i]]@assays$RNA@counts,dbr =0.07, clusters=integrated_all_list[[i]]$minor_label_transferAnchors_BaseAtlas)
  124. # Extract doublet annotations from the results
  125. doublets_anno <- as.vector(sceDblF@colData$scDblFinder.class)
  126. names(doublets_anno) <- row.names(sceDblF@colData)
  127. # Append the doublet annotations to the list
  128. doublets_sceDblF <- c(doublets_sceDblF,doublets_anno)
  129. # Add the doublet classification to the Seurat object metadata
  130. integrated_all_list[[i]]$doublets <- doublets_anno[colnames(integrated_all_list[[i]])]
  131. # Subset the Seurat object to retain only the singlets
  132. integrated_all_list[[i]] <- subset(integrated_all_list[[i]], subset=doublets=="singlet")
  133. }
  135. ################################
  136. # CCA Integration and clustering
  137. ################################
  138. # Normalize data and find variable features
  139. for(sample in names(integrated_all_list)){
  140. obj <- integrated_all_list[[sample]]
  141. obj <- NormalizeData(obj)
  142. obj <- FindVariableFeatures(obj)
  143. integrated_all_list[[sample]] <- obj
  144. }
  145. # Find integration anchors between datasets
  146. i.anchors <- FindIntegrationAnchors(object.list = integrated_all_list, dims = 1:30, reduction = 'cca', scale = T, k.anchor = 5, k.filter = 100, k.score = 15, anchor.features = 3000)
  147. # Integrate the data using the anchors
  148. integrated_v2 <- IntegrateData(anchorset = i.anchors, dims = 1:30, normalization.method ='LogNormalize', k.weight=100)
  149. DefaultAssay(integrated_v2) <- 'integrated'
  150. integrated_v2 <- ScaleData(integrated_v2, verbose = FALSE, vars.to.regress = c("nFeature_RNA",'percent.mt','ccDiff'))
  151. integrated_v2 <- RunPCA(integrated_v2, npcs = 60)
  152. ElbowPlot(integrated_v2, ndims = 60)
  153. # Determine the optimal number of principal components (PCs) for downstream analysis
  154. data.use.integrated <- PrepDR(object = integrated_v2, genes.use = VariableFeatures(object = integrated_v2), use.imputed = F, assay.type = "integrated")
  155. path_data <- getwd()
  156. nPCs.data.use5 <- PCA_estimate_nPC(data.use.integrated,
  157. whereto = paste0(path_data, "/optimal_nPCs_5_integrated.RDS"),
  158. k = 5, by.nPC = 5, from.nPC = 30, to.nPC = 50) #check ElbowPlot and adjust range acoordingly
  159. nPCs.data.use <- PCA_estimate_nPC(data.use.integrated,
  160. whereto = paste0(path_data, "/optimal_nPCs_integrated.RDS"),
  161. k = 5, by.nPC = 1, from.nPC = nPCs.data.use5 - 5, to.nPC = nPCs.data.use5 + 5)
  162. integrated_v2 <- RunUMAP(integrated_v2, dims=1:nPCs.data.use)
  163. integrated_v2 <- FindNeighbors(integrated_v2, dims=1:nPCs.data.use)
  164. integrated_v2 <- FindClusters(integrated_v2, resolution = c(0.4))
  166. ######################################
  167. # PERFORM QC AND REMOVE STRESSED CELLS
  168. ######################################
  169. DefaultAssay(integrated_v2) <- 'RNA'
  170. integrated_v2 <- ScaleData(integrated_v2)
  171. # Extract UMAP embeddings (2D coordinates for visualization)
  172. umap <- Embeddings(integrated_v2,reduction = "umap")
  173. # Loop through a list of stress-related genes to visualize their expression on UMAP and export in pdf
  174. for (gene in c("Hsp90b1","Hspa5","Mapk8")){
  175. # Get the gene expression data
  176. data <- integrated_v2@assays$RNA@data[gene,]
  177. # Combine the UMAP coordinates with gene expression values
  178. umap_gene <- cbind(umap[colnames((integrated_v2@assays$RNA@data)),1:2], data)
  179. # Order the data by expression values (ascending order)
  180. umap_gene <- umap_gene[order(umap_gene[,3], decreasing = FALSE),]
  181. colnames(umap_gene) = c("umap_1","umap_2","Expression")
  182. umap_gene <- as.data.frame(umap_gene)
  183. pdf(paste("GeneExpression_",gene,"_umap.pdf",sep=""))
  184. #print(ggplot(umap_gene, aes(umap_1, umap_2)) + geom_point(aes(colour = Expression), size=1) + scale_color_continuous_sequential(palette='Purple_Yellow') + theme(panel.background = element_rect(fill='white', colour='black')) + theme(legend.position="none"))
  185. print(ggplot(umap_gene, aes(umap_1, umap_2)) + geom_point(aes(colour = Expression), size=1) + scale_color_continuous_sequential(palette='Purple_Yellow') + theme(panel.background = element_rect(fill='white', colour='black'))+ ggtitle( paste0(gene)) )
  186. dev.off()
  187. }
  188. # Create UMAP plots based on feature count (nFeature) and mitochondrial percentage (percent.mt)
  189. dittoDimPlot(integrated_v2, "nFeature_RNA", reduction.use = "umap", min.color = "lightgrey", max.color = "blue")
  190. dittoDimPlot(integrated_v2, "percent.mt", reduction.use = "umap", min.color = "lightgrey", max.color = "blue")
  191. # Create UMAP plots for different clustering resolutions and export in pdf
  192. pdf('umap_plot_atlas_v2_clusters.pdf')
  193. Idents(integrated_v2) <- "integrated_snn_res.0.4"
  194. DimPlot(integrated_v2, reduction = "umap",raster=FALSE, label=TRUE) + ggtitle("Resolution: 0.4")
  195. dev.off()
  196. # Set the clustering identity to the specific resolution (0.4) and generate bar plots of samples per cluster
  197. Idents(integrated_v2)<-'integrated_snn_res.0.4'
  198. dittoBarPlot(integrated_v2, var = "batch", group.by = "integrated_snn_res.0.4") + labs(title = NULL)
  199. # Generate violin plots for expression of various genes by cluster (based on the integrated SNN resolution 0.4)
  200. VlnPlot(integrated_v2, "Hsp90b1", pt.size = 0, group.by = "integrated_snn_res.0.4")
  201. VlnPlot(integrated_v2, "Hspa5", pt.size = 0, group.by = "integrated_snn_res.0.4")
  202. VlnPlot(integrated_v2, "Mapk8", pt.size = 0, group.by = "integrated_snn_res.0.4")
  203. # Add a new feature for ribosomal RNA percentage (ribo genes)
  204. integrated_v2[["percent.ribo"]] <- PercentageFeatureSet(integrated_v2, pattern = "^Rp[Sl]")
  205. # Violin plot for ribosomal RNA percentage by cluster
  206. VlnPlot(integrated_v2, "percent.ribo", pt.size = 0, group.by = "integrated_snn_res.0.4")
  207. # Violin plot for mitochondrial RNA percentage by cluster
  208. VlnPlot(integrated_v2, "percent.mt", pt.size = 0, group.by = "integrated_snn_res.0.4")
  209. # Violin plots for total RNA count and feature count by cluster
  210. VlnPlot(integrated_v2, "nCount_RNA", pt.size = 0, group.by = "integrated_snn_res.0.4") + ylim(0,30000)
  211. VlnPlot(integrated_v2, "nFeature_RNA", pt.size = 0, group.by = "integrated_snn_res.0.4")
  212. # Filter cells - remove clusters with high expression of stress markers or mitochondrial genes, as well as clusters with low nFeatures or unbalanced sample composition.
  213. # Specify clusters you want to remove
  214. integrated_v2_filtered<-subset(integrated_v2, subset=integrated_snn_res.0.4 %in% c(8,14,15), invert = TRUE)
  216. #################################
  217. # CCA Integration after filtering
  218. #################################
  219. DefaultAssay(integrated_v2_filtered) <- 'RNA'
  220. integrated_all_list <- SplitObject(integrated_v2_filtered, split.by = "batch")
  221. # Loop over each sample and normalize the data, then find variable features
  222. for(sample in names(integrated_all_list)){
  223. # Normalize the data for each sample
  224. obj <- integrated_all_list[[sample]]
  225. obj <- NormalizeData(obj)
  226. obj <- FindVariableFeatures(obj)
  227. # Save the updated sample object back to the list
  228. integrated_all_list[[sample]] <- obj
  229. }
  230. # Find integration anchors between the different samples (using CCA for dimensionality reduction)
  231. i.anchors <- FindIntegrationAnchors(object.list = integrated_all_list, dims = 1:30, reduction='cca',scale=T, k.anchor=5, k.filter=100, k.score=15,anchor.features=3000)
  232. integrated_v2_filtered <- IntegrateData(anchorset = i.anchors, dims = 1:30, normalization.method = 'LogNormalize', k.weight=80)
  233. DefaultAssay(integrated_v2_filtered) <- 'integrated'
  234. integrated_v2_filtered <- ScaleData(integrated_v2_filtered, verbose = FALSE, vars.to.regress = c("nFeature_RNA",'percent.mt','ccDiff'))
  235. integrated_v2_filtered <-RunPCA(integrated_v2_filtered, npcs = 60)
  236. ElbowPlot(integrated_v2, ndims = 60)
  237. # Determine the optimal number of principal components (PCs) for downstream analysis
  238. data.use.integrated<- PrepDR(object = integrated_v2_filtered, genes.use = VariableFeatures(object = integrated_v2_filtered), use.imputed = F, assay.type = "integrated")
  239. path_data <- getwd()
  240. nPCs.data.use5 <- PCA_estimate_nPC(data.use.integrated,
  241. whereto = paste0(path_data, "/optimal_nPCs_5_integrated_filtered.RDS"),
  242. k = 5, by.nPC = 5, from.nPC = 40, to.nPC = 60) # Check Elbow plot and set the range for optimal PC
  243. nPCs.data.use <- PCA_estimate_nPC(data.use.integrated,
  244. whereto = paste0(path_data, "/optimal_nPCs_integrated_filtered.RDS"),
  245. k = 2, by.nPC = 1, from.nPC = nPCs.data.use5 - 5, to.nPC = nPCs.data.use5 + 5)
  246. integrated_v2_filtered <- RunUMAP(integrated_v2_filtered, dims=1:nPCs.data.use)
  247. integrated_v2_filtered <- FindNeighbors(integrated_v2_filtered, dims=1:nPCs.data.use)
  248. integrated_v2_filtered <- FindClusters(integrated_v2_filtered, resolution = c(0.4,0.5,0.6,0.7))
  249. # Here, another round of quality checking is performed
  251. ##################
  252. # Cells annotation
  253. ##################
  254. # We annotate cells to major classes using Atlas-v0 as reference
  255. DefaultAssay(integrated_v2_filtered) <- 'RNA'
  256. integrated_all_list <- SplitObject(integrated_v2_filtered, split.by = "batch")
  257. base_atlas <- subset(integrated_v2_filtered, subset= batch %in% c("BaseAtlas_E16","BaseAtlas_lim_P5_fixed_sorted","BaseAtlas_P1","BaseAtlas_P5"))
  258. DefaultAssay(base_atlas) <- 'RNA'
  259. base_atlas <- NormalizeData(base_atlas)
  260. base_atlas <- FindVariableFeatures(base_atlas)
  261. base_atlas <- ScaleData(base_atlas, vars.to.regress = c("nFeature_RNA",'percent.mt','ccDiff'))
  262. base_atlas<-RunPCA(base_atlas,npcs = 60)
  263. # Initialize empty vectors to store predicted labels and scores
  264. minor_label <- c()
  265. major_label <- c()
  266. pred_score_minor <- c()
  267. pred_score_major <- c()
  268. # Loop through each sample, normalize, identify variable features, scale data, and run PCA
  269. for (sample in c("E16_Lim4","P1_Lim3","P1_Lim5","P5_WT1_Lim1","P5_WT23_Lim2","E18_Lippi","P1_EMI014_Lim","Wu_p2","E16_EMI018")) {
  270. # Normalize, find variable features, and scale the data for each sample
  271. integrated_all_list[[sample]] <- NormalizeData(integrated_all_list[[sample]])
  272. integrated_all_list[[sample]] <- FindVariableFeatures(integrated_all_list[[sample]])
  273. integrated_all_list[[sample]] <- ScaleData(integrated_all_list[[sample]], vars.to.regress = c("nFeature_RNA",'percent.mt','ccDiff'))
  274. integrated_all_list[[sample]] <- RunPCA(integrated_all_list[[sample]],npcs = 50)
  275. # Perform anchor finding for label transfer based on PCA reduction and intersected features
  276. transfer.anchors <- FindTransferAnchors(reference = base_atlas, query = integrated_all_list[[sample]], dims = 1:40,reference.reduction = "pca", features=intersect(rownames(base_atlas), rownames(integrated_all_list[[sample]])))
  277. # Transfer the predicted minor labels (clusters)
  278. predictions <- TransferData(anchorset = transfer.anchors, refdata = base_atlas$cluster_label_trained_with_all, dims = 1:40)
  279. minor_pred <- predictions$predicted.id
  280. names(minor_pred) <- rownames(predictions)
  281. minor_label <- c(minor_label,minor_pred)
  282. # Capture the prediction score for each minor label prediction
  283. pred_score_tmp <- predictions$prediction.score.max
  284. names(pred_score_tmp) <- rownames(predictions)
  285. pred_score_minor <- c(pred_score_minor,pred_score_tmp)
  286. # Transfer the predicted major labels (major subsets)
  287. predictions <- TransferData(anchorset = transfer.anchors, refdata = base_atlas$major_cluster_label_trained_with_all, dims = 1:40)
  288. major_pred <- predictions$predicted.id
  289. names(major_pred) <- rownames(predictions)
  290. major_label <- c(major_label,major_pred)
  291. # Capture the prediction score for each major label prediction
  292. pred_score_tmp <- predictions$prediction.score.max
  293. names(pred_score_tmp) <- rownames(predictions)
  294. pred_score_major <- c(pred_score_major,pred_score_tmp)
  295. }
  296. saveRDS(minor_label,"Minor_label_labelTranfer.RDS")
  297. saveRDS(major_label,"Major_label_labelTranfer.RDS")
  298. # Assign minor and major labels to the meta-data of the integrated filtered dataset
  299. integrated_v2_filtered$minor_label_transferAnchors_BaseAtlas <- integrated_v2_filtered$cluster_label_trained_with_all
  300. [email hidden][names(minor_label),'minor_label_transferAnchors_BaseAtlas'] <- minor_label
  301. integrated_v2_filtered$major_label_transferAnchors_BaseAtlas <- integrated_v2_filtered$major_cluster_label_trained_with_all
  302. [email hidden][names(major_label),'major_label_transferAnchors_BaseAtlas'] <- major_label
  303. saveRDS(integrated_v2_filtered, "Integrated_atlas_V2_filtered_annotated.RDS")
  304. # Generate UMAP plots for major labels
  305. ClusterCol <- c('#35C1D5', '#4A2884', '#E05F36')
  306. DimPlot(integrated_v2_filtered, group.by="major_label_transferAnchors_BaseAtlas", reduction = 'umap', cols=ClusterCol)
  308. #######################################
  309. # SAVE objects for clusters computation
  310. #######################################
  311. integrated_v2_filtered_LRP <- subset(integrated_v2_filtered, subset=major_label_transferAnchors_BaseAtlas == "LRP")
  312. saveRDS(integrated_v2_filtered_LRP,'Integrated_atlas_v2_LRP.RDS')
  313. integrated_v2_filtered_Martinotti <- subset(integrated_v2_filtered, subset=major_label_transferAnchors_BaseAtlas == "Martinotti")
  314. saveRDS(integrated_v2_filtered_Martinotti,'Integrated_atlas_v2_Martinotti.RDS')
  315. integrated_v2_filtered_NonMartinotti <- subset(integrated_v2_filtered, subset=major_label_transferAnchors_BaseAtlas == "Non-Martinotti")
  316. saveRDS(integrated_v2_filtered_NonMartinotti,'Integrated_atlas_v2_NonMartinotti.RDS')

Module_3_part_1.R at commit cca2307, no license · at the source

Overview

Authors: Minhui Liu1,2, Facundo Ferrero Restelli1,2, Elia Micoli1,2, Giulia Barbiera3, Rani Moors1,2, Evelien Nouboers1,2, Malou Reverendo1, Jessica Xinyun Du4, Hannah Bertels2, Dimitris Konstantopoulos3, Keimpe Wierda1, Aya Takeoka5, Giordano Lippi4, Lynette Lim1,2
ORCID iDs: Evelien Nouboers, Malou Reverendo, Jessica Xinyun Du, Keimpe Wierda, Aya Takeoka, Giordano Lippi, Lynette Lim
  1. VIB-KU Leuven Center for Neuroscience, Leuven, Belgium
  2. Department of Neurosciences, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
  3. Genevia Technologies Oy, Tampere, Finland
  4. Department of Neuroscience, Scripps Research Institute, La Jolla, CA USA
  5. RIKEN Center for Brain Science, Saitama, Japan
Institutions: VIB-KU Leuven Center for Brain & Disease Research (Belgium); KU Leuven (Belgium); Scripps Research Institute (United States); RIKEN Center for Brain Science (Japan)
Journal: Nature neuroscience, volume 29, issue 9, pages 2283-2295
Dates: received 11 February 2025; accepted 24 June 2026; published online 3 August 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02387-w · PMID 42547805 · PMCID PMC13533839 · OpenAlex W7172317050
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, Connectivity, Statistics, fMRI & imaging
Keywords: Cell type diversity, Cell fate and cell lineage, Differentiation, Neuronal development, Molecular neuroscience
MeSH: Cerebral Cortex*, Neural Inhibition*, Neurons*, Transcriptome*, Animals, Mice, Neurodevelopment, Single-Cell Analysis, Single-Cell Gene Expression Analysis, Somatostatin (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Vlaams Instituut voor Biotechnologie (Flanders Institute for Biotechnology) (Lim lab); NINDS NIH HHS (F31 NS118982); NIMH NIH HHS (RF1 MH126719); U.S. Department of Health & Human Services | NIH | Center for Scientific Review (NIH Center for Scientific Review) (F31NS118982, RF1MH126719); International Foundation for Research in Paraplegia (Internationale Stiftung für Forschung in Paraplegie) (P188)
Citations: not cited yet (Europe PMC); 73 references in the paper
Research resources: rabbit anti-Dach1 RRID:AB_2230330, rabbit antinitric oxide synthase 1—NOS1 RRID:AB_572256, WT C57BL/6J RRID:IMSR_JAX:000664, RC:FLTG RRID:IMSR_JAX:026932, Nkx2.1flp RRID:IMSR_JAX:028577, RCE RRID:MMRRC_032037-JAX, RRID:SCR_024672

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

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Limlab-VIBCBD/Dev-SST-atlas

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: cca2307aea99a332c5f0899c68af487142c797ac, 14 September 2026
Languages: R (16)
Size: 54 files, 16 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (Nextflow_pipeline/docker/Dockerfile), 1 notebook
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Seurat (15 files), tidyverse (15 files), ggplot2 (6 files), circlize (2 files), ComplexHeatmap (2 files), patchwork (2 files), reshape2 (2 files), anndata (1 file), cowplot (1 file), mgcv (1 file), pandas (1 file), randomForest (1 file), Scanpy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
17 files
At the source: github.com/Limlab-VIBCBD/Dev-SST-atlas

Code availability statement

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 23 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data availability statement

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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 3, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 5 keywords, 10 MeSH terms, 5 funders, 70 references, 7 RRIDs.

Cite

This paper

Liu, M., Restelli, F. F., Micoli, E., Barbiera, G., Moors, R., Nouboers, E., Reverendo, M., Du, J. X., Bertels, H., Konstantopoulos, D., Wierda, K., Takeoka, A., Lippi, G., & Lim, L. (2026). Developing mouse inhibitory neuron single-cell transcriptomes reveal distinct modes of cell-type diversification. Nature neuroscience, 29(9), 2283-2295. https://doi.org/10.1038/s41593-026-02387-w

BibTeX

@article{liu2026developing,
author = {Liu, Minhui and Restelli, Facundo Ferrero and Micoli, Elia and Barbiera, Giulia and Moors, Rani and Nouboers, Evelien and Reverendo, Malou and Du, Jessica Xinyun and Bertels, Hannah and Konstantopoulos, Dimitris and Wierda, Keimpe and Takeoka, Aya and Lippi, Giordano and Lim, Lynette},
title = {{Developing mouse inhibitory neuron single-cell transcriptomes reveal distinct modes of cell-type diversification}},
journal = {Nature neuroscience},
year = {2026},
month = aug,
volume = {29},
number = {9},
pages = {2283--2295},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02387-w},
url = {https://doi.org/10.1038/s41593-026-02387-w},
pmid = {42547805},
pmcid = {PMC13533839}
}

RIS

TY - JOUR
AU - Liu, Minhui
AU - Restelli, Facundo Ferrero
AU - Micoli, Elia
AU - Barbiera, Giulia
AU - Moors, Rani
AU - Nouboers, Evelien
AU - Reverendo, Malou
AU - Du, Jessica Xinyun
AU - Bertels, Hannah
AU - Konstantopoulos, Dimitris
AU - Wierda, Keimpe
AU - Takeoka, Aya
AU - Lippi, Giordano
AU - Lim, Lynette
TI - Developing mouse inhibitory neuron single-cell transcriptomes reveal distinct modes of cell-type diversification
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/08/03
VL - 29
IS - 9
SP - 2283
EP - 2295
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02387-w
UR - https://doi.org/10.1038/s41593-026-02387-w
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41593-026-02387-w",
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"title": "Developing mouse inhibitory neuron single-cell transcriptomes reveal distinct modes of cell-type diversification",
"container-title": "Nature neuroscience",
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