OSCR

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.

Code ↔ Paper

40 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 40 matches · 11 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › CODEX data integration and annotation ↔ CODEX/02_Integrate_Samples_rPCA_CLR2.R, lines 1–39 · score 1.00 · HLA DR, CX3CR1, NeuN, S100B, Collagen IV, CD11b
  2. [2] § STAR★Methods › Method details › CODEX data integration and annotation ↔ CODEX/04_Integrate_Myeloid_rPCA.R, lines 1–42 · score 0.99 · HLA DR, CX3CR1, NeuN, S100B, Collagen IV, CD11b
  3. [3] § STAR★Methods › Method details › Myeloid subtype analysis ↔ Microenvironment/Myeloid_snRNA_seq_Analysis.R, lines 136–186 · score 0.99 · k.anchor, nCount_RNA, IntegrateData, SplitObject, SelectIntegrationFeatures, AddModuleScore
  4. [4] § STAR★Methods › Method details › snRNA-seq malignant cell state analysis ↔ snRNA-seq_Analysis/rna_malignant_integration.R, the whole file · a weak match · score 0.97 · k.weight, IntegrateData, SplitObject, SelectIntegrationFeatures, FindIntegrationAnchors, ScaleData
  5. [5] § Results › Tumor immune microenvironment is dominated by diverse myeloid populations ↔ Microenvironment/Myeloid_snRNA_seq_Analysis.R, lines 188–262 · score 0.95 · pro angiogenic TAMs, lipid associated TAMs, inflammatory TAMs, BMD TAM, dendritic cells, pre active
  6. [6] § Results › Tumor immune microenvironment is dominated by diverse myeloid populations ↔ Microenvironment/LIANA_Interactions_Final.R, lines 75–158 · score 0.95 · pro angiogenic TAMs, lipid associated TAMs, inflammatory TAMs, BMD TAM, dendritic cells, pre active
  7. [7] § STAR★Methods › Method details › snRNA-seq malignant cell state analysis ↔ snRNA-seq_Analysis/pathway_analysis_resion.R, the whole file · a weak match · score 0.94 · GO_Biological_Process_2023, KEGG_2021_Human, MSigDB_Hallmark_2020, Reactome_2022, enrichR, pct
  8. [8] § STAR★Methods › Method details › CODEX data integration and annotation ↔ CODEX/06_Merge_Annotations_Filter.R, lines 1–46 · score 0.92 · Collagen IV, CD11b, P2RY12, endothelial cells, CD31, NFP
  9. [9] § Results › Tumor immune microenvironment is dominated by diverse myeloid populations ↔ Microenvironment/LIANA_Interactions_Final.R, lines 75–158 · score 0.91 · pro angiogenic TAMs, IGF1R, CALM1, ERBB2, FN1, HBEGF
  10. [10] § STAR★Methods › Method details › CODEX data integration and annotation ↔ CODEX/04_Integrate_Myeloid_rPCA.R, lines 1–42 · score 0.91 · Collagen IV, CD11b, P2RY12, myeloid cells, CD31, NFP
  11. [11] § Results › Oligodendrocytes coordinate post-therapy-enriched cellular neighborhoods ↔ CODEX/07_Neighborhood_Spatial_Analysis.R, lines 104–160 · score 0.90 · infiltrating tumor, immune enriched, vascular tumor, CN11, CN2, CN3
  12. [12] § STAR★Methods › Method details › SCENIC transcriptional regulatory network (TRN) analysis of myeloid cells ↔ Microenvironment/Run_pySCENIC_Myeloid.sh, the whole file · a weak match · score 0.89 · grnboost2, pySCENIC, Regulatory Network, AUCell, v9, regulons
  13. [13] § STAR★Methods › Method details › Multiomic sequencing data analysis ↔ snATAC-seq_Analysis/Multiome/02_Annotate_Cell_Types.R, lines 43–128 · score 0.88 · white blood cells, leiden_resolution, InferCNV, Multiomic, denoise, HMM
  14. [14] § STAR★Methods › Method details › Deconvolution of bulk RNA-sequencing data ↔ BulkRNA_deconvolution/makeCIBERSORTscRef.R, the whole file · a weak match · score 0.88 · combined endothelial cells, Bulk RNA, vascular cells, deconvoluted, CIBERSORTx, normal cell
  15. [15] § STAR★Methods › Method details › Sample integration, clustering, and cell type annotation of snRNA-Seq data ↔ Microenvironment/Myeloid_snRNA_seq_Analysis.R, lines 136–186 · score 0.87 · SelectIntegrationFeatures, FindIntegrationAnchors, SCTransform, FindNeighbors, RunUMAP, FindClusters
  16. [16] § STAR★Methods › Method details › Xenium ligand-receptor interaction analysis ↔ Xenium/03_Plotting_LR_Results.R, lines 1–54 · score 0.85 · empirical shuffling, LR pair, myeloid tumor, Xenium, fold change, Stouffer
  17. [17] § STAR★Methods › Method details › Cohort-level analysis of pre-versus post-treatment gene expression changes ↔ snRNA-seq_Analysis/CPTCA_pHGG_Dream_Analysis.R, lines 44–112 · score 0.85 · prior history, linear mixed model, Limma Voom, patient ID, C15498, dream
  18. [18] § STAR★Methods › Method details › Construction of transcriptional regulatory network ↔ TRN_Analysis/coembedding_perSample_metacell.R, lines 189–256 · score 0.85 · metacell expression matrix, max_shared, min_cells, seurat_clusters, ident, pca
  19. [19] § STAR★Methods › Method details › Deconvolution of bulk RNA-sequencing data ↔ BulkRNA_deconvolution/OpenPedCan_bulkRNA_cleanup.R, the whole file · a weak match · score 0.85 · OpenPedCan, Bulk RNA seq, deconvoluted, PBTA, CIBERSORTx, TPM
  20. [20] § STAR★Methods › Method details › CODEX data segmentation ↔ CODEX/Mesmer_Segmentation_Script.py, lines 45–74 · score 0.84 · pixel expansion, Nuclear segmentation, interior threshold, Maxima threshold, Mesmer, raw
  21. [21] § STAR★Methods › Method details › Inference of neoplastic versus normal cells by copy number alteration analysis ↔ snATAC-seq_Analysis/Multiome/02_Annotate_Cell_Types.R, lines 43–128 · score 0.82 · white blood cells, mature neurons, InferCNV, HMM, subclusters, vascular
  22. [22] § STAR★Methods › Method details › Xenium ligand-receptor interaction analysis ↔ Xenium/01_Integration_Clustering.R, lines 64–144 · score 0.81 · nCount_Xenium, nFeature_Xenium, RPCA integration, log normalization, cropped, Seurat
  23. [23] § STAR★Methods › Method details › snATAC-seq cell type annotation ↔ TRN_Analysis/coembedding_perSample_metacell.R, lines 1–55 · score 0.80 · CreateAssayObject, gene body, gene activity, log normalization, transfer, Seurat
  24. [24] § STAR★Methods › Method details › SCENIC transcriptional regulatory network (TRN) analysis of myeloid cells ↔ Microenvironment/Review_pySCENIC_Myeloid.R, the whole file · a weak match · score 0.80 · AUCell, logfc.threshold, FindAllMarkers, SCENIC, regulons, loom
  25. [25] § STAR★Methods › Method details › snATAC-seq cell type annotation ↔ snRNA-seq_Analysis/Projection_to_Moreno_etal_Adult_GBM_Atlas.R, the whole file · a weak match · score 0.78 · TransferData, query.assay, NormalizeData, snRNA, refdata, Seurat
  26. [26] § STAR★Methods › Method details › CODEX data segmentation ↔ deepcell/applications/nuclear_segmentation.py, lines 54–199 · score 0.74 · Nuclear segmentation, interior threshold, Maxima threshold, pixel, channel
  27. [27] § STAR★Methods › Method details › Malignant subclone analysis ↔ Clonal_analysis/smooth_snRNA_seg_inferCNV_normal.R, lines 45–89 · score 0.74 · CNVkit, CNV segment, Paired WGS, iteratively, Clonalscope, copy
  28. [28] § STAR★Methods › Method details › Reference mapping of published datasets on pHGG neoplastic cell atlas ↔ snRNA-seq_Analysis/Projection_to_Moreno_etal_Adult_GBM_Atlas.R, the whole file · a weak match · score 0.73 · FindTransferAnchors, variable features, pHGG, snRNA, atlas, query
  29. [29] § STAR★Methods › Method details › Cohort-level analysis of pre-versus post-treatment gene expression changes ↔ Clonal_analysis/Clonal_fishplot_SharedScripts.R, lines 1–46 · score 0.71 · patient ID, C1061121, C107625, C176874, C2399853, C2542041
  30. [30] § STAR★Methods › Method details › Malignant subclone analysis ↔ Clonal_analysis/snRNA_lineage_trace_inferN_smooth_seg.R, lines 45–84 · score 0.71 · CNVkit, Paired WGS, snRNA, iteratively, Clonalscope, copy
  31. [31] § Results › Tumor subclone dynamics reveal recurrent genomic alterations ↔ Clonal_analysis/snRNA_lineage_trace_inferN_smooth_seg.R, lines 88–129 · score 0.69 · lineage traced, tumor subclones, snRNA, WGS, Clonalscope, cell state
  32. [32] § STAR★Methods › Method details › Cell proximity analysis ↔ CODEX/08_Distance_Analysis_Permutations.R, lines 1–78 · score 0.66 · target cell, distfun, ppp, psp, permutation, median
  33. [33] § STAR★Methods › Method details › Differentiation trajectory analysis of malignant cells ↔ TRN_Analysis/scDataAnalysis_Utilities_simp.R, lines 424–488 · score 0.62 · nCount_RNA, SCTransform, variable features, Seurat, PCA, filtered
  34. [34] § STAR★Methods › Method details › Malignant subclone analysis ↔ Clonal_analysis/plot_help_functions.R, the whole file · a weak match · score 0.61 · fishPlot, tumor subclones, spline, Clonalscope, patient, clustering
  35. [35] § STAR★Methods › Method details › Transcription factor motif analysis ↔ snATAC-seq_Analysis/atac_stateanalysis_revision.R, lines 362–422 · score 0.60 · chromVAR, motifs enriched, TFs, deviation, scores, Seq
  36. [36] § STAR★Methods › Method details › Differentiation trajectory analysis of malignant cells ↔ snRNA-seq_Analysis/Monocle_Pseudotime.R, the whole file · a weak match · score 0.58 · learn_graph, Pseudotemporal, snRNA, root, Monocle3, GPC
  37. [37] § STAR★Methods › Method details › Malignant subclone analysis ↔ Clonal_analysis/smooth_snRNA_seg_inferCNV_normal.R, lines 45–89 · score 0.58 · chromosome arm, CNV segments, hg38, Clonalscope, copy, gene
  38. [38] § STAR★Methods › Method details › Differentiation trajectory analysis of malignant cells ↔ R/order_cells.R, lines 2–141 · score 0.58 · learn_graph, root node, Pseudotemporal, Monocle3, trajectory, matrices
  39. [39] § Results › Pediatric gliomas exhibit distinct neoplastic cell states ↔ BulkRNA_deconvolution/OpenPedCan_bulkRNA_cleanup.R, the whole file · a weak match · score 0.55 · bulk RNA seq, deconvolute, pHGG, composition, cohort, glioma
  40. [40] § STAR★Methods › Method details › In vitro drug screening and synergy experiments ↔ Figure_Generation/Generate_Figures_Revision_Curves.R, lines 128–209 · score 0.54 · Drug concentrations, DMSO, SEM, fluorescent, timepoint, cells

Paper

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The authors' code

R · 263 lines · 14 KB · no license · 3 matches

  1. library(Seurat)
  2. library(ggplot2)
  3. library(patchwork)
  4. library(openxlsx)
  5. library(readxl)
  6. library(dplyr)
  7. library(tibble)
  8. library(data.table)
  9. library(dittoSeq)
  10. library(cowplot)
  11. library(RColorBrewer)
  12. library(Matrix)
  13. library(ggpubr)
  14. library(UCell)
  15. library(car)
  16. setwd("/mnt/isilon/tan_lab/sussmanj/pHGG/snRNA-seq/Macrophages")
  17. source("../../Figures/Figure_Functions.R")
  19. ###Load latest file
  20. seurat.rna <- readRDS(file = "../Final_Cohort_All_snRNA-seq.RDS")
  22. #Subset myeloid
  23. macrophage.rna <- subset(x=seurat.rna, subset = merged_cellType == "Macrophage/Microglia")
  24. DimPlot(macrophage.rna, group.by = "timepoint")
  26. #Add ribosomal gene content
  27. C<-GetAssayData(object = macrophage.rna, slot = "counts")
  28. rb.genes <- rownames(macrophage.rna)[grep("^RP[SL]",rownames(macrophage.rna))]
  29. percent.ribo <- Matrix::colSums(C[rb.genes,])/Matrix::colSums(C)*100
  30. macrophage.rna <- AddMetaData(macrophage.rna, percent.ribo, col.name = "percent.ribo")
  32. DefaultAssay(macrophage.rna) <- 'RNA'
  33. macrophage.rna <- SCTransform(macrophage.rna, method = "glmGamPoi",
  34. vars.to.regress = c("nCount_RNA", "percent.mito", "percent.ribo"),
  35. verbose = TRUE)
  36. macrophage.rna <- RunPCA(object = macrophage.rna)
  37. macrophage.rna = RunUMAP(macrophage.rna, dims = 1:30, reduction = 'pca', reduction.name = 'umap')
  38. s.genes <- cc.genes$s.genes
  39. g2m.genes <- cc.genes$g2m.genes
  40. macrophage.rna <- CellCycleScoring(macrophage.rna, s.features = s.genes, g2m.features = g2m.genes)
  42. DimPlot(macrophage.rna, group.by = "patient_id") + coord_fixed()
  44. #rPCA Integration
  45. seurat.list = SplitObject(macrophage.rna, split.by = 'patient_id')
  46. seurat.list = lapply(seurat.list, FUN = SCTransform, method = 'glmGamPoi',
  47. vars.to.regress = c("nCount_RNA", "percent.mito", "percent.ribo"))
  48. features <- SelectIntegrationFeatures(seurat.list, nfeatures = 3000)
  49. seurat.list = PrepSCTIntegration(seurat.list, anchor.features = features)
  50. seurat.list = lapply(seurat.list, FUN = RunPCA, features = features)
  51. anchors <- FindIntegrationAnchors(object.list = seurat.list, normalization.method = "SCT",
  52. anchor.features = features, dims = 1:30,
  53. reduction = "rpca", k.anchor = 20)
  54. macrophage.rna <- IntegrateData(anchorset = anchors, normalization.method = "SCT", dims = 1:30)
  55. macrophage.rna <- RunPCA(macrophage.rna, verbose = TRUE, reduction.name = "rpca_integrated")
  56. macrophage.rna <- RunUMAP(macrophage.rna, reduction = "rpca_integrated",
  57. dims = 1:30, reduction.name = "umap_rpca")
  58. #Timepoint
  59. Idents(macrophage.rna) <- "timepoint"
  60. macrophage.rna <- RenameIdents(macrophage.rna, "Initial CNS Tumor" = "Initial CNS Tumor",
  61. "Recurrence" = "Timepoint_2",
  62. "Progressive (Non-Autopsy)" = "Timepoint_2",
  63. "Progressive (Autopsy)" = "Timepoint_2")
  64. macrophage.rna$timemerge <- Idents(macrophage.rna)
  66. #Clusters
  67. DefaultAssay(macrophage.rna) <- "integrated"
  68. DimPlot(macrophage.rna, group.by = "timepoint") + coord_fixed()
  70. macrophage.rna = FindNeighbors(macrophage.rna, reduction = 'rpca_integrated',
  71. dims = 1:30, verbose = T)
  72. macrophage.rna = FindClusters(macrophage.rna, verbose = T,
  73. resolution = c(0.05, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0))
  75. #Annotation
  76. #TAM Gene lists
  77. DefaultAssay(macrophage.rna) <- "RNA"
  78. module.genes <- read.xlsx('TAM_S5.xlsx')
  79. macrophage.rna <- AddModuleScore(macrophage.rna, features = list(na.omit(module.genes$MG_Markers)), name = "Microglia")
  80. macrophage.rna <- AddModuleScore(macrophage.rna, features = list(module.genes$Mac_Markers), name = "Macrophage")
  82. p1 <- FeaturePlot(macrophage.rna, features = 'Microglia1', min.cutoff = 'q1', max.cutoff = 'q90', reduction = "umap_rpca",
  83. raster = FALSE) + ggtitle('Microglia') + coord_fixed()
  84. p2 <- FeaturePlot(macrophage.rna, features = 'Macrophage1',min.cutoff = 'q1', max.cutoff = 'q90', reduction = "umap_rpca",
  85. raster = FALSE) + ggtitle('Macrophage') + coord_fixed()
  86. p1+p2
  87. saveRDS(macrophage.rna, file = "Reanalysis_scRNA_macrophage3.rds")
  90. #DEGs
  91. DefaultAssay(macrophage.rna) <- "RNA"
  92. Idents(macrophage.rna) <- "integrated_snn_res.0.2"
  93. markers_res.0.2 <- FindAllMarkers(macrophage.rna, only.pos = TRUE,
  94. min.pct = 0.10, min.diff.pct = 0.15, logfc.threshold = 0.15)
  95. macrophage.rna <- ScaleData(macrophage.rna)
  96. markers_res.0.3 %>%
  97. group_by(cluster) %>%
  98. top_n(n = 10, wt = avg_log2FC) -> top
  99. write.table(markers_res.0.2, file = "Reanalysis_Macrophage_Markers_res0.2.txt", sep = '\t')
  100. DotPlot(macrophage.rna, features = unique(top$gene), cols="RdBu") +
  101. RotatedAxis() +
  102. theme(axis.text.x = element_text(size = 8))
  103. DoHeatmap(macrophage.rna, features = top$gene) +
  104. scale_fill_gradientn(colors = c("blue", "white", "red"))
  106. #Projection to Harmonized Atlas
  107. harmonized_gbm_core <- readRDS("../Harmonized_Atlas/Harmonized_Reintegrated_rPCA_Author.RDS")
  109. anchors <- FindTransferAnchors(
  110. reference = harmonized_gbm_core,
  111. query = macrophage.rna,
  112. reference.assay = "RNA", query.assay = "RNA",
  113. reference.reduction = "integrated.rpca",
  114. dims = 1:50
  115. )
  116. Query_2_map <- MapQuery(
  117. anchorset = anchors,
  118. query = macrophage.rna.f,
  119. reference = harmonized_gbm_core,
  120. refdata = list(
  121. celltype.l1 = "annotation_level_1",
  122. celltype.l2 = "annotation_level_2",
  123. celltype.l3 = "annotation_level_3",
  124. celltype.l4 = "annotation_level_4"
  125. ),
  126. reference.reduction = "integrated.rpca",
  127. reduction.model = "umap.rpca"
  128. )
  129. saveRDS([email hidden], "Renalysis_Projection_2_Metadata.rds")
  130. DimPlot(Query_2_map, group.by = "predicted.celltype.l4", label = TRUE,
  131. label.size = 3, repel = TRUE, reduction = 'umap_rpca') + coord_fixed()
  132. FeaturePlot(Query_2_map, reduction = 'umap_rpca', features = "predicted.celltype.l1.score", cols = c("lightgrey", "darkred"),lbel.size = 3.5)
  133. table(Query_2_map$integrated_snn_res.0.3, Query_2_map$predicted.celltype.l1)
  134. dittoBarPlot(Query_2_map, "predicted.celltype.l3", group.by = "integrated_snn_res.0.6")
  136. #################################################
  137. #Remove putative neoplastic and contaminating cells and reintegrate
  138. macrophage.rna.f <- subset(macrophage.rna, integrated_snn_res.0.1 %in% c("1", "5"), invert = TRUE)
  139. table(macrophage.rna.f$integrated_snn_res.0.3)
  140. macrophage.rna.f[["SCT"]] = NULL
  141. macrophage.rna.f[["integrated"]] = NULL
  143. #rPCA Integration
  144. seurat.list = SplitObject(macrophage.rna.f, split.by = 'patient_id')
  145. seurat.list = lapply(seurat.list, FUN = SCTransform, method = 'glmGamPoi',
  146. vars.to.regress = c("nCount_RNA", "percent.mito", "percent.ribo"))
  147. features <- SelectIntegrationFeatures(seurat.list, nfeatures = 3000)
  148. seurat.list = PrepSCTIntegration(seurat.list, anchor.features = features)
  149. seurat.list = lapply(seurat.list, FUN = RunPCA, features = features)
  150. anchors <- FindIntegrationAnchors(object.list = seurat.list, normalization.method = "SCT",
  151. anchor.features = features, dims = 1:30,
  152. reduction = "rpca", k.anchor = 20)
  153. macrophage.rna.f <- IntegrateData(anchorset = anchors, normalization.method = "SCT", dims = 1:30)
  154. macrophage.rna.f <- RunPCA(macrophage.rna.f, verbose = TRUE, reduction.name = "rpca_integrated")
  155. macrophage.rna.f <- RunUMAP(macrophage.rna.f, reduction = "rpca_integrated",
  156. dims = 1:30, reduction.name = "umap_rpca")
  157. #Timepoint
  158. Idents(macrophage.rna.f) <- "timepoint"
  159. macrophage.rna.f <- RenameIdents(macrophage.rna.f, "Initial CNS Tumor" = "Initial CNS Tumor",
  160. "Recurrence" = "Timepoint_2",
  161. "Progressive (Non-Autopsy)" = "Timepoint_2",
  162. "Progressive (Autopsy)" = "Timepoint_2")
  163. macrophage.rna.f$timemerge <- Idents(macrophage.rna.f)
  165. #Clusters
  166. DefaultAssay(macrophage.rna.f) <- "integrated"
  167. DimPlot(macrophage.rna.f, group.by = "timepoint") + coord_fixed()
  169. macrophage.rna.f = FindNeighbors(macrophage.rna.f, reduction = 'rpca_integrated',
  170. dims = 1:30, verbose = T)
  171. macrophage.rna.f = FindClusters(macrophage.rna.f, verbose = T,
  172. resolution = c(0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0))
  174. #Annotation
  175. #TAM Gene lists
  176. DefaultAssay(macrophage.rna.f) <- "RNA"
  177. module.genes <- read.xlsx('TAM_S5.xlsx')
  178. macrophage.rna.f <- AddModuleScore(macrophage.rna.f, features = list(na.omit(module.genes$MG_Markers)), name = "Microglia")
  179. macrophage.rna.f <- AddModuleScore(macrophage.rna.f, features = list(module.genes$Mac_Markers), name = "Macrophage")
  181. p1 <- FeaturePlot(macrophage.rna.f, features = 'Microglia1', min.cutoff = 'q1', max.cutoff = 'q90', reduction = "umap_rpca",
  182. raster = FALSE) + ggtitle('Microglia') + coord_fixed()
  183. p2 <- FeaturePlot(macrophage.rna.f, features = 'Macrophage1',min.cutoff = 'q1', max.cutoff = 'q90', reduction = "umap_rpca",
  184. raster = FALSE) + ggtitle('Macrophage') + coord_fixed()
  185. p1+p2
  186. saveRDS(macrophage.rna.f, file = "Reanalysis_scRNA_macrophage_filtered2.rds")
  188. #DEGs
  189. DefaultAssay(macrophage.rna.f) <- "RNA"
  190. Idents(macrophage.rna.f) <- "integrated_snn_res.0.6"
  191. markers_res.0.6 <- FindAllMarkers(macrophage.rna.f, only.pos = TRUE,
  192. min.pct = 0.10, min.diff.pct = 0.10, logfc.threshold = 0.10)
  193. macrophage.rna.f <- ScaleData(macrophage.rna.f)
  194. markers_res.0.6 %>%
  195. group_by(cluster) %>%
  196. top_n(n = 10, wt = avg_log2FC) -> top
  197. write.table(markers_res.0.6, file = "Reanalysis_Macrophage_Markers_res0.6.txt", sep = '\t')
  198. DotPlot(macrophage.rna.f, features = unique(top$gene), cols="RdBu") +
  199. RotatedAxis() +
  200. theme(axis.text.x = element_text(size = 8))
  202. #Signatures
  203. signatures <- read_excel("S5_Signatures.xlsx")
  204. DefaultAssay(macrophage.rna.f) <- "RNA"
  205. siglist = list()
  206. siglist$Angiogenesis = as.character(na.omit(signatures$Angiogenesis))
  207. macrophage.rna.f <- AddModuleScore(macrophage.rna.f, features = list(as.character(na.omit(signatures$M1))), name = "M1_Phenotype")
  208. macrophage.rna.f <- AddModuleScore(macrophage.rna.f, features = list(as.character(na.omit(signatures$M2))), name = "M2_Phenotype")
  209. macrophage.rna.f <- AddModuleScore(macrophage.rna.f, features = list(as.character(na.omit(signatures$Angiogenesis))), name = "Angiogenesis")
  210. macrophage.rna.f <- AddModuleScore(macrophage.rna.f, features = list(as.character(na.omit(signatures$Phagocytosis))), name = "Phagocytosis")
  212. p1 <- FeaturePlot(macrophage.rna.f, features = macrophage.rna.f, min.cutoff = 'q1', max.cutoff = 'q90', reduction = "umap_rpca",
  213. raster = FALSE) + ggtitle('Angiogenesis') + coord_fixed()
  214. p2 <- FeaturePlot(macrophage.rna.f, features = 'VEGFA', min.cutoff = 'q1', max.cutoff = 'q90', reduction = "umap_rpca",
  215. raster = FALSE) + ggtitle('Phagocytosis1') + coord_fixed()
  216. p3 <- FeaturePlot(macrophage.rna.f, features = 'M1_Phenotype1', min.cutoff = 'q1', max.cutoff = 'q90', reduction = "umap_rpca",
  217. raster = FALSE) + ggtitle('M1_Phenotype') + coord_fixed()
  218. p4 <- FeaturePlot(macrophage.rna.f, features = 'M2_Phenotype1', min.cutoff = 'q1', max.cutoff = 'q90', reduction = "umap_rpca",
  219. raster = FALSE) + ggtitle('M2_Phenotype') + coord_fixed()
  220. p1+p2+p3+p4
  221. VlnPlot(macrophage.rna.f, features = "Angiogenesis1", group.by = 'integrated_snn_res.0.6', pt.size = 0)
  222. VlnPlot(macrophage.rna.f, features = "Phagocytosis1", group.by = 'integrated_snn_res.0.6', pt.size = 0)
  224. #Rename Clusters
  225. Idents(macrophage.rna.f) <- "integrated_snn_res.0.6"
  226. macrophage.rna.f <- RenameIdents(macrophage.rna.f,
  227. `0`="Undetermined MG",
  228. `1`="Pre-Active MG",
  229. `2`="BMD TAM 1",
  230. `3`="Lipid-Associated TAMs",
  231. `4`="Homeostatic MG",
  232. `5`="BMD TAM 2",
  233. `6`="BMD TAM 2",
  234. `7`="Proliferating Myeloid",
  235. `8`="Pro-Angiogenic TAM",
  236. `9`="BMD TAM 1",
  237. `10`="IFN-Responsive TAM",
  238. `11`="Inflammatory TAM",
  239. `12`="Pro-Angiogenic TAM",
  240. `13`="Dendritic Cells")
  241. macrophage.rna.f$cell_labels1 <- Idents(macrophage.rna.f)
  242. DimPlot(macrophage.rna.f, group.by = "cell_labels1", reduction = 'umap_rpca',
  243. label = T) + coord_fixed()
  244. DimPlot(macrophage.rna.f, group.by = "timepoint", reduction = 'umap_rpca',
  245. label = F) + coord_fixed()
  246. DimPlot(macrophage.rna.f, group.by = "patient_id", reduction = 'umap_rpca',
  247. label = F) + coord_fixed()
  248. labels = c("Undetermined MG", "Pre-Active MG","BMD TAM 1","BMD TAM 2", "Lipid-Associated TAMs",
  249. "Homeostatic MG","Proliferating Myeloid","Pro-Angiogenic TAM","IFN-Responsive MG","Inflammatory TAM",
  250. "Dendritic Cells")
  251. ggplot([email hidden], aes(x=cell_labels1, fill=patient_id)) + geom_bar(position = "fill") + scale_y_continuous(expand = c(0,0)) +
  252. theme_bw() + RotatedAxis()
  254. markers_celltypes1 <- FindAllMarkers(macrophage.rna.f, only.pos = TRUE,
  255. min.pct = 0.10, min.diff.pct = 0.10, logfc.threshold = 0.10)
  256. markers_celltypes1 %>%
  257. group_by(cluster) %>%
  258. top_n(n = 15, wt = avg_log2FC) -> top
  259. write.table(markers_celltypes1, file = "Reanalysis_Macrophage_Markers_celltypes1.txt", sep = '\t')
  260. DotPlot(macrophage.rna.f, features = unique(top$gene), cols="RdBu") +
  261. RotatedAxis() + coord_flip() +
  262. theme(axis.text.y = element_text(size = 6.5))

Myeloid_snRNA_seq_Analysis.R at commit 0051e32, no license · at the source

Overview

Authors: Jonathan H Sussman1,2, Derek A Oldridge3,4, Wenbao Yu5,6, Chia-Hui Chen5, Abigail M Zellmer4, Jiazhen Rong2,7, Arianne Parvaresh-Rizi8, Anusha Thadi5, Austin Yang5, Joseph S Tumulty5, Barbara Xiong1,2, David W Wu1,2, Yusha Sun1,9, Shovik Bandyopadhyay1,10, Jason Xu1,2, Stephanie Brosius5, Una Yamamoto Barkardottir5, Isabelle Seka5, Kyung Jin Ahn5, Omar Elghawy11
and 12 other authorsAmy E Baxter4, Mateusz P Koptyra12, Rami S Vanguri4, Stephanie McGrory5, Phillip B Storm12, Nduka M Amankulor13, Mariarita Santi3, Angela N Viaene3, Nancy Zhang7, Thomas De Raedt3,5, Kristina Cole5,6, Kai Tan5,6,14
ORCID iDs: Kai Tan
14 affiliations
  1. Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  2. Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  3. Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
  4. Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
  5. Center for Childhood Cancer Research, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
  6. Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
  7. Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
  8. Department of Medicine, Warren Alpert Medical School of Brown University, Providence, RI, USA
  9. Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  10. Cellular and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  11. Department of General Internal Medicine, University of Pennsylvania, Philadelphia, PA, USA
  12. Department of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
  13. Department of Neurosurgery, Perelman School of Medicine, Philadelphia, PA, USA
  14. Center for Single Cell Biology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
Institutions: University of Pennsylvania (United States); Children's Hospital of Philadelphia (United States); University of Rhode Island (United States); Brown University (United States)
Journal: Cell reports. Medicine, volume 7, issue 5, article 102766
Dates: received 31 July 2025; accepted 25 March 2026; published online 23 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.xcrm.2026.102766 · PMID 42030938 · PMCID PMC13198283 · OpenAlex W4392578156
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.03.06.583588
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: pediatric high-grade glioma, glioblastoma, central nervous system tumors, tumor microenvironment, single-cell multiomics, spatial proteomics, transcriptional regulation
MeSH: Brain Neoplasms*, Glioma*, Single-Cell Analysis*, Adolescent, Child, Child, Preschool, Female, Gene Expression Regulation, Neoplastic, Humans, Longitudinal Studies, Male, Multiomics, Neoplasm Grading, Proteomics, Spatial Transcriptomics, Tumor Microenvironment (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institutes of Health National Cancer Institute (F30-CA-277965, CA233285, F30-CA-268782); NCI NIH HHS (U2C CA233285, K12 CA076931); National Heart Lung and Blood Institute (U54HL165442, R38 HL143613, T32 CA009140); National Institutes of Health; Parker Institute for Cancer Immunotherapy; National Institute of General Medical Sciences (T32 GM07170); National Cancer Institute Division of Cancer Epidemiology and Genetics (F30-CA-298606); Children&amp;apos;s Hospital of Philadelphia
Citations: cited by 6 papers (Europe PMC); 129 references in the paper
Research resources: Anti-CD47 (clone polyclonal) RRID:AB_1026196, Anti-ATRX (clone polyclonal) RRID:AB_1078249, Anti-MOG (clone EP4281) RRID:AB_10863418, Anti-FOSL1 (clone D-3) RRID:AB_11012022, Anti-CD206 (clone polyclonal) RRID:AB_2063019, Anti-OLIG1 (clone 257219) RRID:AB_2157534, Anti-Nestin (clone 196908) RRID:AB_2251304, Anti-H3K27M (clone RM192) RRID:AB_2316432, Anti-GFAP (clone 2.2B10) RRID:AB_2532994, Anti-PAX5 (clone D7H5X) RRID:AB_2798001, Anti-P2RY12 (clone polyclonal) RRID:AB_2861284, Anti-CD11b (clone EPR1344) RRID:AB_2864378, Anti-SPP1 (clone EPR21139-316) RRID:AB_2894860, Anti-Ki-67 (clone B56) RRID:AB_2895046, Anti-CD31 (clone EP3095) RRID:AB_2915935, Anti-CD8 (clone C8/144B) RRID:AB_2915960, Anti-OLIG2 (clone EPR2673) RRID:AB_2923001, Anti-Collagen IV (clone EPR209660) RRID:AB_2927676, Anti-MPO (clone E1E7I) RRID:AB_2927678, Anti-FOXP3 (clone 236A/E7) RRID:AB_2927679, Anti-VIM (clone 091D3) RRID:AB_2935889, Anti-CD68 (clone KP1) RRID:AB_2935894, Anti-CD163 (clone EPR19518) RRID:AB_2935895, Anti-CD3e (clone EP449E) RRID:AB_2936080, Anti-CD44 (clone 156-3C11) RRID:AB_2936081, Anti-CD56 (clone CAL53) RRID:AB_2936082, Anti-PCNA (clone PC10) RRID:AB_2936083, Anti-S100B (clone EP1576Y) RRID:AB_3073612, Anti-PD-L1 (clone 73–10) RRID:AB_3073663, Anti-HLA-DR (clone EPR3692) RRID:AB_3080864, Anti-HIF-1α (clone EP1215Y) RRID:AB_3082972, Anti-CD38 (clone E7Z8C) RRID:AB_3082976, Anti-CD79a (clone D1X5C) RRID:AB_3082977, Anti-CD14 (clone EPR3653) RRID:AB_3083457, Anti-CD4 (clone EPR6855) RRID:AB_3094499, Anti-SOX2 (clone SP76) RRID:AB_3094504, Anti-PD1 (clone D4W2J) RRID:AB_3096407, Anti-CD16 (clone D1N9L) RRID:AB_3280014, Anti-NeuN (clone EPR12763) RRID:AB_3695640, Anti-CD133 (clone D2V8Q) RRID:AB_3717833, Anti-P53 (clone E26) RRID:AB_3718659, Anti-GLUT1 (clone EPR3915) RRID:AB_3719889, Anti-ASCL1 (clone 24B72D11.1) RRID:AB_396479

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.

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tanlabcode/pHGG_Atlas

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Evidence: files inventoried
Commit: 0051e32b5e1d37435fdc267ec099759064900496, 11 March 2026
Languages: R (55), Jupyter (6), Shell (5), Python (3)
Size: 74 files, 69 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, 6 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (41 files), tidyverse (41 files), ggplot2 (35 files), patchwork (20 files), data.table (15 files), pandas (9 files), pheatmap (9 files), ggpubr (8 files), Matplotlib (7 files), NumPy (7 files), SingleCellExperiment (6 files), edgeR (5 files), reshape2 (5 files), tifffile (5 files), cowplot (4 files), circlize (3 files), ComplexHeatmap (3 files), Harmony (3 files), imageio (3 files), NetworkX (3 files), Pillow (3 files), scikit-image (3 files), car (2 files), clusterProfiler (2 files), scikit-learn (2 files), SciPy (2 files), anndata (1 file), Monocle 3 (1 file), Plotly (1 file), pROC (1 file), Scanpy (1 file), scVelo (1 file), seaborn (1 file), UMAP (1 file), WGCNA (1 file)
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At the source: github.com/tanlabcode/pHGG_Atlas

chris-mcginnis-ucsf/DoubletFinder

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Commit: 1b244d8f0d54b4b1cb4365639931bbb16f01e1cd, 21 March 2025
Languages: R (12)
Size: 38 files, 12 scripts
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hub.docker.com/r/trinityctat

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cole-trapnell-lab/monocle3

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Languages: R (56), C++ (2), Python (1)
Size: 227 files, 59 scripts
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velocyto-team/velocyto.py

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Languages: Python (31), C (1)
Size: 70 files, 32 scripts
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At the source: github.com/velocyto-team/velocyto.py

vanvalenlab/deepcell-tf

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At the source: github.com/vanvalenlab/deepcell-tf/blob/master/notebooks/applications/Mesmer-Application.ipynb

YuLab-SMU/clusterProfiler

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GabrielHoffman/variancePartition

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Size: 608 files, 71 scripts
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kids-first/kf-tumor-workflow

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Size: 74 files, 2 scripts
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Found in: the text, “Whole genome sequencing (WGS) analysis”
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hub.docker.com/r/broadinstitute

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kids-first

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 32 authors, 7 keywords, 16 MeSH terms, 8 funders, 128 references, 43 RRIDs.

Cite

This paper

Sussman, J. H., Oldridge, D. A., Yu, W., Chen, C.-H., Zellmer, A. M., Rong, J., Parvaresh-Rizi, A., Thadi, A., Yang, A., Tumulty, J. S., Xiong, B., Wu, D. W., Sun, Y., Bandyopadhyay, S., Xu, J., Brosius, S., Barkardottir, U. Y., Seka, I., Ahn, K. J., . . . Tan, K. (2026). A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma. Cell reports. Medicine, 7(5), 102766. https://doi.org/10.1016/j.xcrm.2026.102766

BibTeX

@article{sussman2026longitudinal,
author = {Sussman, Jonathan H and Oldridge, Derek A and Yu, Wenbao and Chen, Chia-Hui and Zellmer, Abigail M and Rong, Jiazhen and Parvaresh-Rizi, Arianne and Thadi, Anusha and Yang, Austin and Tumulty, Joseph S and Xiong, Barbara and Wu, David W and Sun, Yusha and Bandyopadhyay, Shovik and Xu, Jason and Brosius, Stephanie and Barkardottir, Una Yamamoto and Seka, Isabelle and Ahn, Kyung Jin and Elghawy, Omar and Baxter, Amy E and Koptyra, Mateusz P and Vanguri, Rami S and McGrory, Stephanie and Storm, Phillip B and Amankulor, Nduka M and Santi, Mariarita and Viaene, Angela N and Zhang, Nancy and De Raedt, Thomas and Cole, Kristina and Tan, Kai},
title = {{A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma}},
journal = {Cell reports. Medicine},
year = {2026},
month = apr,
volume = {7},
number = {5},
pages = {102766},
publisher = {Elsevier},
issn = {2666-3791},
doi = {10.1016/j.xcrm.2026.102766},
url = {https://doi.org/10.1016/j.xcrm.2026.102766},
pmid = {42030938},
pmcid = {PMC13198283}
}

RIS

TY - JOUR
AU - Sussman, Jonathan H
AU - Oldridge, Derek A
AU - Yu, Wenbao
AU - Chen, Chia-Hui
AU - Zellmer, Abigail M
AU - Rong, Jiazhen
AU - Parvaresh-Rizi, Arianne
AU - Thadi, Anusha
AU - Yang, Austin
AU - Tumulty, Joseph S
AU - Xiong, Barbara
AU - Wu, David W
AU - Sun, Yusha
AU - Bandyopadhyay, Shovik
AU - Xu, Jason
AU - Brosius, Stephanie
AU - Barkardottir, Una Yamamoto
AU - Seka, Isabelle
AU - Ahn, Kyung Jin
AU - Elghawy, Omar
AU - Baxter, Amy E
AU - Koptyra, Mateusz P
AU - Vanguri, Rami S
AU - McGrory, Stephanie
AU - Storm, Phillip B
AU - Amankulor, Nduka M
AU - Santi, Mariarita
AU - Viaene, Angela N
AU - Zhang, Nancy
AU - De Raedt, Thomas
AU - Cole, Kristina
AU - Tan, Kai
TI - A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma
T2 - Cell reports. Medicine
J2 - Cell Rep Med
PY - 2026
DA - 2026/04/23
VL - 7
IS - 5
SP - 102766
SN - 2666-3791
PB - Elsevier
DO - 10.1016/j.xcrm.2026.102766
UR - https://doi.org/10.1016/j.xcrm.2026.102766
LA - en
ER -

CSL-JSON

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{
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"given": "Kristina"
},
{
"family": "Tan",
"given": "Kai"
}
],
"container-title-short": "Cell Rep Med",
"volume": "7",
"issue": "5",
"page": "102766",
"DOI": "10.1016/j.xcrm.2026.102766",
"PMID": "42030938",
"PMCID": "PMC13198283",
"ISSN": "2666-3791",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.xcrm.2026.102766",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
23
]
]
}
}

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

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