OSCR

Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain.

Code ↔ Paper

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The 32 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] § Results › Context-dependent differentiation of human GPCs in callosal white matter ↔ Scripts/21_Invitro_Invivo_Analysis_withATAC.Rmd, lines 309–401 · score 0.96 · MYCN signaling, THRA signaling, TCF7L2, SOX11 signaling, EGR2 signaling, cell contact
  2. [2] § Methods › Bulk CUT&Tag analysis ↔ Scripts/06_CUT&Tag_genomicCoverage.sh, lines 83–168 · score 0.90 · computeMatrix, plotHeatmap, H3K27ac, H3K27me3, H3K4me3, deeptools
  3. [3] § Results › Resident mouse brain cells drive the specification and differentiation of human GPCs in vivo ↔ Scripts/25_NicheNet_Analysis_and_Figure.Rmd, lines 398–441 · score 0.90 · IL1A, IL1B, APOE, EBI3, ICAM1, MMP13
  4. [4] § Methods › Xenium processing and analysis ↔ Scripts/28_Xenium_Inital_Analysis.ipynb, lines 439–475 · score 0.89 · cross reactive genes, perfect human genes, negative score, composite score, human probe, penalize
  5. [5] § Methods › scATAC-seq of human GPCs ↔ Scripts/20_scATAC_Analysis.Rmd, lines 116–141 · score 0.85 · blacklist ratio, nucleosome signal, TSS enrichment, fragments, scATAC, log
  6. [6] § Methods › Xenium processing and analysis ↔ Scripts/26_Process_MagicBlast_Output.Rmd, lines 27–56 · score 0.85 · Magic BLAST, mouse genome, human genome, composite score, human probe, alignment
  7. [7] § Methods › Integration with external fetal scRNA-/snRNA-seq datasets ↔ Scripts/17_Invitro_Integration_van_Bruggen.Rmd, lines 156–211 · score 0.82 · van Bruggen, FindTransferAnchors, TransferData, RPCA, external, query
  8. [8] § Methods › Bulk CUT & Tag of human PSCs or GPCs ↔ Scripts/06_CUT&Tag_genomicCoverage.sh, lines 83–168 · score 0.82 · CUT Tag, H3K27ac, H3K27me3, H3K4me3, chromatin, histone
  9. [9] § Methods › Cell-cell interaction analysis ↔ Scripts/25_NicheNet_Analysis_and_Figure.Rmd, lines 96–160 · score 0.81 · niche de, sender cells, receiver cells, NicheNet, vitro GPC4, Seurat
  10. [10] § Methods › Bulk CUT&Tag analysis ↔ Scripts/05_CUT&Tag_scRNA_Intersection.Rmd, lines 78–163 · score 0.79 · GeneHancer, DiffBind, promoter region, log2fc, database, enhancers
  11. [11] § Results › Context-dependent differentiation of human GPCs in callosal white matter ↔ Scripts/21_Invitro_Invivo_Analysis_withATAC.Rmd, lines 403–444 · score 0.78 · CAMK2N1, vivo GPC4, NNAT, DCX, FOS, JUNB
  12. [12] § Results › hESC-derived hGPCs are transcriptionally analogous to fetal glial progenitors ↔ Scripts/18_Label_Transfer_Figure_Generation.R, the whole file · a weak match · score 0.77 · van Bruggen, wk GA, 17–41, IPCs, RG, Velmeshev
  13. [13] § Methods › Integration with external fetal scRNA-/snRNA-seq datasets ↔ Scripts/15_Invitro_Integration_Ramos.Rmd, lines 256–307 · score 0.76 · FindTransferAnchors, TransferData, Ramos, RPCA, external, query
  14. [14] § Results › Resident mouse brain cells drive the specification and differentiation of human GPCs in vivo ↔ Scripts/25_NicheNet_Analysis_and_Figure.Rmd, lines 648–714 · score 0.75 · ligand target networks, SULF2, BAMBI, CSPG4, CSPG5, TNR
  15. [15] § Methods › Cell-cell interaction analysis ↔ Scripts/25_NicheNet_Analysis_and_Figure.Rmd, lines 648–714 · score 0.74 · ligand target weights, NicheNet, ligand activity, niches, receiver, activation
  16. [16] § Results › hGPCs are comprised of distinct stage-defined subpopulations ↔ Scripts/25_NicheNet_Analysis_and_Figure.Rmd, lines 60–74 · score 0.70 · dual species, immature oligodendrocytes, maOL, imOL, cell
  17. [17] § Results › Human donor cells can be recovered from host brains as glia, without residual pluripotent stem cells ↔ Scripts/19_Invivo_Analysis.Rmd, lines 784–823 · score 0.66 · cGPC, imAstrocyte, cAPC, maOL, imOL, canonical
  18. [18] § Methods › Bulk CUT & Tag of human PSCs or GPCs ↔ Scripts/05_CUT&Tag_scRNA_Intersection.Rmd, lines 78–163 · score 0.66 · K4me3, CUT Tag, histone mark, chromatin, Bulk
  19. [19] § Results › hESC-derived hGPCs are transcriptionally analogous to fetal glial progenitors ↔ Scripts/18_Label_Transfer_Figure_Generation.Rmd, lines 127–156 · score 0.64 · van Bruggen, wk GA, RG, OPC, fetal, 8–10
  20. [20] § Results › Transcriptionally-distinct cycling pools of bipotential and astrocyte-biased progenitors appear in vivo ↔ Scripts/19_Invivo_Analysis.Rmd, lines 910–940 · score 0.63 · cGPC, cAPC, Log2FC, NXPH1, PLLP, CNTN1
  21. [21] § Results › A distinct set of transcriptional regulators directs human astrocytic differentiation in vivo ↔ Scripts/29_Xenium_Figure_Construction.Rmd, lines 122–154 · score 0.59 · SLC1A3, SLC1A2, EGFR, AQP4, SOX9, OLIG2
  22. [22] § Methods › Identification of transcription factor regulons ↔ Scripts/21_Invitro_Invivo_Analysis_withATAC.Rmd, lines 535–667 · score 0.58 · pySCENIC, transcription factor, AUC, repressive, regulons, log2
  23. [23] § Results › Downstream regulators drive human oligodendrocytic maturation and myelination in vivo ↔ Scripts/19_Invivo_Analysis.Rmd, lines 329–388 · score 0.58 · regulon target, transcription factor, maOL, imOL, TF, networks
  24. [24] § Methods › Pseudotime analysis ↔ Scripts/19_Invivo_Analysis.Rmd, lines 673–719 · score 0.58 · fitGAM, regulon activity, slingshot, pseudotime, Gene expression, Scores
  25. [25] § Results › Human donor cells can be recovered from host brains as glia, without residual pluripotent stem cells ↔ Scripts/19_Invivo_Analysis.Rmd, lines 970–1022 · score 0.57 · SLC1A2, cAPC, MOBP, MKI67, AQP4, BCAS1
  26. [26] § Results › In vitro GPC differentiation yields compositions free of pluripotent signatures ↔ Scripts/07_PSC_vs_GPC_Figure_Assembly.Rmd, lines 111–164 · score 0.57 · co expressed, LIN28A, POU5F1, PSCs, vitro, GPC
  27. [27] § Results › In vitro GPC differentiation yields compositions free of pluripotent signatures ↔ Scripts/07_PSC_vs_GPC_Figure_Assembly.Rmd, lines 111–164 · score 0.56 · PSC stage, LIN28A, POU5F1, GPC stage, vitro, cells
  28. [28] § Methods › Xenium processing and analysis ↔ Scripts/27_Xenium_Read_and_Crop.ipynb, lines 221–234 · score 0.55 · interactive session, engrafted human cells, napari, Xenium, WA09, subset
  29. [29] § Results › Predictive trajectory modeling of transplanted hGPC differentiation trajectories ↔ Scripts/14_Invitro_Analysis.Rmd, lines 496–506 · score 0.55 · BCL11B, mature NPCs, MEIS2, SYT1
  30. [30] § Methods › Alignment and preprocessing of scRNA-seq samples ↔ Scripts/24_NicheNet_Processing.Rmd, lines 193–242 · score 0.54 · dual species, Seurat, human genes, mouse, cell
  31. [31] § Methods › FACS isolation of chimeric white matter cells for scRNA-Seq ↔ Scripts/01_Processing.Rmd, lines 29–67 · score 0.51 · tagged C27s, EGFP, mouse, human, cells
  32. [32] § Methods › Pseudotime analysis ↔ Scripts/12_Invitro_Invivo_Processing.Rmd, lines 134–155 · score 0.51 · terminal cells, AGT, NSG2, DLX5, MOBP, palantir

Paper

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The authors' code

R Markdown · 833 lines · 36 KB · MIT · 5 matches

  1. ---
  2. title: "Differential NicheNet Analysis of in vivo and in vitro hGPCs"
  3. author: "John Mariani"
  4. date: "3/6/2023"
  5. output: github_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  10. knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
  12. ```
  14. ## Load in Libraries
  16. ```{r}
  20. library(dplyr)
  21. library(Seurat)
  22. library(devtools)
  23. library(patchwork)
  24. library(dplyr)
  25. library(SeuratDisk)
  26. library(biomaRt)
  27. library(Matrix)
  28. library(nichenetr)
  29. library(RColorBrewer)
  30. library(scPlottingTools)
  31. library(data.table)
  33. options(future.globals.maxSize = 16000 * 1024^2)
  35. ```
  37. ## Read in Human and Mouse Counts
  39. ```{r}
  41. source("Scripts/HelperFunctions.r")
  43. dualSpecies <- readRDS("output/RDS/finalNiche.rds")
  45. ```
  48. ## Read in NicheNetV2 Databases
  49. ```{r}
  51. ligand_target_matrix = readRDS("data_for_import/NicheNet/ligand_target_matrix_nsga2r_final.rds")
  52. lr_network = readRDS("data_for_import/NicheNet/lr_network_human_21122021.rds")
  53. lr_network = lr_network %>% mutate(bonafide = ! database %in% c("ppi_prediction","ppi_prediction_go"))
  54. lr_network = lr_network %>% dplyr::rename(ligand = from, receptor = to) %>% distinct(ligand, receptor, bonafide)
  56. weighted_networks = readRDS("data_for_import/NicheNet/weighted_networks_nsga2r_final.rds")
  58. ```
  60. ## Make counts to edit
  61. ```{r}
  63. dualSpecies$stage2 <- dualSpecies$stage
  64. dualSpecies$stage2 <- gsub(x = dualSpecies$stage2, pattern = "In Vitro - GPC Stage", replacement = "In Vitro")
  65. unique(dualSpecies$stage2)
  67. dualSpecies$cellStage <- paste0(dualSpecies$stage2, " ", dualSpecies$cellType)
  68. dualSpecies$cellStage <- gsub(x = dualSpecies$cellStage, pattern = "Immature Oligodendrocyte", replacement = "imOL")
  69. dualSpecies$cellStage <- gsub(x = dualSpecies$cellStage, pattern = "Mature Oligodendrocyte", replacement = "maOL")
  71. table(dualSpecies$cellStage)
  74. ```
  76. ## Showing ligand differences between in vitro cell states
  77. ```{r}
  79. ligands <- unique(lr_network$ligand)
  81. GPC1 <- read.delim("output/DE/GPC1.vs.Rest.sig.txt")
  82. GPC2 <- read.delim("output/DE/GPC2.vs.Rest.sig.txt")
  83. GPC3 <- read.delim("output/DE/GPC3.vs.Rest.sig.txt")
  84. GPC4 <- read.delim("output/DE/GPC4.vs.Rest.sig.txt")
  85. NPC <- read.delim("output/DE/NPC.vs.Rest.sig.txt")
  87. invitroStateMarkers <- rbind(GPC1, GPC2, GPC3, GPC4, NPC)
  88. invitroStateLigands <- invitroStateMarkers[invitroStateMarkers$gene %in% ligands,]
  89. invitroStateLigands <- invitroStateLigands[!duplicated(invitroStateLigands$gene),]
  92. ```
  96. ```{r, warning=F}
  98. niches = list(
  99. "In Vivo" = list(
  100. "sender" = c("In Vivo Astrocyte","In Vivo imOL", "In Vivo maOL", "In Vivo Mouse Astrocyte", "In Vivo Mouse Endothelial", "In Vivo Mouse Ependymal", "In Vivo Mouse Macrophage", "In Vivo Mouse GPC", "In Vivo Mouse imOL", "In Vivo Mouse maOL", "In Vivo Mouse Microglia", "In Vivo Mouse NPC", "In Vivo Mouse Pericyte"),
  101. "receiver" = c("In Vivo GPC4")),
  102. "In Vitro" = list(
  103. "sender" = c("In Vitro GPC1","In Vitro GPC2", "In Vitro GPC3", "In Vitro NPC"),
  104. "receiver" = c("In Vitro GPC4"))
  105. )
  109. Idents(dualSpecies) <- dualSpecies$cellStage
  111. one2oneGenes <- read.csv("data_for_import/NicheNet/one2oneGenes.csv")
  113. #DE_sender = calculate_niche_de(seurat_obj = dualSpecies %>% subset(features = lr_network$ligand %>% intersect(one2oneGenes$x)), niches = niches, type = "sender", assay_oi = "RNA") # only ligands important for sender cell types
  116. #write.csv(DE_sender, "output/NicheNet/DE_sender.csv", quote = F, row.names = F)
  118. DE_sender <- read.csv("output/NicheNet/DE_sender.csv")
  119. DE_sender$sender <- gsub(x = DE_sender$sender, pattern = "Immature Oligodendrocyte", replacement = "imOL")
  120. DE_sender$sender_other_niche <- gsub(x = DE_sender$sender_other_niche, pattern = "Immature Oligodendrocyte", replacement = "imOL")
  121. DE_sender$sender <- gsub(x = DE_sender$sender, pattern = "Mature Oligodendrocyte", replacement = "maOL")
  122. DE_sender$sender_other_niche <- gsub(x = DE_sender$sender_other_niche, pattern = "Mature Oligodendrocyte", replacement = "maOL")
  123. DE_sender$sender <- gsub(x = DE_sender$sender, pattern = "Immature Oligo", replacement = "imOL")
  124. DE_sender$sender_other_niche <- gsub(x = DE_sender$sender_other_niche, pattern = "Immature Oligo", replacement = "imOL")
  125. DE_sender$sender <- gsub(x = DE_sender$sender, pattern = "Mature Oligo", replacement = "maOL")
  126. DE_sender$sender_other_niche <- gsub(x = DE_sender$sender_other_niche, pattern = "Mature Oligo", replacement = "maOL")
  129. deGPC4 <- read.delim("output/DE/Invivo.vs.Invitro.GPC4.txt")
  130. deGPC4.sig <- read.delim("output/DE/Invivo.vs.Invitro.GPC4.sig.txt")
  131. deGPC4 <- deGPC4[complete.cases(deGPC4),]
  132. deGPC4 <- rbind(deGPC4, deGPC4.sig[deGPC4.sig$gene %not in% deGPC4$gene,])
  134. #Format Prior Differential Expression for receiver cells
  136. pct.expr <- DotPlot(dualSpecies, features = deGPC4$gene, idents = c("In Vivo GPC4", "In Vitro GPC4"), group.by = "cellStage")$data
  137. pct.expr.invivo <- pct.expr[pct.expr$id == "In Vivo GPC4",]
  138. pct.expr.invitro <- pct.expr[pct.expr$id == "In Vitro GPC4",]
  141. DE_receiver_invivo <- data.frame(gene = deGPC4$gene, p_val = deGPC4$FDR, avg_log2FC = deGPC4$logFC, pct.1 = pct.expr.invivo$pct.exp, pct.2 = pct.expr.invitro$pct.exp, p_val_adj = deGPC4$FDR, receiver = "In Vivo GPC4", receiver_other_niche = "In Vitro GPC4")
  143. DE_receiver_invitro <- data.frame(gene = deGPC4$gene, p_val = deGPC4$FDR, avg_log2FC = (deGPC4$logFC*-1), pct.1 = pct.expr.invitro$pct.exp, pct.2 = pct.expr.invivo$pct.exp, p_val_adj = deGPC4$FDR, receiver = "In Vitro GPC4", receiver_other_niche = "In Vivo GPC4")
  146. DE_receiver <- rbind(DE_receiver_invivo, DE_receiver_invitro)
  149. DE_receiver_receptors <- DE_receiver[DE_receiver$gene %in% lr_network$receptor,]
  151. #DE_receiver = calculate_niche_de(seurat_obj = dualSpecies %>% subset(features = lr_network$receptor %>% unique()), niches = niches, type = "receiver", assay_oi = "RNA") # only receptors now, later on: DE analysis to find targets
  153. DE_sender = DE_sender %>% mutate(avg_log2FC = ifelse(avg_log2FC == Inf, max(avg_log2FC[is.finite(avg_log2FC)]), ifelse(avg_log2FC == -Inf, min(avg_log2FC[is.finite(avg_log2FC)]), avg_log2FC)))
  155. DE_receiver_receptors = DE_receiver_receptors %>% mutate(avg_log2FC = ifelse(avg_log2FC == Inf, max(avg_log2FC[is.finite(avg_log2FC)]), ifelse(avg_log2FC == -Inf, min(avg_log2FC[is.finite(avg_log2FC)]), avg_log2FC)))
  160. ```
  162. ```{r}
  164. expression_pct = 10
  165. DE_sender_processed = process_niche_de(DE_table = DE_sender, niches = niches, expression_pct = expression_pct, type = "sender")
  166. DE_receiver_processed = process_niche_de(DE_table = DE_receiver_receptors, niches = niches, expression_pct = expression_pct, type = "receiver")
  168. specificity_score_LR_pairs = "min_lfc"
  169. DE_sender_receiver = combine_sender_receiver_de(DE_sender_processed, DE_receiver_processed, lr_network, specificity_score = specificity_score_LR_pairs)
  173. ```
  176. ```{r}
  178. lfc_cutoff = 0.25 # recommended for 10x as min_lfc cutoff.
  179. specificity_score_targets = "min_lfc"
  181. #DE_receiver_targets = calculate_niche_de_targets(seurat_obj = dualSpecies, niches = niches, lfc_cutoff = lfc_cutoff, expression_pct = expression_pct, assay_oi = "RNA")
  183. DE_receiver_processed_targets = process_receiver_target_de(DE_receiver = DE_receiver, niches = niches, expression_pct = expression_pct, specificity_score = specificity_score_targets)
  185. background = DE_receiver_processed_targets %>% pull(target) %>% unique()
  187. geneset_InVivo = DE_receiver_processed_targets %>% filter(receiver == niches$`In Vivo`$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>% pull(target) %>% unique()
  190. geneset_Invitro = DE_receiver_processed_targets %>% filter(receiver == niches$`In Vitro`$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>% pull(target) %>% unique()
  192. geneset_InVivo %>% setdiff(rownames(ligand_target_matrix))
  193. geneset_Invitro %>% setdiff(rownames(ligand_target_matrix))
  197. ```
  199. ```{r, warning=F}
  201. top_n_target = 1000
  203. niche_geneset_list = list(
  204. "In_Vivo_niche" = list(
  205. "receiver" = "In Vivo GPC4",
  206. "geneset" = geneset_InVivo,
  207. "background" = background),
  208. "In_Vitro_niche" = list(
  209. "receiver" = "In Vitro GPC4",
  210. "geneset" = geneset_Invitro ,
  211. "background" = background))
  213. ligand_activities_targets = get_ligand_activities_targets(niche_geneset_list = niche_geneset_list, ligand_target_matrix = ligand_target_matrix, top_n_target = top_n_target)
  215. #write.table(ligand_activities_targets, "output/NicheNet/ligand_activities_targets.txt", sep = "\t", row.names = F, quote = F)
  218. ```
  220. ```{r}
  222. features_oi = union(lr_network$ligand, lr_network$receptor) %>% union(ligand_activities_targets$target) %>% setdiff(NA)
  224. dotplot = suppressWarnings(Seurat::DotPlot(dualSpecies %>% subset(idents = niches %>% unlist() %>% unique()), features = features_oi, assay = "RNA"))
  226. exprs_tbl = dotplot$data %>% as_tibble()
  227. exprs_tbl = exprs_tbl %>% dplyr::rename(celltype = id, gene = features.plot, expression = avg.exp, expression_scaled = avg.exp.scaled, fraction = pct.exp) %>%
  228. mutate(fraction = fraction/100) %>% as_tibble() %>% dplyr::select(celltype, gene, expression, expression_scaled, fraction) %>% distinct() %>% arrange(gene) %>% mutate(gene = as.character(gene))
  230. exprs_tbl_ligand = exprs_tbl %>% filter(gene %in% lr_network$ligand) %>% dplyr::rename(sender = celltype, ligand = gene, ligand_expression = expression, ligand_expression_scaled = expression_scaled, ligand_fraction = fraction)
  232. exprs_tbl_receptor = exprs_tbl %>% filter(gene %in% lr_network$receptor) %>% dplyr::rename(receiver = celltype, receptor = gene, receptor_expression = expression, receptor_expression_scaled = expression_scaled, receptor_fraction = fraction)
  234. exprs_tbl_target = exprs_tbl %>% filter(gene %in% ligand_activities_targets$target) %>% dplyr::rename(receiver = celltype, target = gene, target_expression = expression, target_expression_scaled = expression_scaled, target_fraction = fraction)
  236. exprs_tbl_ligand = exprs_tbl_ligand %>% mutate(scaled_ligand_expression_scaled = scale_quantile_adapted(ligand_expression_scaled)) %>% mutate(ligand_fraction_adapted = ligand_fraction) %>% mutate_cond(ligand_fraction >= expression_pct, ligand_fraction_adapted = expression_pct) %>% mutate(scaled_ligand_fraction_adapted = scale_quantile_adapted(ligand_fraction_adapted))
  238. exprs_tbl_receptor = exprs_tbl_receptor %>% mutate(scaled_receptor_expression_scaled = scale_quantile_adapted(receptor_expression_scaled)) %>% mutate(receptor_fraction_adapted = receptor_fraction) %>% mutate_cond(receptor_fraction >= expression_pct, receptor_fraction_adapted = expression_pct) %>% mutate(scaled_receptor_fraction_adapted = scale_quantile_adapted(receptor_fraction_adapted))
  240. ```
  243. ```{r}
  245. exprs_sender_receiver = lr_network %>%
  246. inner_join(exprs_tbl_ligand, by = c("ligand")) %>%
  247. inner_join(exprs_tbl_receptor, by = c("receptor")) %>% inner_join(DE_sender_receiver %>% distinct(niche, sender, receiver))
  249. ligand_scaled_receptor_expression_fraction_df = exprs_sender_receiver %>% group_by(ligand, receiver) %>% mutate(rank_receptor_expression = dense_rank(receptor_expression), rank_receptor_fraction = dense_rank(receptor_fraction)) %>% mutate(ligand_scaled_receptor_expression_fraction = 0.5*( (rank_receptor_fraction / max(rank_receptor_fraction)) + ((rank_receptor_expression / max(rank_receptor_expression))) ) ) %>% distinct(ligand, receptor, receiver, ligand_scaled_receptor_expression_fraction, bonafide) %>% distinct() %>% ungroup()
  251. # No Spatial info
  252. spatial_info = tibble(celltype_region_oi = NA, celltype_other_region = NA) %>% mutate(niche = niches %>% names() %>% head(1), celltype_type = "sender")
  255. ```
  257. ## No Spatial info
  258. ```{r}
  261. include_spatial_info_sender = F # if not spatial info to include: put this to false
  262. include_spatial_info_receiver = FALSE # if spatial info to include: put this to true
  265. if(include_spatial_info_sender == FALSE & include_spatial_info_receiver == FALSE){
  266. spatial_info = tibble(celltype_region_oi = NA, celltype_other_region = NA) %>% mutate(niche = niches %>% names() %>% head(1), celltype_type = "sender")
  267. }
  270. if(include_spatial_info_sender == TRUE){
  271. sender_spatial_DE = calculate_spatial_DE(seurat_obj = seurat_obj %>% subset(features = lr_network$ligand %>% unique()), spatial_info = spatial_info %>% filter(celltype_type == "sender"), assay_oi = assay_oi)
  272. sender_spatial_DE_processed = process_spatial_de(DE_table = sender_spatial_DE, type = "sender", lr_network = lr_network, expression_pct = expression_pct, specificity_score = specificity_score_spatial)
  274. # add a neutral spatial score for sender celltypes in which the spatial is not known / not of importance
  275. sender_spatial_DE_others = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "sender", lr_network = lr_network)
  276. sender_spatial_DE_processed = sender_spatial_DE_processed %>% bind_rows(sender_spatial_DE_others)
  278. sender_spatial_DE_processed = sender_spatial_DE_processed %>% mutate(scaled_ligand_score_spatial = scale_quantile_adapted(ligand_score_spatial))
  280. } else {
  281. # # add a neutral spatial score for all sender celltypes (for none of them, spatial is relevant in this case)
  282. sender_spatial_DE_processed = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "sender", lr_network = lr_network)
  283. sender_spatial_DE_processed = sender_spatial_DE_processed %>% mutate(scaled_ligand_score_spatial = scale_quantile_adapted(ligand_score_spatial))
  285. }
  287. if(include_spatial_info_receiver == TRUE){
  288. receiver_spatial_DE = calculate_spatial_DE(seurat_obj = seurat_obj %>% subset(features = lr_network$receptor %>% unique()), spatial_info = spatial_info %>% filter(celltype_type == "receiver"), assay_oi = assay_oi)
  289. receiver_spatial_DE_processed = process_spatial_de(DE_table = receiver_spatial_DE, type = "receiver", lr_network = lr_network, expression_pct = expression_pct, specificity_score = specificity_score_spatial)
  291. # add a neutral spatial score for receiver celltypes in which the spatial is not known / not of importance
  292. receiver_spatial_DE_others = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "receiver", lr_network = lr_network)
  293. receiver_spatial_DE_processed = receiver_spatial_DE_processed %>% bind_rows(receiver_spatial_DE_others)
  295. receiver_spatial_DE_processed = receiver_spatial_DE_processed %>% mutate(scaled_receptor_score_spatial = scale_quantile_adapted(receptor_score_spatial))
  297. } else {
  298. # # add a neutral spatial score for all receiver celltypes (for none of them, spatial is relevant in this case)
  299. receiver_spatial_DE_processed = get_non_spatial_de(niches = niches, spatial_info = spatial_info, type = "receiver", lr_network = lr_network)
  300. receiver_spatial_DE_processed = receiver_spatial_DE_processed %>% mutate(scaled_receptor_score_spatial = scale_quantile_adapted(receptor_score_spatial))
  301. }
  305. ```
  308. ```{r}
  310. prioritizing_weights = c("scaled_ligand_score" = 5,
  311. "scaled_ligand_expression_scaled" = 1,
  312. "ligand_fraction" = 1,
  313. "scaled_ligand_score_spatial" = 0,
  314. "scaled_receptor_score" = 0.5,
  315. "scaled_receptor_expression_scaled" = 0.5,
  316. "receptor_fraction" = 1,
  317. "ligand_scaled_receptor_expression_fraction" = 1,
  318. "scaled_receptor_score_spatial" = 0,
  319. "scaled_activity" = 0,
  320. "scaled_activity_normalized" = 1,
  321. "bona_fide" = 1)
  324. output = list(DE_sender_receiver = DE_sender_receiver, ligand_scaled_receptor_expression_fraction_df = ligand_scaled_receptor_expression_fraction_df, sender_spatial_DE_processed = sender_spatial_DE_processed, receiver_spatial_DE_processed = receiver_spatial_DE_processed,
  325. ligand_activities_targets = ligand_activities_targets, DE_receiver_processed_targets = DE_receiver_processed_targets, exprs_tbl_ligand = exprs_tbl_ligand, exprs_tbl_receptor = exprs_tbl_receptor, exprs_tbl_target = exprs_tbl_target)
  327. prioritization_tables = get_prioritization_tables(output, prioritizing_weights)
  329. prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[1]]$receiver) %>% head(10)
  330. prioritization_tables$prioritization_tbl_ligand_receptor %>% filter(receiver == niches[[2]]$receiver) %>% head(10)
  335. prioritization_tables$prioritization_tbl_ligand_receptor = prioritization_tables$prioritization_tbl_ligand_receptor %>% mutate(receiver = factor(receiver, levels = c("In Vivo GPC4", "In Vitro GPC4")), niche = factor(niche, levels = c("In Vivo","In Vitro")))
  337. prioritization_tables$prioritization_tbl_ligand_target = prioritization_tables$prioritization_tbl_ligand_target %>% mutate(receiver = factor(receiver, levels = c("In Vivo GPC4", "In Vitro GPC4")), niche = factor(niche, levels = c("In Vivo", "In Vitro")))
  341. ```
  343. ## Ligand Activities for picking targets
  345. ```{r}
  348. ligandActivities <- ligand_activities_targets[!duplicated(ligand_activities_targets[c("ligand", "receiver")]),]
  349. ligandActivities <- ligandActivities[,-c(4:5)]
  351. ligandActivitiesInVivo <- ligandActivities[ligandActivities$receiver == "In Vivo GPC4",]
  352. ligandActivitiesInVitro <- ligandActivities[ligandActivities$receiver == "In Vitro GPC4",]
  354. ligandActivitiesInVivo <- ligandActivitiesInVivo[,-4]
  355. ligandActivitiesInVitro <- ligandActivitiesInVitro[,-4]
  358. names(ligandActivitiesInVivo)[2:6] <- paste(names(ligandActivitiesInVivo)[2:6], "InVivo", sep = "_")
  359. names(ligandActivitiesInVitro)[2:6] <- paste(names(ligandActivitiesInVitro)[2:6], "InVitro", sep = "_")
  361. ligandActivities <- merge(ligandActivitiesInVivo, ligandActivitiesInVitro, by.x = 1, by.y = 1)
  362. ligandActivities <- ligandActivities[,c(1,2,7,3,8,4,9,5,10,6,11)]
  364. ligandActivities$activity_normalized_diff <- ligandActivities$activity_normalized_InVivo - ligandActivities$activity_normalized_InVitro
  365. ligandActivities$activity_normalized_div <- ligandActivities$activity_normalized_InVivo / ligandActivities$activity_normalized_InVitro
  368. # ligandActivities$aupr_corrected_diff <- ligandActivities$aupr_corrected_InVivo - ligandActivities$aupr_corrected_InVitro
  369. # ligandActivities$aupr_corrected_div <- ligandActivities$aupr_corrected_InVivo / ligandActivities$aupr_corrected_InVitro
  370. #
  371. # ligandActivities$activity_diff <- ligandActivities$activity_InVivo - ligandActivities$activity_InVitro
  372. # ligandActivities$activity_div <- ligandActivities$activity_InVivo / ligandActivities$activity_InVitro
  373. #
  374. #
  375. # ligandActivities$scaled_activity_normalized_diff <- ligandActivities$scaled_activity_normalized_InVivo - ligandActivities$scaled_activity_normalized_InVitro
  376. # ligandActivities$scaled_activity_normalized_div <- ligandActivities$scaled_activity_normalized_InVivo / ligandActivities$scaled_activity_normalized_InVitro
  378. ligandActivities <- ligandActivities[order(ligandActivities$activity_normalized_diff, decreasing = T),]
  380. pri <- prioritization_tables$prioritization_tbl_ligand_receptor
  382. priInvivo <- pri[pri$niche == "In Vivo",]
  383. priInvivo <- priInvivo[priInvivo$receptor_fraction > .1,]
  384. priInvivo <- priInvivo[priInvivo$ligand %in% ligandActivities[ligandActivities$activity_normalized_diff > 0.6,]$ligand,]
  385. priInvivo <- priInvivo[order(priInvivo$ligand_score, decreasing = T),]
  386. #priInvivo <- priInvivo[priInvivo$receptor %in% deGPC4[deGPC4$logFC > 0,]$gene,]
  388. ligandActivitiesInvivoReceptor <- ligandActivities[ligandActivities$ligand %in% priInvivo$ligand,]
  389. ligandActivitiesInvivoReceptor <- ligandActivitiesInvivoReceptor[ligandActivitiesInvivoReceptor$activity_normalized_diff > 0.6,]
  391. # Differentially regulated in vitro ligands
  392. ligandActivitiesInvivoReceptor[ligandActivitiesInvivoReceptor$ligand %in% invitroStateLigands$gene,]
  395. ```
  398. ## Ligand Activity and Expression for plots
  399. ```{r}
  401. plotting_tbl <- exprs_tbl
  403. plotting_tbl$stage <- ifelse(grepl("Vivo",plotting_tbl$celltype),"In Vivo","In Vitro")
  404. plotting_tbl$species <- ifelse(grepl("Mouse",plotting_tbl$celltype),"Mouse","Human")
  405. plotting_tbl$ct <- gsub(pattern = "In Vitro ", replacement = "", plotting_tbl$celltype)
  406. plotting_tbl$ct <- gsub(pattern = "In Vivo ", replacement = "", plotting_tbl$ct)
  407. plotting_tbl$ct <- gsub(pattern = "Mouse ", replacement = "", plotting_tbl$ct)
  408. unique(plotting_tbl$ct)
  410. temp <- unique(plotting_tbl$ct)
  411. temp
  412. names(temp) <- c("imOL", "maOL", "Astrocyte", "GPC1", "GPC2", "NPC", "GPC3", "GPC", "Microglia", "Ependymal", "Endothelial", "Pericyte", "Macrophage", "GPC4")
  414. temp
  415. plotting_tbl$ctRenamed <- plyr::mapvalues(plotting_tbl$ct, from = temp, to = names(temp))
  417. unique(plotting_tbl$ctRenamed)
  419. plotting_tbl$ctRenamed <- factor(plotting_tbl$ctRenamed, levels = c("GPC1", "GPC2", "GPC3", "GPC4", "GPC", "imOL", "maOL", "Astrocyte", "Microglia", "Macrophage", "NPC", "Ependymal", "Endothelial", "Pericyte"))
  421. write.table(plotting_tbl, "output/NicheNet/plotting_tbl.txt", row.names = F, quote = F, sep = "\t")
  423. ligandsCurated <- c("PDGFA", "OSM", "IL1A", "CSF1", "EGF", "TNF", "EBI3", "IL1B", "NOG", "SLITRK1", "TNFSF10", "GHRH", "TGFB1", "GAS6", "ICAM1", "EDN1", "CCN2", "DSCAM", "MMP13", "ADM", "APOE", "OCLN", "HBEGF", "FGF1")
  425. #ligandsCurated <- ligandActivitiesInvivoReceptor$ligand[81:95]
  431. # ligandsActivity <- c("SLITRK1", "PDGFA", "OSM", "LRRC4", "IL1A", "IFNK", "EGF", "EBI3", "CSF1", "CMTM8", "CLCF1",
  432. # "TNFSF10", "TNF", "NOG", "GAS6", "DSCAM", "DKK3", "BDNF")
  433. #
  434. # ligandsScore <- c("APOE", "CCN2", "DKK3", "EBI3", "GAS6", "ICAM1", "IL1A", "OCLN", "OSM", "PDGFA", "PGF", "TNFSF10", "FGF1",)
  435. #
  436. # ligandsCurated <- unique(c(ligandsActivity, ligandsScore))
  441. ```
  443. ```{r}
  445. ligandOrder <- rev(ligandActivities[ligandActivities$ligand %in% ligandsCurated,]$ligand)
  446. ligandPlot <- plotting_tbl[plotting_tbl$gene %in% ligandsCurated,]
  447. ligandPlot$gene <- factor(ligandPlot$gene, levels = ligandOrder)
  449. activityPlot <- ligandActivities[ligandActivities$ligand %in% ligandsCurated,]
  450. activityPlot$ligand <- factor(activityPlot$ligand, levels = ligandOrder)
  454. ```
  459. ## Ligand Activity and Expression plot
  460. ```{r,fig.width = 10.5, fig.height=10}
  462. ligandActivityGG <- ggplot(activityPlot, aes(x = "In Vivo", y = ligand, fill = scale(activity_normalized_diff))) +
  463. geom_tile(colour = "black") +
  464. viridis::scale_fill_viridis(option = "D", expand = c(0,0)) +
  465. scale_x_discrete(expand = c(0,0)) +
  466. scale_y_discrete(expand = c(0,0)) +
  467. theme_bw() +
  468. theme_manuscript +
  469. theme(axis.title = element_blank(), legend.title = element_blank(), legend.position = "left", axis.text.y = element_blank()) +
  470. labs(title = "Ligand Activity", tag = "C")
  473. ligandExpressionGG <- ggplot(ligandPlot, aes(x = ctRenamed, y = gene, colour = expression_scaled, size = ifelse(fraction==0, NA, fraction))) +
  474. geom_point() +
  475. viridis::scale_color_viridis(option = "D") +
  476. facet_grid(rows = ~species + stage, scales = "free", space = "free") +
  477. theme_bw() +
  478. theme_manuscript +
  479. theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5), legend.position = "bottom", axis.title = element_blank(), legend.title = element_blank()) +
  480. scale_size(range = c(0,4)) +
  481. labs(title = "Ligand Expression", tag = "D")
  484. (ligandActivityGG | ligandExpressionGG) + plot_layout(widths = c(.5,10))
  486. ```
  489. ## Circos
  490. ```{r}
  492. curatedCircos <- c("PDGFA", "IL1A", "OSM", "EBI3", "TGFB1", "TNF", "MMP13", "GAS6", "HBEGF", "ICAM1", "IL1B", "APOE", "EGF", "CSF1", "NOG", "ADM", "TNFSF10", "OCLN", "EDN1", "ADM")
  494. curatedCircos <- c("PDGFA", "OSM", "IL1A", "EBI3", "EGF", "CSF1", "TGFB1", "TNF", "ICAM1", "OCLN")
  496. tempPri <- prioritization_tables$prioritization_tbl_ligand_receptor
  497. tempPri <- tempPri[tempPri$sender %not in% c("In Vivo Mouse NPC", "In Vivo imOL", "In Vivo maOL", "In Vivo Mouse GPC", "In Vivo Mouse Ependymal", "In Vivo Astrocyte"),]
  499. tempPri <- tempPri[tempPri$niche == "In Vivo",]
  500. tempPri <- tempPri[tempPri$receptor_fraction > .1,]
  503. top_ligand_receptor_niche_df = tempPri %>% dplyr::select(niche, sender, receiver, ligand, receptor, prioritization_score) %>% group_by(ligand, receptor) %>% top_n(1, prioritization_score) %>% ungroup() %>% dplyr::select(ligand, receptor, niche)
  506. prioritized_tbl_oi = tempPri %>% dplyr::filter(ligand %in% curatedCircos) %>% dplyr::select(niche, sender, receiver, ligand, receptor, ligand_receptor, prioritization_score) %>% distinct() %>% inner_join(top_ligand_receptor_niche_df) %>% group_by(ligand) %>% dplyr::filter(receiver == "In Vivo GPC4") %>% top_n(2, prioritization_score) %>% ungroup()
  508. unique(prioritized_tbl_oi$sender)
  510. colors_sender <- c("#00BF7D","#D89000", "#E76BF3", "#00B0F6", "#00BFC4", "#39B600", "#A3A500")
  511. names(colors_sender) <- unique(prioritized_tbl_oi$sender)
  513. colors_receiver <- "turquoise"
  514. names(colors_receiver) <- unique(prioritized_tbl_oi$receiver)
  517. circos_output = make_circos_lr(prioritized_tbl_oi, colors_sender, colors_receiver)
  519. unique(prioritized_tbl_oi$sender)
  524. circos_output = make_circos_lr(prioritized_tbl_oi, colors_sender, colors_receiver)
  527. ```
  528. ## Receptors for Supp Table
  529. ```{r}
  531. receptorSup <- pri
  532. receptorSup <- receptorSup[,c(2,5,6,18,20)]
  533. receptorSup <- receptorSup[!duplicated(receptorSup),]
  534. receptorSup <- receptorSup[order(receptorSup$receptor, receptorSup$ligand),]
  536. #write.table(receptorSup, "output/NicheNet/receptorSuppTable.txt", quote = F, row.names = F, sep = "\t")
  538. ```
  541. ## Receptor Expression for plotting
  543. ```{r}
  545. ##
  546. tempDF <- pri[pri$ligand %in% ligandsCurated,]
  547. length(unique(tempDF$receptor))
  548. tempDF <- tempDF[,c(1, 4:6, 18:20)]
  549. tempDF <- tempDF[!duplicated(tempDF),]
  550. unique(tempDF$receptor)
  552. tempDF <- tempDF[tempDF$receptor %in% priInvivo$receptor,]
  554. tempDF$niche <- factor(tempDF$niche, levels = c("In Vivo", "In Vitro"))
  558. #activityPlot <- activityPlot[activityPlot$ligand %not in% ligandRemove,]
  559. #ligandPlot <- ligandPlot[ligandPlot$gene %not in% ligandRemove,]
  561. tempDF$ligand_receptor <- factor(tempDF$ligand_receptor, levels = unique(tempDF[order(tempDF$ligand, tempDF$receptor),]$ligand_receptor))
  563. tempDF$receptor_fraction <- tempDF$receptor_fraction * 100
  566. ```
  568. ## Receptor Plot
  569. ```{r, fig.width = 15, fig.height = 6}
  571. receptorGG <- ggplot(tempDF, aes(y = niche, x = ligand_receptor, fill = receptor_expression_scaled, size = ifelse(receptor_fraction==0, NA, receptor_fraction))) +
  572. geom_point(color = "black", pch = 21) +
  573. theme_bw() +
  574. theme_manuscript +
  575. scale_fill_gradientn(colors = PurpleAndYellow()) +
  576. theme(legend.position = "right", panel.spacing=unit(0, "lines"), axis.title = element_blank(), axis.text = element_text(angle = 90, hjust = 1)) +
  577. scale_size(range = c(0,20)) +
  578. labs(tag = "C", title = "In Vivo and In Vitro GPC Receptor Expression of Curated Ligands", size = "% Expressed", fill = "Scaled Expression") +
  579. guides(colour = guide_colorbar(title.position = "top", title.theme = element_text(size = axisTitleSize)),
  580. size = guide_legend(title.position = "top", title.theme = element_text(size = axisTitleSize)))
  582. receptorGG
  584. ```
  586. ## Combined Plots
  587. ```{r, fig.width = 8.5, fig.height = 11}
  588. mouse <- readRDS("output/RDS/mouse.rds")
  590. mouseDim <- DimPlotCustom(mouse, group.by = "cellType", label = T) + theme_bw() + theme_manuscript + theme(legend.position = "bottom") + labs(tag = "A")
  592. # This will limit which come from the next function
  593. mouseMarkers <- c("Pdgfra", "Ptprz1",
  594. "Gpr17", "Bcas1",
  595. "Nkx6-2", "Mog",
  596. "Gfap", "Aqp4",
  597. "P2ry12", "Itgam",
  598. "Cldn5", "Pecam1",
  599. "Acta2", "Des",
  600. "Dlx2", "Elavl4",
  601. "Pf4", "Cd163",
  602. "Tmem212", "Ccdc153")
  604. #You can extract percent expression from this seurat function... providing features makes it way faster. $data is what you want
  605. mouseDotPlot <- DotPlot(mouse, features = mouseMarkers)$data
  607. # For ordering in the plot
  608. mouseLevels <- c("GPC", "imOL", "maOL", "Astrocyte", "Microglia", "Endothelial", "Pericyte", "NPC", "Macrophage", "Ependymal")
  609. mouseDotPlot$id <- factor(mouseDotPlot$id , levels = rev(mouseLevels))
  615. figMouseB <- ggplot(mouseDotPlot, aes(size = pct.exp, color = avg.exp.scaled, y = id, x = features.plot)) +
  616. geom_point() +
  617. scale_size_area() +
  618. viridis::scale_color_viridis() +
  619. theme_bw() +
  620. theme_manuscript +
  621. theme(axis.title = element_blank(), axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5), legend.position = "bottom") +
  622. labs(tag = "B", title = "Canonical Marker Expression", size = "% Expressed", colour = "Scaled Expression") +
  623. guides(colour = guide_colorbar(title.position = "top", title.theme = element_text(size = axisTitleSize)),
  624. size = guide_legend(title.position = "top", title.theme = element_text(size = axisTitleSize))) +
  625. scale_size(range = c(0,4))
  627. figMouseB
  629. top <- (mouseDim | figMouseB) + plot_layout(widths = c(1,1.5))
  632. middle <- (ligandActivityGG | ligandExpressionGG) + plot_layout(widths = c(.5,10))
  634. ```
  637. ```{r, fig.width = 17, fig.height = 26}
  638. (top / middle / receptorGG )
  640. #ggsave("output/Figures/Nichenet/nichenet_figure.pdf", width = 8.5, height = 12)
  644. ```
  648. ## Ligand, receptor, target Network construction
  651. ```{r}
  653. invivoNetwork <- ligand_activities_targets[ligand_activities_targets$ligand %in% tempDF$ligand,]
  654. invivoNetwork <- invivoNetwork[invivoNetwork$receiver == "In Vivo GPC4",]
  656. sigRegulons <- read.csv("output/DE/sigRegulons.csv")
  657. sigRegulons <- sigRegulons[sigRegulons$Gene_Log2FC >0 & sigRegulons$AUC_Log2FC > 0,]
  660. oligoTargets <- c("KLF6", "CCND1", "MBP", "CSPG4", "SULF2", "PCDH9", "BCAS1", "GPR17", "SEMA3E", "CA10", "OMG", "PLP1", "BAMBI", "CSPG5", "S100B", "APOD", "NRXN1", "TNR", "PLLP", "CNP", "CNTN1", "SOX10", "OLIG1", "OLIG2", "NKX2-2","PTPRZ1", "LUZP2" )
  662. regulonEdges <- invivoNetwork[invivoNetwork$target %in% sigRegulons$Gene,]
  663. regulonEdges$type <- "Regulon"
  664. regulonEdges$tempLigand <- paste0(regulonEdges$ligand, "_", regulonEdges$type)
  665. regulonEdges$tempTarget <- paste0(regulonEdges$target, "_", regulonEdges$type)
  666. regulonNodes <- data.frame(node = unique(c(regulonEdges$ligand, regulonEdges$target)),
  667. network = "Regulon")
  670. oligoEdges <- invivoNetwork[invivoNetwork$target %in% oligoTargets,]
  671. oligoEdges$type <- "Oligo"
  672. oligoEdges$tempLigand <- paste0(oligoEdges$ligand, "_", oligoEdges$type)
  673. oligoEdges$tempTarget <- paste0(oligoEdges$target, "_", oligoEdges$type)
  674. oligoNodes <- data.frame(node = unique(c(oligoEdges$ligand, oligoEdges$target)),
  675. network = "Oligo")
  678. receptorEdges <- invivoNetwork[invivoNetwork$target %in% tempDF$receptor,]
  679. receptorEdges$type <- "Receptor"
  680. receptorEdges$tempLigand <- paste0(receptorEdges$ligand, "_", receptorEdges$type)
  681. receptorEdges$tempTarget <- paste0(receptorEdges$target, "_", receptorEdges$type)
  682. receptorNodes <- data.frame(node = unique(c(receptorEdges$ligand, receptorEdges$target)),
  683. network = "Receptor")
  685. invivoNetworkFilt <- rbindlist(list(regulonEdges, oligoEdges, receptorEdges))
  686. invivoNetworkFilt <- invivoNetworkFilt[invivoNetworkFilt$ligand_target_weight > 0.05,]
  689. invivoNodes <- rbindlist(list(regulonNodes, oligoNodes, receptorNodes))
  691. invivoNodes$type <- "Gene Target"
  692. invivoNodes$type <- ifelse(invivoNodes$node %in% tempDF$receptor, "Receptor", invivoNodes$type)
  693. invivoNodes$type <- ifelse(invivoNodes$node %in% tempDF$ligand, "Ligand", invivoNodes$type)
  694. invivoNodes$type <- ifelse(invivoNodes$node %in% sigRegulons$Gene, "Regulon", invivoNodes$type)
  695. invivoNodes <- merge(invivoNodes, deGPC4.sig, by.x = "node", by.y = "gene", all.x = T)
  696. invivoNodes <- invivoNodes[,c(1,2,3,6)]
  697. invivoNodes$tempNode <- paste0(invivoNodes$node, "_", invivoNodes$network)
  698. invivoNodes
  700. TF_Functions <- read.csv("data_for_import/TF_Functions.csv")
  702. invivoNodes[invivoNodes$type == "Regulon" & invivoNodes$node %not in% c(TF_Functions$Activators, TF_Functions$Repressors),]
  704. invivoNodes <- invivoNodes[invivoNodes$node %not in% TF_Functions$Repressors,]
  706. invivoNetworkFilt <- invivoNetworkFilt[invivoNetworkFilt$target %not in% TF_Functions$Repressors,]
  709. #write.table(invivoNetworkFilt, "output/Networks/NicheNet/invivoNetworkNichenet.txt", sep = "\t", quote = F, row.names = F)
  710. #write.table(invivoNodes, "output/Networks/NicheNet/invivoNodesNichenet.txt", sep = "\t", quote = F, row.names = F)
  714. ```
  716. # Supplementary
  717. ```{r}
  721. dimLeidenFig <- DimPlotCustom(mouse, group.by = "leidenClusters", ncol = 1, label = T, pt.size = 3) & theme_bw() & theme_manuscript & NoLegend() & ggtitle("In Vivo Mouse Leiden Clusters") & labs(tag = "A")
  723. dimLeidenFig
  725. ```
  728. ## Leiden Markers
  729. ```{r, fig.width = 11, fig.height = 10.5}
  731. DefaultAssay(mouse) <- "RNA"
  733. canonicalMarkers <- c("Pdgfra", "Ptprz1",
  734. "Gpr17", "Bcas1",
  735. "Nkx6-2", "Mog",
  736. "Gfap", "Aqp4",
  737. "P2ry12", "Itgam",
  738. "Cldn5", "Pecam1",
  739. "Acta2", "Des",
  740. "Dlx2", "Elavl4",
  741. "Pf4", "Cd163",
  742. "Tmem212", "Ccdc153")
  744. markerDotPlotSupp <- DotPlot(mouse, features = canonicalMarkers, group.by = "leidenClusters")$data
  746. leidenTemp <- c("0" = "maOL",
  747. "1" = "maOL",
  748. "2" = "maOL",
  749. "3" = "maOL",
  750. "4" = "maOL",
  751. "5" = "maOL",
  752. "6" = "maOL",
  753. "7" = "Endothelial",
  754. "8" = "maOL",
  755. "9" = "maOL",
  756. "10" = "maOL",
  757. "11" = "maOL",
  758. "12" = "maOL",
  759. "13" = "Microglia",
  760. "14" = "imOL",
  761. "15" = "GPC",
  762. "16" = "Astrocyte",
  763. "17" = "NPC",
  764. "18" = "Ependymal",
  765. "19" = "Pericyte",
  766. "20" = "Macrophage")
  769. leidenTemp <- paste0(0:20, " - ", leidenTemp)
  770. names(leidenTemp) <- 0:20
  772. markerDotPlotSupp$id <- plyr::mapvalues(as.character(markerDotPlotSupp$id), from = names(leidenTemp), to = leidenTemp)
  774. markerDotPlotSupp$id <- factor(markerDotPlotSupp$id, levels = rev(c("15 - GPC",
  775. "14 - imOL",
  776. "5 - maOL",
  777. "0 - maOL",
  778. "1 - maOL",
  779. "2 - maOL",
  780. "3 - maOL",
  781. "4 - maOL",
  782. "6 - maOL",
  783. "8 - maOL",
  784. "9 - maOL",
  785. "10 - maOL",
  786. "11 - maOL",
  787. "12 - maOL",
  788. "16 - Astrocyte",
  789. "13 - Microglia",
  790. "7 - Endothelial",
  791. "19 - Pericyte",
  792. "17 - NPC",
  793. "20 - Macrophage",
  794. "18 - Ependymal")))
  798. figSuppMarkerPlot<- ggplot(markerDotPlotSupp, aes(size = pct.exp, fill = avg.exp.scaled, y = id, x = features.plot)) +
  799. geom_point(color = "black", pch = 21) +
  800. scale_size_area(max_size = 15) +
  801. scale_fill_gradientn(colors = PurpleAndYellow()) +
  802. theme_bw() +
  803. theme_manuscript +
  804. theme(axis.title = element_blank(), axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5), legend.position = "right", panel.spacing=unit(0, "lines")) +
  805. labs(tag = "B", title = "Canonical Marker Expression", size = "% Expressed", fill = "Scaled Expression") +
  806. guides(colour = guide_colorbar(title.position = "top", title.theme = element_text(size = axisTitleSize)),
  807. size = guide_legend(title.position = "top", title.theme = element_text(size = axisTitleSize)))
  810. figSuppMarkerPlot
  813. ```
  816. ## Piece together
  818. ```{r, fig.width=30, fig.height = 20}
  820. ((dimLeidenFig | figSuppMarkerPlot) / receptorGG) + plot_layout(heights = c(1,.25))
  822. #ggsave("output/Figures/Nichenet/nichenetSupplement.pdf", width = 30, height = 20)
  825. ```
  828. ```{r}
  830. sessionInfo()
  832. ```

25_NicheNet_Analysis_and_Figure.Rmd at commit 760cd2f, under MIT · at the source

Overview

Authors: John N. Mariani1, Steven J. Schanz1, Benjamin Mansky1, Xiaolu Wei1, Carter C. Long1, Devin Chandler-Militello1, Hannah E. Aichelman1, Nguyen P. T. Huynh1,2, Steven A. Goldman1,2
ORCID iDs: John N. Mariani, Xiaolu Wei, Hannah E. Aichelman, Steven A. Goldman
  1. Center for Translational Neuromedicine, University of Rochester Medical Center,Rochester, NY USA
  2. Center for Translational Neuromedicine, University of Copenhagen Faculty of Health,Copenhagen, Denmark
Institutions: University of Rochester Medicine (United States); University of Copenhagen (Denmark)
Journal: Nature communications, volume 17, issue 1, article 5609
Dates: received 12 April 2025; accepted 31 March 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71803-3 · PMID 42026046 · PMCID PMC13315746 · OpenAlex W7155386802
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), multiple sclerosis (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing
Keywords: Gliogenesis, Oligodendrocyte, Astrocyte, Multiple sclerosis, Stem-cell differentiation
MeSH: Brain*, Neuroglia*, Stem Cell Transplantation*, Animals, Astrocytes, Cell Differentiation, Humans, Mice, Myelin Sheath, Oligodendroglia, Pluripotent Stem Cells, Single-Cell Gene Expression Analysis (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute on Aging (R01AG072298); Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (Dr. Miriam & Sheldon G. Adelson Medical Research Foundation)
Citations: not cited yet (Europe PMC); 138 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 32 matches between paragraphs and lines of code.

CTNGoldmanLab/Glial_Chimera_Maturation

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 760cd2f41c90ccfb279b633e2943e03526a0c510, 12 November 2025
Languages: R (24), Jupyter (7), Shell (1)
Size: 227 files, 32 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 28 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (20 files), ggplot2 (17 files), patchwork (17 files), Seurat (17 files), data.table (12 files), Matplotlib (7 files), NumPy (7 files), Scanpy (7 files), PyTorch (4 files), pandas (3 files), anndata (2 files), BEDTools (2 files), cowplot (2 files), h5py (2 files), pheatmap (2 files), SciPy (2 files), seaborn (2 files), Squidpy (2 files), deepTools (1 file), DESeq2 (1 file), ggpubr (1 file), Numba (1 file), Plotly (1 file), reticulate (1 file), SAMtools (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
34 files
At the source: github.com/CTNGoldmanLab/Glial_Chimera_Maturation

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-71803-3.

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 32 scripts, each with its path and the digest of its content;
  • 32 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

Code and data availability statement

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

Read it in the paper: doi.org/10.1038/s41467-026-71803-3.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 12 MeSH terms, 2 funders, 132 references.

Cite

This paper

Mariani, J. N., Schanz, S. J., Mansky, B., Wei, X., Long, C. C., Chandler-Militello, D., Aichelman, H. E., Huynh, N. P. T., & Goldman, S. A. (2026). Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain. Nature communications, 17(1), 5609. https://doi.org/10.1038/s41467-026-71803-3

BibTeX

@article{mariani2026charting,
author = {Mariani, John N. and Schanz, Steven J. and Mansky, Benjamin and Wei, Xiaolu and Long, Carter C. and Chandler-Militello, Devin and Aichelman, Hannah E. and Huynh, Nguyen P. T. and Goldman, Steven A.},
title = {{Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5609},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71803-3},
url = {https://doi.org/10.1038/s41467-026-71803-3},
pmid = {42026046},
pmcid = {PMC13315746}
}

RIS

TY - JOUR
AU - Mariani, John N.
AU - Schanz, Steven J.
AU - Mansky, Benjamin
AU - Wei, Xiaolu
AU - Long, Carter C.
AU - Chandler-Militello, Devin
AU - Aichelman, Hannah E.
AU - Huynh, Nguyen P. T.
AU - Goldman, Steven A.
TI - Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/23
VL - 17
IS - 1
SP - 5609
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71803-3
UR - https://doi.org/10.1038/s41467-026-71803-3
LA - en
ER -

CSL-JSON

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

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