OSCR

Iba1 deficiency impairs microglial synaptic remodeling and neuronal survival after axonal injury.

Code ↔ Paper

7 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 7 matches
  1. [1] § Materials and methods › GO enrichment analysis ↔ go_enrichment_cluster_degs.R, lines 1–50 · score 0.74 · GO enrichment, clusterProfiler, gene symbols, db, mm, DEGs
  2. [2] § Results › Iba1 loss diverts microglial signaling from adhesion to inflammation ↔ nichenet_microglia_to_chat.R, lines 85–117 · score 0.67 · H2 D1, H2 K1, Anxa2, Mmp2, Tgfbi, Aplp2
  3. [3] § Materials and methods › Volcano plot analysis ↔ volcano_highlight_validation_genes.R, lines 44–98 · score 0.60 · log2 fold change, log2fc, Volcano, gene
  4. [4] § Results › Iba1 loss skews axotomy-evoked microglial states toward interferon-responsive programs ↔ violin_microglia_cytokines.R, lines 89–133 · score 0.60 · Aif1l, growth factor, cytokine, chemokine, microglia
  5. [5] § Results › Iba1 loss diverts microglial signaling from adhesion to inflammation ↔ nichenet_microglia_to_chat.R, lines 85–117 · score 0.54 · NicheNet, Itga3, Itgav, Itgb1, Itgb4, Aplp2
  6. [6] § Results › Microglial contact and synapse number ↔ microglia_reclustering_ad_state_annotation.R, lines 576–639 · score 0.53 · odds ratio, pairwise Fisher, CL, Microglial, cell
  7. [7] § Results › Iba1 loss diverts microglial signaling from adhesion to inflammation ↔ microglia_reclustering_ad_state_annotation.R, lines 356–422 · score 0.53 · H2 D1, H2 K1, microglial

Paper

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The authors' code

R · 629 lines · 23 KB · no license · 2 matches

  1. ################################################################################
  2. # NicheNet pipeline (Receiver: ChAT; Sender: Microglia)
  3. #
  4. # Comparisons (default):
  5. # 1) KO-axotomy vs KO-sham
  6. # 2) WT-axotomy vs WT-sham
  7. #
  8. # Main outputs (per comparison):
  9. # - DEG tables (receiver)
  10. # - Ligand activity (AUPR corrected)
  11. # - Ligand→target links (top)
  12. # - Ligand–receptor candidates (filtered by expression)
  13. # - Barplot / Heatmap / Chord / Sankey / DotPlot (receptors)
  14. #
  15. # Cross-comparison outputs:
  16. # - WT vs KO ligand activity scatter
  17. # - Top15 overlap CSV + barplot + UpSet
  18. #
  19. # Usage:
  20. # Rscript nichenet_chat_from_mgl_public_safe.R \
  21. # --chat_rds path/to/seurat_ChAT_4groups.rds \
  22. # --mgl_rds path/to/seurat_Microglia_4groups.rds \
  23. # --group_col group \
  24. # --out results/NicheNet_ChAT_from_MGL
  25. #
  26. # Notes (public safety):
  27. # - No absolute paths are hard-coded or printed.
  28. # - Output filenames do not include local paths.
  29. ################################################################################
  31. set.seed(1)
  33. #-----------------------------#
  34. # 0) Dependencies #
  35. #-----------------------------#
  36. need <- c(
  37. "Seurat","dplyr","tibble","readr","stringr","tidyr","purrr","Matrix",
  38. "nichenetr","AnnotationDbi","org.Mm.eg.db","babelgene",
  39. "ggplot2","ggrepel","ggalluvial","ComplexUpset","pheatmap",
  40. "circlize","networkD3","htmlwidgets"
  41. )
  43. miss <- need[!vapply(need, requireNamespace, logical(1), quietly = TRUE)]
  44. if (length(miss)) {
  45. message("Missing packages detected (names only): ", paste(miss, collapse = ", "))
  46. stop("Please install the missing packages, then rerun this script.")
  47. }
  49. suppressPackageStartupMessages({
  50. library(Seurat)
  51. library(dplyr)
  52. library(tibble)
  53. library(readr)
  54. library(stringr)
  55. library(tidyr)
  56. library(purrr)
  57. library(Matrix)
  59. library(nichenetr)
  60. library(AnnotationDbi)
  61. library(org.Mm.eg.db)
  62. library(babelgene)
  64. library(ggplot2)
  65. library(ggrepel)
  66. library(ggalluvial)
  67. library(ComplexUpset)
  68. library(pheatmap)
  69. library(circlize)
  70. library(networkD3)
  71. library(htmlwidgets)
  72. })
  74. #-----------------------------#
  75. # 1) Argument parsing #
  76. #-----------------------------#
  77. args <- commandArgs(trailingOnly = TRUE)
  79. get_arg <- function(flag, default = NULL) {
  80. hit <- which(args == flag)
  81. if (length(hit) == 1 && length(args) >= hit + 1) return(args[hit + 1])
  82. default
  83. }
  85. path_chat <- get_arg("--chat_rds")
  86. path_mgl <- get_arg("--mgl_rds")
  87. group_col <- get_arg("--group_col", default = "group")
  88. out_dir <- get_arg("--out", default = file.path("results", "NicheNet_ChAT_from_MGL"))
  90. if (is.null(path_chat)) stop("Missing required argument: --chat_rds <path_to_receiver_seurat_rds>")
  91. if (is.null(path_mgl)) stop("Missing required argument: --mgl_rds <path_to_sender_seurat_rds>")
  92. if (!file.exists(path_chat)) stop("Receiver RDS not found (path not printed).")
  93. if (!file.exists(path_mgl)) stop("Sender RDS not found (path not printed).")
  95. dir.create(out_dir, showWarnings = FALSE, recursive = TRUE)
  97. message("Starting NicheNet pipeline (paths are not printed).")
  98. message("Outputs will be written under the specified output directory.")
  100. #-----------------------------#
  101. # 2) User-configurable values #
  102. #-----------------------------#
  103. cmp_list <- list(
  104. KO = list(case = "KO-axotomy", ctrl = "KO-sham", tag = "KO_axotomy_vs_KO_sham"),
  105. WT = list(case = "WT-axotomy", ctrl = "WT-sham", tag = "WT_axotomy_vs_WT_sham")
  106. )
  108. # Optional: UMAP FeaturePlot targets (only for WT comparison in this script)
  109. genes_umap_chat <- c(
  110. "Aplp2","Ddr1","Dysf","Egfr","F2r","Fgfr1","Igsf11",
  111. "Itga3","Itga6","Itga7","Itga9","Itgav","Itgb1","Itgb4","Itgb5",
  112. "Plat","Sdc2"
  113. )
  114. genes_umap_mgl <- c(
  115. "Anxa2","F13a1","H2-D1","H2-K1","H2-Q4","H2-Q6","H2-Q7",
  116. "Mmp2","Tgfbi","Vsir"
  117. )
  119. #-----------------------------#
  120. # 3) Utilities #
  121. #-----------------------------#
  122. normalize_symbol <- function(x) {
  123. x <- as.character(x)
  124. x <- trimws(x)
  125. x[nchar(x) == 0] <- NA_character_
  126. x
  127. }
  129. extract_symbolish <- function(x) {
  130. x <- as.character(x)
  131. x <- gsub("\\.[0-9]+$", "", x) # strip trailing ".1" etc
  132. x <- gsub("(?i)^(gene|symbol|target|geneid|ensembl)[:_ -]*", "", x, perl = TRUE)
  133. x <- gsub("(?i)[:_ -]*(gene|symbol|target)$", "", x, perl = TRUE)
  134. x <- gsub("\\s+", "", x)
  135. x
  136. }
  138. expressed_genes <- function(seu, pct = 0.05, assay = NULL) {
  139. if (!is.null(assay)) DefaultAssay(seu) <- assay
  140. mat <- GetAssayData(seu, slot = "data")
  141. frac <- Matrix::rowMeans(mat > 0)
  142. names(frac)[frac >= pct]
  143. }
  145. # Convert NicheNet extdata (human symbols) to mouse symbols via babelgene orthologs
  146. # Returns: list(ltm = ligand_target_matrix_mouse, lr = lr_network_mouse)
  147. relabel_to_mouse <- function(ltm, lr) {
  148. mapL <- babelgene::orthologs(colnames(ltm), species = "mouse")
  149. mapR <- babelgene::orthologs(rownames(ltm), species = "mouse")
  151. mapL <- mapL[!is.na(mapL$mouse_symbol) & nzchar(mapL$mouse_symbol),
  152. c("human_symbol","mouse_symbol")] %>% distinct()
  153. mapR <- mapR[!is.na(mapR$mouse_symbol) & nzchar(mapR$mouse_symbol),
  154. c("human_symbol","mouse_symbol")] %>% distinct()
  156. col_new <- setNames(mapL$mouse_symbol, mapL$human_symbol)[colnames(ltm)]
  157. row_new <- setNames(mapR$mouse_symbol, mapR$human_symbol)[rownames(ltm)]
  159. ltm_m <- as.matrix(ltm)
  161. # Collapse duplicated ligands (columns)
  162. col_dup <- split(seq_along(col_new), col_new)
  163. ltm2 <- do.call(cbind, lapply(col_dup, function(idx) Matrix::rowSums(ltm_m[, idx, drop = FALSE])))
  164. # Apply receptor renaming
  165. rownames(ltm2) <- row_new
  167. # Collapse duplicated receptors (rows)
  168. row_dup <- split(seq_len(nrow(ltm2)), rownames(ltm2))
  169. ltm3 <- do.call(rbind, lapply(row_dup, function(idx) Matrix::colSums(ltm2[idx, , drop = FALSE])))
  171. # LR network renaming
  172. lr_m <- lr %>%
  173. mutate(ligand_h = ligand, receptor_h = receiver) %>%
  174. left_join(rename(mapL, ligand_h = human_symbol, ligand = mouse_symbol), by = "ligand_h") %>%
  175. left_join(rename(mapL, receptor_h = human_symbol, receiver = mouse_symbol), by = "receptor_h") %>%
  176. transmute(
  177. ligand = coalesce(ligand, ligand_h),
  178. receiver = coalesce(receiver, receptor_h)
  179. ) %>%
  180. distinct()
  182. list(ltm = ltm3, lr = lr_m)
  183. }
  185. get_links_top <- function(ligands, ligand_target_matrix, n = 100) {
  186. if (missing(ligands) || !length(ligands)) {
  187. return(tibble(ligand = character(), target = character(), weight = numeric()))
  188. }
  189. lig_all <- colnames(ligand_target_matrix)
  190. map_col <- setNames(lig_all, normalize_symbol(lig_all))
  191. lig_use <- intersect(normalize_symbol(ligands), names(map_col))
  192. if (!length(lig_use)) {
  193. return(tibble(ligand = character(), target = character(), weight = numeric()))
  194. }
  196. cols <- unname(map_col[lig_use])
  197. bind_rows(lapply(cols, function(cx) {
  198. sc <- ligand_target_matrix[, cx, drop = FALSE]
  199. ord <- order(sc[, 1], decreasing = TRUE)
  200. take <- head(ord, n = min(n, length(ord)))
  201. tibble(
  202. ligand = colnames(sc)[1],
  203. target = rownames(sc)[take],
  204. weight = as.numeric(sc[take, 1])
  205. )
  206. }))
  207. }
  209. build_node_colors <- function(ligands, receptors) {
  210. col_lig <- "#4477AA"
  211. col_rec <- "#AA7733"
  212. nodes <- unique(c(ligands, receptors))
  213. side <- ifelse(nodes %in% ligands, "ligand", "receptor")
  214. setNames(ifelse(side == "ligand", col_lig, col_rec), nodes)
  215. }
  217. save_dual <- function(p, base, w = 8, h = 5, dpi = 400) {
  218. ggsave(paste0(base, ".pdf"), p, width = w, height = h, units = "in", device = "pdf", useDingbats = FALSE)
  219. ggsave(paste0(base, ".tiff"), p, width = w, height = h, units = "in", dpi = dpi, device = "tiff", compression = "lzw")
  220. }
  222. save_heatmap_dual <- function(mat, base, ann_col = NULL, w = 8, h = 10, dpi = 400) {
  223. grDevices::pdf(paste0(base, ".pdf"), width = w, height = h)
  224. pheatmap::pheatmap(mat, cluster_rows = TRUE, cluster_cols = TRUE,
  225. annotation_col = ann_col, show_rownames = FALSE)
  226. grDevices::dev.off()
  228. grDevices::tiff(paste0(base, ".tiff"), width = w, height = h, units = "in", res = dpi, compression = "lzw")
  229. pheatmap::pheatmap(mat, cluster_rows = TRUE, cluster_cols = TRUE,
  230. annotation_col = ann_col, show_rownames = FALSE)
  231. grDevices::dev.off()
  232. }
  234. make_chord <- function(df_pairs, outbase, top_links = 60, dpi = 400, size_in = 10) {
  235. if (nrow(df_pairs) == 0) return(invisible(NULL))
  236. wcol <- if ("sumscore" %in% names(df_pairs)) "sumscore" else if ("count" %in% names(df_pairs)) "count" else names(df_pairs)[3]
  237. df_top <- df_pairs %>% arrange(desc(.data[[wcol]])) %>% slice_head(n = min(top_links, n()))
  239. ligands <- unique(df_top$ligand)
  240. receptors <- unique(df_top$receptor)
  241. grid.col <- build_node_colors(ligands, receptors)
  243. draw_chord <- function(device_fun) {
  244. circos.clear()
  245. circos.par(
  246. gap.after = c(rep(2, max(0, length(ligands) - 1)), 8, rep(2, max(0, length(receptors) - 1)), 8),
  247. start.degree = 90,
  248. track.margin = c(0.01, 0.01)
  249. )
  251. device_fun()
  252. chordDiagram(
  253. df_top %>% select(ligand, receptor, !!wcol),
  254. grid.col = grid.col,
  255. order = c(ligands, receptors),
  256. directional = 0,
  257. transparency = 0.25,
  258. annotationTrack = "grid",
  259. preAllocateTracks = list(track.height = 0.08)
  260. )
  261. circos.trackPlotRegion(track.index = 1, panel.fun = function(x, y) {
  262. circos.text(CELL_META$xcenter, CELL_META$ycenter, CELL_META$sector.index,
  263. facing = "bending", niceFacing = TRUE, cex = 0.6)
  264. }, bg.border = NA)
  265. grDevices::dev.off()
  266. circos.clear()
  267. }
  269. draw_chord(function() grDevices::pdf(paste0(outbase, ".pdf"), width = size_in, height = size_in))
  270. draw_chord(function() grDevices::tiff(paste0(outbase, ".tiff"), width = size_in, height = size_in, units = "in",
  271. res = dpi, compression = "lzw"))
  272. }
  274. dotplot_receptors <- function(seu, receptors, base, group_col, order_groups = NULL, ordered = FALSE, dpi = 400) {
  275. receptors <- intersect(receptors, rownames(seu))
  276. if (!length(receptors)) return(invisible(NULL))
  278. if (!group_col %in% colnames([email hidden])) return(invisible(NULL))
  280. if (!is.null(order_groups)) {
  281. seu[[group_col]] <- factor(seu[[group_col]][, 1], levels = order_groups)
  282. }
  283. Idents(seu) <- seu[[group_col]][, 1]
  285. feats <- receptors
  286. if (ordered) {
  287. avg <- AverageExpression(seu, features = feats, assays = DefaultAssay(seu), group.by = group_col)$RNA
  288. ord <- order(rowMeans(avg), decreasing = TRUE)
  289. feats <- rownames(avg)[ord]
  290. }
  292. p <- DotPlot(seu, features = feats, cols = c("#BBBBBB", "#4477AA")) +
  293. theme_minimal(base_size = 10) +
  294. theme(axis.text.x = element_text(angle = 60, hjust = 1))
  296. save_dual(p, base, w = 10, h = 6, dpi = dpi)
  297. }
  299. feature_umap <- function(seu, genes, out_dir, prefix, dpi = 400, tiff_only_for = NULL) {
  300. if (!"umap" %in% Reductions(seu)) {
  301. seu <- RunPCA(seu, verbose = FALSE)
  302. seu <- RunUMAP(seu, dims = 1:30, verbose = FALSE)
  303. }
  304. for (g in genes) {
  305. if (!g %in% rownames(seu)) next
  306. p <- FeaturePlot(seu, features = g, reduction = "umap") + theme_void() + ggtitle(g)
  307. pdf_path <- file.path(out_dir, sprintf("%s_%s.pdf", prefix, g))
  308. ggsave(pdf_path, p, width = 5, height = 4, units = "in", device = "pdf", useDingbats = FALSE)
  310. if (is.null(tiff_only_for) || g %in% tiff_only_for) {
  311. tiff_path <- file.path(out_dir, sprintf("%s_%s.tiff", prefix, g))
  312. ggsave(tiff_path, p, width = 5, height = 4, units = "in", dpi = dpi, device = "tiff", compression = "lzw")
  313. }
  314. }
  315. }
  317. sankey_alluvial <- function(df_pairs, base, top_links = 60, dpi = 400) {
  318. if (nrow(df_pairs) == 0) return(invisible(NULL))
  319. if (!"sumscore" %in% names(df_pairs)) return(invisible(NULL))
  321. df_top <- df_pairs %>% arrange(desc(sumscore)) %>% slice_head(n = min(top_links, n()))
  323. # Static (ggalluvial)
  324. p <- ggplot(df_top, aes(axis1 = ligand, axis2 = receptor, y = sumscore)) +
  325. scale_x_discrete(limits = c("Ligand", "Receptor"), expand = c(.1, .1)) +
  326. geom_alluvium(aes(fill = ligand), alpha = .6) +
  327. geom_stratum(width = 1/10) +
  328. geom_text(stat = "stratum", aes(label = after_stat(stratum)), size = 3) +
  329. theme_minimal(base_size = 10) +
  330. theme(legend.position = "none")
  332. save_dual(p, base, w = 12, h = 6, dpi = dpi)
  334. # Interactive HTML (networkD3)
  335. try({
  336. ligs <- unique(df_top$ligand)
  337. recs <- unique(df_top$receptor)
  338. nodes <- data.frame(name = c(ligs, recs))
  340. links <- df_top %>%
  341. mutate(
  342. source = match(ligand, nodes$name) - 1,
  343. target = match(receptor, nodes$name) - 1,
  344. value = sumscore
  345. ) %>%
  346. select(source, target, value)
  348. sn <- sankeyNetwork(
  349. Links = links, Nodes = nodes,
  350. Source = "source", Target = "target",
  351. Value = "value", NodeID = "name",
  352. fontSize = 12, nodeWidth = 20
  353. )
  355. htmlwidgets::saveWidget(sn, file = paste0(base, ".html"), selfcontained = TRUE)
  356. }, silent = TRUE)
  357. }
  359. #-----------------------------#
  360. # 4) Load input data #
  361. #-----------------------------#
  362. chat0 <- readRDS(path_chat)
  363. mgl0 <- readRDS(path_mgl)
  365. if (!inherits(chat0, "Seurat")) stop("Receiver object is not a Seurat object.")
  366. if (!inherits(mgl0, "Seurat")) stop("Sender object is not a Seurat object.")
  367. if (!group_col %in% colnames([email hidden])) stop("group_col not found in receiver meta.data.")
  368. if (!group_col %in% colnames([email hidden])) stop("group_col not found in sender meta.data.")
  370. #-----------------------------#
  371. # 5) NicheNet networks (mouse)#
  372. #-----------------------------#
  373. ligand_target_matrix_h <- readRDS(system.file("extdata", "ligand_target_matrix.rds", package = "nichenetr"))
  374. lr_network_h <- readRDS(system.file("extdata", "lr_network.rds", package = "nichenetr"))
  376. mm <- relabel_to_mouse(ligand_target_matrix_h, lr_network_h)
  377. ligand_target_matrix_m <- mm$ltm
  378. lr_network_m <- mm$lr
  379. rm(mm, ligand_target_matrix_h, lr_network_h)
  381. #-----------------------------#
  382. # 6) Core runner per compare #
  383. #-----------------------------#
  384. run_cmp <- function(tag, case_label, ctrl_label,
  385. chat0, mgl0, group_col, out_dir,
  386. ligand_target_matrix_m, lr_network_m) {
  388. message("=== Running comparison: ", tag, " ===")
  390. # Receiver (ChAT): restrict to 2 groups
  391. chat <- subset(chat0, subset = !!as.name(group_col) %in% c(case_label, ctrl_label))
  392. Idents(chat) <- chat[[group_col]][, 1]
  394. # Sender (Microglia): restrict to same 2 groups for expression filters
  395. mgl <- subset(mgl0, subset = !!as.name(group_col) %in% c(case_label, ctrl_label))
  397. # DEG in receiver
  398. deg <- FindMarkers(
  399. chat, ident.1 = case_label, ident.2 = ctrl_label,
  400. logfc.threshold = 0.1, min.pct = 0.05, test.use = "wilcox",
  401. verbose = FALSE
  402. ) %>%
  403. rownames_to_column("gene") %>%
  404. mutate(gene = extract_symbolish(gene))
  406. write_csv(deg, file.path(out_dir, sprintf("DEG_receiver_%s.csv", tag)))
  408. geneset_up <- deg %>%
  409. filter(p_val_adj < 0.05, avg_log2FC > 0.15) %>%
  410. arrange(desc(avg_log2FC)) %>%
  411. pull(gene) %>%
  412. unique()
  414. background_expressed <- expressed_genes(chat, pct = 0.05)
  416. # Expression-constrained LR network
  417. sender_expressed <- expressed_genes(mgl, pct = 0.05)
  418. receiver_expressed <- expressed_genes(chat, pct = 0.05)
  420. lr_expressed <- lr_network_m %>%
  421. filter(ligand %in% sender_expressed, receiver %in% receiver_expressed) %>%
  422. distinct()
  424. potential_ligands <- intersect(lr_expressed$ligand, colnames(ligand_target_matrix_m))
  426. ligand_activities <- get_ligand_activities(
  427. geneset = geneset_up,
  428. background_expressed_genes = background_expressed,
  429. ligand_target_matrix = ligand_target_matrix_m,
  430. potential_ligands = potential_ligands
  431. )
  433. write_csv(ligand_activities, file.path(out_dir, sprintf("LigandActivity_mouse_%s.csv", tag)))
  435. # Top ligands
  436. topN <- 30
  437. top_ligands <- ligand_activities %>%
  438. arrange(desc(aupr_corrected)) %>%
  439. slice_head(n = min(topN, n())) %>%
  440. pull(test_ligand)
  442. summary10 <- ligand_activities %>%
  443. arrange(desc(aupr_corrected)) %>%
  444. mutate(rank = row_number()) %>%
  445. slice_head(n = min(10, n()))
  447. write_csv(summary10, file.path(out_dir, sprintf("Summary_top10_ligands_%s.csv", tag)))
  449. # Ligand→target links (top per ligand)
  450. links_top <- get_links_top(top_ligands, ligand_target_matrix_m, n = 100)
  451. write_csv(links_top, file.path(out_dir, sprintf("LigandTargetLinks_mouse_%s.csv", tag)))
  453. # Aggregate to LR candidates
  454. ligand_score <- links_top %>%
  455. group_by(ligand) %>%
  456. summarise(sumscore = sum(weight), count = dplyr::n(), .groups = "drop")
  458. lr_pairs <- lr_expressed %>%
  459. inner_join(ligand_score, by = "ligand") %>%
  460. transmute(ligand, receptor = receiver, sumscore, count) %>%
  461. arrange(desc(sumscore))
  463. write_csv(lr_pairs, file.path(out_dir, sprintf("LR_candidates_mouse_%s.csv", tag)))
  465. # Barplot: top ligands
  466. p_bar <- ligand_activities %>%
  467. arrange(desc(aupr_corrected)) %>%
  468. slice_head(n = 20) %>%
  469. mutate(test_ligand = factor(test_ligand, levels = rev(test_ligand))) %>%
  470. ggplot(aes(x = test_ligand, y = aupr_corrected)) +
  471. geom_col() +
  472. coord_flip() +
  473. labs(x = "Ligand", y = "AUPR (corrected)", title = tag) +
  474. theme_minimal(base_size = 11)
  476. save_dual(p_bar, file.path(out_dir, sprintf("Barplot_TopLigands_%s", tag)), w = 6, h = 6)
  478. # Heatmap: ligand-target (top links)
  479. top_targets <- links_top %>%
  480. arrange(desc(weight)) %>%
  481. slice_head(n = 200) %>%
  482. pull(target) %>%
  483. unique()
  485. mat_ht <- ligand_target_matrix_m[
  486. intersect(top_targets, rownames(ligand_target_matrix_m)),
  487. intersect(top_ligands, colnames(ligand_target_matrix_m)),
  488. drop = FALSE
  489. ]
  491. if (nrow(mat_ht) > 1 && ncol(mat_ht) > 1) {
  492. save_heatmap_dual(mat = mat_ht,
  493. base = file.path(out_dir, sprintf("Heatmap_LigandTarget_mouse_%s", tag)),
  494. w = 8, h = 10)
  495. }
  497. # Chord + Sankey for LR candidates
  498. make_chord(lr_pairs, outbase = file.path(out_dir, sprintf("Chord_LR_%s", tag)), top_links = 60)
  499. sankey_alluvial(lr_pairs, base = file.path(out_dir, sprintf("Sankey_LR_%s", tag)), top_links = 60)
  501. # DotPlot: receptor expression in receiver
  502. rec_all <- unique(lr_expressed$receiver)
  503. rec_top <- lr_pairs %>% arrange(desc(sumscore)) %>% pull(receptor) %>% unique() %>% head(50)
  505. dotplot_receptors(chat, rec_all,
  506. base = file.path(out_dir, sprintf("DotPlot_receiver_receptors_%s", tag)),
  507. group_col = group_col, order_groups = NULL, ordered = FALSE)
  509. dotplot_receptors(chat, rec_all,
  510. base = file.path(out_dir, sprintf("DotPlot_receiver_receptors_%s_ordered", tag)),
  511. group_col = group_col, order_groups = NULL, ordered = TRUE)
  513. # Optional canonical ordering (useful when plotting all groups together in a shared object)
  514. canonical_order <- c("WT-sham","WT-axotomy","KO-sham","KO-axotomy")
  515. dotplot_receptors(chat, rec_all,
  516. base = file.path(out_dir, sprintf("DotPlot_receiver_receptors_canonical_order_%s", tag)),
  517. group_col = group_col, order_groups = canonical_order, ordered = TRUE)
  519. dotplot_receptors(chat, rec_top,
  520. base = file.path(out_dir, sprintf("DotPlot_receiver_receptors_topLigands_%s", tag)),
  521. group_col = group_col, order_groups = NULL, ordered = TRUE)
  523. list(
  524. tag = tag,
  525. activities = ligand_activities %>%
  526. select(test_ligand, aupr_corrected) %>%
  527. rename(!!tag := aupr_corrected),
  528. top15 = ligand_activities %>% arrange(desc(aupr_corrected)) %>% slice_head(n = 15) %>% pull(test_ligand)
  529. )
  530. }
  532. #-----------------------------#
  533. # 7) Run comparisons #
  534. #-----------------------------#
  535. res_KO <- run_cmp(
  536. tag = cmp_list$KO$tag, case_label = cmp_list$KO$case, ctrl_label = cmp_list$KO$ctrl,
  537. chat0 = chat0, mgl0 = mgl0, group_col = group_col, out_dir = out_dir,
  538. ligand_target_matrix_m = ligand_target_matrix_m, lr_network_m = lr_network_m
  539. )
  541. res_WT <- run_cmp(
  542. tag = cmp_list$WT$tag, case_label = cmp_list$WT$case, ctrl_label = cmp_list$WT$ctrl,
  543. chat0 = chat0, mgl0 = mgl0, group_col = group_col, out_dir = out_dir,
  544. ligand_target_matrix_m = ligand_target_matrix_m, lr_network_m = lr_network_m
  545. )
  547. #-----------------------------#
  548. # 8) WT vs KO summary #
  549. #-----------------------------#
  550. act_join <- full_join(res_WT$activities, res_KO$activities, by = "test_ligand") %>%
  551. replace_na(setNames(as.list(rep(0, 2)), c(cmp_list$WT$tag, cmp_list$KO$tag)))
  553. write_csv(act_join, file.path(out_dir, "LigandActivity_WT_vs_KO.csv"))
  555. p_sc <- ggplot(act_join, aes(x = .data[[cmp_list$WT$tag]], y = .data[[cmp_list$KO$tag]], label = test_ligand)) +
  556. geom_point() +
  557. geom_abline(slope = 1, intercept = 0, linetype = "dashed") +
  558. ggrepel::geom_text_repel(size = 3, max.overlaps = 20) +
  559. labs(
  560. x = paste0("AUPR: ", cmp_list$WT$tag),
  561. y = paste0("AUPR: ", cmp_list$KO$tag),
  562. title = "Ligand activities: WT vs KO"
  563. ) +
  564. theme_minimal(base_size = 11)
  566. save_dual(p_sc, file.path(out_dir, "Scatter_LigandActivity_WT_vs_KO"), w = 6, h = 6)
  568. # Top15 overlap
  569. top15_WT <- res_WT$top15
  570. top15_KO <- res_KO$top15
  571. ovl <- intersect(top15_WT, top15_KO)
  572. only_WT <- setdiff(top15_WT, top15_KO)
  573. only_KO <- setdiff(top15_KO, top15_WT)
  575. write_csv(tibble(ligand = ovl), file.path(out_dir, "TopLigandOverlap_WTvsKO_top15.csv"))
  576. write_csv(tibble(ligand = only_WT), file.path(out_dir, "TopLigands_WTminusKO.csv"))
  577. write_csv(tibble(ligand = only_KO), file.path(out_dir, "TopLigands_KOminusWT.csv"))
  579. # Overlap barplot for shared ligands
  580. if (length(ovl) > 0) {
  581. act_for_bar <- act_join %>%
  582. filter(test_ligand %in% ovl) %>%
  583. pivot_longer(cols = -test_ligand, names_to = "comparison", values_to = "AUPR")
  585. p_bar_ovl <- ggplot(act_for_bar, aes(x = reorder(test_ligand, AUPR, FUN = median), y = AUPR, fill = comparison)) +
  586. geom_col(position = "dodge") +
  587. coord_flip() +
  588. labs(x = "Ligands (overlap in top15)", y = "AUPR", title = "WT vs KO overlap (top15)") +
  589. theme_minimal(base_size = 11)
  591. save_dual(p_bar_ovl, file.path(out_dir, "Bar_OverlapTopLigands_WTvsKO_top15"), w = 7, h = 6)
  592. }
  594. # UpSet (ComplexUpset)
  595. sets_df <- tibble(
  596. ligand = unique(c(top15_WT, top15_KO)),
  597. WT = ligand %in% top15_WT,
  598. KO = ligand %in% top15_KO
  599. )
  601. p_up <- ComplexUpset::upset(
  602. sets_df,
  603. sets = c("WT", "KO"),
  604. name = "Top15 ligands",
  605. base_annotations = list("Intersection size" = intersection_size())
  606. ) +
  607. ggtitle("Overlap of Top15 ligands (WT vs KO)")
  609. save_dual(p_up, file.path(out_dir, "UpSet_TopLigands_WTvsKO_top15"), w = 8, h = 5)
  611. #-----------------------------#
  612. # 9) Optional UMAP plots (WT) #
  613. #-----------------------------#
  614. # Receiver (ChAT)
  615. chat_WT <- subset(chat0, subset = !!as.name(group_col) %in% c("WT-sham", "WT-axotomy"))
  616. feature_umap(chat_WT, genes_umap_chat, out_dir = out_dir,
  617. prefix = "UMAP_receiver_WT_axotomy_vs_WT_sham", tiff_only_for = c("Itga9"))
  619. # Sender (Microglia)
  620. mgl_WT <- subset(mgl0, subset = !!as.name(group_col) %in% c("WT-sham", "WT-axotomy"))
  621. feature_umap(mgl_WT, genes_umap_mgl, out_dir = out_dir,
  622. prefix = "UMAP_sender_WT_axotomy_vs_WT_sham", tiff_only_for = c("F13a1"))
  624. #-----------------------------#
  625. # 10) Reproducibility #
  626. #-----------------------------#
  627. writeLines(capture.output(sessionInfo()), file.path(out_dir, "sessionInfo.txt"))
  629. message("All outputs completed.")

nichenet_microglia_to_chat.R at commit c14569a, no license · at the source

Overview

Authors: Koji Sekiguchi1, Hirotaka Shoji2, Tomoko Shindo3, Jumpei Sasabe3, Daiki Tokuyasu1, Jin Nakahara1, Tsuyoshi Miyakawa2, Daisuke Ito1,4
  1. Department of Neurology, Keio University School of Medicine,35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582 Japan
  2. Division of Systems Medical Science, Center for Medical Science, Fujita Health University,Toyoake, Aichi 470-1192 Japan
  3. Electron Microscope Laboratory, Keio University School of Medicine,Tokyo, 160-8582 Japan
  4. Memory Center, Keio University School of Medicine,Tokyo, 160-8582 Japan
Institutions: Keio University (Japan); Fujita Health University (Japan)
Journal: Journal of neuroinflammation, volume 23, issue 1, article 223
Dates: received 21 February 2026; accepted 27 April 2026; published online 7 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12974-026-03848-6 · PMID 42098744 · PMCID PMC13321657 · OpenAlex W7160513941
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, fMRI & imaging
Keywords: Microglia, Facial nerve axotomy, Synaptic stripping, Synaptic remodeling, Integrated analysis, Microglia–neuron interaction, Neuronal survival
MeSH: Axons*, Calcium-Binding Proteins*, Facial Nerve Injuries*, Microfilament Proteins*, Microglia*, Motor Neurons*, Synapses*, Animals, Axotomy, Cell Survival, Male, Mice, Mice, Inbred C57BL, Mice, Knockout (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: Japan Society for the Promotion of Science (21H02812 and JP22H04922); MEXT Promotion of the Distinctive Joint Research Center Program (FY2021–2023 and JPMXP0621467949)
Citations: not cited yet (Europe PMC); 57 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

sekikoji54-crypto/single-nucleus-RNA-seq-code

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: c14569a8692b96dd9f5bbebfc0647dfcad6959a8, 3 May 2026
Languages: R (9)
Size: 10 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (9 files), tidyverse (8 files), Seurat (6 files), patchwork (2 files), pheatmap (2 files), circlize (1 file), clusterProfiler (1 file), igraph (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
10 files
At the source: github.com/sekikoji54-crypto/single-nucleus-RNA-seq-code

Zenodo 19996424

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (9 files), tidyverse (8 files), Seurat (6 files), patchwork (2 files), pheatmap (2 files), circlize (1 file), clusterProfiler (1 file), igraph (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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10 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1186/s12974-026-03848-6.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 scripts, each with its path and the digest of its content;
  • 7 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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 7 keywords, 14 MeSH terms, 2 funders, 57 references.

Cite

This paper

Sekiguchi, K., Shoji, H., Shindo, T., Sasabe, J., Tokuyasu, D., Nakahara, J., Miyakawa, T., & Ito, D. (2026). Iba1 deficiency impairs microglial synaptic remodeling and neuronal survival after axonal injury. Journal of neuroinflammation, 23(1), 223. https://doi.org/10.1186/s12974-026-03848-6

BibTeX

@article{sekiguchi2026iba1,
author = {Sekiguchi, Koji and Shoji, Hirotaka and Shindo, Tomoko and Sasabe, Jumpei and Tokuyasu, Daiki and Nakahara, Jin and Miyakawa, Tsuyoshi and Ito, Daisuke},
title = {{Iba1 deficiency impairs microglial synaptic remodeling and neuronal survival after axonal injury}},
journal = {Journal of neuroinflammation},
year = {2026},
month = may,
volume = {23},
number = {1},
pages = {223},
publisher = {BMC},
issn = {1742-2094},
doi = {10.1186/s12974-026-03848-6},
url = {https://doi.org/10.1186/s12974-026-03848-6},
pmid = {42098744},
pmcid = {PMC13321657}
}

RIS

TY - JOUR
AU - Sekiguchi, Koji
AU - Shoji, Hirotaka
AU - Shindo, Tomoko
AU - Sasabe, Jumpei
AU - Tokuyasu, Daiki
AU - Nakahara, Jin
AU - Miyakawa, Tsuyoshi
AU - Ito, Daisuke
TI - Iba1 deficiency impairs microglial synaptic remodeling and neuronal survival after axonal injury
T2 - Journal of neuroinflammation
J2 - J Neuroinflammation
PY - 2026
DA - 2026/05/07
VL - 23
IS - 1
SP - 223
SN - 1742-2094
PB - BMC
DO - 10.1186/s12974-026-03848-6
UR - https://doi.org/10.1186/s12974-026-03848-6
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s12974-026-03848-6",
"type": "article-journal",
"title": "Iba1 deficiency impairs microglial synaptic remodeling and neuronal survival after axonal injury",
"container-title": "Journal of neuroinflammation",
"author": [
{
"family": "Sekiguchi",
"given": "Koji"
},
{
"family": "Shoji",
"given": "Hirotaka"
},
{
"family": "Shindo",
"given": "Tomoko"
},
{
"family": "Sasabe",
"given": "Jumpei"
},
{
"family": "Tokuyasu",
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{
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"given": "Jin"
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{
"family": "Miyakawa",
"given": "Tsuyoshi"
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{
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"given": "Daisuke"
}
],
"container-title-short": "J Neuroinflammation",
"volume": "23",
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"DOI": "10.1186/s12974-026-03848-6",
"PMID": "42098744",
"PMCID": "PMC13321657",
"ISSN": "1742-2094",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s12974-026-03848-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
7
]
]
}
}

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