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Response dynamics of discrete subiculum→retrosplenial cortex projections underlying trace fear conditioning.

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The authors' code

R · 605 lines · 24 KB · no license

  1. options(stringsAsFactors = FALSE)
  2. Sys.getenv('R_MAX_VSIZE')
  4. library(Seurat)
  5. library(dplyr)
  6. library(cowplot)
  7. library(ggplot2)
  8. library(SeuratWrappers)
  9. library(doBy)
  10. library(fgsea)
  11. library(data.table)
  12. library(pheatmap)
  13. library(gprofiler2)
  14. library(scDblFinder)
  15. library(SoupX)
  16. library(lsa)
  17. library(msigdbr)
  19. ###functions to use###############
  20. pc_select <- function(seu){
  21. .seu <- seu
  22. .pct <- .seu[["pca"]]@stdev / sum(.seu[["pca"]]@stdev) * 100
  23. .cumu <- cumsum(.pct)
  24. .co1 <- which(.cumu > 90 & .pct < 5)[1]
  25. .co2 <- sort(which((.pct[1:length(.pct) - 1] - .pct[2:length(.pct)]) > 0.1), decreasing = T)[1] + 1
  26. .pc_num <- min(.co1, .co2)
  27. return(.pc_num)
  28. }
  32. #####Integration with soupX decontamination########################
  33. soupx_lib1 <- load10X("cellranger_output_for_soupX/lib1_outs/")
  34. soupx_lib1 <- autoEstCont(soupx_lib1)
  35. soupx_lib1 <- adjustCounts(soupx_lib1)
  37. set.seed(1234)
  38. lib1 <- CreateSeuratObject(counts = soupx_lib1, project = "lib1")
  39. dblet <- scDblFinder(GetAssayData(lib1, slot="counts"))
  40. lib1$scDblFinder.class <- dblet$scDblFinder.class
  41. lib1##13102 cells
  42. lib1$condition <- "GFP_CFC"
  43. lib1[["percent.mt"]] <- PercentageFeatureSet(lib1, pattern = "^mt-")
  44. lib1 <- RenameCells(lib1, add.cell.id = "lib1")
  46. soupx_lib2 <- load10X("cellranger_output_for_soupX/lib2_outs/")
  47. soupx_lib2 <- autoEstCont(soupx_lib2)
  48. soupx_lib2 <- adjustCounts(soupx_lib2)
  50. set.seed(1234)
  51. lib2 <- CreateSeuratObject(counts = soupx_lib2, project = "lib2")
  52. dblet <- scDblFinder(GetAssayData(lib2, slot="counts"))
  53. lib2$scDblFinder.class <- dblet$scDblFinder.class
  54. lib2##11792 cells
  55. lib2$condition <- "CRE_CFC"
  56. lib2[["percent.mt"]] <- PercentageFeatureSet(lib2, pattern = "^mt-")
  57. lib2 <- RenameCells(lib2, add.cell.id = "lib2")
  59. soupx_lib3 <- load10X("cellranger_output_for_soupX/lib3_outs/")
  60. soupx_lib3 <- autoEstCont(soupx_lib3)
  61. soupx_lib3 <- adjustCounts(soupx_lib3)
  63. set.seed(1234)
  64. lib3 <- CreateSeuratObject(counts = soupx_lib3, project = "lib3")
  65. dblet <- scDblFinder(GetAssayData(lib3, slot="counts"))
  66. lib3$scDblFinder.class <- dblet$scDblFinder.class
  67. lib3##11197 cells
  68. lib3$condition <- "GFP_none"
  69. lib3[["percent.mt"]] <- PercentageFeatureSet(lib3, pattern = "^mt-")
  70. lib3 <- RenameCells(lib3, add.cell.id = "lib3")
  73. soupx_lib4 <- load10X("cellranger_output_for_soupX/lib4_outs/")
  74. soupx_lib4 <- autoEstCont(soupx_lib4)
  75. soupx_lib4 <- adjustCounts(soupx_lib4)
  78. set.seed(1234)
  79. lib4 <- CreateSeuratObject(counts = soupx_lib4, project = "lib4")
  80. dblet <- scDblFinder(GetAssayData(lib4, slot="counts"))
  81. lib4$scDblFinder.class <- dblet$scDblFinder.class
  82. lib4##16301 cells
  83. lib4$condition <- "CRE_none"
  84. lib4[["percent.mt"]] <- PercentageFeatureSet(lib4, pattern = "^mt-")
  85. lib4 <- RenameCells(lib4, add.cell.id = "lib4")
  88. soupx_lib5 <- load10X("cellranger_output_for_soupX/lib5_outs/")
  89. #soupx_lib5 <- autoEstCont(soupx_lib5)
  90. soupx_lib5 = setContaminationFraction(soupx_lib5, 0.2)
  91. soupx_lib5 <- adjustCounts(soupx_lib5)
  93. set.seed(1234)
  94. lib5 <- CreateSeuratObject(counts = soupx_lib5, project = "lib5")
  95. dblet <- scDblFinder(GetAssayData(lib5, slot="counts"))
  96. lib5$scDblFinder.class <- dblet$scDblFinder.class
  97. lib5##9950 cells
  98. lib5$condition <- "CRE_none"
  99. lib5[["percent.mt"]] <- PercentageFeatureSet(lib5, pattern = "^mt-")
  100. lib5 <- RenameCells(lib5, add.cell.id = "lib5")
  105. ####filtration############################
  106. lib1 <- subset(lib1, subset = nFeature_RNA > 1000 & nFeature_RNA < 4000 &percent.mt < 5 & scDblFinder.class == "singlet")##8254
  107. lib2 <- subset(lib2, subset = nFeature_RNA > 1000 & nFeature_RNA < 4000 & percent.mt < 5 & scDblFinder.class == "singlet")##7568
  108. lib2
  109. lib3 <- subset(lib3, subset = nFeature_RNA > 1000 & nFeature_RNA < 4000 & percent.mt < 5 & scDblFinder.class == "singlet")##8147
  110. lib3
  111. lib4 <- subset(lib4, subset = nFeature_RNA > 1000 & nFeature_RNA < 4000 & percent.mt < 5 & scDblFinder.class == "singlet")##12210
  112. lib4
  113. lib5 <- subset(lib5, subset = nFeature_RNA > 1000 & nFeature_RNA < 4000 & percent.mt < 5 & scDblFinder.class == "singlet")##7962
  114. lib5
  119. list3 <- c(lib1,lib2,lib3,lib4,lib5)
  121. list3 <- lapply(X = list3, FUN = function(x) {
  122. x <- NormalizeData(x, normalization.method = "LogNormalize", scale.factor = 10000)
  123. x <- FindVariableFeatures(x, verbose = FALSE)
  124. x <- SCTransform(x, vst.flavor = "v2", verbose = TRUE) %>% RunPCA(npcs = 50, verbose = TRUE)
  125. })
  127. features <- SelectIntegrationFeatures(object.list = list3, nfeatures = 3000)
  128. list3 <- PrepSCTIntegration(object.list = list3, anchor.features = features)
  131. TLR9.fourSamples.withDbltRemoved.withSoupX.anchors <- FindIntegrationAnchors(object.list = list3, normalization.method = "SCT",
  132. anchor.features = features)
  134. TLR9.fourSamples.withDbltRemoved.withSoupX.sct <- IntegrateData(anchorset = TLR9.fourSamples.withDbltRemoved.withSoupX.anchors, normalization.method = "SCT")
  137. TLR9.fourSamples.withDbltRemoved.withSoupX.sct <- RunPCA(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, npcs = 50, verbose = FALSE)
  139. pc_num <- pc_select(TLR9.fourSamples.withDbltRemoved.withSoupX.sct)
  141. TLR9.fourSamples.withDbltRemoved.withSoupX.sct <- TLR9.fourSamples.withDbltRemoved.withSoupX.sct %>%
  142. RunUMAP(reduction = "pca", dims = 1:pc_num, verbose = FALSE) %>%
  143. FindNeighbors(reduction = "pca", dims = 1:pc_num) %>%
  144. FindClusters(resolution = 0.5)
  147. ####Cluster markers######################################
  148. cluster.ALLmarkers <- FindAllMarkers(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, assay = "RNA", only.pos = TRUE, min.pct = 0.1, logfc.threshold = 0.25)
  150. ordered.ALLmarkers <- cluster.ALLmarkers %>%
  151. group_by(cluster) %>%
  152. arrange(desc(avg_log2FC), .by_group = TRUE)
  156. filtered.markers <- cluster.ALLmarkers %>%
  157. group_by(cluster) %>%
  158. slice_max(n = 25, order_by = avg_log2FC)
  161. ####Differential analysis#######################################
  162. DefaultAssay(TLR9.fourSamples.withDbltRemoved.withSoupX.sct) <- "RNA"
  163. TLR9.fourSamples.withDbltRemoved.withSoupX.sct.GFPpos <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, subset= `EGFP-gn` > 0)
  167. for (clst in c(0:4,6:8,10:29)) {
  169. std.combined.cluster <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct,ident=clst)
  171. Idents(std.combined.cluster) <- "orig.ident"
  173. if (sum(std.combined.cluster$orig.ident == "lib1") > 3 & sum(std.combined.cluster$orig.ident == "lib3") > 3){
  174. cluster.marker.by.condition1 <- FindMarkers(std.combined.cluster, assay = "RNA",ident.1 = "lib1", ident.2 = "lib3",verbose = TRUE,
  175. logfc.threshold = 0,min.pct = 0.01)
  176. }
  178. if (sum(std.combined.cluster$orig.ident == "lib2") > 3 & sum(std.combined.cluster$orig.ident == "lib1") > 3){
  179. cluster.marker.by.condition2 <- FindMarkers(std.combined.cluster, assay = "RNA",ident.1 = "lib2", ident.2 = "lib1",verbose = TRUE,
  180. logfc.threshold = 0,min.pct = 0.01)
  181. }
  183. if (sum(std.combined.cluster$orig.ident == "lib4") > 3 & sum(std.combined.cluster$orig.ident == "lib3") > 3){
  184. cluster.marker.by.condition3 <- FindMarkers(std.combined.cluster, assay = "RNA",ident.1 = "lib4", ident.2 = "lib3",verbose = TRUE,
  185. logfc.threshold = 0,min.pct = 0.01)
  186. }
  189. write.csv(cluster.marker.by.condition1, paste0("soupX_corrected_analysis/differential_analysis/cluster_",clst,"_Lib1vsLib3_full_diff.csv"))
  190. write.csv(cluster.marker.by.condition2, paste0("soupX_corrected_analysis/differential_analysis/cluster_",clst,"_Lib2vsLib1_full_diff.csv"))
  191. write.csv(cluster.marker.by.condition3, paste0("soupX_corrected_analysis/differential_analysis/cluster_",clst,"_Lib4vsLib3_full_diff.csv"))
  193. diff_sig1 <- cluster.marker.by.condition1 %>% filter(abs(avg_log2FC) > 0.585 & p_val_adj < 0.01)
  194. diff_sig2 <- cluster.marker.by.condition2 %>% filter(abs(avg_log2FC) > 0.585 & p_val_adj < 0.01)
  195. diff_sig3 <- cluster.marker.by.condition3 %>% filter(abs(avg_log2FC) > 0.585 & p_val_adj < 0.01)
  197. if (length(diff_sig1$p_val) != 0){
  198. write.csv(diff_sig1,paste0("soupX_corrected_analysis/differential_analysis/cluster_",clst,"_Lib1vsLib3_SIG_diff.csv"),row.names = T)
  199. }
  201. if (length(diff_sig2$p_val) != 0){
  202. write.csv(diff_sig2,paste0("soupX_corrected_analysis/differential_analysis/cluster_",clst,"_Lib2vsLib1_SIG_diff.csv"),row.names = T)
  203. }
  205. if (length(diff_sig3$p_val) != 0){
  206. write.csv(diff_sig3,paste0("soupX_corrected_analysis/differential_analysis/cluster_",clst,"_Lib4vsLib3_SIG_diff.csv"),row.names = T)
  207. }
  209. print(paste0("Finished for cluster ",clst))
  211. }
  217. for (clst in c(0:4,6:8,10:29)) {
  219. std.combined.cluster <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct.GFPpos,ident=clst)
  221. Idents(std.combined.cluster) <- "orig.ident"
  223. if (sum(std.combined.cluster$orig.ident == "lib1") > 3 & sum(std.combined.cluster$orig.ident == "lib3") > 3){
  224. cluster.marker.by.condition1 <- FindMarkers(std.combined.cluster, assay = "RNA",ident.1 = "lib1", ident.2 = "lib3",verbose = TRUE,
  225. logfc.threshold = 0,min.pct = 0.01)
  226. }
  228. if (sum(std.combined.cluster$orig.ident == "lib2") > 3 & sum(std.combined.cluster$orig.ident == "lib1") > 3){
  229. cluster.marker.by.condition2 <- FindMarkers(std.combined.cluster, assay = "RNA",ident.1 = "lib2", ident.2 = "lib1",verbose = TRUE,
  230. logfc.threshold = 0,min.pct = 0.01)
  231. }
  233. if (sum(std.combined.cluster$orig.ident == "lib4") > 3 & sum(std.combined.cluster$orig.ident == "lib3") > 3){
  234. cluster.marker.by.condition3 <- FindMarkers(std.combined.cluster, assay = "RNA",ident.1 = "lib4", ident.2 = "lib3",verbose = TRUE,
  235. logfc.threshold = 0,min.pct = 0.01)
  236. }
  239. write.csv(cluster.marker.by.condition1, paste0("soupX_corrected_analysis/differential_analysis_GFPpos/cluster_",clst,"_Lib1vsLib3_full_diff.csv"))
  240. write.csv(cluster.marker.by.condition2, paste0("soupX_corrected_analysis/differential_analysis_GFPpos/cluster_",clst,"_Lib2vsLib1_full_diff.csv"))
  241. write.csv(cluster.marker.by.condition3, paste0("soupX_corrected_analysis/differential_analysis_GFPpos/cluster_",clst,"_Lib4vsLib3_full_diff.csv"))
  243. diff_sig1 <- cluster.marker.by.condition1 %>% filter(abs(avg_log2FC) > 0.585 & p_val_adj < 0.1)
  244. diff_sig2 <- cluster.marker.by.condition2 %>% filter(abs(avg_log2FC) > 0.585 & p_val_adj < 0.1)
  245. diff_sig3 <- cluster.marker.by.condition3 %>% filter(abs(avg_log2FC) > 0.585 & p_val_adj < 0.1)
  247. if (length(diff_sig1$p_val) != 0){
  248. write.csv(diff_sig1,paste0("soupX_corrected_analysis/differential_analysis_GFPpos/cluster_",clst,"_Lib1vsLib3_SIG_diff.csv"),row.names = T)
  249. }
  251. if (length(diff_sig2$p_val) != 0){
  252. write.csv(diff_sig2,paste0("soupX_corrected_analysis/differential_analysis_GFPpos/cluster_",clst,"_Lib2vsLib1_SIG_diff.csv"),row.names = T)
  253. }
  255. if (length(diff_sig3$p_val) != 0){
  256. write.csv(diff_sig3,paste0("soupX_corrected_analysis/differential_analysis_GFPpos/cluster_",clst,"_Lib4vsLib3_SIG_diff.csv"),row.names = T)
  257. }
  259. print(paste0("Finished for cluster ",clst))
  261. }
  265. ####Pathway analysis#####################################
  266. ######GO#####
  267. msigdbr_go <- msigdbr("Mus musculus", "C5")
  268. msigdbr_go <- msigdbr_go %>% filter(gs_subcat %in% c("GO:BP","GO:CC","GO:MF"))
  270. msigdbr_list2 = split(x = msigdbr_go$gene_symbol, f = msigdbr_go$gs_name)
  272. for (path in c(1:length(msigdbr_list2))){
  273. msigdbr_list2[[path]] <- unique(msigdbr_list2[[path]])
  275. }
  278. for (clst in c(0:4,6:8,10:29)) {
  279. diff_table_combined <- read.csv(paste0("soupX_corrected_analysis/differential_analysis_allCells/cluster_",clst,"_Lib2vsLib1_full_diff.csv"))
  281. ##GESA pathway analysis
  282. ranks <- diff_table_combined$avg_log2FC
  283. names(ranks) <- diff_table_combined$X
  285. fgseaRes1 <- fgsea(msigdbr_list2, ranks, minSize=15, maxSize = 500)
  287. for (n in c(1:nrow(fgseaRes1))){
  288. fgseaRes1$leadingEdgeSize[n] <- length(fgseaRes1[,8][[1]][[n]])
  289. }
  290. fgseaRes1<- fgseaRes1[,c(1:7,9,8)] %>% arrange(pval)
  291. fwrite(fgseaRes1,file = paste0("soupX_corrected_analysis/pathway_analysis_allCells/cluster_",clst,"_Lib2vsLib1_GSEA_FULL_GO_pathway.csv"),sep = ",",sep2 = c(""," ",""))
  293. gsea_output1 <- fgseaRes1[which(fgseaRes1$padj < 0.05)][order(pval, -abs(NES)), ]
  295. ##print out significant regulated pathways
  296. if (length(gsea_output1$pathway) != 0){
  297. fwrite(gsea_output1, paste0("soupX_corrected_analysis/pathway_analysis_allCells/cluster_",clst,"_Lib2vsLib1_GSEA_SIG_GO_pathway.csv"),sep = ",",sep2 = c(""," ",""))
  298. }
  300. print(paste0("Finished for cluster ",clst))
  301. }
  306. for (clst in c(0:4,6:8,10:29)) {
  308. diff_table_combined <- read.csv(paste0("soupX_corrected_analysis/differential_analysis_allCells/cluster_",clst,"_Lib1vsLib3_full_diff.csv"))
  310. ##GESA pathway analysis
  311. ranks <- diff_table_combined$avg_log2FC
  312. names(ranks) <- diff_table_combined$X
  314. fgseaRes1 <- fgsea(msigdbr_list2, ranks, minSize=15, maxSize = 500)
  316. for (n in c(1:nrow(fgseaRes1))){
  317. fgseaRes1$leadingEdgeSize[n] <- length(fgseaRes1[,8][[1]][[n]])
  318. }
  319. fgseaRes1<- fgseaRes1[,c(1:7,9,8)] %>% arrange(pval)
  320. fwrite(fgseaRes1,file = paste0("soupX_corrected_analysis/pathway_analysis_allCells/cluster_",clst,"_Lib1vsLib3_GSEA_FULL_GO_pathway.csv"),sep = ",",sep2 = c(""," ",""))
  322. gsea_output1 <- fgseaRes1[which(fgseaRes1$padj < 0.05)][order(pval, -abs(NES)), ]
  324. ##print out significant regulated pathways
  325. if (length(gsea_output1$pathway) != 0){
  326. fwrite(gsea_output1, paste0("soupX_corrected_analysis/pathway_analysis_allCells/cluster_",clst,"_Lib1vsLib3_GSEA_SIG_GO_pathway.csv"),sep = ",",sep2 = c(""," ",""))
  327. }
  329. print(paste0("Finished for cluster ",clst))
  330. }
  334. ####plots and tables#########
  335. ######Stats for up/down regulated genes per cluster###################################
  336. table <- data.frame()
  337. for (clst in c(0:4,6:8,10:29)) {
  339. if(file.exists(paste0("soupX_corrected_analysis/differential_analysis_allCells/cluster_",clst,"_Lib1vsLib3_SIG_diff.csv"))){
  340. diff1 <- read.csv(paste0("soupX_corrected_analysis/differential_analysis_allCells/cluster_",clst,"_Lib1vsLib3_SIG_diff.csv"))
  342. up1 <- length(diff1 %>% filter(avg_log2FC > 0) %>% pull(X))
  343. down1 <- length(diff1 %>% filter(avg_log2FC < 0) %>% pull(X))
  345. table <- rbind(table,c(clst,up1,down1))
  346. }
  348. else{
  349. table <- rbind(table,c(clst,0,0))
  350. }
  352. print(paste0("Finished for cluster ",clst))
  353. }
  356. table2 <- data.frame()
  357. for (clst in c(0:4,6:8,10:29)) {
  359. if(file.exists(paste0("soupX_corrected_analysis/differential_analysis_allCells/cluster_",clst,"_Lib2vsLib1_SIG_diff.csv"))){
  360. diff1 <- read.csv(paste0("soupX_corrected_analysis/differential_analysis_allCells/cluster_",clst,"_Lib2vsLib1_SIG_diff.csv"))
  362. up1 <- length(diff1 %>% filter(avg_log2FC > 0) %>% pull(X))
  363. down1 <- length(diff1 %>% filter(avg_log2FC < 0) %>% pull(X))
  365. table2 <- rbind(table2,c(clst,up1,down1))
  366. }
  368. else{
  369. table2 <- rbind(table2,c(clst,0,0))
  370. }
  372. print(paste0("Finished for cluster ",clst))
  373. }
  375. colnames(table) <- c("cluster","up", "down")
  376. colnames(table2) <- c("cluster","up", "down")
  379. write.csv(table,"soupX_corrected_analysis/lib1vs3_significant_diff_stats.csv")
  380. write.csv(table2,"soupX_corrected_analysis/lib2vs1_significant_diff_stats.csv")
  382. ######Violin plots of Hsp90b1################################
  383. lib1 <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, subset=orig.ident == "lib1")
  384. lib2 <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, subset=orig.ident == "lib2")
  385. lib3 <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, subset=orig.ident == "lib3")
  387. lib1 <- subset(lib1, subset=seurat_clusters != "9")
  388. lib2 <- subset(lib2, subset=seurat_clusters != "9")
  389. lib3 <- subset(lib3, subset=seurat_clusters != "9")
  391. p1 <- VlnPlot(lib3, features = c("Hsp90b1"), pt.size = 0) + BoldTitle() + ylab("lib3") + ggtitle("Hsp90b1") + SeuratAxes() +
  392. theme(legend.position = "none",axis.title.y=element_text(angle=0,vjust=0.5), axis.title.x = element_blank(),axis.text.x=element_blank())
  393. p2 <- VlnPlot(lib1, features = c("Hsp90b1"), pt.size = 0) + BoldTitle() + ylab("lib1") + SeuratAxes() +
  394. theme(legend.position = "none",axis.title.x = element_blank(),axis.text.x=element_blank(),axis.title.y=element_text(angle=0,vjust=0.5),plot.title = element_blank())
  395. p3 <- VlnPlot(lib2, features = c("Hsp90b1"), pt.size = 0) + SeuratAxes() + ylab("lib2") +
  396. theme(legend.position = "none",axis.title.x = element_blank(),axis.title.y=element_text(angle=0,vjust=0.5),plot.title = element_blank())
  399. pdf("soupX_corrected_analysis/Vlnplot_Hsp90b1.pdf",width = 12, height = 8)
  400. wrap_plots(p1,p2,p3,ncol=1)
  401. dev.off()
  404. z1 <- FeaturePlot(lib1, features = "Hsp90b1") + ggtitle("Lib1")
  405. z2 <- FeaturePlot(lib2, features = "Hsp90b1")+ ggtitle("Lib2")
  406. z3 <- FeaturePlot(lib3, features = "Hsp90b1")+ ggtitle("Lib3")
  409. pdf("soupX_corrected_analysis/FeaturePlot_Hsp90b1.pdf",width = 12, height = 8)
  410. wrap_plots(z3,z1,z2,ncol=3)
  411. dev.off()
  414. ###split violinplots
  415. cluster19 <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, subset=seurat_clusters == "19")
  416. cluster20 <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, subset=seurat_clusters == "20")
  418. cluster19_sample123 <- subset(cluster19, subset=orig.ident %in% c("lib1","lib2","lib3"))
  419. cluster20_sample123 <- subset(cluster20, subset=orig.ident %in% c("lib1","lib2","lib3"))
  421. table(cluster19$seurat_clusters)
  422. table(cluster19$orig.ident)
  423. table(cluster19_sample123$orig.ident)
  424. table(cluster20_sample123$orig.ident)
  427. cluster19_sample123$orig.ident <- factor(cluster19_sample123$orig.ident,levels = c("lib3","lib1","lib2"))
  428. cluster20_sample123$orig.ident <- factor(cluster20_sample123$orig.ident,levels = c("lib3","lib1","lib2"))
  430. pdf("soupX_corrected_analysis/Hsp90b1_plots/cluster19_vlnPlot_Hsp90b1.pdf",width = 8, height = 6)
  431. VlnPlot(cluster19_sample123, group.by = "orig.ident",features = c("Hsp90b1"), pt.size = 0.5)
  432. dev.off()
  434. pdf("soupX_corrected_analysis/Hsp90b1_plots/cluster20_vlnPlot_Hsp90b1.pdf",width = 8, height = 6)
  435. VlnPlot(cluster20_sample123, group.by = "orig.ident",features = c("Hsp90b1"), pt.size = 0.5)
  436. dev.off()
  438. dittoPlot(cluster19_sample123, "Hsp90b1", group.by = "orig.ident",
  439. plots = c("vlnplot", "jitter"))
  441. dittoBoxPlot(cluster19_sample123, "Hsp90b1", group.by = "orig.ident")
  444. ###heatmap on selected genes
  445. if (!requireNamespace("BiocManager", quietly = TRUE))
  446. install.packages("BiocManager")
  448. # Install dittoSeq
  449. BiocManager::install("dittoSeq")
  450. library(dittoSeq)
  452. gelist <- c("Hsp90b1","Hspa5","Atp6v0c","Bsg","Cck","Itm2b","Grina","Pcsk1n","Ly6h","Apoe","Ttr","Cdh13","Galntl6","Unc5d","Nrg1","Pcdh15","Sorcs1")
  454. pdf("soupX_corrected_analysis/Hsp90b1_plots/cluster19_heatmap.pdf",width = 8, height = 6)
  455. dittoHeatmap(cluster19_sample123, assay = "RNA", slot = "data", gelist,group.by = "orig.ident",annot.by = "orig.ident")
  456. dev.off()
  458. pdf("soupX_corrected_analysis/Hsp90b1_plots/cluster20_heatmap.pdf",width = 8, height = 6)
  459. dittoHeatmap(cluster20_sample123, assay = "RNA", slot = "data", gelist,group.by = "orig.ident",annot.by = "orig.ident")
  460. dev.off()
  464. ######correlation between Hsp90b1 and Atp6v0c########################
  465. cluster19_sample123$Hsp90b1 <- cluster19_sample123@assays$RNA@data["Hsp90b1",]
  466. cluster19_sample123$Atp6v0c <- cluster19_sample123@assays$RNA@data["Atp6v0c",]
  468. cluster20_sample123$Hsp90b1 <- cluster20_sample123@assays$RNA@data["Hsp90b1",]
  469. cluster20_sample123$Atp6v0c <- cluster20_sample123@assays$RNA@data["Atp6v0c",]
  471. test <- data.frame(cluster19_sample123$Hsp90b1,cluster19_sample123$Atp6v0c)
  473. cosine(cluster19_sample123$Hsp90b1,cluster19_sample123$Atp6v0c)##0.608
  474. cosine(cluster20_sample123$Hsp90b1,cluster20_sample123$Atp6v0c)##0.591
  476. ###cosine similarity and permutation test
  477. cs_list <- c()
  478. gene_pool <- read.csv("soupX_corrected_analysis/differential_analysis_allCells/cluster_19_Lib1vsLib3_full_diff.csv")
  479. gene_pool <- data.frame(gene_pool$X)
  480. for (i in c(1:10000)){
  481. rand <- sample(1:15745,2)
  482. rand1 <- rand[1]
  483. rand2 <- rand[2]
  485. cs <- cosine(cluster19_sample123@assays$RNA@data[gene_pool[rand1,],],cluster19_sample123@assays$RNA@data[gene_pool[rand2,],])
  487. cs_list <- c(cs_list, cs)
  488. }
  490. table(cs_list > 0.608)
  493. cs_list <- c()
  494. gene_pool <- read.csv("soupX_corrected_analysis/differential_analysis_allCells/cluster_20_Lib1vsLib3_full_diff.csv")
  495. gene_pool <- data.frame(gene_pool$X)
  496. for (i in c(1:10000)){
  497. rand <- sample(1:15745,2)
  498. rand1 <- rand[1]
  499. rand2 <- rand[2]
  501. cs <- cosine(cluster20_sample123@assays$RNA@data[gene_pool[rand1,],],cluster20_sample123@assays$RNA@data[gene_pool[rand2,],])
  503. cs_list <- c(cs_list, cs)
  504. }
  506. table(cs_list > 0.591)#0.008
  508. table(cluster19_sample123$Hsp90b1 > 0)##0.45
  509. table(cluster19_sample123$Atp6v0c > 0)##0.41
  510. table(cluster19_sample123$Hsp90b1 > 0 & cluster19_sample123$Atp6v0c > 0)##0.26
  513. table(cluster20_sample123$Hsp90b1 > 0)##0.44
  514. table(cluster20_sample123$Atp6v0c > 0)##0.39
  515. table(cluster20_sample123$Hsp90b1 > 0 & cluster20_sample123$Atp6v0c > 0)##0.23
  517. cosine(cluster20_sample123$Hsp90b1,cluster20_sample123$Atp6v0c)##0.59
  520. ######Rad51,Trp53bp1,Cetn2#####################################
  522. #########lib4 vs lib3##################
  523. lib3_4 <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, subset=orig.ident %in% c("lib3","lib4"))
  526. ##Rad51, Trp53BP1, and centrin2
  527. lib3_4$Rad51 <- lib3_4@assays$RNA@data["Rad51",]
  528. lib3_4$Trp53bp1 <- lib3_4@assays$RNA@data["Trp53bp1",]
  529. lib3_4$Cetn2 <- lib3_4@assays$RNA@data["Cetn2",]
  531. data.summary <- [email hidden] %>%
  532. group_by(orig.ident) %>%
  533. summarise(
  534. Rad51 = mean(Rad51),
  535. Trp53bp1 = mean(Trp53bp1),
  536. Cetn2 = mean(Cetn2)
  537. )
  540. DefaultAssay(lib3_4) <- "RNA"
  542. z1 <- VlnPlot(lib3_4, features = ("Rad51"), split.by = "orig.ident")
  543. z2 <- VlnPlot(lib3_4, features = ("Trp53bp1"), split.by = "orig.ident")
  544. z3 <- VlnPlot(lib3_4, features = ("Cetn2"), split.by = "orig.ident")
  546. pdf("soupX_corrected_analysis/libr3_vs_lib4/VlnPlot_Rad51_Trp53bp1_Cetn2.pdf",width = 12, height = 6)
  547. wrap_plots(z1,z2,z3,ncol=1)
  548. dev.off()
  552. #######lib2 vs lib1##################
  553. lib1_2 <- subset(TLR9.fourSamples.withDbltRemoved.withSoupX.sct, subset=orig.ident %in% c("lib1","lib2"))
  556. ##Rad51, Trp53BP1, and centrin2
  557. lib1_2$Rad51 <- lib1_2@assays$RNA@data["Rad51",]
  558. lib1_2$Trp53bp1 <- lib1_2@assays$RNA@data["Trp53bp1",]
  559. lib1_2$Cetn2 <- lib1_2@assays$RNA@data["Cetn2",]
  561. data.summary <- [email hidden] %>%
  562. group_by(seurat_clusters,orig.ident) %>%
  563. summarise(
  564. Rad51 = mean(Rad51),
  565. Trp53bp1 = mean(Trp53bp1),
  566. Cetn2 = mean(Cetn2)
  567. )
  574. z1 <- VlnPlot(lib1_2, features = ("Rad51"), split.by = "orig.ident") + ylim(-0.1,2.5)
  575. z2 <- VlnPlot(lib1_2, features = ("Trp53bp1"), split.by = "orig.ident")+ ylim(-0.1,2.5)
  576. z3 <- VlnPlot(lib1_2, features = ("Cetn2"), split.by = "orig.ident")+ ylim(-0.1,2.5)
  578. p1 <- VlnPlot(lib1_2, features = c("Rad51"),split.by = "orig.ident") + BoldTitle() + ylab("Rad51") + SeuratAxes() + ylim(-0.1,2.5) +
  579. theme(legend.position = "top",axis.title.x = element_blank(),axis.title.y=element_text(angle=0,vjust=0.5),plot.title = element_blank())+ scale_fill_manual(values=c("blue", "green3"))
  580. p2 <- VlnPlot(lib1_2, features = c("Trp53bp1"),split.by = "orig.ident") + BoldTitle() + ylab("Trp53bp1") + SeuratAxes() + ylim(-0.1,2.5) +
  581. theme(legend.position = "none",axis.title.x = element_blank(),axis.title.y=element_text(angle=0,vjust=0.5),plot.title = element_blank())+ scale_fill_manual(values=c("blue", "green3"))
  582. p3 <- VlnPlot(lib1_2, features = c("Cetn2"),split.by = "orig.ident") + BoldTitle() + ylab("Cetn2") + SeuratAxes() + ylim(-0.1,2.5) +
  583. theme(legend.position = "none",axis.title.x = element_blank(),axis.title.y=element_text(angle=0,vjust=0.5),plot.title = element_blank())+ scale_fill_manual(values=c("blue", "green3"))
  589. pdf("soupX_corrected_analysis/libr3_vs_lib4/lib1vs2_VlnPlot_Rad51_Trp53bp1_Cetn2_v2.pdf",width = 12, height = 6)
  590. wrap_plots(p1,p2,p3,ncol=1)
  591. dev.off()
  594. my_table <- data.frame()
  595. for (clst in c(0:4,6:8,10:29)) {
  597. diff_table <- read.csv(paste0("soupX_corrected_analysis/differential_analysis_allCells/cluster_",clst,"_Lib2vsLib1_full_diff.csv"))
  599. diff_table <- diff_table %>% filter(X %in% c("Rad51","Trp53bp1","Cetn2"))
  600. diff_table$cluster <- clst
  602. my_table <- rbind(my_table,diff_table)
  603. }
  605. write.csv(my_table, "soupX_corrected_analysis/libr3_vs_lib4/Lib2vsLib1_Rad51_Trp53bp1_Cetn2_diff.csv")

code_scRNAseq_analysis.R at commit a80f1ab, no license · at the source

Overview

Authors: Thomas E. Bassett1, Yi-Zhi Wang2, Elizabeth M. Wood1, Hui Zhang1, Zorica Petrovic1, Vivien Prifti1, Vladimir Jovasevic3, Naoki Yamawaki4,5,6, Lynn Ren7, Natalia Khalatyan2, Viktoriya Grayson7, Jeffrey N. Savas2, Jelena Radulovic1,4,5,6, Ana Cicvaric1
ORCID iDs: Naoki Yamawaki, Ana Cicvaric
  1. Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
  2. Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
  3. Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
  4. Department of Biomedicine, Aarhus University, Aarhus, Denmark
  5. PROMEMO, Aarhus University, Aarhus, Denmark
  6. DANDRITE, Aarhus University, Aarhus, Denmark
  7. Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
Institutions: Albert Einstein College of Medicine (United States); Northwestern University (United States); Aarhus University (Denmark)
Journal: iScience, volume 29, issue 4, article 115317
Dates: received 24 July 2025; accepted 6 March 2026; published online 13 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.115317 · PMID 41971990 · PMCID PMC13068534 · OpenAlex W7135212675
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality)
Methods: Preprocessing, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: neuroscience, omics, sensory neuroscience
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (MH108837, MH078064); FWF; Austrian Science Fund; NARSAD; Individual Biomedical Research Award; Hartwell Foundation; NIH (S10OD032464); Albert Einstein College of Medicine
Citations: cited by 2 papers (Europe PMC); 79 references in the paper
Research resources: Rabbit anti-mCherry RRID:AB_2571870, Chicken anti-GFP RRID:AB_300798, AAV8-EF1a-Nuc-flox(mCherry)-EGFP RRID:Addgene_112677, AAVrg-EF1a-DO_DIO-TdTomato_EGFP-WPRE-pA RRID:Addgene_37120, AAV8-hSyn-HA-hM4D(Gi)-mCherry RRID:Addgene_44362, AAV8-hSyn-DIO-hM4D(Gi)-mCherry RRID:Addgene_50475, Vglut2-Cre mice RRID:IMSR_JAX:016963, Vglut1-Cre mice RRID:IMSR_JAX:037512

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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RadulovicLab/Nature-2024

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a80f1abfe4458e10102c5ddfbf8a9b84df98dc0e, 5 February 2024
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (1 file), data.table (1 file), ggplot2 (1 file), pheatmap (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: github.com/RadulovicLab/Nature-2024

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Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 3 keywords, 8 funders, 77 references, 8 RRIDs.

Cite

This paper

Bassett, T. E., Wang, Y.-Z., Wood, E. M., Zhang, H., Petrovic, Z., Prifti, V., Jovasevic, V., Yamawaki, N., Ren, L., Khalatyan, N., Grayson, V., Savas, J. N., Radulovic, J., & Cicvaric, A. (2026). Response dynamics of discrete subiculum→retrosplenial cortex projections underlying trace fear conditioning. iScience, 29(4), 115317. https://doi.org/10.1016/j.isci.2026.115317

BibTeX

@article{bassett2026response,
author = {Bassett, Thomas E. and Wang, Yi-Zhi and Wood, Elizabeth M. and Zhang, Hui and Petrovic, Zorica and Prifti, Vivien and Jovasevic, Vladimir and Yamawaki, Naoki and Ren, Lynn and Khalatyan, Natalia and Grayson, Viktoriya and Savas, Jeffrey N. and Radulovic, Jelena and Cicvaric, Ana},
title = {{Response dynamics of discrete subiculum→retrosplenial cortex projections underlying trace fear conditioning}},
journal = {iScience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {115317},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115317},
url = {https://doi.org/10.1016/j.isci.2026.115317},
pmid = {41971990},
pmcid = {PMC13068534}
}

RIS

TY - JOUR
AU - Bassett, Thomas E.
AU - Wang, Yi-Zhi
AU - Wood, Elizabeth M.
AU - Zhang, Hui
AU - Petrovic, Zorica
AU - Prifti, Vivien
AU - Jovasevic, Vladimir
AU - Yamawaki, Naoki
AU - Ren, Lynn
AU - Khalatyan, Natalia
AU - Grayson, Viktoriya
AU - Savas, Jeffrey N.
AU - Radulovic, Jelena
AU - Cicvaric, Ana
TI - Response dynamics of discrete subiculum→retrosplenial cortex projections underlying trace fear conditioning
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/03/13
VL - 29
IS - 4
SP - 115317
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115317
UR - https://doi.org/10.1016/j.isci.2026.115317
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.115317",
"type": "article-journal",
"title": "Response dynamics of discrete subiculum→retrosplenial cortex projections underlying trace fear conditioning",
"container-title": "iScience",
"author": [
{
"family": "Bassett",
"given": "Thomas E."
},
{
"family": "Wang",
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{
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{
"family": "Zhang",
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{
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{
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{
"family": "Jovasevic",
"given": "Vladimir"
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{
"family": "Yamawaki",
"given": "Naoki"
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{
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"DOI": "10.1016/j.isci.2026.115317",
"PMID": "41971990",
"PMCID": "PMC13068534",
"ISSN": "2589-0042",
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"language": "en",
"issued": {
"date-parts": [
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2026,
3,
13
]
]
}
}

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