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Spatial architecture of autism pathogenesis reveals mosaic structural disarray during early development.

Code ↔ Paper

10 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 10 matches
  1. [1] § Methods › Spatial Signaling Score (S3) ↔ Spatial signaling score/CalculateSpatialSignalingScore.R, lines 87–167 · score 0.88 · edge weight, ligand expression, cell contact, receptor expression, scaled S3, sender cell
  2. [2] § Methods › Spatial Signaling Score (S3) ↔ Spatial signaling score/CalculateSpatialSignalingScore.R, lines 87–167 · score 0.75 · cell contact graph, edge weights, Scaled S3, receiver cell identity, sum, sender
  3. [3] § Methods › Spatial Signaling Score (S3) ↔ Spatial signaling score/Spatialsignalingscore_main.R, lines 1–42 · score 0.71 · literature supported, receptor genes, shortlisted ligand, ligand receptor pairs, S3, Score
  4. [4] § Methods › MERFISH spot assignment ↔ Spot assigment/Assign_spots_EDT.py, lines 231–281 · score 0.67 · segmented cell, RNA spot, cell mask, nucleus, assignment, dilated
  5. [5] § Results › Cell adhesion between ASD progenitors is dysregulated ↔ Spatial signaling score/CalculateSpatialSignalingScore.R, lines 47–85 · score 0.66 · interacting cell pairs, receptor expression, Spatial Signaling Score, receiver cell identity, ligand receptor, sender
  6. [6] § Methods › Spatial Signaling Score (S3) ↔ Spatial signaling score/Spatialsignalingscore_main.R, lines 330–392 · score 0.64 · edge weight, cell contact, ligand receptor pair, sender, S3, receiver
  7. [7] § Methods › Spatial Signaling Score (S3) ↔ Spatial signaling score/Spatialsignalingscore_main.R, lines 330–392 · score 0.60 · cell contact graph, edge weights, cell identity, sender, S3, receiver
  8. [8] § Methods › MERFISH spot assignment ↔ Spot assigment/Assign_spots_EDT.py, lines 231–281 · score 0.59 · segmented region, RNA spots, nuclei, assignment, DAPI, mask
  9. [9] § Methods › Spatial Signaling Score (S3) ↔ Spatial signaling score/Spatialsignalingscore_main.R, lines 394–455 · score 0.53 · randomized control, diagnosis, disorganized, signaling, cell identities, S3
  10. [10] § Methods › MERFISH spot decoding ↔ Spot assigment/Assign_spots_EDT.py, lines 39–130 · score 0.52 · Euclidean distance, closest, spots, pixels, frame, cell

Paper

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The authors' code

R · 685 lines · 40 KB · no license · 4 matches

  1. # The main script for quantifying spatial ligand-receptor interactions
  2. # load libraries
  3. library(patchwork) #1.1.1
  4. library(Rmisc) #1.5
  5. library(reticulate) #1.22
  6. library(data.table) #1.14.2
  7. library(readxl) #1.3.1
  8. library(reshape2) #1.4.4
  9. library(pheatmap) #1.0.12
  10. library(Seurat) #4.0.2
  11. library(ggplot2)
  12. library(readxl)
  13. library(quantmod)
  15. # load our own functions
  16. source("/home/ubuntu/SpatialSignalingScore/DistToDataframe.R")
  17. source("/home/ubuntu/SpatialSignalingScore/AnalysisSpatialNetworks.R")
  18. source("/home/ubuntu/SpatialSignalingScore/CalculateSpatialSignalingScore.R")
  19. source("/home/ubuntu/SpatialSignalingScore/ScaleSpatialSignalingScore.R")
  20. source("/home/ubuntu/SpatialSignalingScore/FindDifferentialSpatialSignalingScore.R")
  21. source("/home/ubuntu/SpatialSignalingScore/VisualizeS3Dotplot.R")
  22. source("/home/ubuntu/SpatialSignalingScore/LoadAllMetrics.R")
  24. #======================================== 1. SHORTLIST LIGANDS AND RECEPTORS FOR ANALYSIS ==============================================
  25. #---------------- Load ligand-receptor pairs from literature: https://static-content.springer.com/esm/art%3A10.1038%2Fs41467-020-18873-z/MediaObjects/41467_2020_18873_MOESM4_ESM.xlsx ---------------
  26. ligand.receptor = read_excel('/home/ubuntu/41467_2020_18873_MOESM4_ESM.xlsx',sheet='literature_support')
  28. #---------------- Select ligands and receptors that exist in scRNAseq panel ---------------
  29. scrnaP <- readRDS("/home/ubuntu/scrnaP.rds")
  30. scrnaN <- readRDS("/home/ubuntu/scrnaN.rds")
  31. scrna<-merge(scrnaN,y = scrnaP)
  32. selected_pairs = data.frame(ligand = character(length=0L),receptor=character(length=0L))
  33. for (i in 1:nrow(ligand.receptor)){
  34. if (ligand.receptor[i,"Ligand gene symbol"] %in% rownames(scrna@assays[["SCT"]])){
  35. if (ligand.receptor[i,"Receptor gene symbol"] %in% rownames(scrna@assays[["SCT"]])){
  36. temp = cbind(as.character(ligand.receptor[i,"Ligand gene symbol"]),
  37. as.character(ligand.receptor[i,"Receptor gene symbol"]),
  38. as.character(ligand.receptor[i,"Ligand location"]))
  39. colnames(temp) = c('ligand','receptor','ligand.location')
  40. selected_pairs=rbind(selected_pairs,temp)
  41. }}}
  42. write.csv(selected_pairs,file='/home/ubuntu/differentialS3/selected_ligand-receptor_pairs_latest2020_20230714.txt')
  44. #---------------- Filter out ligands and receptors that are lowly expressed in scRNAseq ---------------
  45. lig.rec = unique(c(as.character(selected_pairs$ligand),as.character(selected_pairs$receptor)))
  47. # get highest mean expression per cell identity across all cell identities
  48. best.avg = data.frame(row.names=lig.rec)
  49. avg.scRNAseq <- AverageExpression(scrna, features=lig.rec, assays="SCT",slot='counts',group.by = "cell_identity",verbose =F)
  50. best.avg$scRNAseq <- apply(avg.scRNAseq$SCT,1,max)
  51. p <- hist(log(best.avg$scRNAseq),breaks=60)
  52. p$breaks[findValleys(p$density, thresh=0)]
  53. lig.rec.good = lig.rec[(best.avg$scRNAseq)>exp(-2.4)] # threshold selected is at the valley of the bimodel distribution
  55. selected_pairs2= data.frame(ligand = character(length=0L),receptor=character(length=0L))
  56. for (i in 1:nrow(selected_pairs)){
  57. if (selected_pairs[i,"ligand"] %in% lig.rec.good){
  58. if (selected_pairs[i,"receptor"] %in% lig.rec.good){
  59. temp = cbind(as.character(selected_pairs[i,"ligand"]),
  60. as.character(selected_pairs[i,"receptor"]))
  61. colnames(temp) = c('ligand','receptor')
  62. selected_pairs2=rbind(selected_pairs2,temp)
  63. }}}
  64. lig.rec.left = unique(c(as.character(selected_pairs2$ligand),as.character(selected_pairs2$receptor)))
  65. selected_pairs = selected_pairs2
  66. selected_pairs$LR_comb = paste0(as.character(selected_pairs$ligand),'--',as.character(selected_pairs$receptor))
  67. write.csv(selected_pairs,file=paste0(root_dir,'selected_ligand-receptor_pairs_threshold_valley_20230714.txt'))
  69. #---------------- Initialze all paremeters and paths ---------------
  70. meta.mfish <- read.csv("/home/ubuntu/metadata_mfish.csv", header = T)
  71. data_name = meta.mfish$data_name
  72. ctrl_name = meta.mfish$data_name[meta.mfish$diagnosis=="ctrl"]
  73. case_name = meta.mfish$data_name[meta.mfish$diagnosis=="ASD"]
  75. ##--- choose assay 'SCT': measured expression,'SCTpred': imputed expression. Both use 'counts' slot ---
  76. assay = 'SCTpred'
  78. ##--- choose method of Spatial Signaling Score 1:demoninator being # of interacted receiver cells,
  79. ## 2:demoninator being # of all receiver cells,
  80. save.id = 2 # default is 2
  82. ##--- directory of results for all datasets ---
  83. root_dir = '/home/ubuntu/differentialS3/'
  84. data_dir = '/home/ubuntu/differentialS3/panel1/'
  85. results_folder = '/home/ubuntu/differentialS3/results/'
  86. data_dir_wilcoxtest = '/home/ubuntu/differentialS3/wilcoxtest/'
  88. ##--- choose cell identity labels to be processed ---
  89. # labels from imputed expression: 'imputed.cell_identity'
  90. # labels from measured expression: 'cell_identity'
  91. cluster_column = "imputed.cell_identity"
  93. ##--- choose the spatial network, 'SN_2median':physical distance < 2*median are connected
  94. ## 'SN_touched':two cells are touched, juxtacrine signalling
  95. spatial_network_name = 'SN_touched'
  97. ##--- threshold of proportion of interacting receiver cells ---
  98. prop_thrd = 0.2
  100. ##--- load SFARI gene list ---
  101. sfari.gene = read.csv("/home/ubuntu/SFARI-Gene_genes_01-23-2023release_03-02-2023export.csv")
  103. ##--- Format of selected ligand-receptor pairs ---
  104. # "ligand" "receptor" "ligand.location" "LR_comb"
  105. # 1 ...
  106. # 2 ...
  107. # ...
  109. ##--- load selected ligand-receptor pairs ---
  110. LRname = "_exp-5_intersect"
  111. selected_pairs = read.csv(file=paste0(root_dir,'selected_ligand-receptor_pairs_threshold_valley_20230714.txt'))
  112. selected_pairs = selected_pairs[,-1]
  113. selected_pairs$LR_comb = paste0(selected_pairs$ligand,'--',selected_pairs$receptor)
  115. ##--- Subset ligand-receptor pairs for which ligand and/or receptor are SFARI genes ---
  116. selected_pairs_sfari = NULL
  117. for (i in 1:nrow(selected_pairs)){
  118. if ( selected_pairs$ligand[i] %in% sfari.gene$gene.symbol | selected_pairs$receptor[i] %in% sfari.gene$gene.symbol){
  119. selected_pairs_sfari=rbind(selected_pairs_sfari,selected_pairs[i,])
  120. }
  121. }
  122. write.csv(selected_pairs_sfari,file=paste0(root_dir,'sfari_ligand-receptor_pairs_20230714.txt'))
  124. ## NOTE: all steps will saved intermediate results. Subsequent steps will directly load the saved results
  127. #======================================== 2. GENERATE SPATIAL NETWORKS & CALCULATE PROPORTION OF INTERACTING RECEIVER CELLS ==============================================
  128. for (x in c(1:length(data_name))){
  129. results_folder = paste0(root_dir,'panel1/',data_name[x],'/')
  130. if (!dir.exists(results_folder)){
  131. dir.create(results_folder)
  132. }
  133. Mer <- readRDS(paste0("/home/ubuntu/label transfer/dataset/",meta.mfish$data_name[[x]],"_imputed_18k_genes_-SCT-anchor-SCT.rds"))
  134. gene.list = unique(c(rownames(Mer@assays$SCT), selected_pairs$ligand, selected_pairs$receptor))
  135. gene.list = gene.list[gene.list %in% rownames(Mer)]
  136. Mer <- subset(Mer, features = gene.list)
  137. cell_id = names(Idents(Mer))
  138. cell_id = substr(cell_id,7+nchar(data_name[x]),16+nchar(data_name[x]))
  139. Idents(Mer) = as.numeric(cell_id)
  140. Mer<-RenameCells(Mer, new.names =as.character(Idents(Mer)))
  141. mer.loc <- data.frame(row.names = Idents(Mer))
  142. mer.loc$X = Mer$X
  143. mer.loc$Y = Mer$Y
  144. actual_height = max(Mer$Y) - min(Mer$Y)
  145. actual_width = max(Mer$X) - min(Mer$X)
  146. height = actual_height/1000 # plot height
  147. width = actual_width/1000
  148. size = width/100
  150. #---------------- generate spatial networks ----------------
  151. if (spatial_network_name == 'SN_touched'){
  152. # cell-cell contact network/graph generated on python, directly load here
  153. annot_network = read.csv(paste0('/home/ubuntu/',meta.mfish$data_name[[x]],'/touched_cell_networks.csv'))
  154. # delete the edge of cells filtered in preprocessing
  155. annot_network = annot_network[annot_network$from %in% Idents(Mer) &annot_network$to %in% Idents(Mer),]
  156. annot_network$from = factor(annot_network$from)
  157. annot_network$to = factor(annot_network$to)
  158. annot_network$distance = sqrt((annot_network$sdimx_begin-annot_network$sdimx_end)**2+(annot_network$sdimy_begin-annot_network$sdimy_end)**2)
  159. print(paste0(meta.mfish$data_name[[x]], ":median of cell cell distance is ", median(annot_network$distance)))
  160. Mer@graphs[[spatial_network_name]] = list(name = spatial_network_name, method = 'touched',networkDT = annot_network)
  162. } else if ( spatial_network_name == 'SN_2median'){
  163. # find median of nearest neighbor
  164. nn_spatial <- dbscan::kNN(loc , k=1)
  165. median_neighbors = median(nn_spatial[["dist"]])
  166. print(paste0(data_name[dataset],': median of nearest neighbor is ',median_neighbors))
  167. # spatial networks, distance < 2* median
  168. Mer<-GenerateSpatialNetwork(Mer, threshold=median_neighbors*2,
  169. name = spatial_network_name,full_network = FALSE)
  170. }
  172. #---------------- Calculate proportion of interacting receiver cells. results are stored in Mer@graphs[[spatial_network_name]][['proportion']][[cluster_column]] ----------------
  173. Mer<-AnalysisSpatialNetworks(object=Mer,
  174. cluster_column = 'imputed.cell_identity',
  175. selected_clusters = selected_clusters,
  176. selected_pairs = selected_pairs,
  177. networks = spatial_network_name)
  178. Mer<-AnalysisSpatialNetworks(object=Mer,
  179. cluster_column = 'cell_identity',
  180. selected_clusters = selected_clusters,
  181. selected_pairs = selected_pairs,
  182. networks = spatial_network_name)
  183. saveRDS(Mer,paste0(results_folder,"Seurat_object_",spatial_network_name,".rds"))
  184. }
  186. #---------------- Filter proportion of interacting receiver cells ----------------
  187. ##--- Load proportion for all datasets ----
  188. cluster_column = 'imputed.cell_identity'
  189. proportion_all = NULL
  190. for(dataset in c(ctrl_name, case_name)){
  191. data_folders = paste0(data_dir, dataset, '/')
  192. print(dataset)
  193. Mer<-readRDS(paste0(data_folders,"Seurat_object_", spatial_network_name, ".rds"))
  194. print(unique(Mer$imputed.cell_identity))
  195. spatial_all_scores<-Mer@graphs[[spatial_network_name]][['proportion']][[cluster_column]]
  196. spatial_all_scores$data = dataset
  197. proportion_all = rbind(proportion_all,spatial_all_scores)
  198. }
  199. write.csv(proportion_all,paste0(root_dir,'proportion/proportion_all_',cluster_column,'_',spatial_network_name,'.csv'))
  201. ##--- Calculate the mean proportions within each diagnosis ----
  202. proportion_mean = data.frame(row.names = unique(proportion_all$LR_cell_comb))
  203. for (cell_identity_pairs in unique(proportion_all$LR_cell_comb)){
  204. # control
  205. temp =proportion_all[proportion_all$LR_cell_comb %in%cell_identity_pairs &proportion_all$data %in% ctrl_name,]
  206. proportion_mean[cell_identity_pairs,'mean_ctrl'] = mean(temp$proportion)
  207. proportion_mean[cell_identity_pairs,'standard_error_ctrl'] = sd(temp$proportion)/sqrt(nrow(temp)-1)
  208. # case
  209. temp =proportion_all[proportion_all$LR_cell_comb %in%cell_identity_pairs &proportion_all$data %in% case_name,]
  210. proportion_mean[cell_identity_pairs,'mean_case'] = mean(temp$proportion)
  211. proportion_mean[cell_identity_pairs,'standard_error_case'] = sd(temp$proportion)/sqrt(nrow(temp)-1)
  212. }
  213. proportion_mean = proportion_mean[!is.na(proportion_mean$standard_error_ctrl) &!is.na(proportion_mean$standard_error_case),]
  214. proportion_mean$LR_cell_comb = rownames(proportion_mean)
  215. write.csv(proportion_mean,paste0(root_dir,'proportion/proportion_mean_',cluster_column,'_',spatial_network_name,'.csv'))
  217. ##--- give the cell identities we are interested ----
  218. selected_clusters = unique(Mer$imputed.cell_identity)
  220. ##--- all the homotypical cell identity pairs A-A and its proportions ---
  221. same_DT = data.table(V1 = selected_clusters, V2 = selected_clusters)
  222. same_DT$LR_cell_comb = paste0(same_DT$V1,'--',same_DT$V2)
  223. proportion_all_homo = proportion_mean[proportion_mean$LR_cell_comb %in% same_DT$LR_cell_comb,]
  224. write.csv(proportion_all_homo,paste0(root_dir,'proportion/proportion_mean_homo_',cluster_column,'_',spatial_network_name,'.csv'))
  226. ##--- apply threshold for homotypical cell identity pairs ---
  227. proportion_homo_filtered = proportion_all_homo[proportion_all_homo$mean_ctrl>prop_thrd,]
  229. ##--- heterotypic cell identity pairs: A-B, B-A ---
  230. combn_DT = as.data.table(t(combn(selected_clusters, m = 2)))
  231. # direction 1: A-B
  232. combn_DT$LR_cell_comb_1 = paste0(combn_DT$V1,'--',combn_DT$V2)
  233. # direction 2: B-A
  234. combn_DT$LR_cell_comb_2 = paste0(combn_DT$V2,'--',combn_DT$V1)
  236. combn_DT$proportion_1 =NA
  237. combn_DT$proportion_2=NA
  239. ##--- for any two cell identities, find the maximum proportion in either direction ---
  240. for (i in seq(1,nrow(combn_DT))){
  241. if (length(proportion_mean[proportion_mean$LR_cell_comb %in% combn_DT$LR_cell_comb_1[i],"mean_ctrl"]!=0)){
  242. combn_DT$proportion_1[i] = proportion_mean[proportion_mean$LR_cell_comb %in% combn_DT$LR_cell_comb_1[i],"mean_ctrl"]
  243. }
  244. if (length(proportion_mean[proportion_mean$LR_cell_comb %in% combn_DT$LR_cell_comb_2[i],"mean_ctrl"]!=0)){
  245. combn_DT$proportion_2[i] = proportion_mean[proportion_mean$LR_cell_comb %in% combn_DT$LR_cell_comb_2[i],"mean_ctrl"]
  246. }
  247. combn_DT$proportion[i] = max(combn_DT$proportion_1[i],combn_DT$proportion_2[i])
  248. }
  249. proportion_hetero_all = combn_DT[!is.na(combn_DT$proportion_1),]
  250. write.csv(proportion_hetero_all,paste0(root_dir,'proportion/proportion_mean_hetero_',cluster_column,'_',spatial_network_name,'.csv'))
  252. ##--- apply threshold for heterotypic cell identity pairs: find the maximum proportion in either direction > threshold ---
  253. proportion_hetero_filtered = proportion_hetero_all[proportion_hetero_all$proportion>prop_thrd,]
  255. ##--- all the cell identity pairs after applying filtering ---
  256. selected_cluster_pairs = c(proportion_hetero_filtered$LR_cell_comb_1,proportion_hetero_filtered$LR_cell_comb_2,rownames(proportion_homo_filtered))
  257. write.csv(selected_cluster_pairs,paste0(root_dir,'proportion/selected_cluster_pairs_proportion_',prop_thrd,'_',cluster_column,'_',spatial_network_name,'.csv'),col.names = NA, row.names = FALSE)
  259. #---------------- Visualize selected proportions of interacting receiver cells ----------------
  260. ##--- load cell identity pairs after applying filtering ---
  261. selected_cluster_pairs = read.csv(paste0(root_dir,'proportion/selected_cluster_pairs_proportion_0.2_',cluster_column,'_',spatial_network_name,'.csv'))
  262. selected_cluster_pairs = sort(selected_cluster_pairs$x)
  263. ##--- load proportion for all cell identity pairs and all datasets ---
  264. proportion_all = read.csv(paste0(root_dir,'proportion/proportion_all_',cluster_column,'_',spatial_network_name,'.csv'))
  265. selDT = as.data.table(proportion_all)
  266. selDT_d = data.table::dcast.data.table(selDT, data~LR_cell_comb, value.var = 'proportion', fill = 0)
  267. dataname = c(ctrl_name,case_name)
  268. rownames = selDT_d$data
  269. selDT_d = selDT_d[,-1]
  270. selDT_d = as.matrix(selDT_d)
  271. rownames(selDT_d) = rownames
  272. selDT_g = selDT_d[dataname,]
  274. ##--- do wilcoxon test on proportions to identify cell identity pairs with significant difference between case and control, p.value<0.05 ---
  275. sign_cluster_pairs = NULL
  276. for (col in 1:ncol(selDT_g)) {
  277. ttest=t.test(selDT_g[ctrl_name,col],selDT_g[case_name,col],na.action=na.omit)
  278. print(paste0(colnames(selDT_g)[col]," proportion p.value: ",ttest$p.value))
  279. if (ttest$p.value<=0.05) sign_cluster_pairs = c(sign_cluster_pairs,colnames(selDT_g)[col])
  280. }
  281. sign_cluster_pairs = intersect(sign_cluster_pairs,selected_cluster_pairs)
  283. ##--- mean case vs mean ctrl ---
  284. proportion_case = data.frame(row.names = selected_cluster_pairs )
  285. proportion_sub = proportion_all[proportion_all$data %in% case_name,]
  286. for (cell_identity_pairs in selected_cluster_pairs ){
  287. temp =proportion_sub[proportion_sub$LR_cell_comb %in%cell_identity_pairs,]
  288. proportion_case[cell_identity_pairs,'mean'] = mean(temp$proportion)
  289. proportion_case[cell_identity_pairs,'standard_error'] = sd(temp$proportion)/sqrt(nrow(temp)-1)
  290. }
  291. proportion_case$diagnosis = 'Case'
  292. proportion_case$cell_identity_pairs = rownames(proportion_case)
  294. proportion_ctrl = data.frame(row.names = selected_cluster_pairs)
  295. proportion_sub = proportion_all[proportion_all$data %in% ctrl_name,]
  297. for (cell_identity_pairs in selected_cluster_pairs ){
  298. temp =proportion_sub[proportion_sub$LR_cell_comb %in%cell_identity_pairs,]
  299. proportion_ctrl[cell_identity_pairs,'mean'] = mean(temp$proportion)
  300. proportion_ctrl[cell_identity_pairs,'standard_error'] = sd(temp$proportion)/sqrt(nrow(temp)-1)
  301. }
  302. proportion_ctrl$diagnosis = 'Control'
  303. proportion_ctrl$cell_identity_pairs = rownames(proportion_ctrl)
  305. proportion_mean = rbind(proportion_case,proportion_ctrl)
  307. x.text <- transpose(as.data.frame(strsplit(sort(unique(proportion_mean$cell_identity_pairs)), split = "--")))
  308. colnames(x.text) = c('sender', 'receiver')
  309. x.text$sender.receiver = paste0(x.text$sender,'--',x.text$receiver)
  310. x.text$color = 'black'
  311. x.text$color[x.text$sender.receiver %in% sign_cluster_pairs] = 'red'
  313. ggplot(data = proportion_mean,aes(x=mean,y=cell_identity_pairs,color=diagnosis))+geom_point(alpha=0.5)+#position = position_dodge(width = 1)
  314. geom_errorbar(aes(xmin = mean-standard_error,xmax = mean+standard_error,color=diagnosis),alpha=0.5) +
  315. theme_bw() + scale_color_manual(values = c('orange','black'))+
  316. theme(axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=0.5,size=16),
  317. legend.title = element_text(size=16), #change legend title font size
  318. legend.text = element_text(size=15))+
  319. annotate(geom = "text", y=c(nrow(x.text)+1), x = -0.5, label = c('receiver'), size = 7,hjust = 0,fontface="bold")+
  320. annotate(geom = "text", y=c(nrow(x.text)+1), x = -0.55, label = c('sender'), size = 7, hjust = 1,fontface="bold")+
  321. annotate(geom = "text", y=c(nrow(x.text)+1), x = -0.525, label = c('-'), size = 7, hjust = 0.5,fontface="bold")+
  322. annotate(geom = "text", y = seq_len(nrow(x.text)), x = -0.5, label = x.text$receiver, size = 6,colour = x.text$color,hjust = 0)+
  323. annotate(geom = "text", y = seq_len(nrow(x.text)), x = -0.55, label = x.text$sender, size = 6,colour = x.text$color,hjust = 1)+
  324. annotate(geom = "text", y = seq_len(nrow(x.text)), x = -0.525, label = '-', size = 6,colour = x.text$color,hjust = 0.5)+
  325. coord_cartesian(xlim=c(0,1),ylim=c(0.5,nrow(x.text)+0.5),expand = FALSE, clip = "off")+
  326. theme(plot.margin=unit(c(2,1,1,20),'lines'),axis.text.y = element_blank(),axis.title = element_blank())
  327. ggsave(paste0(root_dir,'proportion/',cluster_column,'_proportion_',prop_thrd,'_case_vs_ctrl_',spatial_network_name,'_20230714.png'),
  328. width = 9.3,height=30,units = 'in')
  330. #======================================== 3. EVALUATE LARB CUTOFF ==============================================
  331. assay='SCTpred'
  332. data_folders = paste0(data_dir,data_name[4],'/')
  334. ##--- load the merfish dataset with the spatial networks ---
  335. Mer<-readRDS(paste0(data_folders,"Seurat_object_",spatial_network_name,".rds"))
  336. Mer<-subset(Mer,feature = unique(c(selected_pairs$ligand,selected_pairs$receptor)))
  337. expr_select = GetAssayData(object = Mer, assay = assay, slot = 'data')
  338. cell_metadata = [email hidden]
  340. ##--- the annotation of spatial network ---
  341. annot_network_directional = Mer@graphs[[spatial_network_name]][["networkDT"]]
  343. ##--- get the expression ---
  344. colnames(expr_select) = colnames(Mer)
  346. ##--- check if ligand or receptor existing in the expression ---
  347. selected_pairs_sub = selected_pairs[selected_pairs$ligand %in% rownames(expr_select),]
  348. selected_pairs_sub = selected_pairs_sub[selected_pairs_sub$receptor %in% rownames(expr_select),]
  350. ##--- only keep the ligand-receptor pairs for ligand and receptor existing in the expression ---
  351. select_ligands = as.character(selected_pairs_sub$ligand)
  352. select_receptors = as.character(selected_pairs_sub$receptor)
  353. sender.expr = as.matrix(expr_select[select_ligands,as.character(annot_network_directional$from)])
  354. receiver.expr = as.matrix(expr_select[select_receptors,as.character(annot_network_directional$to)])
  356. ##--- edge weight in cell-cell contact graph ---
  357. LARB_expr = sender.expr*receiver.expr
  358. hist(log(LARB_expr),breaks=100)
  359. LARB_cutoff = exp(-5) # 2x peak of log distribution
  362. #======================================== 4. CALCULATE SPATIAL SIGNALING SCORE ==============================================
  363. assay="SCTpred"
  364. cluster_column = "imputed.cell_identity"
  366. for (x in c(1:length(data_name))){
  367. print(data_name[x])
  368. results_folder = paste0('/home/ubuntu/differentialS3/panel1/',data_name[x],'/')
  369. if (!dir.exists(results_folder)){
  370. dir.create(results_folder)
  371. }
  372. data_folders = paste0(data_dir,data_name[x],'/')
  373. Mer<-readRDS(paste0(data_folders,"Seurat_object_",spatial_network_name,".rds"))
  375. if(!dir.exists(paste0(results_folder,'3_lig-rec_cell-cell_communication'))){
  376. dir.create(paste0(results_folder,'3_lig-rec_cell-cell_communication'))
  377. }
  378. ##--- only run for the cell_identity pairs shortlist ---
  379. selected_cluster_pairs = read.csv(paste0(root_dir,'proportion/selected_cluster_pairs_proportion_',prop_thrd,'_imputed.cell_identity_',spatial_network_name,'.csv'))
  380. selected_cluster_pairs = sort(selected_cluster_pairs$x)
  381. Mer<-CalculateSpatialSignalingScore(object=Mer,
  382. cluster_column = cluster_column,
  383. selected_cluster_pairs = selected_cluster_pairs,
  384. normalization = FALSE,
  385. LARB_cutoff = LARB_cutoff,
  386. assay = assay,
  387. slot = 'data',
  388. selected_pairs = selected_pairs,
  389. networks = spatial_network_name,
  390. save.id = save.id)
  391. saveRDS(Mer,paste0(results_folder,save.id,"_Seurat_object_SpatialSignalingScore",spatial_network_name, "_assay_",assay,LRname,".rds"))#"_LARB_",LARB_cutoff,"_scale.factor_",scale.factor,
  392. }
  394. #======================================== 5. CALCULATE CASE-CONTROL DIFFERENTIAL SPATIAL SIGNALING SCORE ==============================================
  395. # get shortlist cell_identity pairs
  396. assay='SCTpred'
  397. cluster_column = 'imputed.cell_identity'
  398. selected_cluster_pairs = read.csv(paste0(root_dir,'proportion/selected_cluster_pairs_proportion_0.2_',cluster_column,'_',spatial_network_name,'.csv'))
  399. selected_cluster_pairs = sort(selected_cluster_pairs$x)
  401. ctrl_name = meta.mfish$data_name[meta.mfish$diagnosis=="ctrl"]
  403. case_name = meta.mfish$data_name[meta.mfish$diagnosis=="ASD"]
  405. phenotypeA = meta.mfish$data_name[meta.mfish$layering=="disorganized" & # bud_mis_layering_no_desease_state_case
  406. meta.mfish$diagnosis=="ASD" &
  407. meta.mfish$state=="canonical" &
  408. meta.mfish$bud=="present"]
  409. phenotypeA = c(phenotypeA, "20221006_JY_T2_B4_108A")
  410. phenotypeA = phenotypeA[! phenotypeA %in% "20220916_JY_T2_B4_50"]
  412. phenotypeB = meta.mfish$data_name[ meta.mfish$diagnosis=="ASD" &
  413. meta.mfish$state=="diseased"]
  415. grps = list("case_name" = case_name,
  416. "phenotypeA" = phenotypeA,
  417. "phenotypeB" = phenotypeB)
  419. for (group in names(grps)){
  420. score_all_dataset = FindDifferentialSpatialSignalingScore(case_name=get(group),
  421. ctrl_name=ctrl_name, selected_pairs= selected_pairs,
  422. cluster_column=cluster_column, selected_cluster_pairs = selected_cluster_pairs,
  423. spatial_network_name=spatial_network_name, data_dir=data_dir,save.id=save.id,
  424. assay = assay,LRname = LRname)
  425. saveRDS(score_all_dataset,paste0(root_dir,'wilcoxtest/',save.id,'_dS3_', group, '_vs-all-ctrls_',cluster_column,'_',spatial_network_name,"_assay_",assay,LRname,'.rds'))
  426. }
  429. #--- compare random sets of controls: we use mean 99th percentile of NMD distribution in 50 randomized control-to-control comparisons as threshold ----
  430. df <- data.frame()
  431. for (itr in 1:50){
  432. ctrl1 <- union(sample(meta.mfish$data_name[meta.mfish$layering %in% "disorganized"], 3), sample(meta.mfish$data_name[meta.mfish$layering %in% "layered"], 4))
  433. ctrl2 <- ctrl_name[!ctrl_name %in% ctrl1]
  434. score_all_dataset = FindDifferentialSpatialSignalingScore(case_name=ctrl2,
  435. ctrl_name=ctrl1, selected_pairs= selected_pairs,
  436. cluster_column=cluster_column, selected_cluster_pairs = selected_cluster_pairs,
  437. spatial_network_name=spatial_network_name, data_dir=data_dir,save.id=save.id,
  438. assay = assay,LRname = LRname)
  439. saveRDS(score_all_dataset,paste0(root_dir,'wilcoxtest/',save.id,'_dS3_ctrlvsctrl-itr', itr, '_',cluster_column,'_',spatial_network_name,"_assay_",assay,LRname,'.rds'))
  441. metric_score = readRDS(paste0(data_dir_wilcoxtest, save.id,'_dS3_ctrlvsctrl-itr', itr, '_',cluster_column,'_',spatial_network_name,"_assay_",assay, LRname, '.rds'))
  442. metric_score <- metric_score[metric_score$p.adjust < thr.padjust, ]
  443. print(nrow(metric_score[metric_score$p.adjust < thr.padjust,]))
  445. df[itr, 1:6] <- summary(metric_score$differential_S3_normalized)
  446. df[itr, 7] <- mean(abs(metric_score$differential_S3_normalized))
  447. }
  448. colnames(df) <- c("min","Q1", "median", "mean", "Q3", "max", "mean abs NMD")
  450. for (itr in 1:50){
  451. metric_score = readRDS(paste0(data_dir_wilcoxtest, save.id,'_dS3_ctrlvsctrl-itr', itr, '_',cluster_column,'_',spatial_network_name,"_assay_",assay, LRname, '.rds'))
  452. print(nrow(metric_score[metric_score$p.adjust < thr.padjust & abs(metric_score$differential_S3_normalized) > mean(df$V9), ]))
  453. }
  454. colnames(df[, 8:10]) <- c("95th percentile", "99th percentile", "99.9th percentile")
  455. saveRDS(df, "/home/ubuntu/differentialS3/wilcoxtest/ctrl-vs-ctrl_iterations.rds")
  458. #======================================== 6. DOTPLOT VISUALIZATION OF CASE-CONTROL DIFFERENTIAL SPATIAL SIGNALING SCORE ==============================================
  459. assay='SCTpred'
  460. cluster_column = 'imputed.cell_identity'
  462. selected_pairs_sfari = read.csv(file=paste0(root_dir,'sfari_ligand-receptor_pairs_20230714.txt'))
  463. selected_pairs_nosfari = selected_pairs[!selected_pairs$LR_comb %in% selected_pairs_sfari$LR_comb,]
  464. # set the parameter
  465. size = "p.adjust"
  466. diff = "differential_S3_normalized"
  467. color = "differential_S3_normalized"
  468. metrics = "score"
  469. thr.pvalue = NULL
  470. filtering = TRUE
  471. thr.diff = 0.006 # mean 99th percentile of NMD distribution in 50 randomized control-to-control comparisons
  472. thr.padjust = 0.05
  473. orderby = color
  474. top_num_per_pair = NULL
  475. top_num_all = 200L
  476. sfari = ""
  477. highlight = sfari.gene$gene.symbol
  478. cluster_on = color
  479. max_diff=NULL
  480. p.circle = 0.05
  481. meanlig_thr = NULL
  482. meanrec_thr = NULL
  484. control.grp = "all"
  485. for (option in c("case_name", "phenotypeA", "phenotypeB")){
  486. metric_score = readRDS(paste0(data_dir_wilcoxtest, save.id, '_dS3_', option, '_vs-', control.grp, '-ctrls_', cluster_column,'_', spatial_network_name, "_assay_", assay, LRname,'.rds'))
  487. print(paste0(option, " ", nrow(metric_score[metric_score$p.adjust<thr.padjust & abs(metric_score$differential_S3_normalized) > 0.006,]), " significant LRCC"))
  489. lig.rep_order = VisualizeS3Dotplot(metric_score=metric_score, size=size,color=color,diff=diff,metrics = metrics,
  490. filtering = filtering, thr.pvalue = thr.pvalue, thr.diff = thr.diff,thr.padjust = thr.padjust,
  491. orderby = orderby,top_num_per_pair = top_num_per_pair,top_num_all=top_num_all,
  492. cluster_on =cluster_on, max_diff=max_diff,results_folder=data_dir_wilcoxtest,
  493. cluster_column=cluster_column,spatial_network_name=spatial_network_name,assay = assay,
  494. selected_pairs=selected_pairs,#_sfari,sfari = sfari,
  495. highlight = highlight, selected_clusters_pairs = selected_clusters_pairs,
  496. clustering='col',cluster_order_row = selected_clusters_pairs$sender.receiver,#cluster_order_col = cluster_order_col,
  497. p.circle=p.circle,save.id=save.id,w.adjust = 5, h.adjust = 4.5, saving_name = paste0('_pearson_',option,'_', control.grp, 'ctrls', LRname))
  498. }
  501. #======================================== 7. HEATMAP VISUALIZATION OF POSITIVE-CONTROL LIGAND-RECEPTOR INTERACTIONS ==============================================
  502. ##--- ligand-receptor pairs from literature ---
  503. lig_rep_positive = read.csv(file='/home/ubuntu/positive_ctrl/lig-rec-shortlist_230705_v2.txt')
  505. ##--- shortlist cell_identity pairs ---
  506. cell_identity_select_1 = expand.grid(selected_clusters,selected_clusters)
  507. colnames(cell_identity_select_1) = c('receiver','sender')
  508. cell_identity_select_1$sender.receiver = paste0(cell_identity_select_1$sender,'--',cell_identity_select_1$receiver)
  510. cell_identity_select = read.csv(file='/home/ubuntu/differentialS3/positive_ctrl/selected cell_identity pairs sender_v4.csv')[,-1]
  511. cell_identity_select_1 = cell_identity_select_1[cell_identity_select_1$sender.receiver %in% cell_identity_select,]
  513. cell_identity_select_1 = transpose(as.data.frame(strsplit(cell_identity_select, split = "--")))
  514. colnames(cell_identity_select_1) = c('sender','receiver')
  516. ##--- get scaled S3 for all datasets and all ligand-receptor pairs ---
  517. ScaledSpatialSignalingScore = readRDS(paste0(data_dir_wilcoxtest,save.id,'_all_ScaledSpatialSignalingScore_',cluster_column,'_',spatial_network_name,"_assay_",assay,LRname,'.rds'))
  518. cell_identity_select_1 = cell_identity_select_1[cell_identity_select_1$sender.receiver %in% unique(ScaledSpatialSignalingScore$LR_cell_comb),]
  519. #cell_identity_select_1 = cell_identity_select_1[cell_identity_select_1$sender.receiver %in% sign_cell_identity_pairs,]
  521. ##--- subset control datasets and their positive-control ligand-receptor interactions
  522. ScaledSpatialSignalingScore = ScaledSpatialSignalingScore[ScaledSpatialSignalingScore$LR_comb %in% lig_rep_positive_1$LR_comb,]
  523. #subset selected cell_identity pairs
  524. ScaledSpatialSignalingScore = ScaledSpatialSignalingScore[ScaledSpatialSignalingScore$LR_cell_comb %in% cell_identity_select_1$sender.receiver,]
  525. cluster_on = "mean_ctrl"
  526. ScaledSpatialSignalingScore_temp = expand.grid(cell_identity_select_1$sender.receiver,lig_rep_positive_1$LR_comb)
  527. colnames(ScaledSpatialSignalingScore_temp) = c("LR_cell_comb","LR_comb" )
  528. ScaledSpatialSignalingScore_temp$LR_CC = paste0(ScaledSpatialSignalingScore_temp$LR_comb,'---',ScaledSpatialSignalingScore_temp$LR_cell_comb)
  529. ScaledSpatialSignalingScore_temp$mean_ctrl = 0
  531. ##--- get the mean scaled S3 across all controls ---
  532. ScaledSpatialSignalingScore = rbind(ScaledSpatialSignalingScore[,c("LR_cell_comb","LR_comb","LR_CC" ,"mean_ctrl" )],
  533. ScaledSpatialSignalingScore_temp[!ScaledSpatialSignalingScore_temp$LR_CC%in%ScaledSpatialSignalingScore$LR_CC,])
  534. selDT = as.data.table(ScaledSpatialSignalingScore)
  535. selDT_d = data.table::dcast.data.table(selDT, LR_cell_comb ~LR_comb, value.var = cluster_on, fill = 0)
  536. rownames = as.character(selDT_d[[1]])
  537. write.csv(selDT_d,file=paste0(root_dir,"wilcoxtest/","pos-ctrl-ScaledSpatialSignalingScore.csv"))
  539. clus_sort_names_row = cell_identity_select_1$sender.receiver
  540. y.text <- transpose(as.data.frame(strsplit(clus_sort_names_row, split = "--")))
  541. colnames(y.text ) = c('sender', 'receiver')
  542. y.text$sender.receiver = paste0(y.text$sender,"--",y.text$receiver)
  543. y.text$color = 'black'
  544. y.text$symble = "-"
  545. selDT[, `:=`(LR_cell_comb, factor(LR_cell_comb, clus_sort_names_row))]
  547. ##--- change the col order ---
  548. clus_sort_names_col = lig_rep_positive_1$LR_comb
  549. x.text <- transpose(as.data.frame(strsplit(clus_sort_names_col, split = "--")))
  550. colnames(x.text) = c('ligand', 'receptor')
  551. selDT[, `:=`(LR_comb, factor(LR_comb, clus_sort_names_col))]
  553. selDT$mean_ctrl[selDT$mean_ctrl>0.05]=0.05
  554. max = max(selDT$mean_ctrl)
  555. median = median(selDT$mean_ctrl)
  556. mean = mean(selDT$mean_ctrl)
  557. min= min(selDT$mean_ctrl)
  558. ggplot()+ geom_tile(data = selDT, aes_string(x = "LR_comb", y = "LR_cell_comb", fill = "mean_ctrl")) +
  559. theme_classic() +
  560. labs(fill='mean\ncontrol\nScaledSpatialSignalingScore')+
  561. annotate(geom = "text", x=c(0), y = -3.5, label = c('receptor'), size = 3.5, angle=90,hjust = 0,fontface="bold")+
  562. annotate(geom = "text", x=c(0), y = -4, label = c('ligand'), size = 3.5, angle=90,hjust = 1,fontface="bold")+
  563. annotate(geom = "text", y =c(nrow(y.text)+1), x = -6.5, label = c('sender'), size = 3.5,hjust = 1,fontface="bold")+
  564. annotate(geom = "text", y = c(nrow(y.text)+1), x = -6, label = c('receiver'), size = 3.5,hjust = 0,fontface="bold")+
  565. annotate(geom = "text", y = c(nrow(y.text)+1), x = -6.25, label = c('-'), size = 3.5,hjust = 0.5,fontface="bold")+
  566. annotate(geom = "text", x = seq_len(nrow(x.text)), y = -3.5, label = x.text$receptor, size = 2.7, angle=90,hjust = 0)+
  567. annotate(geom = "text", x = seq_len(nrow(x.text)), y = -4, label = x.text$ligand, size = 2.7, angle=90,hjust = 1)+
  568. annotate(geom = "text", y = seq_len(nrow(y.text)), x = -6.5, label = y.text$sender, colour=y.text$color,size = 2.7,hjust = 1)+
  569. annotate(geom = "text", y = seq_len(nrow(y.text)), x = -6.25, label = y.text$symble, colour=y.text$color,size = 2.7,hjust = 0.5)+
  570. annotate(geom = "text", y = seq_len(nrow(y.text)), x = -6, label = y.text$receiver, colour=y.text$color,size = 2.7,hjust = 0)+
  571. coord_cartesian(ylim = c(0.5, nrow(y.text)+0.5),xlim=c(0.5,nrow(x.text)+0.5),expand = FALSE, clip = "off")+
  572. theme(plot.margin=unit(c(1.5,1,6,11),'lines'),axis.text = element_blank(),axis.title = element_blank())+
  573. scale_fill_gradient2(low='blue',high='red',mid='yellow',midpoint =(min+max)/2)
  574. ggsave(paste0(root_dir,"wilcoxtest/",save.id,'_',cluster_column,'_mean_ScaledSpatialSignalingScore_of_positive_lig-rep_on_ctrl_dataset_',spatial_network_name,LRname,'_colclip0.05.png'),
  575. width = 7.5,height=5.6,units = 'in')
  578. #======================================== 8. COMPARE SCALED S3 CALCULATED WITH MEASURED AND IMPUTED EXPRESSION ==============================================
  579. assay = 'SCT'
  580. cluster_column = 'cell_identity'
  581. ScaledSpatialSignalingScore_measured = readRDS(paste0(data_dir_wilcoxtest,save.id,'_all_ScaledSpatialSignalingScore_',cluster_column,'_',spatial_network_name,"_assay_",assay,LRname,'.rds'))
  583. assay = 'SCTpred'
  584. cluster_column = 'imputed.cell_identity'
  585. ScaledSpatialSignalingScore_imputed = readRDS(paste0(data_dir_wilcoxtest,save.id,'_all_ScaledSpatialSignalingScore_',cluster_column,'_',spatial_network_name,"_assay_",assay,LRname,'.rds'))
  587. ##--- get all ligand-receptor pairs present in MERFISH panel ---
  588. lig_rec = unique(ScaledSpatialSignalingScore_measured$LR_comb)
  590. ##--- calculate max LR_expr across all cell identity pairs for measured and imputed separately ---
  591. max_LR_expr_pred = data.frame(row.names = paste(lig_rec))
  592. assay = 'SCTpred'
  593. cluster_column = 'imputed.cell_identity'
  595. for (dataset in data_name){
  596. results_folder = paste0(data_dir,'/',dataset,'/')
  597. Mer = readRDS(paste0(results_folder,save.id,"_Seurat_object_SpatialSignalingScore",spatial_network_name, "_assay_",assay,LRname,".rds"))
  598. spatial_all_scores<-Mer@graphs[[spatial_network_name]]$SpatialSignalingScore[[cluster_column]]
  599. LR_expr = spatial_all_scores[,c('LR_comb','LR_cell_comb','LR_expr')]
  600. colnames(LR_expr)[3] = 'Value'
  601. LR_expr = dcast(LR_expr,LR_comb~LR_cell_comb)
  602. rownames(LR_expr) = LR_expr$LR_comb
  603. LR_expr = LR_expr[lig_rec,]
  604. LR_expr = LR_expr[,-1]
  605. # max lig_expr across all cell identity pairs for each ligand-receptor pair
  606. max_LR_expr_pred[[dataset]] = apply(LR_expr,1,max)
  607. }
  609. max_LR_expr = data.frame(row.names = paste(lig_rec))
  610. assay = 'SCT'
  611. cluster_column = 'cell_identity'
  613. for (dataset in data_name){
  614. results_folder = paste0(data_dir,'/',dataset,'/')
  615. Mer = readRDS(paste0(results_folder,save.id,"_Seurat_object_SpatialSignalingScore",spatial_network_name, "_assay_",assay,LRname,".rds"))
  616. spatial_all_scores<-Mer@graphs[[spatial_network_name]]$SpatialSignalingScore[[cluster_column]]
  617. LR_expr = spatial_all_scores[,c('LR_comb','LR_cell_comb','LR_expr')]
  618. colnames(LR_expr)[3] = 'Value'
  619. LR_expr = dcast(LR_expr,LR_comb~LR_cell_comb)
  620. rownames(LR_expr) = LR_expr$LR_comb
  621. LR_expr = LR_expr[lig_rec,]
  622. LR_expr = LR_expr[,-1]
  623. # max lig_expr across all interactions for each ligand-receptor pair
  624. max_LR_expr[[dataset]] = apply(LR_expr,1,max)
  625. }
  626. max_LR_expr$LR = rownames(max_LR_expr)
  627. max_LR_expr_pred$LR = rownames(max_LR_expr_pred)
  628. max_LR_expr_1 = melt(max_LR_expr,id.vars =c("LR"))
  629. colnames(max_LR_expr_1)[3] = 'measured'
  630. max_LR_expr_pred_1 = melt(max_LR_expr_pred,id.vars =c("LR"))
  631. colnames(max_LR_expr_pred_1)[3] = 'imputed'
  632. max_LR_expr_all = max_LR_expr_1
  633. max_LR_expr_all$imputed = max_LR_expr_pred_1$imputed
  635. ggplot(max_LR_expr_all,aes(x=measured,y=imputed))+geom_point(color='red',shape=1)+ggtitle('max_LR_expr_all')
  636. ggsave(paste0(root_dir,'/measured_imputed/imputed-measured.png'),height=10,width=10)
  638. ##--- filter out the lowly expressed ligand-receptor pairs in both measured and imputed expression ---
  639. cutoff = 0.05
  640. max_LR_expr_all_select = max_LR_expr_all[max_LR_expr_all$imputed>cutoff & !is.na(max_LR_expr_all$measured)&max_LR_expr_all$measured>cutoff,]
  642. ##--- scatter plot ---
  643. all_scores = NULL
  644. for (dataset in data_name){
  645. results_folder = paste0(data_dir,'/',dataset,'/')
  646. print(dataset)
  647. if (!dir.exists(results_folder)){
  648. dir.create(results_folder)}
  650. LR_comb_select = max_LR_expr_all_select$LR[max_LR_expr_all_select$variable %in% dataset ]
  651. if (length(LR_comb_select)!=0){
  652. assay = 'SCTpred'
  653. cluster_column = "imputed.cell_identity"
  654. Mer = readRDS(paste0(results_folder,save.id,"_Seurat_object_SpatialSignalingScore",spatial_network_name, "_assay_",assay,LRname,".rds"))
  655. spatial_all_scores<-Mer@graphs[[spatial_network_name]]$SpatialSignalingScore[[cluster_column]]
  656. spatial_all_scores_pred = spatial_all_scores[spatial_all_scores$LR_comb %in% LR_comb_select,c('LR_comb',"LR_cell_comb","ScaledSpatialSignalingScore")]
  658. assay = 'SCT'
  659. cluster_column = "cell_identity"
  660. Mer = readRDS(paste0(results_folder,save.id,"_Seurat_object_SpatialSignalingScore",spatial_network_name, "_assay_",assay,LRname,".rds"))
  661. spatial_all_scores<-Mer@graphs[[spatial_network_name]]$SpatialSignalingScore[[cluster_column]]
  662. spatial_all_scores = spatial_all_scores[spatial_all_scores$LR_comb %in% LR_comb_select,c('LR_comb',"LR_cell_comb","ScaledSpatialSignalingScore")]
  663. colnames(spatial_all_scores)[3] = c("ScaledSpatialSignalingScore_measured")
  664. spatial_all_scores$LRCC = paste0(spatial_all_scores$LR_comb,"---",spatial_all_scores$LR_cell_comb)
  665. spatial_all_scores_pred$LRCC = paste0(spatial_all_scores_pred$LR_comb,"---",spatial_all_scores_pred$LR_cell_comb)
  666. lrcc.common = intersect(spatial_all_scores$LRCC,spatial_all_scores_pred$LRCC )
  667. spatial_all_scores = spatial_all_scores[spatial_all_scores$LRCC %in%lrcc.common,]
  668. spatial_all_scores_pred = spatial_all_scores_pred[spatial_all_scores_pred$LRCC %in%lrcc.common,]
  669. spatial_all_scores$ScaledSpatialSignalingScore_imputed = spatial_all_scores_pred$ScaledSpatialSignalingScore[match(spatial_all_scores_pred$LRCC,spatial_all_scores$LRCC)]
  670. spatial_all_scores$dataset = dataset
  671. all_scores = rbind(all_scores,spatial_all_scores)
  672. }
  673. }
  674. saveRDS(all_scores,paste0(root_dir,"measured_imputed/ScaledSpatialSignalingScore_measured_vs_imputed_common_LR_0.05.rds"))
  675. all_scores<-readRDS(paste0(root_dir,"measured_imputed/ScaledSpatialSignalingScore_measured_vs_imputed_common_LR_0.05.rds"))
  677. hist(all_scores$ScaledSpatialSignalingScore_measured,breaks=100)
  678. cutoff = 0.0015
  679. all_scores_subset = all_scores[all_scores$ScaledSpatialSignalingScore_measured>cutoff & all_scores$ScaledSpatialSignalingScore_imputed>cutoff,]
  680. ggplot(data = all_scores_subset,aes(x=log2(ScaledSpatialSignalingScore_imputed),
  681. y=log2(ScaledSpatialSignalingScore_measured)))+geom_point(aes(color=dataset))+
  682. geom_abline(slope=1,intercept = 0)+theme_bw()+
  683. theme(title=element_text(size=20),axis.title= element_text(size=20),legend.text= element_text(size=20),axis.text = element_text(size=15) )
  684. ggsave(paste0(root_dir,"measured_imputed/log2_ScaledSpatialSignalingScore_measured_vs_imputed_color_data_cutoff_",cutoff,".png"),width = 15,height=7,units = 'in')
  685. cor(log2(all_scores_subset$ScaledSpatialSignalingScore_measured),log2(all_scores_subset$ScaledSpatialSignalingScore_imputed))

Spatialsignalingscore_main.R at commit 0008b95, no license · at the source

Overview

Authors: Li Lin1, Tzuen Yih Saw2, Nigel Chou2, Jie Lin Jolene Goh2, Jing Eugene Kwa2, Wan Kee Chock2, Vipul Singhal2,3, Zheng Li2, Mike J. Huang2, Huck Hui Ng2,4,5,6,7,8, Chiea Chuen Khor2,9,10, Hwee Lee Kuan1,5,11,12,13, Kok Hao Chen2, Shyam Prabhakar2,14, Jinyue Liu2
ORCID iDs: Jing Eugene Kwa, Wan Kee Chock, Vipul Singhal, Zheng Li, Chiea Chuen Khor, Jinyue Liu
14 affiliations
  1. Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR),Singapore, Republic of Singapore
  2. Genome Institute of Singapore (GIS), Agency for Science Technology and Research (A*STAR),Singapore, Republic of Singapore
  3. Terray Therapeutics Inc., Monrovia, CA USA
  4. Lee Kong Chian School of Medicine, Nanyang Technological University,Singapore, Republic of Singapore
  5. School of Biological Sciences, Nanyang Technological University,Singapore, Republic of Singapore
  6. Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore,Singapore, Republic of Singapore
  7. Department of Biological Sciences, National University of Singapore,Singapore, Republic of Singapore
  8. School of Medicine, Nazarbayev University,Astana, Republic of Kazakhstan
  9. Singapore Eye Research Institute (SERI),Singapore, Republic of Singapore
  10. SingHealth and Duke-NUS Ophthalmology & Visual Sciences Academic Clinical Programme (EYE ACP),Singapore, Republic of Singapore
  11. School of Computing, National University of Singapore,Singapore, Republic of Singapore
  12. International Research Laboratory on Artificial Intelligence, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR),Singapore, Republic of Singapore
  13. Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR),Singapore, Republic of Singapore
  14. National Cancer Centre Singapore, 30 Hospital Boulevard,Singapore, Republic of Singapore
Institutions: Agency for Science, Technology and Research (Singapore); Nanyang Technological University (Singapore); National University of Singapore (Singapore); Nazarbayev University (Kazakhstan); Singapore Eye Research Institute (Singapore); National Cancer Centre Singapore (Singapore)
Journal: Nature communications, volume 17, issue 1, article 7522
Dates: received 3 June 2025; accepted 7 May 2026; published online 13 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74320-5 · PMID 42288509 · PMCID PMC13408761 · OpenAlex W7164641945
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), autism (population), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, Machine learning
Keywords: Development of the nervous system, Computational biology and bioinformatics
MeSH: Autism Spectrum Disorder*, Brain*, Mosaicism*, Cerebral Cortex, Female, Humans, Male, Neurodevelopment, Neurons, Organoids, Single-Cell Analysis, Spatial Transcriptomics (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Funding was provided by Singapore Ministry of Health’s National Medical Research Council Open Fund – Young Individual Research Grant Project No. MOH-000239-00 (J.L.), Single-Cell In Situ Spatial Omics at subcellular Resolution (SCISSOR) IAF-PP-H18/01/a0/020 (K.H.C., H.K.L., S.P.), National Medical Research Council of Singapore OFIRG20nov-0056 (K.H.C.), National Research Foundation Singapore NRF-CRP25-10 2020-0001(K.H.C., J.L.), and Agency for Science, Technology and Research Career Development Award 202D800031 (J.L.)
Citations: cited by 1 paper (Europe PMC); 70 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

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Zenodo 19229995

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (2 files), Matplotlib (2 files), NumPy (2 files), OpenCV (2 files), pandas (2 files), Pillow (2 files), scikit-image (2 files), ggplot2 (1 file), h5py (1 file), patchwork (1 file), pheatmap (1 file), reshape2 (1 file), reticulate (1 file), SciPy (1 file), Seurat (1 file), tifffile (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
10 files
At the source:

jinyueliu0/spatial-signaling-score

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0008b95ed8775f1a50b48eb3051416de5e190318, 5 September 2023
Languages: R (7), Python (2)
Size: 13 files, 9 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (2 files), Matplotlib (2 files), NumPy (2 files), OpenCV (2 files), pandas (2 files), Pillow (2 files), scikit-image (2 files), ggplot2 (1 file), h5py (1 file), patchwork (1 file), pheatmap (1 file), reshape2 (1 file), reticulate (1 file), SciPy (1 file), Seurat (1 file), tifffile (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/jinyueliu0/Spatial-Signaling-Score/tree/v1.0.1

Code availability statement

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  • it points to the authors' code: Zenodo 19229995

Read it in the paper: doi.org/10.1038/s41467-026-74320-5.

Tracing map

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What the map holds:

  • 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;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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/s41467-026-74320-5.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 2 keywords, 12 MeSH terms, 1 funder, 70 references.

Cite

This paper

Lin, L., Saw, T. Y., Chou, N., Goh, J. L. J., Kwa, J. E., Chock, W. K., Singhal, V., Li, Z., Huang, M. J., Ng, H. H., Khor, C. C., Kuan, H. L., Chen, K. H., Prabhakar, S., & Liu, J. (2026). Spatial architecture of autism pathogenesis reveals mosaic structural disarray during early development. Nature communications, 17(1), 7522. https://doi.org/10.1038/s41467-026-74320-5

BibTeX

@article{lin2026spatial,
author = {Lin, Li and Saw, Tzuen Yih and Chou, Nigel and Goh, Jie Lin Jolene and Kwa, Jing Eugene and Chock, Wan Kee and Singhal, Vipul and Li, Zheng and Huang, Mike J. and Ng, Huck Hui and Khor, Chiea Chuen and Kuan, Hwee Lee and Chen, Kok Hao and Prabhakar, Shyam and Liu, Jinyue},
title = {{Spatial architecture of autism pathogenesis reveals mosaic structural disarray during early development}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7522},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74320-5},
url = {https://doi.org/10.1038/s41467-026-74320-5},
pmid = {42288509},
pmcid = {PMC13408761}
}

RIS

TY - JOUR
AU - Lin, Li
AU - Saw, Tzuen Yih
AU - Chou, Nigel
AU - Goh, Jie Lin Jolene
AU - Kwa, Jing Eugene
AU - Chock, Wan Kee
AU - Singhal, Vipul
AU - Li, Zheng
AU - Huang, Mike J.
AU - Ng, Huck Hui
AU - Khor, Chiea Chuen
AU - Kuan, Hwee Lee
AU - Chen, Kok Hao
AU - Prabhakar, Shyam
AU - Liu, Jinyue
TI - Spatial architecture of autism pathogenesis reveals mosaic structural disarray during early development
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/13
VL - 17
IS - 1
SP - 7522
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74320-5
UR - https://doi.org/10.1038/s41467-026-74320-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-74320-5",
"type": "article-journal",
"title": "Spatial architecture of autism pathogenesis reveals mosaic structural disarray during early development",
"container-title": "Nature communications",
"author": [
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"family": "Lin",
"given": "Li"
},
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{
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{
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{
"family": "Kwa",
"given": "Jing Eugene"
},
{
"family": "Chock",
"given": "Wan Kee"
},
{
"family": "Singhal",
"given": "Vipul"
},
{
"family": "Li",
"given": "Zheng"
},
{
"family": "Huang",
"given": "Mike J."
},
{
"family": "Ng",
"given": "Huck Hui"
},
{
"family": "Khor",
"given": "Chiea Chuen"
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{
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"given": "Hwee Lee"
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{
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"given": "Kok Hao"
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{
"family": "Prabhakar",
"given": "Shyam"
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{
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"container-title-short": "Nat Commun",
"volume": "17",
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"DOI": "10.1038/s41467-026-74320-5",
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"PMCID": "PMC13408761",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
13
]
]
}
}

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