OSCR

Perinatal brain developmental transition revealed by transcriptomic and proteomic analyses of Bama miniature pigs.

Code ↔ Paper

15 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 15 matches
  1. [1] § Methods › Cross-species integrative analysis › RNA-seq data integration ↔ MF2.R, lines 257–328 · score 0.92 · AgeEffect, lmFit, makeContrasts, age regulated genes, limma, logDays
  2. [2] § Methods › Cell type deconvolution and cell type-related DVG analysis ↔ MF4.R, lines 333–404 · score 0.71 · low confidence cell, high confidence cell, related DVGs, unknown, brain region, spec
  3. [3] § Results › Pig model recapitulates primate perinatal neurodevelopment ↔ MF2.R, lines 257–328 · score 0.70 · age regulated genes, Select genes, logDays, HSB155, HSB194, AK5
  4. [4] § Methods › Cell type deconvolution and cell type-related DVG analysis ↔ Function.R, lines 128–143 · score 0.69 · low confidence cell, high confidence cell, related DVGs, unknown, spec, genes
  5. [5] § Methods › Cell type deconvolution and cell type-related DVG analysis ↔ MF4.R, lines 333–404 · score 0.68 · low confidence cell, related DVGs, high confidence, brain region, spec
  6. [6] § Methods › Cell type deconvolution and cell type-related DVG analysis ↔ Function.R, lines 128–143 · score 0.66 · low confidence cell, related DVGs, high confidence, spec
  7. [7] § Methods › WGCNA analysis › Co-expression network construction ↔ MF5.R, lines 85–140 · score 0.64 · soft thresholding, power, reassignment, cut, networks, seq
  8. [8] § Methods › Cross-species integrative analysis › RNA-seq data integration ↔ MF2.R, lines 898–966 · score 0.62 · ge im, logDays, ae, rabbit, log10, predicting
  9. [9] § Methods › Dynamic characterization of brain disease risk genes in transcriptomic/proteomic profiles › Eigengene trajectory analysis of disease risk genes during development ↔ MF6.R, lines 57–141 · score 0.59 · mid fetal, early fetal, adult, Cluster, correlation
  10. [10] § Results › Spatiotemporal overview of transcriptome and proteome in the developing Bama miniature pig brain ↔ MF1.R, lines 1–64 · score 0.59 · body weight, Brain weight, ratio, sex, E76, E94
  11. [11] § Results › Developmental velocities reveal spatial divergence in the perinatal phase ↔ MF5.R, lines 459–506 · score 0.58 · granule cell, purkinje cells, ExNs, Glias, MSNs, matched
  12. [12] § Methods › WGCNA analysis › Cell type and DVG/DVP enrichment test ↔ MF5.R, lines 459–506 · score 0.58 · purkinje cells, enricher, DG, granule, MSN, SPEC
  13. [13] § Methods › TF-RNA correlation analysis ↔ MF5.R, lines 683–740 · score 0.58 · TF target, target genes, ITFP, TRRUST, TFs
  14. [14] § Methods › WGCNA analysis › Transcriptional-translational co-regulatory network analysis based on WGCNA modules ↔ MF5.R, lines 683–740 · score 0.54 · TF target gene, canonical, RBPs, TFs, co, WGCNA
  15. [15] § Methods › Quality assessment of transcriptome and proteome ↔ MF1.R, lines 1–64 · score 0.51 · body weight, brain weight, sex, age

Paper

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The authors' code

R · 825 lines · 25 KB · no license · 5 matches

  1. library(tidyverse)
  2. library(WGCNA)
  3. library(fastcluster)
  4. library(clusterProfiler)
  5. library(openxlsx)
  6. library(dlookr)
  8. #####—————integrate gene and protein—————#####
  9. set.seed(100)
  11. age_ref <- c("E76","E85","E94","E104","E109","P0","P3","P30")
  12. reg_ref <- c("PFC","TMP","OCC","STR","THA","HIP","CERE")
  14. load("/home/wangzm/Project/PBA/basic_analysis/MF5_related/PBAtlas.geneExpr.norm.RData")
  15. gexpr.g = gexpr
  16. meta.g = meta
  18. load("/home/wangzm/Project/PBA/basic_analysis/MF5_related/PBAtlas.proteinExpr.norm.RData")
  19. gexpr.p = combat_edata1
  20. meta.p = meta
  22. sampList = intersect(colnames(gexpr.g), colnames(gexpr.p))
  23. x01 = match(sampList, colnames(gexpr.g))
  24. x02 = match(sampList, colnames(gexpr.p))
  25. gexpr2.g = gexpr.g[,x01]
  26. gexpr2.p = gexpr.p[,x02]
  27. meta2.g = meta.g[x01, ]
  28. meta2.p = meta.p[x02, ]
  30. genes.pc = intersect(rownames(gexpr2.g), rownames(gexpr2.p))
  31. gexpr2.g = gexpr2.g[genes.pc, ]
  32. gexpr2.p = gexpr2.p[genes.pc, ]
  33. gexpr2.p <- as.matrix(gexpr2.p)
  34. gexpr2.g
  36. bc_trans<- function(x){
  37. x = 2^x
  38. bc_res = boxcox(x ~ 1, plotit = F)
  39. best_lambda = bc_res$x[which.max(bc_res$y)]
  40. y = (x^best_lambda - 1)/best_lambda
  41. z = sign(y) * log2(abs(y))
  42. return(z)
  43. }
  44. gexpr2.p = apply(gexpr2.p, 2, bc_trans)
  46. for(i in 1:nrow(gexpr2.p)){
  47. eachline = gexpr2.p[i,]
  48. df = data.frame(sample = names(eachline), value = eachline)
  49. xx= imputate_outlier(df, value, method = "capping", cap_ntiles = c(0.01, 0.95))
  50. outlier_num = length(attr(xx, "outliers"))
  51. ##
  52. if(outlier_num > 0 ){
  53. gexpr2.p[i,] = as.numeric(xx)
  54. }
  55. }
  57. gexpr2.g = base::scale(gexpr2.g, center = T, scale = T)
  58. gexpr2.p = base::scale(gexpr2.p, center = T, scale = T)
  59. boxplot(gexpr2.g, outline = F)
  60. boxplot(gexpr2.p, outline = F)
  62. ##merge data
  63. rownames(gexpr2.g) = paste0("g.",rownames(gexpr2.g))
  64. rownames(gexpr2.p) = paste0("p.",rownames(gexpr2.p))
  65. gexpr <- NULL
  66. gexpr = rbind(gexpr2.g, gexpr2.p)
  67. geneList = rownames(gexpr)
  68. meta = cbind(meta2.g, meta2.p)
  70. ageList = meta$Age
  71. dayList = ageList
  72. dayList = rep(0,length(ageList))
  73. dayList[which(ageList == "E76")] = 76
  74. dayList[which(ageList == "E85")] = 85
  75. dayList[which(ageList == "E94")] = 94
  76. dayList[which(ageList == "E104")] = 104
  77. dayList[which(ageList == "E109")] = 109
  78. dayList[which(ageList == "P0")] = 115
  79. dayList[which(ageList == "P3")] = 118
  80. dayList[which(ageList == "P30")] = 145
  81. meta$Day = dayList
  83. save(gexpr, meta, file="/PBA/pg.WGCNA.RData")
  85. #####—————WGCNA—————#####
  86. set.seed(100)
  87. load("/PBA/pg.WGCNA.RData")
  89. recGenexp = t(gexpr)
  90. powers = c(c(1:15), seq(from = 15, to=30, by=2))
  91. sft = pickSoftThreshold(recGenexp, powerVector = powers, verbose = 3)
  93. recGenexp = t(gexpr)
  94. meta = meta
  96. geneNet = blockwiseModules(recGenexp, power=6, minModuleSize=10,
  97. mergeCutHeight=0.2,networkType = "signed",
  98. TOMType = "signed",numericLabels=T,pamRespectsDendro=F,
  99. saveTOMs=F,verbose=0)
  100. mergedColors = labels2colors(geneNet$colors)
  101. plotDendroAndColors(geneNet$dendrograms[[1]],mergedColors[geneNet$blockGenes[[1]]],
  102. "Module colors", addTextGuide=T, dendroLabels = F, hang = 0.03,addGuide = TRUE,
  103. guideHang = 0.05,marAll = c(1, 5, 3,1))
  105. save(gexpr, geneNet, meta, file="/PBA/pg.WGCNA.gene-net-signed.RData")
  107. load("/PBA/pg.WGCNA.gene-net-signed.RData")
  109. eigenexp = geneNet$MEs
  110. moduleList = colnames(eigenexp)
  111. gexpr = t(gexpr)
  112. geneList = colnames(gexpr)
  113. colorList = geneNet$colors
  115. outFile = "/PBA/pg.WGCNA.geneModule.reassigned.xls"
  116. if(file.exists(outFile)) file.remove(outFile)
  118. res <- c()
  119. for(i in 0:max(colorList)){
  120. myindex = which(colorList == i)
  121. clusterGene = as.character(geneList[myindex])
  122. cat(i, "\n")
  124. for(j in 1:length(clusterGene)){
  125. geneName = as.character(clusterGene[j])
  126. myindex2 = which(geneList == geneName)
  127. oneGenexp = as.numeric(gexpr[,myindex2])
  128. oneGenecor = cor(oneGenexp, eigenexp,method="p",use = 'pairwise.complete.obs')
  130. moduleName.old = paste("ME",i,sep="")
  131. myindex3 = which(moduleList == moduleName.old)
  133. moduleCor.old = oneGenecor[myindex3]
  134. moduleCor.new = max(oneGenecor)
  136. output = c(geneName, moduleName.old)
  137. if((moduleCor.new -moduleCor.old) >0.3){
  138. maxIndex = which(oneGenecor == max(oneGenecor))
  139. output = c(geneName, moduleList[maxIndex])
  140. }
  142. ##recording
  143. if(length(res) == 0){
  144. res = output
  145. }
  146. else{
  147. res = rbind(res,output)
  148. }
  149. }
  150. }
  152. for(i in 0:max(colorList)){
  153. moduleName = paste("ME",i,sep="")
  154. myindex = which(as.character(res[,2]) == moduleName)
  155. clusterGene = as.character(res[myindex,1])
  156. moduleSize = length(clusterGene)
  157. geneNum = length(grep("^g", clusterGene))
  158. proteinNum = length(grep("^p", clusterGene))
  159. cat(moduleName,moduleSize, geneNum, proteinNum, "\n",sep="\t")
  160. cat(moduleName,clusterGene,"\n",file = outFile, sep="\t",append=T)
  161. }
  163. annotation_colors <- list(
  164. Age = c(
  165. "E76" = "#f3993a",
  166. "E85" = "#ffee6f",
  167. "E94" = "#add5a2",
  168. "E104" = "#7a7b78",
  169. "E109" = "#9933cc",
  170. "P0" = "#0066ff",
  171. "P3" = "#33cccc",
  172. "P30" = "#ff66cc"),
  173. Region=c(
  174. "PFC" = "#d71345",
  175. "TMP" = "#f26522",
  176. "OCC" = "#f7acbc",
  177. "STR" = "#6950a1",
  178. "THA" = "#009ad6",
  179. "HIP" = "#3CB371",
  180. "CERE" = "#8B4513"
  181. )
  182. )
  183. set.seed(100)
  185. ageRef = c("E76","E85","E94", "E104", "E109","P0","P3","P30")
  186. dayRef = c(76, 85, 94, 104, 109, 115, 118, 145)
  187. regionRef = c("PFC","TMP","OCC","STR","THA","HIP","CERE")
  188. dayList = meta$Day
  189. ageList = meta$Age
  191. gexpr= t(gexpr)
  192. sampList = rownames(gexpr)
  193. geneList = colnames(gexpr)
  195. modData = readLines("/PBA/pg.WGCNA.geneModule.reassigned.xls")
  196. resEigen <- c()
  197. smEigen <- c()
  198. recPlts <- list()
  200. ##
  201. for(i in 1:length(modData)){
  202. rec = unlist(strsplit(as.character(modData[i]),split="\t",fixed=T))
  203. modName = rec[1]
  204. clustGene = rec[2:length(rec)]
  206. moduleSize = length(clustGene)
  207. geneNum = length(grep("^g", clustGene))
  208. proteinNum = length(grep("^p", clustGene))
  209. cat(modName,moduleSize, geneNum, proteinNum, "\n",sep="\t")
  210. eachName = paste(modName,moduleSize, geneNum, proteinNum, sep = "_")
  212. idx = match(clustGene,geneList)
  213. moduleGenexp = gexpr[,idx]
  215. myEigen = moduleEigengenes(moduleGenexp,colors=rep("red",ncol(moduleGenexp)))
  216. geneigen = unlist(myEigen$eigengenes)
  218. if(length(resEigen) == 0){
  219. resEigen = geneigen
  220. } else{
  221. resEigen = rbind(resEigen,geneigen)
  222. }
  224. res <- NULL
  225. res = data.frame(eigen.val = as.numeric(geneigen), Age = ageList, Day = dayList,
  226. Region = meta$Region, Sex = meta$Sex)
  227. res$Region = factor(res$Region, regionRef)
  229. p0 <- ggplot(data=res, aes(x = Day, y = eigen.val)) +
  230. labs(title = eachName, x = "", y = "Eigengene") +
  231. theme_bw() +
  232. theme(axis.text.x = element_text(angle = 45, hjust = 1),
  233. axis.text.y = element_text(size = 12),
  234. axis.title = element_text(size = 16),
  235. panel.background = element_rect(fill = "white", color = "white"),
  236. panel.grid.major = element_line(color = "grey80"),
  237. panel.grid.minor = element_line(color = "grey90"),
  238. panel.border = element_rect(color = "black", fill = NA, linewidth = 1))+
  239. geom_point(aes(x = Day, y = eigen.val, colour = Region)) +
  240. scale_x_continuous(breaks = dayRef, labels = ageRef) +
  241. scale_colour_manual(name = "Region",values = annotation_colors$Region) +
  242. geom_smooth(level = 0, span = 0.75, aes( group = Region,colour = Region,fill = Region,alpha=0.5),size=1.5) +
  243. scale_fill_manual(name = "Region",values = annotation_colors$Region) +
  244. geom_vline(xintercept = 115,colour="grey20",linewidth =1,linetype = "dashed")
  246. recPlts[[modName]] = p0
  248. smooth_values <- ggplot_build(p0)$data[[1]]
  249. if(length(smEigen) == 0){
  250. smEigen = smooth_values$y
  251. } else{
  252. smEigen = rbind(smEigen,smooth_values$y)
  253. }
  254. }
  256. colnames(resEigen) = sampList
  257. rownames(resEigen) = paste("ME",0:(length(modData)-1),sep="")
  259. colnames(smEigen) = sampList
  260. rownames(smEigen) = paste("ME",0:(length(modData)-1),sep="")
  262. save(gexpr, meta, geneNet, resEigen, smEigen,recPlts, file="/PBA/pg.WGCNA.eigengenes.RData")
  264. #####—————Traits—————#####
  265. load(file="/PBA/pg.WGCNA.eigengenes.RData")
  266. modList = rownames(resEigen)
  267. dayList = meta$Day
  268. regionRef = c("PFC","TMP","OCC", "STR","THA", "HIP", "CERE")
  269. modData = readLines("/PBA/pg.WGCNA.geneModule.reassigned.xls")
  271. pctMat <- NULL
  272. for(i in 1:length(modData)){
  273. rec = unlist(strsplit(as.character(modData[i]),split="\t",fixed=T))
  274. modName = rec[1]
  275. clustGene = rec[2:length(rec)]
  276. moduleSize = length(clustGene)
  278. eachout <- NULL
  280. genes = substring(clustGene[grep("^g", clustGene)],3)
  281. proteins = substring(clustGene[grep("^p", clustGene)],3)
  282. eachout = c(length(genes)/moduleSize , length(proteins)/moduleSize)
  283. if(length(pctMat) == 0){
  284. pctMat = eachout
  285. }else{
  286. pctMat = rbind(pctMat, eachout)
  287. }
  288. }
  289. colnames(pctMat) = c("Gene", "Protein")
  290. rownames(pctMat) = modList
  292. pctMat_long <- as.data.frame(pctMat) %>%
  293. rownames_to_column("module") %>%
  294. pivot_longer(cols = c(Gene, Protein),
  295. names_to = "Type",
  296. values_to = "Percentage")
  298. pctMat_long$Type <- factor(pctMat_long$Type,levels = c("Gene","Protein"))
  299. pctMat_long$module <- factor(pctMat_long$module,levels = rownames(pctMat))
  301. ggplot(pctMat_long, aes(x = module, y = Percentage, fill = Type)) +
  302. geom_bar(stat = "identity") +
  303. scale_fill_manual(values = c("#F5F5DC","#F4A460")) +
  304. theme_classic() +
  305. labs(y = "Region", x = "Age") +
  306. theme(strip.background = element_rect(color = "black", fill="black",),
  307. strip.text = element_text(size = 12,color = "white"),
  308. legend.position = "left",
  309. axis.title.y = element_blank(),
  310. axis.text.y = element_blank()
  311. )
  313. cls = hclust(dist(smEigen), method = "ave")
  314. dend <- as.dendrogram(cls)
  315. kMax = 11
  316. plot(dend)
  318. myTree = fastcluster::hclust(dist(smEigen),method="ave")
  319. groupMods = cutree(myTree, 11)
  321. mat.st <- NULL
  323. for(i in 1:nrow(resEigen)){
  324. modName = rownames(resEigen)[i]
  326. df = data.frame(Region = meta$Region, Age = meta$Age, Day = dayList, value = resEigen[i,], value.sm = smEigen[i, ])
  327. mod.t = lm(value.sm ~ Day, df)
  328. mod.s = lm(value.sm ~ Region, df)
  330. all.pval.t = (-1) * log10(overall_p(mod.t))
  331. all.pval.s = (-1) * log10(overall_p(mod.s))
  333. cor.t = cor(df$value.sm, df$Day)
  334. sep.pval.s = (-1) * log10(summary(mod.s)$coefficients[,4])
  335. names(sep.pval.s)[1] = "CERE"
  336. names(sep.pval.s) = gsub("Region", "", names(sep.pval.s))
  337. sep.pval.s = sep.pval.s[regionRef]
  338. sep.pval.s = t(sep.pval.s)
  340. eachout = data.frame(mods = modName, all.pval.t = all.pval.t, cor.t = cor.t,
  341. all.pval.s = all.pval.s, sep.pval.s)
  343. if(length(mat.st) == 0){
  344. mat.st = eachout
  345. }else{
  346. mat.st = rbind(mat.st, eachout)
  347. }
  348. }
  349. rownames(mat.st) = mat.st$mods
  350. mat.st = as.matrix(mat.st[, -1])
  352. spTest <- NULL
  353. for(i in 1:nrow(mat.st)){
  354. rec = mat.st[i,]
  355. eachout = rep(0, length(rec))
  357. if(rec[1] > 2){
  358. eachout[1] = "t"
  359. if(rec[2] > 0.25) eachout[2] = "plus"
  360. if(rec[2] < (-0.25)) eachout[2] = "minus"
  361. }
  363. if(rec[3] > 2){
  364. eachout[3] = "s"
  365. rem = rec[4:length(rec)]
  366. idx.tops = findTops(rem)
  367. eachout[3 + idx.tops] = "spatial"
  368. }
  370. if(length(spTest) == 0){
  371. spTest = eachout
  372. }else{
  373. spTest = rbind(spTest, eachout)
  374. }
  375. }
  376. colnames(spTest) = colnames(mat.st)
  377. rownames(spTest) = modList
  378. overall_means <- rowMeans(smEigen)
  380. Spatial2 <- matrix(0,
  381. nrow = nrow(smEigen),
  382. ncol = length(regionRef),
  383. dimnames = list(rownames(smEigen), regionRef))
  385. for(mod in rownames(smEigen)) {
  386. for(region in regionRef) {
  387. region_values <- smEigen[mod, meta$Region == region]
  388. region_mean <- mean(region_values)
  389. if(region_mean > overall_means[mod]) {
  390. Spatial2[mod, region] <- 1
  391. } else {
  392. Spatial2[mod, region] <- -1
  393. }
  394. }
  395. }
  397. for(mod in rownames(spTest)) {
  398. for(region in regionRef) {
  399. if(spTest[mod, region] != "spatial") {
  400. Spatial2[mod, region] <- 0
  401. }
  402. }
  403. }
  404. Spatial2
  406. pfc_ct_path <- c("/PBA/PFC/pfc_hum.spec.xls")
  407. tmp_ct_path <- c("/PBA/TMP/tmp_hum.spec.xls")
  408. occ_ct_path <- c("/PBA/OCC/occ_hum.spec.xls")
  409. str_ct_path <- c("/PBA/STR/str_hum.spec.xls")
  410. tha_ct_path <- c("/PBA/THA/tha_hum.spec.xls")
  411. hip_ct_path <- c("/PBA/HIP/hip_hum.spec.xls")
  412. cere_ct_path <- c("/PBA/CERE/cere_hum.spec.xls")
  414. pfc_ct_mar <- extract_spectop(pfc_ct_path)
  415. tmp_ct_mar <- extract_spectop(tmp_ct_path)
  416. occ_ct_mar <- extract_spectop(occ_ct_path)
  417. str_ct_mar <- extract_spectop(str_ct_path)
  418. tha_ct_mar <- extract_spectop(tha_ct_path)
  419. hip_ct_mar <- extract_spectop(hip_ct_path)
  420. cere_ct_mar <- extract_spectop(cere_ct_path)
  422. pfc_ct_mat <- get_ct_mat(pfc_ct_mar)
  423. tmp_ct_mat <- get_ct_mat(tmp_ct_mar)
  424. occ_ct_mat <- get_ct_mat(occ_ct_mar)
  425. str_ct_mat <- get_ct_mat(str_ct_mar)
  426. tha_ct_mat <- get_ct_mat(tha_ct_mar)
  427. hip_ct_mat <- get_ct_mat(hip_ct_mar)
  428. cere_ct_mat <- get_ct_mat(cere_ct_mar)
  430. ct_ref <- Reduce(union, list(
  431. unique(pfc_ct_mat$ctID),
  432. unique(tmp_ct_mat$ctID),
  433. unique(occ_ct_mat$ctID),
  434. unique(str_ct_mat$ctID),
  435. unique(tha_ct_mat$ctID),
  436. unique(hip_ct_mat$ctID),
  437. unique(cere_ct_mat$ctID)
  438. ))
  440. ct_ref_mat <- rbind(pfc_ct_mat,tmp_ct_mat,occ_ct_mat,str_ct_mat,tha_ct_mat,hip_ct_mat,cere_ct_mat)
  441. rownames(ct_ref_mat) <- NULL
  442. ct_ref_mat <- unique(ct_ref_mat)
  443. add_rows <- data.frame(
  444. ctID = rep("MSN",times = 2),
  445. geneID = c("DRD1","DRD2")
  446. )
  447. ct_ref_mat <- rbind(ct_ref_mat,add_rows)
  449. recMarkers.lister = readRDS("/PBA/Lister_Cell_2022.perinatal.celltypeSpec.gene.rds")
  450. ##CERE data
  451. df.pc = read.csv("/PBA/01MinusPC.tsv", header = T, sep = "\t")
  452. df.gn = read.csv("/PBA/04MinusGN.tsv", header = T, sep = "\t")
  453. recMarkers.millen = data.frame(cluster = c(rep("PC", nrow(df.pc)), rep("GN", nrow(df.gn))),
  454. gene = c(df.pc$Gene, df.gn$Gene))
  455. ##STR data
  456. recMarkers.msn = readRDS("/PBA/Stauffer_CurrentBiology2021.celltypeSpec.gene.rds")
  457. recMarkers.msn = subset(recMarkers.msn, cluster %in% c("DRD1", "DRD2"))
  459. ##DG
  460. load("/PBA/DG_exn_mar.RData") #gc_mar
  461. dg_df <- data.frame(
  462. ctID = "DG.ExN",
  463. geneID = gc_mar$gene
  464. )
  466. ##
  467. recMarkers = rbind(recMarkers.lister[,c(6,7)], recMarkers.millen, recMarkers.msn[, c(6,7)])
  468. celltypeSpec = data.frame(pathID = recMarkers$cluster,
  469. geneID = recMarkers$gene)
  471. celltypeSpec$pathID <- as.character(celltypeSpec$pathID)
  472. celltypeSpec$pathID[celltypeSpec$pathID %in% c("Micro","Astro","OPC","Oligo")] <- "Glia"
  473. celltypeSpec$pathID[celltypeSpec$pathID %in% c("DRD2","DRD1")] <- "MSN"
  474. celltypeSpec$pathID[celltypeSpec$pathID == "PC"] <- "Purkinje.cell"
  475. celltypeSpec$pathID[celltypeSpec$pathID == "GN"] <- "CB.granule.cell"
  477. celltypeSpec <- subset(celltypeSpec,celltypeSpec$pathID %in% unique(ct_ref_mat$ctID))
  478. colnames(celltypeSpec) <- c("ctID","geneID")
  480. celltypeSpec <- rbind(celltypeSpec, dg_df)
  481. ct_ref_mat <- rbind(ct_ref_mat,celltypeSpec)
  482. table(ct_ref_mat$ctID)
  484. celltypeEnrich <- array(0, dim = c(nrow(mat.st), length(ct_ref)))
  485. rownames(celltypeEnrich) = rownames(mat.st)
  486. colnames(celltypeEnrich) = ct_ref
  487. library(clusterProfiler)
  488. for(i in 1:length(modData)){
  489. rec = unlist(strsplit(as.character(modData[i]),split="\t",fixed=T))
  490. modName = rec[1]
  491. clustGene = unique(substring(rec[2:length(rec)],3))
  492. miTest = enricher(clustGene, TERM2GENE = ct_ref_mat, maxGSSize = 8000, qvalueCutoff = 0.05)
  493. miTest.df = as.data.frame(miTest)
  494. if(nrow(miTest.df) > 0){
  495. cat(modName, length(clustGene), miTest.df$ID, "\n", sep = "\t")
  496. idx.sort = match(miTest.df$ID, ct_ref)
  497. celltypeEnrich[i, idx.sort] = 1
  498. }
  499. }
  501. transMat <- NULL
  502. for(i in 1:length(modData)){
  503. rec = unlist(strsplit(as.character(modData[i]),split="\t",fixed=T))
  504. modName = rec[1]
  505. clustGene = rec[2:length(rec)]
  506. moduleSize = length(clustGene)
  508. ###
  509. eachout <- NULL
  510. ###
  511. genes = substring(clustGene[grep("^g", clustGene)],3)
  512. proteins = substring(clustGene[grep("^p", clustGene)],3)
  513. op = intersect(genes, proteins)
  515. idx = which(groupMods == groupMods[i])
  516. clustGene <- NULL
  517. for(k in idx){
  518. rec = unlist(strsplit(as.character(modData[k]),split="\t",fixed=T))
  519. clustGene = c(clustGene, rec[2:length(rec)])
  520. }
  521. moduleSize = length(clustGene)
  523. ###
  524. proteins = substring(clustGene[grep("^p", clustGene)],3)
  525. op2 = intersect(genes, proteins)
  527. ##
  528. geneNum = length(genes)+1e-6
  529. eachout = c(length(op)/geneNum , length(op2)/geneNum)
  530. if(length(transMat) == 0){
  531. transMat = eachout
  532. }else{
  533. transMat = rbind(transMat, eachout)
  534. }
  535. }
  537. rownames(transMat) = modList
  538. colnames(transMat) = c("Percent.trans.mod", "Percent.trans.grp")
  540. tfAnnot.guo = read.table("/PBA/TF-Target-information.txt", header = T, sep = "\t")
  541. load("/PBA/tftargets.rda")
  543. ln = readLines("/PBA/c3.tft.v2024.1.Hs.symbols.gmt")
  545. gsea <- list()
  546. for(i in 1:length(ln)){
  547. rec = unlist(strsplit(as.character(ln[i]), "\t"))
  548. targets = rec[-(1:2)]
  549. rem = unlist(strsplit(as.character(rec[1]), "_"))
  550. tfname = rem[1]
  551. if(length(gsea[[tfname]]) == 0){
  552. gsea[[tfname]] = rec
  553. }else{
  554. gsea[[tfname]] = unique(c(gsea[[tfname]], rec))
  555. }
  556. }
  558. tf.tot = unique(c(unique(tfAnnot.guo$TF), names(ITFP), names(Marbach2016), names(TRRUST), names(gsea)))
  559. tf2target <- list()
  560. for(i in 1:length(tf.tot)){
  561. eachtf = tf.tot[i]
  562. eachtarget = unique(c(ITFP[[eachtf]], Marbach2016[[eachtf]], TRRUST[[eachtf]], gsea[[eachtf]]))
  564. xi = which(tfAnnot.guo$TF == eachtf)
  565. eachtarget = unique(c(eachtarget, tfAnnot.guo$target[xi]))
  567. tf2target[[eachtf]] = eachtarget
  568. }
  570. regMat <- NULL
  571. for(i in 1:length(modData)){
  572. rec = unlist(strsplit(as.character(modData[i]),split="\t",fixed=T))
  573. modName = rec[1]
  574. clustGene = rec[2:length(rec)]
  575. moduleSize = length(clustGene)
  577. eachout <- NULL
  578. genes = substring(clustGene[grep("^g", clustGene)],3)
  579. proteins = substring(clustGene[grep("^p", clustGene)],3)
  580. tfs = intersect(proteins, names(tf2target))
  582. targets = unique(unlist(tf2target[proteins]))
  583. op2 = intersect(genes, targets)
  585. idx = which(groupMods == groupMods[i])
  586. clustGene <- NULL
  587. for(k in idx){
  588. rec = unlist(strsplit(as.character(modData[k]),split="\t",fixed=T))
  589. clustGene = c(clustGene, rec[2:length(rec)])
  590. }
  591. moduleSize = length(clustGene)
  593. proteins = substring(clustGene[grep("^p", clustGene)],3)
  594. tfs = intersect(proteins, names(tf2target))
  596. targets = unique(unlist(tf2target[proteins]))
  597. op3 = intersect(genes, targets)
  599. geneNum = length(genes)+1e-6
  600. eachout = c(length(op2)/geneNum, length(op3)/geneNum)
  601. if(length(regMat) == 0){
  602. regMat = eachout
  603. }else{
  604. regMat = rbind(regMat, eachout)
  605. }
  607. }
  608. rownames(regMat) = modList
  609. colnames(regMat) = c("Percent.reg.mod", "Percent.reg.grp")
  611. proteinAnnot = read.table("/PBA/9606.protein.info.v11.0.txt", header = T, sep = "\t")
  612. proteinLink = read.table("/PBA/9606.protein.links.v11.0.txt", header = T, sep = " ")
  613. ##
  614. idx01 = match(proteinLink[,1], proteinAnnot[,1])
  615. idx02 = match(proteinLink[,2], proteinAnnot[,1])
  616. stringdb = cbind(proteinAnnot[idx01, 2], proteinAnnot[idx02, 2])
  618. ##
  619. intMat <- NULL
  620. for(i in 1:length(modData)){
  621. rec = unlist(strsplit(as.character(modData[i]),split="\t",fixed=T))
  622. modName = rec[1]
  623. clustGene = rec[2:length(rec)]
  624. moduleSize = length(clustGene)
  626. eachout <- NULL
  627. genes = substring(clustGene[grep("^g", clustGene)],3)
  628. clustGene = substring(clustGene,3)
  629. idx01 = which(stringdb[,1] %in% genes)
  630. idx02 = which(stringdb[,2] %in% clustGene)
  631. op5 = length(intersect(idx01, idx02))
  633. idx = which(groupMods == groupMods[i])
  634. clustGene <- NULL
  635. for(k in idx){
  636. rec = unlist(strsplit(as.character(modData[k]),split="\t",fixed=T))
  637. clustGene = c(clustGene, rec[2:length(rec)])
  638. }
  639. clustGene = substring(clustGene,3)
  640. idx02 = which(stringdb[,2] %in% clustGene)
  641. op6 = length(intersect(idx01, idx02))
  644. ##
  645. geneNum = length(genes)+1e-6
  646. eachout = c(op5/geneNum, op6/geneNum)
  647. if(length(intMat) == 0){
  648. intMat = eachout
  649. }else{
  650. intMat = rbind(intMat, eachout)
  651. }
  653. }
  654. rownames(intMat) = modList
  655. colnames(intMat) = c("Norm.int.mod", "Norm.int.grp")
  657. #####—————Transcription-Translation—————#####
  658. load("/PBA/pg.WGCNA.eigengenes.RData")
  659. WGCNA.info <- read.xlsx("/PBA/Table S13.xlsx")
  660. colnames(WGCNA.info) <- WGCNA.info[1,]
  661. WGCNA.info <- WGCNA.info[-1,]
  662. load("/PBA/PBAtlas.geneExpr.norm.RData")
  663. gexpr.g = gexpr
  664. meta.g = meta
  666. load("/PBA/PBAtlas.proteinExpr.norm.RData")
  667. gexpr.p = combat_edata1
  668. meta.p = meta
  669. #
  670. sampList = intersect(colnames(gexpr.g), colnames(gexpr.p))
  671. x01 = match(sampList, colnames(gexpr.g))
  672. x02 = match(sampList, colnames(gexpr.p))
  673. gexpr2.g = gexpr.g[,x01]
  674. gexpr2.p = gexpr.p[,x02]
  675. meta2.g = meta.g[x01, ]
  676. meta2.p = meta.p[x02, ]
  677. #
  678. genes.pc = intersect(rownames(gexpr2.g), rownames(gexpr2.p))
  679. #
  680. gexpr2.g = gexpr2.g[genes.pc, ]
  681. gexpr2.p = gexpr2.p[genes.pc, ]
  683. ###RBP
  684. RBP_ls <- read.csv("/PBA/Homo_sapiens-RBPs.csv")
  685. cRBP_ls <- subset(RBP_ls,RBP.type == "Canonical_RBPs")
  686. cRBP_vec <- sort(unique(cRBP_ls$Gene.symbol))
  687. coRBP <- sort(intersect(cRBP_vec,rownames(gexpr2.p)))
  689. ###TF
  690. tfAnnot.guo = read.table("/PBA/TF-Target-information.txt", header = T, sep = "\t")
  691. #
  692. load("/PBA/tftargets.rda")
  693. #
  694. ln = readLines("/PBA/c3.tft.v2024.1.Hs.symbols.gmt")
  695. #
  696. gsea <- list()
  697. for(i in 1:length(ln)){
  698. rec = unlist(strsplit(as.character(ln[i]), "\t"))
  699. targets = rec[-(1:2)]
  700. rem = unlist(strsplit(as.character(rec[1]), "_"))
  701. tfname = rem[1]
  702. if(length(gsea[[tfname]]) == 0){
  703. gsea[[tfname]] = rec
  704. }else{
  705. gsea[[tfname]] = unique(c(gsea[[tfname]], rec))
  706. }
  707. }
  708. #
  709. tf.tot = unique(c(unique(tfAnnot.guo$TF), names(ITFP), names(Marbach2016), names(TRRUST), names(gsea)))
  710. tf2target <- list()
  711. for(i in 1:length(tf.tot)){
  712. eachtf = tf.tot[i]
  713. eachtarget = unique(c(ITFP[[eachtf]], Marbach2016[[eachtf]], TRRUST[[eachtf]], gsea[[eachtf]]))
  714. xi = which(tfAnnot.guo$TF == eachtf)
  715. eachtarget = unique(c(eachtarget, tfAnnot.guo$target[xi]))
  716. eachtarget = eachtarget[eachtarget != eachtf]
  717. tf2target[[eachtf]] = eachtarget
  718. }
  720. for (i in 1:5) {
  721. cat("\nTF:", names(tf2target)[i], "\n")
  722. print(head(tf2target[[i]], 5))
  723. }
  725. clean_tf2target <- lapply(names(tf2target), function(tf_name) {
  726. genes <- tf2target[[tf_name]]
  727. genes <- genes[!grepl("https://", genes)]
  728. genes <- genes[!grepl("_TARGET_GENES", genes)]
  729. genes <- genes[genes %in% rownames(gexpr2.g)]
  730. genes <- genes[genes != tf_name]
  731. })
  732. names(clean_tf2target) <- names(tf2target)
  733. valid_tfs <- names(clean_tf2target)[names(clean_tf2target) %in% rownames(gexpr2.p)]
  734. tf2target <- clean_tf2target[valid_tfs]
  736. modules <- unique(WGCNA.info$Module)
  737. module_results <- list()
  739. for (module in modules) {
  740. module_data <- WGCNA.info[WGCNA.info$Module == module, ]
  742. module_proteins <- module_data$`Gene/Protein Name`[module_data$Type == "Protein"]
  743. module_genes <- module_data$`Gene/Protein Name`[module_data$Type == "Gene"]
  744. gene_protein_pairs <- intersect(module_proteins, module_genes)
  745. if (length(gene_protein_pairs) == 0) next
  747. module_tfs <- intersect(names(tf2target), module_proteins)
  748. result_df <- data.frame(TF = character(),
  749. Gene = character(),
  750. Protein = character(),
  751. stringsAsFactors = FALSE)
  752. for (tf in module_tfs) {
  753. module_targets <- intersect(tf2target[[tf]], module_genes)
  754. valid_targets <- intersect(module_targets, gene_protein_pairs)
  756. if (length(valid_targets) > 0) {
  757. result_df <- rbind(result_df,
  758. data.frame(TF = tf,
  759. Gene = valid_targets,
  760. Protein = valid_targets,
  761. stringsAsFactors = FALSE))
  762. }
  763. }
  765. module_rbps <- intersect(coRBP, module_proteins)
  767. module_results[[module]] <- list(
  768. TF_gene_protein = result_df,
  769. RB_proteins = module_rbps
  770. )
  772. }
  774. for (module in names(module_results)) {
  775. cat("\nModule:", module, "\n\n")
  777. if (nrow(module_results[[module]]$TF_gene_protein) > 0 &length(module_results[[module]]$RB_proteins) > 0) {
  778. cat("TF-RNA-Protein: ",nrow(module_results[[module]]$TF_gene_protein))
  779. cat("RBP: ",length(module_results[[module]]$RB_proteins),"\n\n")
  780. }
  781. }
  783. ###
  784. correlation_results <- list()
  785. for (module_name in names(module_results)) {
  786. module_data <- module_results[[module_name]]
  788. if (length(module_data$RB_proteins) == 0 || nrow(module_data$TF_gene_protein) == 0) {
  789. next
  790. }
  792. target_proteins <- unique(module_data$TF_gene_protein$Protein)
  793. rb_proteins <- module_data$RB_proteins
  796. target_proteins <- target_proteins[target_proteins %in% rownames(gexpr2.p)]
  797. rb_proteins <- rb_proteins[rb_proteins %in% rownames(gexpr2.p)]
  799. if (length(target_proteins) == 0 || length(rb_proteins) == 0) {
  800. next
  801. }
  803. cor_matrix <- matrix(NA,
  804. nrow = length(target_proteins),
  805. ncol = length(rb_proteins),
  806. dimnames = list(target_proteins, rb_proteins))
  809. for (target in target_proteins) {
  810. for (rbp in rb_proteins) {
  811. cor_value <- cor(gexpr2.p[target, ], gexpr2.p[rbp, ], use = "complete.obs") #row-protein, col-RBP
  812. cor_matrix[target, rbp] <- cor_value
  813. }
  814. }
  815. correlation_results[[module_name]] <- cor_matrix
  816. }
  818. for(m in names(correlation_results)){
  819. mm <- correlation_results[[m]]
  820. print(class(mm))
  821. mm <- as.matrix(mm)
  822. mm[mm == 1] <- 0
  823. cat(m,"\n\n")
  824. print(max(mm))
  825. }

MF5.R at commit 724136d, no license · at the source

Overview

Authors: Ziming Wang1, Fan Chu1, Xue Wang1, Shulin Li1, Yingjie Gao1, Xiangling Feng1, Yixiang Li1, Menghan Li1, Yaoyi Wang2, Kunhao Mei3, Ying Zhu2, Shaojie Ma4, Qing Lu1,5, Mingfeng Li1,5,6
ORCID iDs: Yixiang Li, Yaoyi Wang, Ying Zhu, Shaojie Ma, Qing Lu, Mingfeng Li
  1. Department of Pharmacology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  2. State Key Laboratory of Brain Function and Disorders, MOE Frontiers Center for Brain Science, Institutes of Brain Science, and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
  3. College of Pharmacy, Henan University, Kaifeng, China
  4. Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
  5. The Key Laboratory for Drug Target Researches and Pharmacodynamic Evaluation of Hubei Province, Wuhan, China
  6. Innovation center for Brain Medical Sciences, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
Institutions: Huazhong University of Science and Technology (China); Shanghai Medical College of Fudan University (China); Fudan University (China); Huashan Hospital (China); Frontiers Center for Brain Science of the Ministry of Education (China); Henan University (China); Center for Excellence in Brain Science and Intelligence Technology (China); University of Chinese Academy of Sciences (China)
Journal: Nature communications, volume 17, issue 1, article 4712
Dates: received 24 July 2025; accepted 18 March 2026; published online 2 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71360-9 · PMID 41922393 · PMCID PMC13212750 · OpenAlex W7147252514
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other (organism), developmental (subfield)
Methods: Connectivity, Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging
Keywords: Neuronal development, Gliogenesis, Developmental neurogenesis
MeSH: Brain*, Proteome*, Swine, Miniature*, Transcriptome*, Animals, Animals, Newborn, Female, Gene Expression Profiling, Gene Expression Regulation, Developmental, Neurodevelopment, Proteomics, Swine (* major topic)
Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (32421003, 32471024, 32470712, 82373869, 32270715, 82071259)
Citations: not cited yet (Europe PMC); 98 references in the paper

Abstract

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

Repository

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lmfeng/PBAtlas_Perinatal_Multiome

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 724136d184831be39f84ccee911a9083900c27e9, 26 November 2025
Languages: R (7)
Size: 8 files, 7 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), circlize (3 files), ComplexHeatmap (3 files), ggplot2 (3 files), limma (2 files), reshape2 (2 files), WGCNA (2 files), clusterProfiler (1 file), patchwork (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
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8 files
At the source: github.com/lmfeng/PBAtlas_Perinatal_Multiome

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Data

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Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 3 keywords, 12 MeSH terms, 1 funder, 92 references.

Cite

This paper

Wang, Z., Chu, F., Wang, X., Li, S., Gao, Y., Feng, X., Li, Y., Li, M., Wang, Y., Mei, K., Zhu, Y., Ma, S., Lu, Q., & Li, M. (2026). Perinatal brain developmental transition revealed by transcriptomic and proteomic analyses of Bama miniature pigs. Nature communications, 17(1), 4712. https://doi.org/10.1038/s41467-026-71360-9

BibTeX

@article{wang2026perinatal,
author = {Wang, Ziming and Chu, Fan and Wang, Xue and Li, Shulin and Gao, Yingjie and Feng, Xiangling and Li, Yixiang and Li, Menghan and Wang, Yaoyi and Mei, Kunhao and Zhu, Ying and Ma, Shaojie and Lu, Qing and Li, Mingfeng},
title = {{Perinatal brain developmental transition revealed by transcriptomic and proteomic analyses of Bama miniature pigs}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {4712},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71360-9},
url = {https://doi.org/10.1038/s41467-026-71360-9},
pmid = {41922393},
pmcid = {PMC13212750}
}

RIS

TY - JOUR
AU - Wang, Ziming
AU - Chu, Fan
AU - Wang, Xue
AU - Li, Shulin
AU - Gao, Yingjie
AU - Feng, Xiangling
AU - Li, Yixiang
AU - Li, Menghan
AU - Wang, Yaoyi
AU - Mei, Kunhao
AU - Zhu, Ying
AU - Ma, Shaojie
AU - Lu, Qing
AU - Li, Mingfeng
TI - Perinatal brain developmental transition revealed by transcriptomic and proteomic analyses of Bama miniature pigs
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/02
VL - 17
IS - 1
SP - 4712
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71360-9
UR - https://doi.org/10.1038/s41467-026-71360-9
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-71360-9",
"type": "article-journal",
"title": "Perinatal brain developmental transition revealed by transcriptomic and proteomic analyses of Bama miniature pigs",
"container-title": "Nature communications",
"author": [
{
"family": "Wang",
"given": "Ziming"
},
{
"family": "Chu",
"given": "Fan"
},
{
"family": "Wang",
"given": "Xue"
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{
"family": "Li",
"given": "Shulin"
},
{
"family": "Gao",
"given": "Yingjie"
},
{
"family": "Feng",
"given": "Xiangling"
},
{
"family": "Li",
"given": "Yixiang"
},
{
"family": "Li",
"given": "Menghan"
},
{
"family": "Wang",
"given": "Yaoyi"
},
{
"family": "Mei",
"given": "Kunhao"
},
{
"family": "Zhu",
"given": "Ying"
},
{
"family": "Ma",
"given": "Shaojie"
},
{
"family": "Lu",
"given": "Qing"
},
{
"family": "Li",
"given": "Mingfeng"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4712",
"DOI": "10.1038/s41467-026-71360-9",
"PMID": "41922393",
"PMCID": "PMC13212750",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71360-9",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
2
]
]
}
}

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

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