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Uncovering the Key Circuit FOSL2/FOS/EGR3/EGR1, Contributing to the Hyperexcitability of Excitatory Neurons in the Epileptic Temporal Cortex and Hippocampus.

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The 26 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § 4. Materials and Methods › 4.3. Cell Subpopulation Re-Annotation ↔ Code/Result2/Cell subpopulation re-annotation_Hoppocampus.R, lines 117–177 · score 1.00 · HS3ST4, SLC47A1, SV2B, TMEM132C, SEMA5A, SLC14A1
  2. [2] § 4. Materials and Methods › 4.2. Quality Control, Cell Clustering and Major Cell Type Identification of snRNA-Seq Data ↔ Code/Result1/Data Processing_Hippocampus.R, lines 54–131 · score 0.91 · oligodendrocyte progenitor cells, oligodendrocyte precursor cells, FindVariableFeatures, FindAllMarkers, endothelial cells, CellMarker
  3. [3] § 2. Results › 2.2. Excitatory Neurons Exhibit Highly Active State in Separate Epileptic Regions ↔ Code/Result2/Cell subpopulation re-annotation_Hoppocampus.R, lines 117–177 · score 0.90 · dentate gyrus, DG_Ex3, DG_Ex4, DG_Ex5, CA_Ex_GAPDH, CA1_Ex
  4. [4] § 2. Results › 2.2. Excitatory Neurons Exhibit Highly Active State in Separate Epileptic Regions ↔ Code/Result2/Neuronal_activation_Score.R, lines 1–68 · score 0.87 · DG_Ex3, DG_Ex4, DG_Ex5, CA_Ex_GAPDH, CA1_Ex, DG_Ex1
  5. [5] § 2. Results › 2.5. Revealing Circuit FOSL2/FOS/EGR3/EGR1 Transregional Crosstalk Promoting Excitatory Neuronal Activation ↔ Code/Result2/Neuronal_activation_Score.R, lines 1–68 · score 0.84 · module score, CA1_Ex, DG_Ex1, L2_3_Cux2, neuronal activation, CA3_Ex
  6. [6] § 4. Materials and Methods › 4.2. Quality Control, Cell Clustering and Major Cell Type Identification of snRNA-Seq Data ↔ Code/Result1/Data Processing_Temporal cortex.R, lines 184–233 · score 0.84 · oligodendrocyte progenitor cells, oligodendrocyte precursor cells, FindAllMarkers, endothelial cells, CellMarker, logfc
  7. [7] § 2. Results › 2.4. Glial Cells Mediate Cellular Junction Assembly and Synaptic Organization Functions Along the Hippocampal Anterior and Posterior Axis ↔ Code/Result4/Glial_subtype.R, lines 270–358 · score 0.84 · CSF1R, P2RY12, CD86, SALL1, SPP1, TMEM119
  8. [8] § 2. Results › 2.4. Glial Cells Mediate Cellular Junction Assembly and Synaptic Organization Functions Along the Hippocampal Anterior and Posterior Axis ↔ Code/Result4/Glial_subtype.R, lines 114–169 · score 0.80 · NR4A2, CD44, ETNPPL, HSPB1, WIF1, CABLES1
  9. [9] § 4. Materials and Methods › 4.2. Quality Control, Cell Clustering and Major Cell Type Identification of snRNA-Seq Data ↔ Code/Result1/kBET_validation.R, the whole file · a weak match · score 0.79 · n_repeat, acceptance rate, kBET, k0, batch, embeddings
  10. [10] § 2. Results › 2.5. Revealing Circuit FOSL2/FOS/EGR3/EGR1 Transregional Crosstalk Promoting Excitatory Neuronal Activation ↔ Code/Result5/Bulk RNA data processing.R, lines 312–348 · score 0.79 · COL4A1, IL1B, bulk RNA, neuronal activation, LASSO, SOCS6
  11. [11] § 2. Results › 2.5. Revealing Circuit FOSL2/FOS/EGR3/EGR1 Transregional Crosstalk Promoting Excitatory Neuronal Activation ↔ Code/Result2/Neuronal_activation_gene_expression.R, the whole file · a weak match · score 0.78 · CA1_Ex, DG_Ex1, L2_3_Cux2, neuronal activation, CA3_Ex, FOSL2
  12. [12] § 2. Results › 2.4. Glial Cells Mediate Cellular Junction Assembly and Synaptic Organization Functions Along the Hippocampal Anterior and Posterior Axis ↔ Code/Result4/Glial_enrichment.R, lines 220–302 · score 0.78 · chemical synaptic transmission, trans synaptic signaling, cell junction assembly, synapse organization, modulation, glial
  13. [13] § 4. Materials and Methods › 4.10. Differential Expression Analysis ↔ Code/Result5/Bulk RNA data processing.R, lines 48–125 · score 0.73 · adj.P.Val, limma, fold change, Bayes, DEGs, GSE256068
  14. [14] § 2. Results › 2.5. Revealing Circuit FOSL2/FOS/EGR3/EGR1 Transregional Crosstalk Promoting Excitatory Neuronal Activation ↔ Code/Result5/Neuronal_activation.R, lines 56–134 · score 0.72 · epilepsy risk genes, neuronal activation, target genes, GABRA1, GABRG2, FOSL2
  15. [15] § 2. Results › 2.4. Glial Cells Mediate Cellular Junction Assembly and Synaptic Organization Functions Along the Hippocampal Anterior and Posterior Axis ↔ Code/Result4/Glial_enrichment.R, lines 220–302 · score 0.71 · chemical synaptic transmission, trans synaptic signaling, synapse organization, assembly, modulation, Oligodendrocytes
  16. [16] § 2. Results › 2.1. Dissecting Transregional Cellular Composition Independent and Joint-Triggering Epileptic Effects ↔ Code/Result1/Data Processing_Hippocampus.R, lines 54–131 · score 0.70 · oligodendrocyte progenitor cells, oligodendrocyte precursor cells, endothelial cells, batch, inhibitory neurons, SNN
  17. [17] § 4. Materials and Methods › 4.8. Construction of Cross-Regional Cell Type-Specific Transcriptional Regulatory Networks ↔ Code/Result5/pySCENIC_Data preparation_Visualization.R, lines 198–255 · score 0.69 · human transcription factors, target gene, Cytoscape, motif, temporal lobe, pySCENIC
  18. [18] § 2. Results › 2.1. Dissecting Transregional Cellular Composition Independent and Joint-Triggering Epileptic Effects ↔ Code/Result1/Data Processing_Temporal cortex.R, lines 184–233 · score 0.67 · oligodendrocyte progenitor cells, oligodendrocyte precursor cells, endothelial cells, inhibitory neurons, SNN, temporal cortex
  19. [19] § 4. Materials and Methods › 4.3. Cell Subpopulation Re-Annotation ↔ Code/Result4/Glial_subtype.R, lines 585–626 · score 0.63 · imOli, Oli1, Oli2, OPCs, subclusters, oligodendrocyte
  20. [20] § 4. Materials and Methods › 4.9. Downloading and Processing of Bulk RNA Data ↔ Code/Result5/Bulk RNA data processing.R, lines 1–45 · score 0.61 · Bulk RNA, gene symbols, GSE256068
  21. [21] § 4. Materials and Methods › 4.7. Reconstruction Pseudo-Time Trajectory by Monocle3 ↔ Code/Result3/monocle3_Temporal cortex.R, lines 46–88 · score 0.60 · graph_test, temporal lobe, monocle3, morans, root, trajectories
  22. [22] § 4. Materials and Methods › 4.11. Identification of Hub Genes and Development of a Diagnostic Signature for Epileptic Neuronal Activation ↔ Code/Result5/Bulk RNA data processing.R, lines 312–348 · score 0.58 · cross validation, glmnet, binomial, LASSO, GSE256068, activation
  23. [23] § 2. Results › 2.5. Revealing Circuit FOSL2/FOS/EGR3/EGR1 Transregional Crosstalk Promoting Excitatory Neuronal Activation ↔ Code/Result5/Neuronal_activation.R, lines 56–134 · score 0.57 · neuronal activation, Target genes, TF, Hippo, FOSL2, EGR1
  24. [24] § 2. Results › 2.5. Revealing Circuit FOSL2/FOS/EGR3/EGR1 Transregional Crosstalk Promoting Excitatory Neuronal Activation ↔ Code/Result5/Bulk RNA data processing.R, lines 262–310 · score 0.56 · neuronal activation, Target genes, Subnetworks, TF, cross, enrichment
  25. [25] § 4. Materials and Methods › 4.7. Reconstruction Pseudo-Time Trajectory by Monocle3 ↔ Code/Result3/monocle3_Hippocampus.R, lines 53–95 · score 0.55 · graph_test, monocle3, morans, root, trajectories, pseudotemporal
  26. [26] § 4. Materials and Methods › 4.2. Quality Control, Cell Clustering and Major Cell Type Identification of snRNA-Seq Data ↔ Code/Result1/Data Processing_Hippocampus.R, lines 1–52 · score 0.52 · nFeature_RNA, Seurat, hippocampus, cells, genes

Paper

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The authors' code

R · 632 lines · 23 KB · MIT · 5 matches

  1. ##BULK RNA------------
  2. # 读取数据
  3. #GSE256068--------------
  4. library(data.table)
  5. GSE256068_raw_data <- fread("GSE256068_raw_data.csv.gz")
  6. class(GSE256068_raw_data)
  7. GSE256068_raw_data <- as.data.frame(GSE256068_raw_data)
  8. library(stringr)
  9. GSE256068_raw_data[1:4,1:4]
  10. library(org.Hs.eg.db)
  12. gene_symbols <- mapIds(
  13. org.Hs.eg.db,
  14. keys = GSE256068_raw_data$V1,
  15. column = "SYMBOL",
  16. keytype = "ENSEMBL",
  17. multiVals = "first"
  18. )
  19. GSE256068_raw_data$gene_symbol <- gene_symbols[GSE256068_raw_data$V1]
  20. GSE256068_raw_data[1:4,1:4]
  21. GSE256068_raw_data[1:4,(ncol(GSE256068_raw_data)-4):ncol(GSE256068_raw_data)]
  22. length(GSE256068_raw_data$gene_symbol)
  23. length(unique(GSE256068_raw_data$gene_symbol))
  25. GSE256068_raw_data<-na.omit(GSE256068_raw_data)
  26. table(duplicated(GSE256068_raw_data$gene_symbol))
  27. library(limma)
  28. GSE256068_raw_data[1:4,c(1,(ncol(GSE256068_raw_data)-4):ncol(GSE256068_raw_data))]
  29. exp_unique<-avereps(GSE256068_raw_data[,-c(1,ncol(GSE256068_raw_data))],ID=GSE256068_raw_data$gene_symbol)
  30. exp_unique[1:4,1:4]
  31. dim(exp_unique)
  32. exp_unique[1:4,c(1,(ncol(exp_unique)-4):ncol(exp_unique))]
  34. GSE256068_exp <- exp_unique
  35. save(GSE256068_exp,file = "./GSE256068_exp.RData")
  37. SampleFile <- fread("./GSE256068_SampleFile.txt")
  38. unique(SampleFile$Tissue)
  39. SampleFile <- SampleFile[SampleFile$Tissue %in% c("Temporal","Hippocampus"),] #96*5
  40. table(SampleFile$Tissue)
  41. # Hippocampus Temporal
  42. # 13 83
  43. colnames(SampleFile)[4] <- "Disease"
  44. table(SampleFile$Disease)
  45. table(SampleFile$Tissue,SampleFile$Disease)
  48. #因子型
  49. SampleFile <- SampleFile[SampleFile$Sample_ID %in% colnames(GSE256068_exp),]
  50. SampleFile <- SampleFile[order(SampleFile$Disease),]
  51. table(SampleFile$Disease)
  52. save(SampleFile,file = "./SampleFile_GSE256068.RData")
  54. GSE256068_exp[1:4,1:4]
  55. dim(GSE256068_exp) #20714 162
  56. #sample <- intersect(SampleFile$Sample,colnames(GSE256068_exp))
  57. GSE256068_exp <- GSE256068_exp[,SampleFile$Sample_ID]
  58. identical(colnames(GSE256068_exp),SampleFile$Sample_ID)
  59. colnames(GSE256068_exp) <- SampleFile$Sample
  60. boxplot(GSE256068_exp,outline=FALSE, notch=T,las=2)
  61. colnames(GSE256068_exp)
  62. #GSE256068_exp_batch_boxplot.pdf
  63. range(GSE256068_exp)# -5.933978 14.931358 在20以内的范围就是已经log2了
  64. dim(GSE256068_exp) #20714 96
  65. save(GSE256068_exp,file = "./GSE256068_exp.RData")
  67. # #1.差异表达分析--------------
  68. #limma-------------
  69. #构建分组矩阵--design
  70. load(file = "./GSE256068_exp.RData")
  71. load(file = "./SampleFile_GSE256068.RData")
  72. # table(SampleFile$Disease)
  73. # # ControlCortex ControlHippocampus FCD2a FCD2b TLE_HS TSC
  74. # # 4 13 3 6 64 6
  75. # SampleFile$Group[SampleFile$Disease %in% c("ControlCortex","ControlHippocampus")] <- "Control"
  76. # SampleFile$Group[SampleFile$Disease %in% c("FCD2a" ,"FCD2b","TLE_HS" ,"TSC")] <- "Epilepsy"
  77. table(SampleFile$Group)
  78. # Control Epilepsy
  79. # 17 79
  80. #save(SampleFile,file = "./SampleFile_GSE256068.RData")
  82. design <- model.matrix(~0+factor(SampleFile$Group))
  83. colnames(design) <- levels(factor(SampleFile$Group))
  85. rownames(design) <- colnames(GSE256068_exp)
  86. #构建比较矩阵——contrast
  87. contrast.matrix <- makeContrasts(Epilepsy-Control,levels = design)
  88. #limma DEG
  89. fit <- lmFit(GSE256068_exp,design)##线性拟合模型构建
  90. fit2 <- contrasts.fit(fit, contrast.matrix)
  91. fit2 <- eBayes(fit2)
  92. DEG <- topTable(fit2, coef = 1,n = Inf)
  94. DEG$type <- ifelse(DEG$adj.P.Val > 0.05, "no_change",
  95. ifelse(DEG$logFC > 1, "up",
  96. ifelse(DEG$logFC < -1, "down", "no_change")))
  97. DEG <- dplyr::filter(DEG, !is.na(DEG$type))
  99. save(DEG,file = "./DEGs.RData")
  100. dif <- DEG[DEG$adj.P.Val<0.05&abs(DEG$logFC)>1,]
  101. dif <- dif[order(dif$logFC),]
  102. save(dif,file = "./DEGs_filter.RData")
  103. dim(dif)
  104. ##1916 7
  105. ##火山图-------------
  106. library(ggplot2)
  107. load(file = "./DEGs.RData")
  108. head(DEG)
  109. table(DEG$type)
  110. # down no_change up
  111. # 927 18798 989
  112. GSE256068_exp[1:4,1:4]
  113. ggplot(DEG, aes(logFC, -log10(adj.P.Val)))+ #读取差异表达结果，X轴为logFC,y轴为-log10(P.Value)
  115. geom_point(aes(col=type))+ #设置点数据的来源
  117. scale_color_manual(values=c("#0072B5","grey","#BC3C28"))+ #这里可以对途中，上调，不变和下调的点的颜色进行设置
  119. labs(x="log2(FoldChange)",y="-log10(adj.P.Val)")+ #设置x轴和y轴的标签
  121. geom_vline(xintercept=c(-0.5,0.5), colour="grey", linetype="dashed")+ #设置x轴的分界线
  123. geom_hline(yintercept = -log10(0.05),colour="grey", linetype="dashed") #设置y轴的分界线
  125. ggsave("./diff_gene_volcano.pdf",height = 5,width = 5)
  127. #2.功能富集分析------------
  128. library(clusterProfiler)
  129. library(org.Hs.eg.db)
  130. ##GO------------
  131. load(file = "./DEGs_filter.RData")
  132. head(dif) #
  133. ##全部---------------
  134. enrich.go <- enrichGO(gene = rownames(dif), #基因列表文件中的基因名称
  135. OrgDb = 'org.Hs.eg.db', #指定物种的基因数据库
  136. keyType = 'SYMBOL', #指定给定的基因名称类型，例如这里以 entrze id 为例
  137. ont = 'ALL', #可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
  138. pAdjustMethod = 'fdr', #指定 p 值校正方法
  139. pvalueCutoff = 0.05, #指定 p 值阈值，不显著的值将不显示在结果中
  140. qvalueCutoff = 0.2, #指定 q 值阈值，不显著的值将不显示在结果中
  141. readable = FALSE)
  142. enrich.go <-as.data.frame(enrich.go) #794
  143. save(enrich.go,file = "./DEGs_GO_enrichment.RData")
  144. load(file = "./DEGs_GO_enrichment.RData")
  145. ##可视化---------
  146. dat <- enrich.go[1:10,]
  147. dat <- dat[order(dat$Count,decreasing = F),]
  148. dat$Description <- factor(dat$Description, levels = dat$Description)
  150. #柱形图，横坐标 p 值的对数转换，纵坐标是 GO Term，颜色按 Category 着色
  151. p2 <- ggplot(dat, aes(Description, Count)) +
  152. geom_col(aes(fill = -log10(pvalue)), width = 0.5) +
  153. scale_fill_gradient(low = "#F5E3DE",high = "#831A1F") +
  154. theme(panel.grid = element_blank(), panel.background = element_rect(color = 'black', fill = 'transparent')) +
  155. scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
  156. coord_flip() +
  157. labs(y = 'Count',x = "", title = "DEGs GO enrichment")+
  158. theme(
  159. axis.title = element_text(size = 13),
  160. axis.text = element_text(size = 11),
  161. plot.title = element_text(size = 14,
  162. hjust = 0.5,vjust = 0.5,
  163. face = "bold"),
  164. legend.title = element_text(size = 13),
  165. legend.text = element_text(size = 11)
  166. )
  167. p2
  168. #上调------------------
  169. dif$logFC
  170. up_genes <- dif %>%
  171. filter(adj.P.Val < 0.05 & logFC > 1) %>%
  172. rownames() #
  173. down_genes <- dif %>%
  174. filter(adj.P.Val < 0.05 & logFC < -1) %>%
  175. rownames() #
  177. enrich.go <- enrichGO(gene = up_genes, #基因列表文件中的基因名称
  178. OrgDb = 'org.Hs.eg.db', #指定物种的基因数据库
  179. keyType = 'SYMBOL',
  180. ont = 'ALL', #可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
  181. pAdjustMethod = 'fdr', #指定 p 值校正方法
  182. pvalueCutoff = 0.05, #指定 p 值阈值，不显著的值将不显示在结果中
  183. qvalueCutoff = 0.2, #指定 q 值阈值，不显著的值将不显示在结果中
  184. readable = FALSE)
  185. enrich.go <-as.data.frame(enrich.go) #826
  186. save(enrich.go,file = "./up_genes_go.RData")
  187. load(file = "./up_genes_go.RData")
  188. ##可视化---------
  189. dat <- enrich.go[1:10,]
  190. dat <- dat[order(dat$Count,decreasing = F),]
  191. dat$Description <- factor(dat$Description, levels = dat$Description)
  192. p2 <- ggplot(dat, aes(Description, Count)) +
  193. geom_col(aes(fill = -log10(pvalue)), width = 0.5) +
  194. scale_fill_gradient(low = "#F5E3DE",high = "#831A1F") +
  195. theme(panel.grid = element_blank(), panel.background = element_rect(color = 'black', fill = 'transparent')) +
  196. scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
  197. coord_flip() +
  198. labs(y = 'Count',x = "", title = "DEGs GO enrichment")+
  199. theme(
  200. axis.title = element_text(size = 13),
  201. axis.text = element_text(size = 11),
  202. plot.title = element_text(size = 14,
  203. hjust = 0.5,vjust = 0.5,
  204. face = "bold"),
  205. legend.title = element_text(size = 13),
  206. legend.text = element_text(size = 11)
  207. )
  208. p2
  210. #下调------------------
  211. enrich.go <- enrichGO(gene = down_genes, #基因列表文件中的基因名称
  212. OrgDb = 'org.Hs.eg.db', #指定物种的基因数据库
  213. keyType = 'SYMBOL',
  214. ont = 'ALL', #可选 BP、MF、CC，也可以指定 ALL 同时计算 3 者
  215. pAdjustMethod = 'fdr', #指定 p 值校正方法
  216. pvalueCutoff = 0.05, #指定 p 值阈值，不显著的值将不显示在结果中
  217. qvalueCutoff = 0.2, #指定 q 值阈值，不显著的值将不显示在结果中
  218. readable = FALSE)
  219. enrich.go <-as.data.frame(enrich.go) #94
  220. save(enrich.go,file = "./down_genes_go.RData")
  221. load(file = "./down_genes_go.RData")
  222. ##可视化---------
  223. dat <- enrich.go[1:10,]
  224. dat <- dat[order(dat$Count,decreasing = F),]
  225. dat$Description <- factor(dat$Description, levels = dat$Description)
  227. p2 <- ggplot(dat, aes(Description, Count)) +
  228. geom_col(aes(fill = -log10(pvalue)), width = 0.5) +
  229. scale_fill_gradient(low = "#F5E3DE",high = "#831A1F") +
  230. theme(panel.grid = element_blank(), panel.background = element_rect(color = 'black', fill = 'transparent')) +
  231. scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
  232. coord_flip() +
  233. labs(y = 'Count',x = "", title = "DEGs GO enrichment")+
  234. theme(
  235. axis.title = element_text(size = 13),
  236. axis.text = element_text(size = 11),
  237. plot.title = element_text(size = 14,
  238. hjust = 0.5,vjust = 0.5,
  239. face = "bold"),
  240. legend.title = element_text(size = 13),
  241. legend.text = element_text(size = 11)
  242. )
  243. p2
  245. ##KEGG------------
  246. load(file = "./DEGs_filter.RData")
  247. head(dif) #
  248. dif$gene <- rownames(dif)
  249. genelist <- bitr(dif$gene, fromType="SYMBOL",
  250. toType="ENTREZID", OrgDb='org.Hs.eg.db')
  251. library(dplyr)
  252. #inner_join() 函数要基于 DEG 数据框的 "Gene" 列和 genelist 数据框的 "SYMBOL" 列进行连接。
  253. dif <- inner_join(dif,genelist,by=c("gene"="SYMBOL"))
  254. enrich.kegg <- enrichKEGG(gene = dif$ENTREZID, #基因列表文件中的基因名称
  255. organism = 'hsa', #指定物种的基因数据库
  256. pvalueCutoff = 0.05, #指定 p 值阈值，不显著的值将不显示在结果中
  257. qvalueCutoff = 0.2 #指定 q 值阈值，不显著的值将不显示在结果中
  258. )
  259. enrich.kegg <-as.data.frame(enrich.kegg) #45
  260. save(enrich.kegg,file = "./dif_gene_kegg.RData")
  262. ##可视化---------
  263. dat <- enrich.kegg[1:10,]
  264. dat <- dat[order(dat$Count,decreasing = F),]
  265. dat$Description <- factor(dat$Description, levels = dat$Description)
  266. p2 <- ggplot(dat, aes(Description, Count)) +
  267. geom_col(aes(fill = -log10(pvalue)), width = 0.5) +
  268. scale_fill_gradient(low = "#F5E3DE",high = "#831A1F") +
  269. theme(panel.grid = element_blank(), panel.background = element_rect(color = 'black', fill = 'transparent')) +
  270. scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
  271. coord_flip() +
  272. labs(y = 'Count',x = "", title = "DEGs KEGG enrichment")+
  273. theme(
  274. axis.title = element_text(size = 13),
  275. axis.text = element_text(size = 11),
  276. plot.title = element_text(size = 14,
  277. hjust = 0.5,vjust = 0.5,
  278. face = "bold"),
  279. legend.title = element_text(size = 13),
  280. legend.text = element_text(size = 11)
  281. )
  282. p2
  283. ##与单细胞神经元激活相关的转录因子和靶基因取交集-----------
  284. #GSE256068--------------------
  285. load(file = "./DEGs_filter.RData")
  286. dim(dif) #
  288. library(readr)
  289. Neuronal_activation_subnetwork <- read_delim("./Neuronal_activation_subnetwork.txt",
  290. delim = "\t", escape_double = FALSE,
  291. trim_ws = TRUE)
  292. TF <- unique(c(Neuronal_activation_subnetwork$TF,Neuronal_activation_subnetwork$TargetGene))
  293. Neuronal_activation_subnetwork_gene <- intersect(unique(c(Neuronal_activation_subnetwork$TF,Neuronal_activation_subnetwork$TargetGene)),
  294. rownames(dif)
  295. )
  297. library(VennDiagram)
  298. library(grid)
  299. # 创建韦恩图对象
  300. venn.plot <- draw.pairwise.venn(
  301. area1 = length(rownames(dif)), # 第一个集合大小
  302. area2 = length(TF), # 第二个集合大小
  303. cross.area = length(Neuronal_activation_subnetwork_gene), # 交集大小
  304. category = c("DEGs", "Neuronal activation gene"), # 集合名称
  305. fill = c("#c86f5e", "skyblue"), # 颜色填充
  306. alpha = 0.7, # 透明度
  307. cat.pos = c(0, 0), # 标签位置
  308. cat.dist = 0.05, # 标签距离
  309. ext.text = FALSE
  310. )
  312. # 显示图形
  313. grid.newpage()
  314. grid.draw(venn.plot)
  315. # [1] "FOS" "EGR3" "EGR1" "HAVCR2" "CD74" "PTPRC" "CCL3" "CX3CR1" "IL1B" "CCL4" "SOCS6"
  316. # [12] "CCL2" "HOMER1" "SLC7A11" "BTG2" "PTGS2" "COL4A1" "EGR2" "FOSB" "TPPP3" "GFAP" "JUNB"
  317. # [23] "ZFP36" "HSPA1A"
  318. gene_expr <- GSE256068_exp[rownames(GSE256068_exp) %in% Neuronal_activation_subnetwork_gene, ] # 选出目标基因表达
  319. gene_expr <- t(gene_expr) # 转置为样本×基因
  321. # group 向量，样本分组，长度与样本数一致
  322. group <- SampleFile$Group # 0=normal, 1=epilepsy
  323. group[group %in% "Control"] <- "0"
  324. group[group %in% "Epilepsy"] <- "1"
  326. group <- as.factor(group) # 0=normal, 1=epilepsy
  327. # LASSO回归筛选特征基因---------
  328. library(glmnet)
  329. # 训练LASSO模型
  330. set.seed(123)
  331. cvfit <- cv.glmnet(
  332. as.matrix(gene_expr),
  333. as.numeric(as.character(group)), # 注意要转为0/1数值
  334. family = "binomial", # 二分类
  335. alpha = 1 # LASSO
  336. )
  337. plot(cvfit$glmnet.fit, xvar="lambda", label=TRUE, main="LASSO coefficient paths")
  338. #lasso_lujing.pdf 8*8
  339. plot(cvfit, main="LASSO cross-validation curve")
  340. abline(v=log(cvfit$lambda.min), col="red", lty=2)
  341. #lasso_CV.pdf 8*8
  343. # 提取最佳lambda时的基因
  344. coef_lasso <- coef(cvfit, s = "lambda.min")
  345. lasso_genes <- rownames(coef_lasso)[which(coef_lasso != 0)][-1]
  346. print(lasso_genes)
  347. #"IL1B" "GFAP" "SLC7A11" "SOCS6" "COL4A1" "HSPA1A"
  348. save(lasso_genes,file = "./lasso_genes.RData")
  350. #SVM递归特征消除（RFE）----------
  351. load(file = "./GSE256068_exp.RData")
  352. load(file = "./SampleFile_GSE256068.RData")
  354. library(caret)
  355. library(e1071)
  356. gene_expr <- GSE256068_exp[rownames(GSE256068_exp) %in% Neuronal_activation_subnetwork_gene, ]
  357. gene_expr <- t(gene_expr)
  359. dat <- data.frame(gene_expr)
  360. dat$group <- group
  361. library(caret)
  362. library(e1071)
  364. x <- dat[, !(names(dat) %in% "group")]
  365. y <- as.factor(dat$group)
  368. stopifnot(!any(is.na(x)))
  369. stopifnot(!any(is.na(y)))
  370. set.seed(123)
  371. ctrl <- rfeControl(functions = rfFuncs, method = "cv", number = 10)
  372. svmProfile <- rfe(
  373. x,
  374. y,
  375. sizes = c(1:10, 15, 20),
  376. rfeControl = ctrl,
  377. method = "rf"
  378. )
  379. plot(svmProfile, type = c("g", "o"), main = "RFE Accuracy vs. Number of Features")
  380. print(svmProfile)
  381. svmProfile$optVariables
  382. predictors(svmProfile)
  383. #"CCL3" "IL1B" "CX3CR1" "CCL4" "COL4A1" "EGR2" "SOCS6" "FOSB"
  384. varImp(svmProfile)
  385. svm_genes <- predictors(svmProfile)
  386. print(svm_genes)
  387. save(svm_genes,file = "./svm_genes.RData")
  389. load("./lasso_genes.RData")
  390. load(file = "./svm_genes.RData")
  391. common_genes <- intersect(lasso_genes, svm_genes)
  392. print(common_genes)
  393. #"IL1B" "SOCS6" "COL4A1"
  396. library(VennDiagram)
  397. library(grid)
  398. # 创建韦恩图对象
  399. venn.plot <- draw.pairwise.venn(
  400. area1 = length(lasso_genes), # 第一个集合大小
  401. area2 = length(svm_genes), # 第二个集合大小
  402. cross.area = length(common_genes), # 交集大小
  403. category = c("Lasso genes", "RF genes"), # 集合名称
  404. fill = c("#c86f5e", "skyblue"), # 颜色填充
  405. alpha = 0.7, # 透明度
  406. cat.pos = c(0, 0), # 标签位置
  407. cat.dist = 0.05, # 标签距离
  408. ext.text = FALSE
  409. )
  411. # 显示图形
  412. grid.newpage()
  413. grid.draw(venn.plot)
  416. library(caret)
  417. library(pROC)
  419. X <- t(GSE256068_exp[common_genes, ])
  420. X <- as.data.frame(X)
  421. y <- as.factor(group)
  423. stopifnot(nrow(X) == length(y))
  425. model <- glm(y ~ ., data = X, family = binomial)
  427. summary(model)
  430. pred_prob <- predict(model, type = "response")
  432. roc_obj <- roc(y, pred_prob)
  433. auc_score <- auc(roc_obj)
  434. cat("模型AUC:", auc_score, "\n")
  435. plot(roc_obj, col="red", lwd=2,main=paste("Logistic Regression ROC, AUC =", round(auc_score, 3)))
  436. coef(summary(model))
  438. library(pROC)
  439. library(RColorBrewer)
  441. y <- as.factor(group)
  442. if (is.character(y) || is.factor(y)) y <- as.numeric(as.character(y))
  444. cols <- brewer.pal(min(6, length(common_genes)), "Set1")
  446. plot(NULL, xlim=c(0,1), ylim=c(0,1), xlab="1-Specificity", ylab="Sensitivity", main="Single Gene ROC Curves")
  447. abline(0,1,lty=2,col="gray")
  449. legend_text <- c()
  450. cols <- c("#99CCCC","#336699","#996699")
  451. for (i in seq_along(common_genes)) {
  452. gene <- common_genes[i]
  453. gene_expr <- as.numeric(GSE256068_exp[gene, ])
  454. roc_obj <- roc(y, gene_expr, quiet=TRUE)
  455. lines(1 - roc_obj$specificities, roc_obj$sensitivities, col=cols[i], lwd=2)
  456. legend_text <- c(legend_text, paste0(gene, " (AUC=", round(auc(roc_obj), 3), ")"))
  457. }
  459. legend("bottomright", legend=legend_text, col=cols, lwd=2, cex=0.9)
  461. ##GSE139914 8癫痫 39正常--------------
  462. library(data.table)
  463. GSE139914_raw_data <- fread("GSE139914_Within_Subject_RawCounts.txt.gz")
  465. GSE139914_exp<-na.omit(GSE139914_raw_data)
  466. table(duplicated(GSE139914_exp$V1))
  467. library(limma)
  468. exp_unique<-avereps(GSE139914_exp[,-1],ID=GSE139914_exp$V1)
  469. exp_unique[1:4,1:4]
  470. dim(exp_unique)
  471. exp_unique[1:4,c(1,(ncol(exp_unique)-4):ncol(exp_unique))]
  473. GSE139914_exp <- exp_unique
  474. range(GSE139914_exp) #0 798857
  475. GSE139914_exp <- log2(GSE139914_exp+1) #0.00000 19.60758
  476. save(GSE139914_exp,file = "./GSE139914_exp.RData")
  478. load(file = "./GSE139914_exp.RData")
  479. GSE139914_SampleFile <- fread("GSE139914_SampleFile.txt")
  481. group_new <- GSE139914_SampleFile$Group
  482. group_new <- factor(group_new, levels = c("Control", "Epilepsy"))
  484. library(pROC)
  485. gene_list <- c("IL1B","SOCS6","COL4A1") #
  487. plot(NULL, xlim=c(0,1), ylim=c(0,1), xlab="1-Specificity", ylab="Sensitivity", main="Single Gene ROC")
  488. abline(0,1,lty=2,col="gray")
  489. cols <- c("#99CCCC","#336699","#996699")[1:length(gene_list)]
  490. legend_text <- c()
  492. for(i in seq_along(gene_list)){
  493. gene <- gene_list[i]
  494. gene_expr <- as.numeric(GSE139914_exp[gene, ])
  495. roc_obj <- roc(group_new, gene_expr, quiet=TRUE, levels=rev(levels(group_new)))
  496. lines(1-roc_obj$specificities, roc_obj$sensitivities, col=cols[i], lwd=2)
  497. legend_text <- c(legend_text, paste0(gene, " (AUC=", round(auc(roc_obj),3), ")"))
  498. }
  499. legend("bottomright", legend=legend_text, col=cols, lwd=2)
  501. X_new <- t(GSE139914_exp[gene_list, ])
  502. X_new <- as.data.frame(X_new)
  504. model_new <- glm(group_new ~ ., data=X_new, family=binomial)
  505. pred_prob_new <- predict(model_new, type="response")
  506. roc_model <- roc(group_new, pred_prob_new, levels=rev(levels(group_new)))
  507. auc_model <- auc(roc_model)
  508. plot(roc_model, col="red", lwd=2, main=paste("3-Gene Model ROC, AUC =", round(auc_model, 3)))
  510. #GSE140393 9正常 12癫痫----------------
  511. library(data.table)
  512. GSE140393_raw_data <- fread("GSE140393_rldnormalized_EGFR.txt.gz")
  513. GSE140393_raw_data <- GSE140393_raw_data[c(1,86:nrow(GSE140393_raw_data))]
  514. colnames(GSE140393_raw_data)[2:4] <- as.character(GSE140393_raw_data[1,2:4])
  515. GSE140393_raw_data <- GSE140393_raw_data[-1,-c(1,6,7)]
  516. GSE140393_exp<-na.omit(GSE140393_raw_data)
  517. table(duplicated(GSE140393_exp$hgnc_symbol))
  518. GSE140393_exp <- GSE140393_exp[!(is.na(GSE140393_exp$hgnc_symbol) & GSE140393_exp$hgnc_symbol == ""), ]
  520. row_data <- GSE140393_exp[ , -4]
  521. non_zero_row <- rowSums(row_data != 0) > 0
  522. GSE140393_exp <- GSE140393_exp[non_zero_row, ]
  524. dim(GSE140393_exp)
  525. table(duplicated(GSE140393_exp$hgnc_symbol))
  527. GSE140393_exp <- as.data.frame(GSE140393_exp)
  528. library(limma)
  529. gene_ids <- as.character(GSE140393_exp$hgnc_symbol)
  530. exp_unique <- avereps(GSE140393_exp[,-4], ID = gene_ids)
  531. if (is.null(rownames(exp_unique))) {
  532. rownames(exp_unique) <- unique(gene_ids)
  533. }
  534. rownames(exp_unique)
  535. exp_unique[1:10,1:3]
  536. exp_unique <- as.matrix(exp_unique)
  537. dim(exp_unique) #20726 3
  539. GSE140393_exp <- exp_unique
  540. range(GSE140393_exp) #"-0.001887921" "9.999648117"
  541. save(GSE140393_exp,file = "./GSE140393_exp.RData")
  543. GSE140393_raw_data_1 <- fread("GSE140393_rldnormalized_nuclei.txt.gz")
  544. GSE140393_raw_data <- GSE140393_raw_data_1[c(1,86:nrow(GSE140393_raw_data_1))]
  545. colnames(GSE140393_raw_data)[2:19] <- as.character(GSE140393_raw_data[1,2:19])
  546. GSE140393_raw_data <- GSE140393_raw_data[-1,-c(1,21,22)]
  547. GSE140393_exp<-na.omit(GSE140393_raw_data)
  548. table(duplicated(GSE140393_exp$hgnc_symbol))
  550. GSE140393_exp <- GSE140393_exp[!(is.na(GSE140393_exp$hgnc_symbol) & GSE140393_exp$hgnc_symbol == ""), ]
  552. row_data <- GSE140393_exp[ , -19]
  553. non_zero_row <- rowSums(row_data != 0) > 0
  554. GSE140393_exp <- GSE140393_exp[non_zero_row, ]
  556. dim(GSE140393_exp)
  557. table(duplicated(GSE140393_exp$hgnc_symbol))
  558. library(limma)
  559. gene_ids <- as.character(GSE140393_exp$hgnc_symbol)
  560. exp_unique <- avereps(GSE140393_exp[,-19], ID = gene_ids)
  561. if (is.null(rownames(exp_unique))) {
  562. rownames(exp_unique) <- unique(gene_ids)
  563. }
  564. rownames(exp_unique)
  565. exp_unique[1:10,1:3]
  566. dim(exp_unique)
  568. GSE140393_exp <- exp_unique
  569. range(GSE140393_exp) #"-0.000123268" "9.999995611"
  570. GSE140393_exp_1 <- GSE140393_exp
  571. save(GSE140393_exp_1,file = "./GSE140393_exp_1.RData")
  574. load("./GSE140393_exp.RData")
  575. load("./GSE140393_exp_1.RData")
  576. GSE140393_exp <- as.data.frame(GSE140393_exp)
  577. GSE140393_exp_1 <- as.data.frame(GSE140393_exp_1)
  579. gene <- intersect(rownames(GSE140393_exp),rownames(GSE140393_exp_1))
  580. GSE140393_exp <- GSE140393_exp[gene,]
  581. GSE140393_exp_1 <- GSE140393_exp_1[gene,]
  583. GSE140393_exp <- cbind(GSE140393_exp,GSE140393_exp_1)
  584. GSE140393_exp_1 <- apply(GSE140393_exp, 2, as.numeric)
  585. rownames(GSE140393_exp_1) <- rownames(GSE140393_exp)
  587. SampleFile <- fread("GSE140393_SampleFile.txt")
  588. SampleFile$Group
  589. GSE140393_exp <-GSE140393_exp_1[,SampleFile$Patient]
  590. colnames(GSE140393_exp) <- SampleFile$Sample
  592. boxplot(GSE140393_exp,outline=FALSE, notch=T,las=2)
  593. #
  594. GSE140393_exp<-na.omit(GSE140393_exp)
  596. range(GSE140393_exp)#-2.359978 21.928633
  599. save(GSE140393_exp,file = "./GSE140393_exp_final.RData")
  601. load(file = "./GSE140393_exp_final.RData")
  602. gene_list <- c("IL1B","SOCS6","COL4A1") #
  604. GSE140393_SampleFile <- fread("GSE140393_SampleFile.txt")
  606. group_new <- GSE140393_SampleFile$Group
  607. group_new <- factor(group_new, levels = c("Control", "Epilepsy"))
  609. library(pROC)
  611. plot(NULL, xlim=c(0,1), ylim=c(0,1), xlab="1-Specificity", ylab="Sensitivity", main="Single Gene ROC")
  612. abline(0,1,lty=2,col="gray")
  613. cols <- c("#99CCCC","#336699","#996699")[1:length(gene_list)]
  614. legend_text <- c()
  616. for(i in seq_along(gene_list)){
  617. gene <- gene_list[i]
  618. gene_expr <- as.numeric(GSE140393_exp[gene, ])
  619. roc_obj <- roc(group_new, gene_expr, quiet=TRUE, levels=rev(levels(group_new)))
  620. lines(1-roc_obj$specificities, roc_obj$sensitivities, col=cols[i], lwd=2)
  621. legend_text <- c(legend_text, paste0(gene, " (AUC=", round(auc(roc_obj),3), ")"))
  622. }
  623. legend("bottomright", legend=legend_text, col=cols, lwd=2)
  625. X_new <- t(GSE140393_exp[gene_list, ])
  626. X_new <- as.data.frame(X_new)
  628. model_new <- glm(group_new ~ ., data=X_new, family=binomial)
  629. pred_prob_new <- predict(model_new, type="response")
  630. roc_model <- roc(group_new, pred_prob_new, levels=rev(levels(group_new)))
  631. auc_model <- auc(roc_model)
  632. plot(roc_model, col="red", lwd=2, main=paste("3-Gene Model ROC, AUC =", round(auc_model, 3)))

Bulk RNA data processing.R at commit adcd5f9, under MIT · at the source

Overview

Authors: Jing Chen1, Bowen Zhao1, Kaiyue Yang1, Wanqi Mi1, Xiaozhi Huang1, Wenqi Jiang1, Congxue Hu1, Zhenzhen Wang2, Yunpeng Zhang1,2, Xia Li1
ORCID iDs: Jing Chen, Wanqi Mi, Yunpeng Zhang, Xia Li
  1. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China; (J.C.); (B.Z.); (K.Y.); (W.M.); (X.H.); (W.J.); (C.H.)
  2. School of Intelligent Medicine and Technology (Big Data Research Center), Hainan Medical University, Haikou 571199, China
Institutions: Harbin Medical University (China); Hainan Medical University (China)
Journal: International journal of molecular sciences, volume 27, issue 10, article 4466
Dates: received 15 April 2026; accepted 13 May 2026; published online 16 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/ijms27104466 · PMID 42196443 · PMCID PMC13206787 · OpenAlex W7161552119
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), epilepsy (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity
Keywords: epilepsy, excitatory neurons, temporal cortex, hippocampus, neuronal hyperexcitability, single-nucleus RNA sequencing
MeSH: Early Growth Response Protein 1*, Early Growth Response Protein 3*, Epilepsy*, Fos-Related Antigen-2*, Hippocampus*, Neurons*, Proto-Oncogene Proteins c-fos*, Temporal Lobe*, Gene Regulatory Networks, Humans (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Heilongjiang Postdoctoral Fund (LBH-Z24210); Key Research and Development Program of Heilongjiang Province (2024ZX12C27); Harbin Medical University Fund (2025-KYYWF-ZR0297); National Natural Science Foundation of China (62472131, 32570792, 62502128, U23A20166); Longjiang New Era Outstanding Doctoral Dissertation Project Grant (LJYXL2024-069); China Postdoctoral Science Foundation (2024M760709); National Science and Technology Major Program (2024ZD0530500)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Epilepsy is mainly characterized by spontaneous seizures caused by hyperactive neural circuits. To delineate the cell-type-specific mechanisms underlying neuronal hyperexcitability, we resolve the hyperexcitability of excitatory neurons across epileptic human brain trans-foci at single-cell resolution to identify the key drivers and potential diagnostic signatures. We constructed a comprehensive atlas encompassing 240,000 cells derived from the temporal cortex and hippocampus, detecting trans-regional cellular and molecular diversity. We further delineated dynamic trajectories, gene expression patterns, and functional reorganization across cell types. Using the LASSO and random forest algorithms, we prioritized the core genes and developed a logistic regression-based diagnostic model. Despite transregional cellular landscape conservation, major cell types varied in abundance. Detailed analysis delineated various excitatory neuron subtypes’ dynamic trajectories, intricate expression, and functional reorganization, with pronounced dysfunction in the posterior hippocampal and temporal cortex networks, indicating hyperactive pro-epileptic effects. Excitatory neurons exhibit an intrinsic ability to autonomously organize themselves into distinct, highly active modules, characterized by a high activation state during epileptogenesis, as illustrated by ten epilepsy-associated functions. Transcription circuits FOSL2/FOS/EGR3/EGR1 promote neuronal hyperexcitability. Integrating epilepsy bulk RNA-seq data, we identified 24 overlapping genes between differential genes and circuit targets. The LASSO and random forest algorithms prioritized three core genes (IL1B, SOCS6, and COL4A1). A logistic regression model based on these three genes showed variable performance, with an apparent AUC of 1.000 in the discovery cohort (GSE256068) and AUCs of 0.974 and 0.722 in and two validation cohorts, indicating the need for further validation. Our study establishes the FOSL2/FOS/EGR3/EGR1 circuit as a master regulator of pathological neuronal hyperactivity across epileptic foci, linking transcriptional activation to network dysfunction. Identifying overactive factors may represent a candidate molecular pathway for future therapeutic exploration against hyperexcitability.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 26 matches between paragraphs and lines of code.

khodosevichlab/Epilepsy19

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b0ea0decba5d2a400139814283dcb4c031159e76, 20 March 2023
Languages: R (23), JavaScript (13)
Size: 221 files, 36 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, environment (DESCRIPTION, gene_modules/renv.lock), documentation, 18 notebooks
Not found: CITATION.cff, tests, continuous integration
Tools: tidyverse (18 files), ggplot2 (12 files), data.table (9 files), cowplot (8 files), Seurat (5 files), limma (4 files), pheatmap (4 files), lme4 (3 files), reshape2 (3 files), clusterProfiler (2 files), igraph (1 file), UMAP (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
38 files
At the source: github.com/khodosevichlab/Epilepsy19

Zhangyunpeng1987/Epilepsy

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: adcd5f942f97fab457ff694e4eb813b3959ce4a7, 12 May 2026
Languages: R (21), Jupyter (3), Shell (1)
Size: 34 files, 25 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, 3 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (18 files), tidyverse (15 files), Seurat (8 files), patchwork (6 files), data.table (5 files), ggpubr (5 files), pheatmap (5 files), reshape2 (5 files), clusterProfiler (4 files), ComplexHeatmap (4 files), circlize (3 files), Monocle 3 (3 files), anndata (2 files), cowplot (2 files), igraph (2 files), NumPy (2 files), reticulate (2 files), Scanpy (2 files), caret (1 file), glmnet (1 file), limma (1 file), Plotly (1 file), pROC (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
27 files
At the source: github.com/Zhangyunpeng1987/Epilepsy

The paper's code and data availability statement is in the Data section.

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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Data

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Other data links

Data Availability Statement

The data that support the findings of this study are openly available in GEO at GSE160189 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160189) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160189, accessed on 10 September 2023) and GitHub (https://github.com/khodosevichlab/Epilepsy19, accessed on 15 October 2023). Bulk RNA-seq datasets analyzed in this study, including GSE256068 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE256068) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE256068, accessed on 15 August 2024), GSE139914 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139914) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139914, accessed on 15 August 2024), and GSE140393 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140393) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140393, accessed on 15 August 2024), were sourced from the Gene Expression Omnibus (GEO) database. All analyses were performed using R (v4.2.2) and Python (v3.9). The complete set of custom scripts used for data processing, analysis, and figure generation is available at https://github.com/Zhangyunpeng1987/Epilepsy (accessed on 6 May 2026). For detailed parameter settings, please refer to the annotated scripts and the README file.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 6 keywords, 10 MeSH terms, 7 funders, 66 references.

Cite

This paper

Chen, J., Zhao, B., Yang, K., Mi, W., Huang, X., Jiang, W., Hu, C., Wang, Z., Zhang, Y., & Li, X. (2026). Uncovering the Key Circuit FOSL2/FOS/EGR3/EGR1, Contributing to the Hyperexcitability of Excitatory Neurons in the Epileptic Temporal Cortex and Hippocampus. International journal of molecular sciences, 27(10), 4466. https://doi.org/10.3390/ijms27104466

BibTeX

@article{chen2026uncovering,
author = {Chen, Jing and Zhao, Bowen and Yang, Kaiyue and Mi, Wanqi and Huang, Xiaozhi and Jiang, Wenqi and Hu, Congxue and Wang, Zhenzhen and Zhang, Yunpeng and Li, Xia},
title = {{Uncovering the Key Circuit FOSL2/FOS/EGR3/EGR1, Contributing to the Hyperexcitability of Excitatory Neurons in the Epileptic Temporal Cortex and Hippocampus}},
journal = {International journal of molecular sciences},
year = {2026},
month = may,
volume = {27},
number = {10},
pages = {4466},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1422-0067},
doi = {10.3390/ijms27104466},
url = {https://doi.org/10.3390/ijms27104466},
pmid = {42196443},
pmcid = {PMC13206787}
}

RIS

TY - JOUR
AU - Chen, Jing
AU - Zhao, Bowen
AU - Yang, Kaiyue
AU - Mi, Wanqi
AU - Huang, Xiaozhi
AU - Jiang, Wenqi
AU - Hu, Congxue
AU - Wang, Zhenzhen
AU - Zhang, Yunpeng
AU - Li, Xia
TI - Uncovering the Key Circuit FOSL2/FOS/EGR3/EGR1, Contributing to the Hyperexcitability of Excitatory Neurons in the Epileptic Temporal Cortex and Hippocampus
T2 - International journal of molecular sciences
J2 - Int J Mol Sci
PY - 2026
DA - 2026/05/16
VL - 27
IS - 10
SP - 4466
SN - 1422-0067
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/ijms27104466
UR - https://doi.org/10.3390/ijms27104466
LA - en
ER -

CSL-JSON

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"id": "10.3390/ijms27104466",
"type": "article-journal",
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"container-title": "International journal of molecular sciences",
"author": [
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"container-title-short": "Int J Mol Sci",
"volume": "27",
"issue": "10",
"page": "4466",
"DOI": "10.3390/ijms27104466",
"PMID": "42196443",
"PMCID": "PMC13206787",
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"language": "en",
"issued": {
"date-parts": [
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