OSCR

Regulation of physical activity and energy expenditure through Phf6 in the medial preoptic area.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Single-cell RNA sequencing data analyses ↔ 2025POA_esr1_postive.r, lines 92 to 125 · score 0.74 · FindNeighbors, RunUMAP, FindClusters, CCA, resolution, Seurat · Report a mismatch
  2. [2] § Methods › Single-cell RNA sequencing data analyses ↔ 2025POA_esr1_phf6_postivecells.r, lines 1 to 60 · score 0.68 · FindNeighbors, RunUMAP, FindClusters, CCA, resolution, Seurat · Report a mismatch
  3. [3] § Results › Phf6 marks a subset of Esr1-expressing neurons in the MPOA ↔ 2025POA_esr1_postive.r, lines 127 to 185 · score 0.52 · UMAP, Slc17a6, Slc32a1, Gad1, RNA, clustering · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

R · 462 lines · 14 KB · no license · 2 matches

  1. # 导入必要的库
  2. library(dplyr)
  3. library(pheatmap)# 加载 Nebulosa 包
  4. library(Nebulosa)
  5. library(Seurat)
  6. library(readr)
  7. library(data.table)
  8. # 读取计数矩阵
  9. rawcount <- fread("./GSE183093/GSE183093_POAcountmatrix.tsv", header = TRUE, stringsAsFactors = FALSE, fill = TRUE)
  10. metadata <- fread("./GSE183093/GSE183093_POA_Cell_metadata.tsv.gz", header = TRUE)
  11. rawcount[1:5,1:5]
  13. POA_count <- as.data.frame(rawcount)
  15. POA_count[1:5,1:5]
  16. tail(colnames(POA_count))
  19. rownames(POA_count) = POA_count$barcode
  20. POA_count <- POA_count[,-1]
  23. POA_count[1:5,1:5]
  25. metadata[1:5,]
  27. # # 转置矩阵
  28. POA_count_t <- t(POA_count)
  30. # 创建 Seurat 对象
  31. # meta_list <- pull(metadata, "Barcode")
  32. # metadata <- metadata[, -1]
  33. # rownames(metadata) <- meta_list
  34. POA_seurat <- CreateSeuratObject(counts = POA_count_t, meta.data = as.data.frame(metadata), min.cells = 3, min.features = 200)
  36. # 对 Seurat 对象进行数据归一化
  37. POA_seurat <- POA_seurat %>% NormalizeData()
  38. [email hidden][1:5,]
  40. # 提取 Esr1 大于 0 的子集
  41. POA_subset_Esr1 <- subset(POA_seurat, subset = Esr1 > 0)
  43. # 读取另一个数据集
  44. POA_1 <- Read10X(data.dir = "./GSE113576/")
  45. POA_1 <- CreateSeuratObject(counts = POA_1, project = "POA", min.cells = 3, min.features = 200)
  47. # 对第二个数据集进行通用分析管道
  48. POA_1 <- POA_1 %>% NormalizeData()
  49. POA_subset_1 <- subset(POA_1, subset = Esr1 > 0)
  51. # 提取共同基因
  52. genes1 <- rownames(POA_subset_Esr1)
  53. genes2 <- rownames(POA_subset_1)
  54. common_genes <- intersect(genes1, genes2)
  55. POA_subset_Esr1_c <- subset(POA_subset_Esr1, features = common_genes)
  56. POA_subset_1_c <- subset(POA_subset_1, features = common_genes)
  58. # 合并变换后的数据
  59. merged_seurat <- merge(x = POA_subset_Esr1_c, y = POA_subset_1_c)
  61. # 对合并后的数据进行标准分析
  62. merged_seurat <- merged_seurat %>%
  63. NormalizeData() %>%
  64. FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%
  65. ScaleData() %>%
  66. RunPCA(npcs = 50, verbose = FALSE)
  69. library(future)
  70. # check the current active plan
  71. plan()
  72. # change the current plan to access parallelization
  73. plan("multisession", workers = 32)
  74. plan()
  76. saveRDS(merged_seurat,'merged_seurat.rds')
  77. # qsave(merged_seurat,'merged_seurat.rds')
  79. setwd('e:/Personal_data/WYX/huangju/')
  80. merged_seurat = readRDS('merged_seurat.rds')
  81. # 使用 CCAIntegration 方法进行整合
  82. merged_seurat <- IntegrateLayers(
  83. object = merged_seurat, method = CCAIntegration,
  84. orig.reduction = "pca", new.reduction = "integrated.cca",
  85. verbose = FALSE
  86. )
  88. merged_seurat <- JoinLayers(merged_seurat)
  90. ElbowPlot(merged_seurat,reduction = "pca")
  92. # 运行 UMAP、找邻居、聚类
  93. merged_seurat <- RunUMAP(merged_seurat, dims = 1:20, reduction = "integrated.cca")
  94. merged_seurat <- FindNeighbors(merged_seurat, dims = 1:20, reduction = "integrated.cca")
  95. options(future.globals.maxSize = 1e11)
  96. merged_seurat <- FindClusters(merged_seurat, resolution = 0.13, reduction = "integrated.cca")
  99. [email hidden]$seurat_clusters = [email hidden]$RNA_snn_res.0.13
  100. [email hidden]$seurat_clusters = paste0('c',[email hidden]$seurat_clusters)
  102. # [email hidden][[email hidden]$seurat_clusters == 'c2',]$seurat_clusters = 'c10'
  103. # [email hidden][[email hidden]$seurat_clusters == 'c1',]$seurat_clusters = 'c2'
  104. # [email hidden][[email hidden]$seurat_clusters == 'c10',]$seurat_clusters = 'c1'
  108. library(ggplot2)
  109. DefaultAssay(merged_seurat) = 'INTE'
  110. DotPlot(merged_seurat, features = "Phf6", dot.scale = 12,group.by = 'seurat_clusters') + scale_color_gradientn(colours = colorRampPalette(c("white",'#F8E4D9','#E9AD93', "#D22D25")) (100))
  112. pdf('D:/PersonalData/WYX/huangju/202503.phf6.dotplot.pdf',width = 3.5,height = 6)
  113. DotPlot(merged_seurat, features = "Phf6", dot.scale = 12,group.by = 'seurat_clusters') + scale_color_gradientn(colours = colorRampPalette(c("white",'#F8E4D9','#E9AD93', "#D22D25")) (100))
  114. dev.off()
  119. DotPlot(merged_seurat,group.by = 'seurat_clusters', features = c('Esr1','Drp2','Pde1c','Serpine2','Slc17a6','St18','Bcl11b','Emx2os','Opn5','Zeb2','Otp',"Phf6",'Slc32a1'), dot.scale = 12) + scale_color_gradientn(colours = colorRampPalette(c("blue", "red")) (100))+RotatedAxis()
  120. DotPlot(merged_seurat,group.by = 'seurat_clusters', features = c('Esr1','Slc17a6',"Phf6",'Slc32a1'), dot.scale = 12) + scale_color_gradientn(colours = colorRampPalette(c("white",'#F8E4D9','#E9AD93', "#D22D25")) (100))+RotatedAxis()
  125. phf6 = subset(merged_seurat,subset = Phf6 > 0)
  127. # 运行 UMAP、找邻居、聚类
  128. phf6 <- RunUMAP(phf6, dims = 1:20, reduction = "integrated.cca")
  129. phf6 <- FindNeighbors(phf6, dims = 1:20, reduction = "integrated.cca")
  130. # options(future.globals.maxSize = 1e11)
  131. phf6 <- FindClusters(phf6, resolution = 0.1, reduction = "integrated.cca")
  132. phf6 <- FindClusters(phf6, resolution = 0.2, reduction = "integrated.cca")
  134. FeaturePlot(
  135. phf6,
  136. features = c("Esr1","Phf6",'Slc17a6','Slc32a1'),max.cutoff = 2.5,split.by = 'Condition')
  138. FeaturePlot(
  139. phf6,
  140. features = c("Phf6",'Slc17a6','Slc32a1'),max.cutoff = 2.5)
  142. unique(phf6$Condition)
  143. Idents(phf6) = 'Condition'
  144. phf6=subset(phf6, idents = c("Male", "FR", "FNR"))
  146. [email hidden]$sex = 'Female'
  147. [email hidden][[email hidden]$Condition=='Male',]$sex = 'Male'
  149. pdf('featureplot.pdf',width = 5,height = 10)
  150. FeaturePlot(
  151. phf6,
  152. features = c("Phf6",'Slc17a6','Slc32a1','Gad1'),max.cutoff = 2.5,split.by = 'sex')
  153. dev.off()
  155. DimPlot(phf6,group.by = "RNA_snn_res.0.1",split.by = 'sex')
  156. DimPlot(phf6,group.by = "RNA_snn_res.0.2")
  157. DimPlot(phf6,group.by = "RNA_snn_res.0.13")
  159. phf6$neutype = 'Glu'
  160. [email hidden][[email hidden]$RNA_snn_res.0.1 %in%c(0,1,4,5),]$neutype = 'Gaba'
  162. VlnPlot(phf6,features = c("Phf6",'Slc17a6','Slc32a1','Gad1'),group.by = 'RNA_snn_res.0.1',stack = T,pt.size = 0)
  163. DimPlot(phf6,group.by = "neutype")
  166. table(phf6$orig.ident)
  168. length([email hidden][phf6$neutype == 'Glu'& phf6$sex == 'Male',]$neutype)
  169. length([email hidden][phf6$neutype == 'Gaba'& phf6$sex == 'Male',]$neutype)
  171. length([email hidden][phf6$neutype == 'Glu'& phf6$sex == 'Female',]$neutype)
  172. length([email hidden][phf6$neutype == 'Gaba'& phf6$sex == 'Female',]$neutype)
  176. counts <- c(Glu = 859, Gaba = 2022)
  178. pct <- round(100 * counts / sum(counts), 1)
  179. labs <- paste0(names(counts), ": ", counts, " (", pct, "%)")
  181. pie(counts,
  182. labels = labs,
  183. clockwise = TRUE,
  184. col = c("#6BAED6", "#FD8D3C"),
  185. main = 'Male')
  190. counts <- c(Glu = 991, Gaba = 1976)
  192. pct <- round(100 * counts / sum(counts), 1)
  193. labs <- paste0(names(counts), ": ", counts, " (", pct, "%)")
  195. pie(counts,
  196. labels = labs,
  197. clockwise = TRUE,
  198. col = c("#6BAED6", "#FD8D3C"),
  199. main = 'Female')
  203. idents(phf6) = 'sex'
  204. phf5.f = subset(phf6, idents = 'Female')
  205. phf5.m = subset(phf6, idents = 'Male')
  207. FeaturePlot(
  208. phf5.f,
  209. features = c('Esr1',"Phf6",'Slc17a6','Gad1'),max.cutoff = 2.5)
  211. FeaturePlot(
  212. phf5.m,
  213. features = c('Esr1',"Phf6",'Slc17a6','Gad1'),max.cutoff = 2.5)
  215. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Gad1_featureplot_female_exp2.pdf',height = 7.4,width = 9)
  216. FeaturePlot(
  217. phf5.f,
  218. features = c('Esr1',"Phf6",'Slc17a6','Gad1'),max.cutoff = 2)
  219. dev.off()
  221. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Gad1_featureplot_male_exp2.pdf',height = 7.4,width = 9)
  222. FeaturePlot(
  223. phf5.m,
  224. features = c('Esr1',"Phf6",'Slc17a6','Gad1'),max.cutoff = 2)
  225. dev.off()
  228. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Slc32a1_featureplot_female_exp2.pdf',height = 7.4,width = 9)
  229. FeaturePlot(
  230. phf5.f,
  231. features = c('Esr1',"Phf6",'Slc17a6','Slc32a1'),max.cutoff = 2)
  232. dev.off()
  234. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Slc32a1_featureplot_male_exp2.pdf',height = 7.4,width = 9)
  235. FeaturePlot(
  236. phf5.m,
  237. features = c('Esr1',"Phf6",'Slc17a6','Slc32a1'),max.cutoff = 2)
  238. dev.off()
  242. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Gad1_featureplot_female_exp2.pdf',height = 7.4,width = 9)
  243. FeaturePlot(
  244. phf5.f,
  245. features = c('Esr1',"Phf6",'Slc17a6','Gad1'),max.cutoff = 2)
  246. dev.off()
  248. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Gad1_featureplot_male_exp2.pdf',height = 7.4,width = 9)
  249. FeaturePlot(
  250. phf5.m,
  251. features = c('Esr1',"Phf6",'Slc17a6','Gad1'),max.cutoff = 2)
  252. dev.off()
  255. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Slc32a1_featureplot_female_exp2.pdf',height = 7.4,width = 9)
  256. FeaturePlot(
  257. phf5.f,
  258. features = c('Esr1',"Phf6",'Slc17a6','Slc32a1'),max.cutoff = 2)
  259. dev.off()
  261. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Slc32a1_featureplot_male_exp2.pdf',height = 7.4,width = 9)
  262. FeaturePlot(
  263. phf5.m,
  264. features = c('Esr1',"Phf6",'Slc17a6','Slc32a1'),max.cutoff = 2)
  265. dev.off()
  266. FeaturePlot(
  267. phf6,
  268. features = c('Syt1'),max.cutoff = 2)
  274. setwd('D:/PersonalData/WYX/huangju/')
  277. for (i in c(0.05,0.1,0.2)) {
  278. phf6 = FindClusters(phf6,resolution = i)
  279. print(i)
  281. pdf(paste0('phf6_res',i,'.pdf'),width = 4.5,height = 4)
  282. print(DimPlot(phf6,group.by = paste0('RNA_snn_res.',i)))
  283. dev.off()
  285. marker= FindAllMarkers(phf6, group.by = paste0('RNA_snn_res.',i),only.pos = T,logfc.threshold = 1)
  286. marker= marker.res0.1[marker.res0.1$p_val_adj<0.05,]
  287. writexl::write_xlsx(marker,paste0('phf6_res.',i,'.xlsx'))
  288. }
  291. saveRDS(phf6,file = '202506phf6.rds')
  292. saveRDS(phf6,file = '202506phf6_2.rds')
  294. phf6 <- phf6 %>%
  295. NormalizeData() %>%
  296. FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%
  297. ScaleData() %>%
  298. RunPCA(npcs = 10, verbose = FALSE)
  301. phf6 <- RunUMAP(phf6, dims = 1:10, reduction = "integrated.cca")
  302. phf6 <- FindNeighbors(phf6, dims = 1:10, reduction = "integrated.cca")
  305. Idents(phf6) = 'sex'
  306. phf5.f = subset(phf6, idents = 'Female')
  307. phf5.m = subset(phf6, idents = 'Male')
  309. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Gad1_featureplot_female_exp2.pdf',height = 7.4,width = 9)
  310. FeaturePlot(
  311. phf5.f,
  312. features = c('Esr1',"Phf6",'Slc17a6','Gad1'),max.cutoff = 2)
  313. dev.off()
  315. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Gad1_featureplot_male_exp2.pdf',height = 7.4,width = 9)
  316. FeaturePlot(
  317. phf5.m,
  318. features = c('Esr1',"Phf6",'Slc17a6','Gad1'),max.cutoff = 2)
  319. dev.off()
  322. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Slc32a1_featureplot_female_exp2.pdf',height = 7.4,width = 9)
  323. FeaturePlot(
  324. phf5.f,
  325. features = c('Esr1',"Phf6",'Slc17a6','Slc32a1'),max.cutoff = 2)
  326. dev.off()
  328. pdf('D:/PersonalData/WYX/huangju/Esr1_Phf6_Slc17a6_Slc32a1_featureplot_male_exp2.pdf',height = 7.4,width = 9)
  329. FeaturePlot(
  330. phf5.m,
  331. features = c('Esr1',"Phf6",'Slc17a6','Slc32a1'),max.cutoff = 2)
  332. dev.off()
  334. for (i in c(0.05,0.1,0.2)) {
  335. phf6 = FindClusters(phf6,resolution = i)
  336. print(i)
  338. pdf(paste0('phf6_res',i,'.pdf'),width = 4.5,height = 4)
  339. print(DimPlot(phf6,group.by = paste0('RNA_snn_res.',i)))
  340. dev.off()
  342. marker= FindAllMarkers(phf6, group.by = paste0('RNA_snn_res.',i),only.pos = T,logfc.threshold = 1)
  343. marker= marker[marker$p_val_adj<0.05,]
  344. writexl::write_xlsx(marker,paste0('phf6_res.',i,'.xlsx'))
  345. }
  348. marker= FindAllMarkers(phf6, group.by = paste0('RNA_snn_res.0.1'),only.pos = T,logfc.threshold = 1)
  349. marker= marker[marker$p_val_adj<0.05,]
  351. marker %>%
  352. group_by(cluster) %>%
  353. dplyr::filter(avg_log2FC > 2) %>%
  354. slice_head(n = 3) %>%
  355. ungroup() -> top3
  358. DotPlot(phf6,c('Esr1','Phf6',top3$gene),group.by = 'RNA_snn_res.0.1')+RotatedAxis()
  359. VlnPlot(phf6,c('Esr1','Phf6',top5$gene),group.by = 'RNA_snn_res.0.1',stack=T)+RotatedAxis()
  361. selectgene = c('Esr1','Phf6', "Ntng1","Nts","Samd3","Adamts2","Emx2os","Bcl11b" ,"Lhx8","Pantr1",
  362. "Slc17a6","Hs3st2","Ptprb", "Nfix","Moxd1","St18")
  364. VlnPlot(phf6,selectgene,group.by = 'RNA_snn_res.0.1',stack=T)+RotatedAxis()
  370. # 寻找差异基因
  371. POA_markers <- FindAllMarkers(merged_seurat, only.pos = TRUE, min.pct = 0.4, logfc.threshold = 1)
  373. # 对每个细胞簇进行排序
  374. POA_markers_sorted <- POA_markers %>%
  375. arrange(cluster, p_val_adj)
  377. # 选择每个细胞簇中 avg_log2FC > 1 且 p_val_adj 最小的唯一基因
  378. selected_markers <- POA_markers_sorted %>%
  379. group_by(cluster) %>%
  380. filter(avg_log2FC> 1.2) %>%
  381. slice_min(order_by = p_val_adj) %>%
  382. distinct(cluster, .keep_all = TRUE)
  384. # 选择每个细胞簇中的 marker 基因
  385. selected_genes <- c(selected_markers$gene)
  387. # 使用 VlnPlot 绘制堆叠小提琴图
  388. # VlnPlot(
  389. # merged_seurat,
  390. # features = selected_genes,
  391. # stack = TRUE,
  392. # group.by = "seurat_clusters",
  393. # pt.size = 0,
  394. # combine = TRUE
  395. # ) +
  396. # ggtitle(NULL) + # Remove the title
  397. # theme(
  398. # axis.title.x = element_blank(), # Remove x-axis label
  399. # axis.text.x = element_blank(), # Remove x-axis ticks
  400. # axis.ticks.x = element_blank(), # Remove x-axis tick marks
  401. # strip.text.x = element_text(size=8,face = "plain",angle = +45, hjust = +0.5) # Rotate individual violin plot titles counterclockwise by 20 degrees and adjust horizontal justification
  402. # )
  404. DefaultAssay(merged_seurat) = "RNA"
  405. DotPlot(merged_seurat,assay = "RNA" ,features = c("Esr1","Phf6",selected_genes), cols = c("white", "red"), dot.scale = 12)+
  406. RotatedAxis()
  408. dotPlot <- DotPlot(merged_seurat, features = "Phf6", cols = c("white", "red"), dot.scale = 12)
  409. dotPlot
  411. plot_data <- data.frame(dotPlot$data,stringsAsFactors = FALSE) %>%
  412. select(pct.exp, id)
  414. # 将百分比转换为小数
  415. pct <- plot_data$pct.exp
  417. plot_data$id <- as.numeric(plot_data$id)
  419. #piechart
  421. # 设置图形布局，2行5列
  422. par(mfrow=c(2, 5))
  424. # 指定颜色
  425. custom_colors <- c()
  427. # 为每个百分比绘制饼图
  428. for (i in 1:length(pct)) {
  429. pie(c(pct[i], 100 - pct[i]), labels = "", col = c(custom_colors[i], "white"),lwd = 1,border = "#bcbdc0")
  430. title(paste("Pie Chart ", i))
  431. }
  433. # 恢复默认图形布局
  434. par(mfrow=c(1, 1))
  436. # seurat_clusters <- as.character(plot_data$id) # Convert to character if not already
  438. # Create a pie chart using ggplot2
  440. p<-FeaturePlot(
  441. merged_seurat,
  442. features = "Phf6",
  443. label = F
  444. )+
  445. plot_annotation(theme = theme(plot.title = element_text(size = 12,hjust = 0.5, face = "bold")))+
  446. scale_color_gradientn(colours = c("#BDBDBD", "lightgrey", "#d1c4e9", "#7e57c2", "#5e35b1","#4527a0","blue")
  447. )
  449. # a<-p+
  450. # labs(title = "")
  451. # a
  452. DimPlot(merged_seurat, label = F)
  454. # 使用 plot_density 函数绘制密度图
  455. plot_density(merged_seurat, features = "Phf6")
  457. # Assuming that 'Phf6' is a column in your Seurat object representing the expression of the Phf6 gene
  459. # Create a binary indicator for Phf6 expression greater than 0
  460. merged_seurat$Phf6_positive <- as.integer(merged_seurat$Phf6 > 0)
  462. #

2025POA_esr1_postive.r at commit e7c0308, no license · at the source

Overview

Authors: Jingjie Wang1,2, Bing Liu1,2, Xiaohua Wu1,2, Jingjing Sun1,2, Yaxin Wang3, Liang He3, Linhua Gan2, Xiangfeng Du1,2, Jingyu Ruan1,2, Lingzhi Pi1,2, Qing-Feng Wu3, Ju Huang1,2
ORCID iDs: Qing-Feng Wu, Ju Huang
  1. Department of Neurology, Songjiang Research Institute, Shanghai Key Laboratory of Emotions and Affective Disorders, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
  2. Department of Anatomy and Physiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  3. Laboratory of Integrative Physiology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
Institutions: Shanghai Jiao Tong University (China); Chinese Academy of Sciences (China); Institute of Genetics and Developmental Biology (China)
Journal: Nature communications, volume 17, issue 1, article 4468
Dates: received 26 May 2025; accepted 6 March 2026; published online 24 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-70962-7 · PMID 41876547 · PMCID PMC13187260 · OpenAlex W7140161793
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging
Keywords: Neural circuits, Molecular neuroscience
MeSH: Energy Metabolism*, Homeodomain Proteins*, Physical Conditioning, Animal*, Preoptic Area*, Animals, Estrogen Receptor alpha, Estrogens, Female, Male, Mice, Mice, Inbred C57BL, Mice, Knockout, Neurons, Obesity (* major topic)
Topic: Hypothalamic control of reproductive hormones (Reproductive Medicine, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (32300838, 81771215, 32070957, 32200820); Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology) (2021ZD0202800/01)
Citations: not cited yet (Europe PMC); 59 references in the paper

Abstract

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

echo1318/MPOA_Phf6_Esr1

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: e7c0308232cb97d067d3144eb89737967c0eea70, 18 December 2025
Languages: R (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), tidyverse (2 files), data.table (1 file), pheatmap (1 file), Seurat (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: github.com/echo1318/MPOA_Phf6_Esr1

Zenodo 17972970

License: CC-BY-4.0
State: the link answers, verified on 30 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: ggplot2 (2 files), tidyverse (2 files), data.table (1 file), pheatmap (1 file), Seurat (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
3 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-70962-7.

Tracing map

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What the map holds:

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  • 4 scripts, each with its path and the digest of its content;
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Data

Datasets cited

Data availability statement

The paper has a 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/s41467-026-70962-7.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 14 MeSH terms, 2 funders, 55 references.

Cite

This paper

Wang, J., Liu, B., Wu, X., Sun, J., Wang, Y., He, L., Gan, L., Du, X., Ruan, J., Pi, L., Wu, Q.-F., & Huang, J. (2026). Regulation of physical activity and energy expenditure through Phf6 in the medial preoptic area. Nature communications, 17(1), 4468. https://doi.org/10.1038/s41467-026-70962-7

BibTeX

@article{wang2026regulation,
author = {Wang, Jingjie and Liu, Bing and Wu, Xiaohua and Sun, Jingjing and Wang, Yaxin and He, Liang and Gan, Linhua and Du, Xiangfeng and Ruan, Jingyu and Pi, Lingzhi and Wu, Qing-Feng and Huang, Ju},
title = {{Regulation of physical activity and energy expenditure through Phf6 in the medial preoptic area}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {4468},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70962-7},
url = {https://doi.org/10.1038/s41467-026-70962-7},
pmid = {41876547},
pmcid = {PMC13187260}
}

RIS

TY - JOUR
AU - Wang, Jingjie
AU - Liu, Bing
AU - Wu, Xiaohua
AU - Sun, Jingjing
AU - Wang, Yaxin
AU - He, Liang
AU - Gan, Linhua
AU - Du, Xiangfeng
AU - Ruan, Jingyu
AU - Pi, Lingzhi
AU - Wu, Qing-Feng
AU - Huang, Ju
TI - Regulation of physical activity and energy expenditure through Phf6 in the medial preoptic area
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/24
VL - 17
IS - 1
SP - 4468
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70962-7
UR - https://doi.org/10.1038/s41467-026-70962-7
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-70962-7",
"type": "article-journal",
"title": "Regulation of physical activity and energy expenditure through Phf6 in the medial preoptic area",
"container-title": "Nature communications",
"author": [
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"family": "Wang",
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{
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},
{
"family": "Wu",
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{
"family": "Sun",
"given": "Jingjing"
},
{
"family": "Wang",
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{
"family": "He",
"given": "Liang"
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{
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"given": "Linhua"
},
{
"family": "Du",
"given": "Xiangfeng"
},
{
"family": "Ruan",
"given": "Jingyu"
},
{
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],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4468",
"DOI": "10.1038/s41467-026-70962-7",
"PMID": "41876547",
"PMCID": "PMC13187260",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-70962-7",
"language": "en",
"issued": {
"date-parts": [
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2026,
3,
24
]
]
}
}

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