OSCR

Esr1-dependent signaling and transcriptional maturation in the medial preoptic area of the hypothalamus shape the development of mating behavior during adolescence.

Code ↔ Paper

9 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 9 matches
  1. [1] § Materials and methods › DEG and trajectory analysis of scRNAseq in Esr1KO experiments ↔ Integrating Esr1KOM and MPOA scRNAseq Fig4.ipynb, lines 285 to 291 · score 0.81 · eVgat9, eVgat3, eVgat4, eVgat5, eVgat1, scRNAseq · Report a mismatch
  2. [2] § Materials and methods › Integrative clustering and differential gene expression analysis ↔ Integrating Esr1KOF and MPOA scRNAseq Fig4.ipynb, lines 26 to 37 · score 0.74 · RunUMAP, FindClusters, FindNeighbors, PCA, resolution, clustering · Report a mismatch
  3. [3] § Materials and methods › Integrative clustering and differential gene expression analysis ↔ Integrating Esr1KOM and MPOA scRNAseq Fig4.ipynb, lines 20 to 31 · score 0.74 · RunUMAP, FindClusters, FindNeighbors, PCA, resolution, clustering · Report a mismatch
  4. [4] § Materials and methods › Integrative clustering and differential gene expression analysis ↔ 210726 doublet_removal_only_paper.ipynb, lines 13 to 144 · score 0.64 · FindVariableFeatures, NormalizeData, transformation, Seurat, neighbor, matrix · Report a mismatch
  5. [5] § Materials and methods › Integrative analysis of MERFISH and scRNAseq data ↔ 210726 doublet_removal_only_paper.ipynb, lines 13 to 144 · score 0.60 · FindVariableFeatures, NormalizeData, POA, Seurat, clusters, V3 · Report a mismatch
  6. [6] § Materials and methods › Integrative clustering and differential gene expression analysis ↔ Integrating Esr1KOF and MPOA scRNAseq Fig4.ipynb, lines 26 to 37 · score 0.59 · FindVariableFeatures, NormalizeData, scores, neighbor, clustering · Report a mismatch
  7. [7] § Materials and methods › DEG and trajectory analysis of scRNAseq in Esr1KO experiments ↔ Integrating Esr1KOM and MPOA scRNAseq Fig4.ipynb, lines 597 to 622 · score 0.59 · eMix1, eVgat1, Esr1KOM, clustering, V3, cells · Report a mismatch
  8. [8] § Materials and methods › DEG and trajectory analysis of scRNAseq in Esr1KO experiments ↔ Integrating Esr1KOF and MPOA scRNAseq Fig4.ipynb, lines 65 to 71 · score 0.58 · eMix1, eVgat1, Esr1KOF, V3 · Report a mismatch
  9. [9] § Results › Transcriptional dynamics of MPOA cell types during adolescent development ↔ MPOA_log_neurons_basic_figures_UMAP_Propotion_Disc_violin_correlation_Fig2.ipynb, lines 649 to 663 · score 0.50 · androgen receptor, steroid hormone receptor, AR, Neurons, gene, MPOA · 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

Jupyter notebook · 731 lines · 43 KB · MIT · 3 matches

  1. # %%
  2. library(Seurat)
  3. library(dplyr)
  4. library(magrittr)
  5. IRdisplay::display_html("<style> .container { width:95% !important; } </style>")
  6. library("xlsx")
  7. #library(mgsa)
  8. library("ggplot2")# note that Seurat v3, they return ggplot object for easy customization
  9. library(parallel)
  10. library(scales)
  12. # %%
  13. Esr1KOM.data <-Read10X(data.dir = "/media/garret/New Volume/scRNAseq_data/backupv3/v3/Esr1KO_M/01_analysis/cellranger_count_v2/P50Esr1KO/raw_feature_bc_matrix")
  14. colnames(Esr1KOM.data) = paste0(colnames(Esr1KOM.data),"Esr1KOM")
  15. Esr1KOM<- CreateSeuratObject(counts = Esr1KOM.data, min.cells = 3, min.features = 200, project = "10X_MPOA")
  16. Neuron_id<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/all_cells/Neuron_bid.rds")
  17. Esr1KOM<-subset(x = Esr1KOM, cells=Neuron_id)
  18. [email hidden]$stim <- "Esr1KOM"
  20. # %%
  21. Esr1KOM<- NormalizeData(object = Esr1KOM,verbose = FALSE)
  22. Esr1KOM<- FindVariableFeatures(object =Esr1KOM,selection.method = "vst", nfeatures = 2000, verbose = FALSE)
  23. Esr1KOM<- ScaleData(object = Esr1KOM, features = rownames(x =Esr1KOM))
  24. Esr1KOM<- RunPCA(object = Esr1KOM, features = VariableFeatures(object =Esr1KOM), verbose = FALSE)
  25. Esr1KOM <- JackStraw(object =Esr1KOM, num.replicate = 100)
  26. Esr1KOM <- ScoreJackStraw(object = Esr1KOM, dims = 1:20)
  27. JackStrawPlot(object = Esr1KOM, dims = 1:20)
  28. ElbowPlot(object =Esr1KOM)
  29. Esr1KOM <- FindNeighbors(object =Esr1KOM, dims = 1:30)
  30. Esr1KOM <- FindClusters(object = Esr1KOM, resolution = 0.8)
  31. Esr1KOM<- RunUMAP(object = Esr1KOM, reduction = "pca", dims = 1:30)
  34. # %%
  35. DefaultAssay(Esr1KOM) <- "RNA"
  37. # %%
  38. FeaturePlot(object =Esr1KOM, features = c("Slc32a1"),order=TRUE)
  39. ggsave(file="/media/garret/New Volume/paper submission/MPOA_Science/Revision_Figures/raw/disc_Esr1KOM_all_#rev3_com#feature_Vgat.pdf",width=10,height=10)
  41. # %%
  42. FeaturePlot(object =Esr1KOM, features = c("Slc17a6"),order=TRUE)
  43. ggsave(file="/media/garret/New Volume/paper submission/MPOA_Science/Revision_Figures/raw/disc_Esr1KOM_all_#rev3_com#feature_Vglu.pdf",width=10,height=10)
  45. # %%
  46. saveRDS(Esr1KOM,file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/Esr1KOM.rds")
  48. # %%
  49. Esr1KOM<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/Esr1KOM.rds")
  50. new.ident <- c("Mix1","Vgat1","Vglu1","Vgat2","Vgat3","Vgat4","Vglu2","Vglu3","Vgat5","Vgat6","Vgat7","Vgat8","Vgat9","Mix2","Vgat10","Vglu4","Vgat11","Vglu5","Vgat12","Vglu6","Ambiguous")
  51. names(x = new.ident) <- levels(x =Esr1KOM)
  52. Esr1KOM<- RenameIdents(object =Esr1KOM, new.ident)
  53. for (i in 1:length(new.ident)){
  54. assign(paste(new.ident[i],"_barcode",sep=""),colnames(Esr1KOM@assays$RNA@data[,which(Idents(object=Esr1KOM) %in% new.ident[i])]))}
  56. # %%
  57. DimPlot(object = Esr1KOM, reduction = "umap", label = TRUE, repel = TRUE)
  58. ggsave(file="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/umap_name.pdf",width=10,height=10)
  60. # %%
  61. violin/disc
  63. # %%
  64. #start from here
  65. Esr1KOM<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/Esr1KOM.rds")
  66. new.ident <- c("eMix1","eVgat1","eVglu1","eVgat2","eVgat3","eVgat4","eVglu2","eVglu3","eVgat5","eVgat6","eVgat7","eVgat8","eVgat9","eMix2","eVgat10","eVglu4","eVgat11","eVglu5","eVgat12","eVglu6","eAmbiguous")
  67. names(x = new.ident) <- levels(x =Esr1KOM)
  68. Esr1KOM<- RenameIdents(object =Esr1KOM, new.ident)
  69. for (i in 1:length(new.ident)){
  70. assign(paste(new.ident[i],"_barcode",sep=""),colnames(Esr1KOM@assays$RNA@data[,which(Idents(object=Esr1KOM) %in% new.ident[i])]))}
  72. # %%
  73. # UMI, gene, fraction, UMAP
  75. # %%
  76. [email hidden]$celltype<-Idents(Esr1KOM)
  77. Celltype<-c("eVgat1","eVgat2","eVgat3","eVgat4","eVgat5","eVgat6","eVgat7","eVgat8","eVgat9","eVgat10","eVgat11","eVgat12","eVglu1","eVglu2","eVglu3","eVglu4","eVglu5","eVglu6","eMix1","eMix2")
  78. Esr1KOM<-subset(Esr1KOM,cells=rownames([email hidden])[[email hidden]$celltype %in% Celltype])
  79. [email hidden]$celltype<-factor([email hidden]$celltype,levels=Celltype)
  80. Idents(Esr1KOM)<-factor(Idents(Esr1KOM),levels=Celltype)
  82. # %%
  83. #colors<-rep("cyan4",length(unique([email hidden]$celltype)))
  85. # %%
  86. colors<-color<-c("lightblue","lightskyblue","skyblue","deepskyblue","lightsteelblue",
  87. "dodgerblue","cornflowerblue","steelblue","cadetblue","mediumslateblue",
  88. "slateblue","darkslateblue","lightsalmon","salmon","darksalmon","lightcoral","indianred",
  89. "#DC143C","darkgray","dimgray")
  91. # %%
  92. #UMAP
  93. DimPlot(object = Esr1KOM, reduction = "umap", label = FALSE, repel = TRUE,cols=color)
  94. ggsave(file="/media/garret/New Volume/paper submission/MPOA_Science/Revision_Figures/raw/Esr1KOM_neuron_umap_name_rec#3com#4.pdf",width=10,height=10)
  96. # %%
  97. #UMI
  98. ggplot([email hidden],aes_string(x="celltype",y="nCount_RNA",fill="celltype"))+geom_violin(scale = "width")+scale_fill_manual(values=colors)+
  99. stat_summary(fun.y=median, geom="point", size=0.6, color="red")+ylab("UMI")+
  100. theme(axis.title.x=element_blank(),
  101. ,axis.text.y=element_text(size=12,color="black"),axis.title.y=element_text(size=20,angle=90,color="black",margin = margin(t = 10, r = 8, b = 0, l = 10),vjust=0.5),axis.text.x=element_text(size=12,h=0.5,v=0.5,angle=50,color="black")
  102. ,axis.title=element_text(size=15,face="bold"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  103. panel.background = element_blank(), axis.line = element_line(colour = "black"),legend.position="none",plot.margin = unit(c(1, 0,0, 0), "cm"))
  104. ggsave(file="/media/garret/New Volume/paper submission/MPOA_Science/Revision_Figures/raw/Esr1KOM_UMI_neuroncelltype_rev#3com4.pdf",height=2, width=10 , paper = "letter")
  106. # %%
  107. #gene
  108. ggplot([email hidden],aes_string(x="celltype",y="nFeature_RNA",fill="celltype"))+geom_violin(scale = "width")+scale_fill_manual(values=colors)+
  109. stat_summary(fun.y=median, geom="point", size=0.6, color="red")+ylab("gene")+
  110. theme(axis.title.x=element_blank(),
  111. ,axis.text.y=element_text(size=12,color="black"),axis.title.y=element_text(size=20,angle=90,color="black",margin = margin(t = 10, r = 8, b = 0, l = 10),vjust=0.5),axis.text.x=element_text(size=12,h=0.5,v=0.5,angle=50,color="black")
  112. ,axis.title=element_text(size=15,face="bold"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  113. panel.background = element_blank(), axis.line = element_line(colour = "black"),legend.position="none",plot.margin = unit(c(1, 0,0, 0), "cm"))
  114. ggsave(file="/media/garret/New Volume/paper submission/MPOA_Science/Revision_Figures/raw/Esr1KOM_gene_neuroncelltype_rev#3com4.pdf",height=2, width=10 , paper = "letter")
  116. # %%
  117. #fraction
  118. Cell_type<-Celltype
  119. # make a data frame for cell number (this data frame is a simple version giving single value for each cluster)
  120. Cell_number<- data.frame("type" =Cell_type)
  121. Numberofcells<-vector(mode="numeric", length=0)
  122. proportion<-vector(mode="numeric", length=0)
  124. for (i in 1:dim(Cell_number)[1]){Numberofcells[i]<-dim(subset([email hidden],celltype==Cell_number$type[i]))[1]}
  125. Cell_number$len<-Numberofcells
  127. for (i in 1:dim(Cell_number)[1]){proportion[i]<-100*Cell_number$len[i]/sum(Cell_number$len)}
  128. Cell_number$prop<-proportion
  130. Cell_number$type<-factor(Cell_number$type,levels=Celltype)
  132. # %%
  133. # proportion of cells
  134. ggplot(Cell_number,aes(x=type,y=proportion,fill=type, width=.75))+geom_bar(stat = "identity",position=position_dodge())+ylab("fraction (%)")+
  135. scale_fill_manual(values=colors)+scale_x_discrete(limits = (levels(Cell_number$type)))+
  136. theme(legend.position="none",axis.title.x=element_text(size=0),axis.title.y=element_text(size=20,color="black"),axis.text.y=element_text(size=12,color="black"),axis.text.x=element_text(size=15,angle = 50, hjust =0.5,v=0.5,color="black"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  137. panel.background = element_blank(), axis.line = element_line(colour = "black",size=0.7))+ggtitle("% cells")
  138. ggsave(file="/media/garret/New Volume/paper submission/MPOA_Science/Revision_Figures/raw/Esr1KOM_neuroncelltypefraction_rev#3com4.pdf",height=6, width=10 , paper = "letter")
  140. # %%
  141. #disc plot
  143. # %%
  144. #ext fig7
  145. Cell_type<-factor(rev(c("eVgat7","eVgat3","eVgat4","eVgat8","eVgat9","eVgat10","eVgat2","eVgat1","eVgat5","eVgat6")),levels=rev(c("eVgat7","eVgat3","eVgat4","eVgat8","eVgat9","eVgat10","eVgat2","eVgat1","eVgat5","eVgat6")))
  146. gene_list<-c("Slc32a1","Slc17a6","Ar","Esr1")
  148. # %%
  149. Cell_number<- data.frame(Date=as.Date(character()),File=character(),User=character(),stringsAsFactors=FALSE)
  151. for (i in 1:length(gene_list)){
  152. L<-length(Cell_type)
  154. Cell_number_t<- data.frame("cluster" =Cell_type, "gene"=c(rep(gene_list[i],L)))
  155. #used normalized uncorrected data
  156. for (p in 1:length(Cell_type)){
  157. Cell_number_t$pct[p]<-100*sum(Esr1KOM@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],"_barcode",sep="")))]>0)/length(eval(parse(text=paste(Cell_type[p],"_barcode",sep=""))))
  158. Cell_number_t$avg[p]<-(mean(Esr1KOM@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],"_barcode",sep="")))])-mean(Esr1KOM@assays$RNA@data[gene_list[i],]))/sd(Esr1KOM@assays$RNA@data[gene_list[i],])
  159. #for avg only consider the expressed cell
  160. #t<-LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],"_barcode",sep="")))]>0
  161. # Cell_number_t$avg[p]<-mean(LHb.integrated@assays$RNA@data[gene_list[i],t])/sd(LHb.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],"_barcode",sep="")))])
  162. }
  163. Cell_number<-rbind(Cell_number_t,Cell_number)}
  164. as.factor(Cell_number$cluster)
  166. # %%
  167. ggplot(Cell_number, aes(gene, cluster)) + geom_point(aes(size = pct, colour=avg)) +
  168. scale_y_discrete(limits = (levels(Cell_number$cluster)))+scale_x_discrete(limits =rev(levels(Cell_number$gene)),position = "top")+
  169. scale_color_gradient(low = "white", high = "darkblue",limits = c(-1,1.5),breaks=seq(-1,1.5,0.5),oob=squish) +scale_size_continuous(range = c(0,8),limits=c(0,100),breaks=seq(0,100,50))+
  170. geom_point(aes(size = pct), pch=21, lwd=0,stroke=0)+
  171. theme(axis.title.y=element_text(size=15),axis.text.y=element_text(size=8),axis.text.x=element_text(size=15,angle = 50, hjust = 0.1,vjust=0.5,color="black",face="bold"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  172. panel.background = element_blank(), axis.line = element_line(colour = "black",size=1),,legend.position ="top",axis.ticks.length=unit(.1, "cm"),axis.ticks = element_line(size = 1), plot.margin = margin(10, 110,30, 30))
  173. ggsave(file="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/disc_hormone_vglu.pdf",height=7, width=3.9 , paper = "letter")
  175. # %%
  176. ggplot(Cell_number, aes(gene, cluster)) + geom_point(aes(size = pct, colour=avg)) +
  177. scale_y_discrete(limits = (levels(Cell_number$cluster)))+scale_x_discrete(limits =rev(levels(Cell_number$gene)),position = "top")+
  178. scale_color_gradient(low = "white", high = "darkblue",limits = c(-1,1),breaks=seq(-1,1,0.5),oob=squish) +scale_size_continuous(range = c(0,8),limits=c(0,100),breaks=seq(0,100,50))+
  179. geom_point(aes(size = pct), pch=21, lwd=0,stroke=0)+
  180. theme(axis.title.y=element_text(size=15),axis.text.y=element_text(size=8),axis.text.x=element_text(size=15,angle = 50, hjust = 0.1,vjust=0.5,color="black",face="bold"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  181. panel.background = element_blank(), axis.line = element_line(colour = "black",size=1),,legend.position ="top",axis.ticks.length=unit(.1, "cm"),axis.ticks = element_line(size = 1), plot.margin = margin(10, 110,30, 30))
  182. ggsave(file="/media/garret/New Volume/paper submission/MPOA_Science/Revision_Figures/raw/disc_Esr1KOM_all_#rev3_com#.pdf",height=7, width=3.9 , paper = "letter")
  184. # %%
  185. Cell_type<-rev(c("eVgat1","eVgat2","eVgat3","eVgat4","eVgat5","eVgat6","eVgat7","eVgat8","eVgat9","eVgat10","eVgat11","eVgat12"))
  186. gene_list<-c("Slc32a1","Ar","Esr1","Dlx1")
  188. # %%
  189. #initialize empty data frame
  190. Cell_number<- data.frame(Date=as.Date(character()),File=character(),User=character(),stringsAsFactors=FALSE)
  192. for (i in 1:length(Cell_type)){
  193. L<-length(eval(parse(text = paste(Cell_type[i],"_barcode",sep=""))))
  195. Cell_number_t<- data.frame("type" =c(rep(Cell_type[i],L)))
  196. #used normalized uncorrected data
  197. for (p in 1:length(gene_list)){
  198. Cell_number_t[gene_list[p]]<-as.vector(Esr1KOM@assays$RNA@data[gene_list[p],eval(parse(text = paste(Cell_type[i],"_barcode",sep="")))])
  199. }
  200. Cell_number<-rbind(Cell_number_t,Cell_number)}
  202. as.factor(Cell_number$type)
  204. # %%
  205. colors<-rep("dodgerblue4",12)
  207. # %%
  208. for (k in 1:length(gene_list))
  209. {
  210. if (k==length(gene_list)){assign(paste("P",k,sep=""),ggplot(Cell_number,aes_string(x="type",y=gene_list[k],fill="type"))+geom_violin(scale = "width")+scale_fill_manual(values=colors)+ylab(gene_list[k])+ theme(axis.title.x=element_blank(),
  211. ,axis.text.y=element_blank(), axis.ticks.y=element_blank(),axis.text.x=element_text(size=12,face="bold",color="black",angle = 90, hjust =0,margin = margin(t = 2, r =0, b = 0, l =0)),axis.title.y=element_text(size=12,angle=0,face="bold",vjust=0.5,margin = margin(t = 5, r = 10, b = 0, l = 10)), axis.title=element_text(size=6,face="bold"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  212. panel.background = element_blank(), axis.line = element_line(colour = "black"),legend.position="none",plot.margin = unit(c(0, 0, 0, 0), "cm")))}
  213. else{
  214. assign(paste("P",k,sep=""),ggplot(Cell_number,aes_string(x="type",y=gene_list[k],fill="type"))+geom_violin(scale = "width")+scale_fill_manual(values=colors)+ylab(gene_list[k])+ theme(axis.title.x=element_blank(),
  215. axis.text.x=element_blank(), axis.ticks.x=element_blank(),axis.text.y=element_blank(), axis.ticks.y=element_blank(),axis.title.y=element_text(size=12,angle=0,face="bold",vjust=0.5,margin = margin(t = 5, r = 10, b = 0, l = 10)), axis.title=element_text(size=12,face="bold"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  216. panel.background = element_blank(), axis.line = element_line(colour = "black"),legend.position="none",plot.margin = unit(c(0, 0, 0, 0), "cm")))}
  218. }
  220. # %%
  221. library(grid)
  223. # %%
  224. #for Slc32a1,gene_list<-c("Slc32a1","Slc17a6","Dlx1","Ar","Esr1","Pgr","Moxd1")
  225. merge<-list()
  226. for (i in length(gene_list):1){
  227. if (length(merge)==0){
  228. merge<-ggplotGrob(eval(parse(text=paste("P",i,sep = ""))))}else{merge<-rbind(ggplotGrob(eval(parse(text=paste("P",i,sep = "")))),merge,size = "last")}}
  230. #all gene
  231. pdf(paste("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/stackplotviolin_all_clusters_hormone",".pdf",sep=""),height=3,width=18, paper = "letter")
  232. grid.newpage()
  233. grid.draw(merge)
  234. dev.off()
  236. # %% [markdown]
  237. # start from here
  239. # %%
  240. MPOA.integrated<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/MPOA.integrated.rds")
  241. P23M<-readRDS(file ="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/P23M.rds")
  242. P35M<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/P35M.rds")
  243. AM<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/AM.rds")
  244. Cast<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/Cast.rds")
  245. P23F<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/P23F.rds")
  246. P35F<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/P35F.rds")
  247. AF<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/AF.rds")
  248. OVX<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/OVX.rds")
  249. new.ident <- c("Mix1","Vgat1","Vglu1","Vgat2","Vgat3","Vgat4","Mix2","Vglu2","Vgat5","Vglu3","Vglu4","Vgat6","Vgat7","Vgat8","Vglu5","Vglu6","Vgat9","Vgat10","Vgat11","Vglu7","Vgat12","Vgat13","Vgat14","Vgat15","Vglu8","Vglu9","Vgat16","Vglu10","Vgat17","Vgat18","Vgat19","Vglu11","Vglu12","Ambiguous1","Mix3","Vgat20")
  250. names(x = new.ident) <- levels(x =MPOA.integrated)
  251. MPOA.integrated<- RenameIdents(object =MPOA.integrated, new.ident)
  252. for (i in 1:length(new.ident)){
  253. assign(paste(new.ident[i],"_barcode",sep=""),colnames(MPOA.integrated@assays$RNA@data[,which(Idents(object=MPOA.integrated) %in% new.ident[i])]))# this gives all barcodes in cluster
  254. assign(paste(new.ident[i],"_barcode_P23M",sep=""),intersect(colnames(P23M@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  255. assign(paste(new.ident[i],"_barcode_P35M",sep=""),intersect(colnames(P35M@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  256. assign(paste(new.ident[i],"_barcode_AM",sep=""),intersect(colnames(AM@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  257. assign(paste(new.ident[i],"_barcode_Cast",sep=""),intersect(colnames(Cast@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  258. assign(paste(new.ident[i],"_barcode_P23F",sep=""),intersect(colnames(P23F@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  259. assign(paste(new.ident[i],"_barcode_P35F",sep=""),intersect(colnames(P35F@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  260. assign(paste(new.ident[i],"_barcode_AF",sep=""),intersect(colnames(AF@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  261. assign(paste(new.ident[i],"_barcode_OVX",sep=""),intersect(colnames(OVX@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  262. }
  264. # %%
  265. [email hidden]$celltype<-Idents(MPOA.integrated)
  267. # %%
  268. Esr1KOM<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/Esr1KOM.rds")
  269. new.ident <- c("eMix1","eVgat1","eVglu1","eVgat2","eVgat3","eVgat4","eVglu2","eVglu3","eVgat5","eVgat6","eVgat7","eVgat8","eVgat9","eMix2","eVgat10","eVglu4","eVgat11","eVglu5","eVgat12","eVglu6","eAmbiguous")
  270. names(x = new.ident) <- levels(x =Esr1KOM)
  271. Esr1KOM<- RenameIdents(object =Esr1KOM, new.ident)
  272. for (i in 1:length(new.ident)){
  273. assign(paste(new.ident[i],"_barcode",sep=""),colnames(Esr1KOM@assays$RNA@data[,which(Idents(object=Esr1KOM) %in% new.ident[i])]))}
  274. [email hidden]$celltype<-Idents(Esr1KOM)
  275. #Celltype may change
  276. Celltype<-c("eVgat1","eVgat2","eVgat3","eVgat4","eVgat5","eVgat6","eVgat7","eVgat8","eVgat9","eVgat10","eVgat11","eVgat12","eVglu1","eVglu2","eVglu3","eVglu4","eVglu5","eVglu6","eMix1","eMix2")
  278. Esr1KOM<-subset(Esr1KOM,cells=rownames([email hidden])[[email hidden]$celltype %in% Celltype])
  279. [email hidden]$celltype<-factor([email hidden]$celltype,levels=Celltype)
  280. Idents(Esr1KOM)<-factor(Idents(Esr1KOM),levels=Celltype)
  282. # %%
  283. celltype<-c(eVglu1_barcode,eVglu2_barcode,eVglu3_barcode,eVglu4_barcode,eVglu5_barcode,eVglu6_barcode)
  285. # %% [markdown]
  286. # # correlation KO and scRNAseq
  288. # %%
  289. #for extFig7, remove Vgat20
  290. MPOA_type<-c("Vgat6","Vgat2","Vgat16","Vgat4","Vgat10","Vgat13","Vgat8","Vgat17","Vgat1","Vgat3","Vgat14","Vgat5","Vgat7","Vgat11","Vgat9","Vgat19","Vgat15","Vgat18","Vgat12")
  291. Esr1KOM_type<-rev(c("eVgat7","eVgat3","eVgat4","eVgat8","eVgat9","eVgat10","eVgat2","eVgat1","eVgat5","eVgat6"))
  293. # %%
  294. corr<- data.frame(matrix(NA, ncol=length(MPOA_type),nrow=length(Esr1KOM_type)))
  295. rownames(corr)<-factor(Esr1KOM_type,levels=Esr1KOM_type)
  296. colnames(corr)<-factor(MPOA_type,levels=MPOA_type)
  298. # %%
  299. p_value<- data.frame(matrix(NA, ncol=length(MPOA_type),nrow=length(Esr1KOM_type)))
  300. rownames(p_value)<-factor(Esr1KOM_type,levels=Esr1KOM_type)
  301. colnames(p_value)<-factor(MPOA_type,levels=MPOA_type)
  303. # %%
  304. genes<-intersect(rownames(Esr1KOM@ reductions$ pca@ feature.loadings),rownames(MPOA.integrated@assays$RNA@data))
  305. MPOA_exp<-MPOA.integrated@assays$RNA@data[genes,]
  306. Esr1KOM_exp<- Esr1KOM@assays$RNA@data[genes,]
  308. # %%
  309. length(genes)
  311. # %%
  312. MPOA_exp<-t(scale(t(as.matrix(MPOA_exp))))
  313. Esr1KOM_exp<-t(scale(t(as.matrix(Esr1KOM_exp))))
  315. # %%
  316. for (i in 1:length(MPOA_type)){
  317. for (j in 1:length(Esr1KOM_type)){
  318. test1<-Esr1KOM_exp[,eval(parse(text = paste(Esr1KOM_type[j],"_barcode",sep="")))]
  319. rownames(test1) <- c()
  320. test1<-rowMeans(test1)
  321. include1<-is.na(test1)
  322. test2<-MPOA_exp[,eval(parse(text = paste(MPOA_type[i],"_barcode",sep="")))]
  323. rownames(test2) <- c()
  324. test2<-rowMeans(test2)
  325. include2<-is.na(test2)
  326. include<-(include1+include2)==0
  327. test1<-test1[include]
  328. test2<-test2[include]
  330. corr[j,i]<-cor(test1,test2)
  331. }}
  333. # %%
  334. for (i in 1:length(MPOA_type)){
  335. for (j in 1:length(Esr1KOM_type)){
  336. test1<-Esr1KOM_exp[,eval(parse(text = paste(Esr1KOM_type[j],"_barcode",sep="")))]
  337. rownames(test1) <- c()
  338. test1<-rowMeans(test1)
  339. include1<-is.na(test1)
  340. test2<-MPOA_exp[,eval(parse(text = paste(MPOA_type[i],"_barcode",sep="")))]
  341. rownames(test2) <- c()
  342. test2<-rowMeans(test2)
  343. include2<-is.na(test2)
  344. include<-(include1+include2)==0
  345. test1<-test1[include]
  346. test2<-test2[include]
  347. p_value[j,i]<-cor.test(test1,test2)[[3]]}}
  349. # %%
  350. library("reshape2")
  352. # %%
  353. test.m <- melt(as.matrix(corr))
  355. # %%
  356. library(scales)
  358. # %%
  359. test.p <- melt(as.matrix(p_value))
  361. # %%
  362. for(i in 1:dim(test.m)[1]){
  363. if (test.p$value[i]>0.05){
  364. test.m$value[i]<-0
  365. }else{
  366. test.m$value[i]<-test.m$value[i]
  367. }
  368. }
  370. # %%
  371. p <- ggplot(test.m, aes(Var2, Var1)) + geom_tile(aes(fill =value),
  372. colour = "white")+ scale_fill_continuous(limits=c(0.3,0.8), breaks=seq(0.0,0.8,by=0.2),low = "white",high = "darkred", oob=squish)
  373. #scale_x_discrete(breaks=c(0,5994,7712,9820,11428,11674,11836,11878))
  374. pp<-p+ylab("Esr1KOM")+xlab("scRNAseq")+theme(axis.text.y=element_text(size=10,colour = "black"),axis.text.x=element_text(size=10,,angle = 50, hjust =1,colour = "black"),axis.title=element_text(size=20,face="bold"),plot.title = element_text(size=16),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  375. panel.background = element_blank(), axis.line = element_line(colour = "black", size = 0.1),axis.ticks.y = element_blank())+ggtitle("correlation between Esr1KOM and scRNAseq")
  376. print(pp)
  378. # %%
  379. p <- ggplot(test.m, aes(Var2, Var1)) + geom_tile(aes(fill =value),
  380. colour = "white")+ scale_fill_continuous(limits=c(0.3,0.8), breaks=seq(0.0,0.8,by=0.2),low = "white",high = "darkred", oob=squish)
  381. #scale_x_discrete(breaks=c(0,5994,7712,9820,11428,11674,11836,11878))
  382. pp<-p+ylab("Esr1KOM")+xlab("scRNAseq")+theme(axis.text.y=element_text(size=15,colour = "black",face="bold"),axis.text.x=element_text(size=14,,angle = 50, hjust =1,colour = "black",face="bold"),axis.title=element_text(size=20,face="bold"),plot.title = element_text(size=16),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  383. panel.background = element_blank(), axis.line = element_line(colour = "black", size = 1),axis.ticks.length=unit(.1, "cm"),axis.ticks = element_line(size = 1), plot.margin = margin(10, 10, 10, 10))
  384. ggsave(file="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/correlation_vgat.pdf",height=5, width=10 , paper = "letter")
  386. # %%
  387. p <- ggplot(test.m, aes(Var2, Var1)) + geom_tile(aes(fill =value),
  388. colour = "white")+ scale_fill_continuous(limits=c(0.3,0.8), breaks=seq(0.0,0.8,by=0.2),low = "white",high = "darkred", oob=squish)
  389. #scale_x_discrete(breaks=c(0,5994,7712,9820,11428,11674,11836,11878))
  390. pp<-p+ylab("Esr1KOM")+xlab("scRNAseq")+theme(axis.text.y=element_text(size=10,colour = "black"),axis.text.x=element_text(size=12,,angle = 50, hjust =1,colour = "black"),axis.title=element_text(size=20,face="bold"),plot.title = element_text(size=16),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  391. panel.background = element_blank(), axis.line = element_line(colour = "black", size = 0.1),axis.ticks.y = element_blank())+ggtitle("correlation between Esr1KOM and scRNAseq")
  392. ggsave(file="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/correlation_vglu.pdf",height=2.2, width=10 , paper = "letter")
  394. # %% [markdown]
  395. # # reference based projection
  397. # %%
  398. #for S-Fig, remove Vgat20 as no correspondence with Vgat20 in correlation analysis
  399. MPOA_type<-c("Vgat6","Vgat2","Vgat16","Vgat4","Vgat10","Vgat13","Vgat8","Vgat17","Vgat1","Vgat3","Vgat14","Vgat5","Vgat7","Vgat11","Vgat9","Vgat19","Vgat15","Vgat18","Vgat12")
  400. Esr1KOM_type<-rev(c("eVgat7","eVgat3","eVgat4","eVgat8","eVgat9","eVgat10","eVgat2","eVgat1","eVgat5","eVgat6"))
  402. # %%
  403. MPOA.integrated<-subset(MPOA.integrated,cells=rownames([email hidden])[[email hidden]$celltype %in% MPOA_type])
  404. Esr1KOM<-subset(Esr1KOM,cells=rownames([email hidden])[[email hidden]$celltype %in% Esr1KOM_type])
  406. # %%
  407. POA.list<-objects() #maybe better using POA.list<-vector(mode = "list")
  408. POA.list$MPOA.integrated<-MPOA.integrated
  409. POA.list$Esr1KOM<-Esr1KOM
  410. reference.list <- POA.list[c("MPOA.integrated","Esr1KOM")]
  412. # %%
  413. MPOA.integrated
  415. # %%
  416. help(FindTransferAnchors)
  418. # %%
  419. genes<-intersect(rownames(Esr1KOM@ reductions$ pca@ feature.loadings),rownames(MPOA.integrated@assays$RNA@data))
  421. # %%
  422. POA.anchors <- FindTransferAnchors(reference = MPOA.integrated, query = Esr1KOM, reduction = "cca", dims = 1:10,features=genes)
  424. # %%
  425. predictions <- TransferData(anchorset = POA.anchors, refdata = MPOA.integrated$celltype, weight.reduction="cca", dims = 1:10)
  426. Esr1KOM <- AddMetaData(Esr1KOM, metadata = predictions)
  428. # %%
  429. [email hidden]$celltype<-factor([email hidden]$celltype,levels=Esr1KOM_type)
  430. [email hidden]$predicted.id<-factor([email hidden]$predicted.id,levels=MPOA_type)
  432. # %%
  433. meta <- [email hidden] %>% select(celltype, predicted.id) %>% group_by(celltype, .drop = FALSE) %>% count(celltype, predicted.id)
  434. adjusted<-numeric()
  435. for(i in 1:dim(meta)[1]){
  436. adjusted[i]<-100*meta$n[i]/sum(subset(meta,predicted.id==meta$predicted.id[i])$n)
  437. }
  438. meta$adjusted<-adjusted
  440. # %%
  441. meta<-meta[meta$predicted.id %in% c("Vgat2","Vgat4","Vgat16"),]
  443. # %%
  444. meta$celltype<-factor(meta$celltype,levels=Esr1KOM_type)
  445. meta$predicted.id<-factor(meta$predicted.id,levels=c("Vgat2","Vgat4","Vgat16"))
  447. # %%
  448. unique(meta$celltype)
  450. # %%
  451. colors=c(c("eVgat7"="lightblue","eVgat3"="darkblue","eVgat4"="indianred","eVgat8"="lightskyblue","eVgat9"="skyblue","eVgat10"="lightgray","eVgat2"="darkgray","eVgat1"="lightsalmon","eVgat5"="darkgray","eVgat6"="dimgray"))
  453. # %%
  454. #% of scRNAseq Vgat Esr1 cell types predicted in KO data
  455. ggplot(meta,aes(x=predicted.id,y=adjusted,fill=celltype,group=celltype))+geom_bar(position="dodge",stat = "identity")+ylab("% of predicted cells")+
  456. scale_fill_manual(values=colors)+
  457. theme(axis.title.y=element_text(size=20,color="black"),axis.title.x=element_blank(),axis.text.y=element_text(size=15,color="black"),axis.text.x=element_text(size=15,angle = 50, hjust = 0.5,vjust=0.5,color="black",face="bold"),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  458. panel.background = element_blank(), axis.line = element_line(colour = "black",size=0.3),axis.ticks.length=unit(.1, "cm"),axis.ticks = element_line(size =0.3), plot.margin = unit(c(0.6, 0,0, 0), "cm"))
  459. ggsave(file="/media/garret/New Volume/paper submission/MPOA_Science/Revision_Figures/raw/Esr1KOM_number_of_predicted_celltype_rev#3_com#4.pdf",height=3, width=10 , paper = "letter")
  462. # %%
  463. help(FindTransferAnchors)
  465. # %% [markdown]
  466. # # DEG analysis
  468. # %%
  469. MPOA.integrated<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/MPOA.integrated.rds")
  470. P23M<-readRDS(file ="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/P23M.rds")
  471. P35M<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/P35M.rds")
  472. AM<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/AM.rds")
  473. Cast<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/Cast.rds")
  474. P23F<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/P23F.rds")
  475. P35F<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/P35F.rds")
  476. AF<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/AF.rds")
  477. OVX<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/OVX.rds")
  478. new.ident <- c("Mix1","Vgat1","Vglu1","Vgat2","Vgat3","Vgat4","Mix2","Vglu2","Vgat5","Vglu3","Vglu4","Vgat6","Vgat7","Vgat8","Vglu5","Vglu6","Vgat9","Vgat10","Vgat11","Vglu7","Vgat12","Vgat13","Vgat14","Vgat15","Vglu8","Vglu9","Vgat16","Vglu10","Vgat17","Vgat18","Vgat19","Vglu11","Vglu12","Ambiguous1","Mix3","Vgat20")
  479. names(x = new.ident) <- levels(x =MPOA.integrated)
  480. MPOA.integrated<- RenameIdents(object =MPOA.integrated, new.ident)
  481. for (i in 1:length(new.ident)){
  482. assign(paste(new.ident[i],"_barcode",sep=""),colnames(MPOA.integrated@assays$RNA@data[,which(Idents(object=MPOA.integrated) %in% new.ident[i])]))# this gives all barcodes in cluster
  483. assign(paste(new.ident[i],"_barcode_P23M",sep=""),intersect(colnames(P23M@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  484. assign(paste(new.ident[i],"_barcode_P35M",sep=""),intersect(colnames(P35M@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  485. assign(paste(new.ident[i],"_barcode_AM",sep=""),intersect(colnames(AM@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  486. assign(paste(new.ident[i],"_barcode_Cast",sep=""),intersect(colnames(Cast@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  487. assign(paste(new.ident[i],"_barcode_P23F",sep=""),intersect(colnames(P23F@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  488. assign(paste(new.ident[i],"_barcode_P35F",sep=""),intersect(colnames(P35F@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  489. assign(paste(new.ident[i],"_barcode_AF",sep=""),intersect(colnames(AF@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  490. assign(paste(new.ident[i],"_barcode_OVX",sep=""),intersect(colnames(OVX@assays$RNA@data),eval(parse(text = paste(new.ident[i],"_barcode",sep="")))))
  491. }
  492. Esr1KOM<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/Esr1KOM.rds")
  493. new.ident <- c("eMix1","eVgat1","eVglu1","eVgat2","eVgat3","eVgat4","eVglu2","eVglu3","eVgat5","eVgat6","eVgat7","eVgat8","eVgat9","eMix2","eVgat10","eVglu4","eVgat11","eVglu5","eVgat12","eVglu6","eAmbiguous")
  494. names(x = new.ident) <- levels(x =Esr1KOM)
  495. Esr1KOM<- RenameIdents(object =Esr1KOM, new.ident)
  496. for (i in 1:length(new.ident)){
  497. assign(paste(new.ident[i],"_barcode",sep=""),colnames(Esr1KOM@assays$RNA@data[,which(Idents(object=Esr1KOM) %in% new.ident[i])]))}
  499. # %%
  500. DefaultAssay(Esr1KOM) <- "RNA"
  501. #celltype<-c(eVgat3_barcode,eVgat4_barcode,eVgat7_barcode) #not used
  502. celltype<-c(eVgat3_barcode,eVgat4_barcode)
  503. KO<-subset(Esr1KOM,cells=celltype)
  505. # %%
  506. DefaultAssay(MPOA.integrated) <- "RNA"
  507. temp<-c(Vgat2_barcode,Vgat4_barcode,Vgat16_barcode)
  508. celltype<-intersect(temp,rownames(subset([email hidden],stim=="AM")))
  509. Intact<-subset(MPOA.integrated,cells=celltype)
  511. # %%
  512. #vgat low
  513. DefaultAssay(MPOA.integrated) <- "RNA"
  514. temp<-c(Vgat14_barcode,Vgat17_barcode,Vgat20_barcode)
  515. celltype<-intersect(temp,rownames(subset([email hidden],stim=="AM")))
  516. Intact<-subset(MPOA.integrated,cells=celltype)
  518. # %%
  519. #Vgatlow
  520. DefaultAssay(Esr1KOM) <- "RNA"
  522. #celltype<-c(eVgat3_barcode,eVgat4_barcode,eVgat7_barcode)
  523. celltype<-c(eVgat9_barcode)
  524. KO<-subset(Esr1KOM,cells=celltype)
  526. # %%
  527. Merge<-merge(x = KO, y = Intact, merge.data = TRUE)
  528. DefaultAssay(Merge) <- "RNA"
  530. # %%
  531. [email hidden]
  533. # %%
  534. celltype.stim<-vector()
  535. for (i in 1:dim([email hidden])[1]){
  536. if([email hidden]$stim[i]=="AM"){celltype.stim[i]<-"AM"}
  537. else if([email hidden]$stim[i]=="Esr1KOM"){celltype.stim[i]<-"Esr1KOM"}
  538. }
  540. [email hidden]$celltype.stim<-celltype.stim
  542. # %%
  543. Merge[["celltype"]] <- Idents(object = Merge)
  544. Idents(Merge = Merge) <- [email hidden]$celltype.stim
  547. # %%
  548. one.markers <- FindMarkers(Merge, ident.1 = "AM", ident.2 ="Esr1KOM", grouping.var = "stim",print.bar = FALSE,logfc.threshold = 0,min.pct = 0,min.cells.gene = 3, min.cells.group = 1)
  550. one.markers$p_val_adjust<-one.markers$p_val*(dim(one.markers)[1]:1)
  551. sort_marker<-one.markers[order(-one.markers["avg_logFC"]),]
  553. # %%
  554. #201105 Vgat2 4 16 all
  555. one.markers <- FindMarkers(Merge, ident.1 = "AM", ident.2 ="Esr1KOM", grouping.var = "stim",print.bar = FALSE,logfc.threshold = 0,min.pct = 0,min.cells.gene = 3, min.cells.group = 1)
  557. one.markers$p_val_adjust<-one.markers$p_val*(dim(one.markers)[1]:1)
  558. sort_marker<-one.markers[order(-one.markers["avg_logFC"]),]
  559. write.xlsx2(subset(sort_marker),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/All_gene.xlsx"))
  561. # %%
  562. #201105 Vgat2 4 16
  563. one.markers <- FindMarkers(Merge, ident.1 = "AM", ident.2 ="Esr1KOM", grouping.var = "stim",print.bar = FALSE,logfc.threshold = 0,min.pct = 0,min.cells.gene = 3, min.cells.group = 1)
  565. one.markers$p_val_adjust<-one.markers$p_val*(dim(one.markers)[1]:1)
  566. sort_marker<-one.markers[order(-one.markers["avg_logFC"]),]
  567. write.xlsx2(subset(sort_marker,avg_logFC>0 & p_val_adjust<0.05 & pct.1>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/gene_adjust_0.1.xlsx"))
  568. write.xlsx2(subset(sort_marker,avg_logFC<0 & p_val_adjust<0.05 & pct.2>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/gene_adjust_0.1n.xlsx"))
  569. write.xlsx2(subset(sort_marker,avg_logFC>0.1 & p_val_adjust<0.05 & pct.1>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/gene_adjust_0.1_0.1.xlsx"))
  571. # %%
  572. #201106 Vgat2 4 16 vs 3 4
  573. one.markers <- FindMarkers(Merge, ident.1 = "AM", ident.2 ="Esr1KOM", grouping.var = "stim",print.bar = FALSE,logfc.threshold = 0,min.pct = 0,min.cells.gene = 3, min.cells.group = 1)
  575. one.markers$p_val_adjust<-one.markers$p_val*(dim(one.markers)[1]:1)
  576. sort_marker<-one.markers[order(-one.markers["avg_logFC"]),]
  577. write.xlsx2(subset(sort_marker,avg_logFC>0 & p_val_adjust<0.05 & pct.1>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/gene_adjust_0.1_246vs34.xlsx"))
  578. write.xlsx2(subset(sort_marker,avg_logFC<0 & p_val_adjust<0.05 & pct.2>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/gene_adjust_0.1n_246vs34.xlsx"))
  579. write.xlsx2(subset(sort_marker,avg_logFC>0.1 & p_val_adjust<0.05 & pct.1>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/gene_adjust_0.1_0.1_246vs34.xlsx"))
  581. # %%
  582. one.markers <- FindMarkers(Merge, ident.1 = "Esr1KOM", ident.2 ="AM", grouping.var = "stim",print.bar = FALSE,logfc.threshold = 0,min.pct = 0,min.cells.gene = 3, min.cells.group = 1)
  584. one.markers$p_val_adjust<-one.markers$p_val*(dim(one.markers)[1]:1)
  585. sort_marker<-one.markers[order(-one.markers["avg_logFC"]),]
  586. write.xlsx2(subset(sort_marker,avg_logFC>0.1 & p_val_adjust<0.05 & pct.1>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/gene_adjust_0.1_0.1_KO.xlsx"))
  588. # %%
  589. one.markers <- FindMarkers(Merge, ident.1 = "AM", ident.2 ="Esr1KOM", grouping.var = "stim",print.bar = FALSE,logfc.threshold = 0,min.pct = 0,min.cells.gene = 3, min.cells.group = 1)
  591. one.markers$p_val_adjust<-one.markers$p_val*(dim(one.markers)[1]:1)
  592. sort_marker<-one.markers[order(-one.markers["avg_logFC"]),]
  593. write.xlsx2(subset(sort_marker,avg_logFC>0 & p_val_adjust<0.05 & pct.1>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/low/gene_adjust_0.1_246vs34.xlsx"))
  594. write.xlsx2(subset(sort_marker,avg_logFC<0 & p_val_adjust<0.05 & pct.2>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/low/gene_adjust_0.1n_246vs34.xlsx"))
  595. write.xlsx2(subset(sort_marker,avg_logFC>0.1 & p_val_adjust<0.05 & pct.1>0.1),file=("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/low/gene_adjust_0.1_0.1_246vs34.xlsx"))
  597. # %% [markdown]
  598. # # heatmap
  600. # %%
  601. MPOA.integrated<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/MPOA.integrated.rds")
  602. new.ident <- c("Mix1","Vgat1","Vglu1","Vgat2","Vgat3","Vgat4","Mix2","Vglu2","Vgat5","Vglu3","Vglu4","Vgat6","Vgat7","Vgat8","Vglu5","Vglu6","Vgat9","Vgat10","Vgat11","Vglu7","Vgat12","Vgat13","Vgat14","Vgat15","Vglu8","Vglu9","Vgat16","Vglu10","Vgat17","Vgat18","Vgat19","Vglu11","Vglu12","Ambiguous1","Mix3","Vgat20")
  603. names(x = new.ident) <- levels(x =MPOA.integrated)
  604. MPOA.integrated<- RenameIdents(object =MPOA.integrated, new.ident)
  605. for (i in 1:length(new.ident)){
  606. assign(paste(new.ident[i],"_barcode",sep=""),colnames(MPOA.integrated@assays$RNA@data[,which(Idents(object=MPOA.integrated) %in% new.ident[i])]))# this gives all barcodes in cluster
  607. }
  608. #temp<-c(Vgat2_barcode,Vgat4_barcode,Vgat16_barcode) #Vgat Esr1
  609. temp<-c(Vgat14_barcode,Vgat17_barcode,Vgat20_barcode) # hormone low
  610. celltype<-intersect(temp,rownames(subset([email hidden],stim=="AM" | stim=="P35M"| stim=="P23M"|stim=="Cast")))
  611. MPOA.integrated<-subset(MPOA.integrated,cells=celltype)
  612. Esr1KOM<-readRDS(file = "/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/Esr1KOM.rds")
  613. new.ident <- c("eMix1","eVgat1","eVglu1","eVgat2","eVgat3","eVgat4","eVglu2","eVglu3","eVgat5","eVgat6","eVgat7","eVgat8","eVgat9","eMix2","eVgat10","eVglu4","eVgat11","eVglu5","eVgat12","eVglu6","eAmbiguous")
  614. names(x = new.ident) <- levels(x =Esr1KOM)
  615. Esr1KOM<- RenameIdents(object =Esr1KOM, new.ident)
  616. for (i in 1:length(new.ident)){
  617. assign(paste(new.ident[i],"_barcode",sep=""),colnames(Esr1KOM@assays$RNA@data[,which(Idents(object=Esr1KOM) %in% new.ident[i])]))}
  618. #201111 3 4
  619. #celltype<-c(eVgat3_barcode,eVgat4_barcode)
  620. celltype<-c(eVgat9_barcode)
  621. KO<-subset(Esr1KOM,cells=celltype)
  622. MPOA.integrated<-merge(x=MPOA.integrated,y=KO)
  624. # %%
  625. Cell_type<-c("Esr1KOM","Cast","AM")
  626. #Cell_type<-c("Esr1KOM","P23M","Cast","P35M","AM")
  627. Cell_type<-factor(Cell_type,levels=Cell_type)
  629. # %%
  630. temp1<-read.xlsx("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/DEG/Esr1_3/AM_over_Cast/gene_adjust_Vgat_0.1.xlsx",sheetIndex=1)
  631. temp3<-read.xlsx("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_integrated/log/Neurons/DEG/Esr1_3/P35M_over_Cast/gene_adjust_Vgat_0.1.xlsx",sheetIndex=1)
  632. temp2<-read.xlsx("/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/gene_adjust_0.1_0.1_246vs34.xlsx",sheetIndex=1)
  633. temp1<-temp1[,1]
  634. temp2<-temp2[,1]
  635. temp3<-temp3[,1]
  636. #both<-intersect(temp1,intersect(temp2,temp3))
  637. both<-intersect(temp1,temp2)
  639. # %%
  640. gene_list<-both
  641. #gene_list<-temp2
  643. # %%
  644. Cell_number<- data.frame(Date=as.Date(character()),File=character(),User=character(),stringsAsFactors=FALSE)
  646. for (i in 1:length(gene_list)){
  647. L<-length(Cell_type)
  649. Cell_number_t<- data.frame("cluster" =Cell_type, "gene"=(rep(gene_list[i],L))) # do not use c if the gene is factorizsed
  650. #used normalized uncorrected data
  651. for (p in 1:length(Cell_type)){
  652. #Cell_number_t$pct[p]<-100*sum(MPOA.integrated@assays$RNA@data[gene_list[i],eval(parse(text=paste(Cell_type[p],"_barcode",sep="")))]>0)/length(eval(parse(text=paste(Cell_type[p],"_barcode",sep=""))))
  653. Cell_number_t$zscore[p]<-(mean(MPOA.integrated@assays$RNA@data[gene_list[i],rownames(subset([email hidden],stim==Cell_type[p]))])-mean(MPOA.integrated@assays$RNA@data[gene_list[i],]))/sd(MPOA.integrated@assays$RNA@data[gene_list[i],])
  654. Cell_number_t$FC[p]<-mean(MPOA.integrated@assays$RNA@data[gene_list[i],rownames(subset([email hidden],stim==Cell_type[p]))])-mean(MPOA.integrated@assays$RNA@data[gene_list[i],rownames(subset([email hidden],stim!=Cell_type[p]))])
  655. Cell_number_t$percent[p]<- 100*sum(MPOA.integrated@assays$RNA@data[gene_list[i],rownames(subset([email hidden],stim==Cell_type[p]))]>0)/length(MPOA.integrated@assays$RNA@data[gene_list[i],rownames(subset([email hidden],stim==Cell_type[p]))])
  656. }
  657. Cell_number<-rbind(Cell_number_t,Cell_number)}
  658. as.factor(Cell_number$cluster)
  660. # %%
  661. Cell_number$gene
  663. # %%
  664. Cell_number$gene<-factor(Cell_number$gene,levels=gene_list)
  666. # %%
  667. temp_gene<-Cell_number$gene
  669. # %%
  670. color<-numeric()
  671. size<-numeric()
  672. for(i in 1:length(temp_gene)){
  673. if(temp_gene[i]=="Gal"| temp_gene[i]=="Guk1" | temp_gene[i]=="Pgr" | temp_gene[i]=="Prkce" | temp_gene[i]=="Greb1" | temp_gene[i]=="Sytl4"| temp_gene[i]=="Cntnap2" |temp_gene[i]=="Sv2c" ){
  674. color[i]<-"black"
  675. size[i]<-20
  676. }else{
  677. color[i]<-"white"
  678. size[i]<-0
  679. }
  680. }
  682. # %%
  683. genes<-c("Slc32a1","Esr1","Ar","Npy2r","Pgr15l","Sytl4","Lamp5","Pgr","Nrip1","Acvr1c","Apoc3","Greb1","Tead1","Napb","Apoe","Pgr","Gal","Apoc3","mt-Nd4",'Cdh4','Acvr1c',"Syt6","Prkce")
  685. # %%
  686. both
  688. # %%
  689. high_C<-"#FCFADE"
  690. middle_C<-"#489588"
  691. low_C<-"#262E68"
  692. low<- -0.0
  693. high<-1
  694. p<-ggplot(data = Cell_number, mapping = aes(x = cluster, y = gene,fill =zscore,label=gene)) + scale_y_discrete(breaks=c(genes))+
  695. geom_tile() + scale_fill_gradient2(limits=c(low, high), breaks=seq(low,high,by=0.1),low = low_C,mid=middle_C,high =high_C, midpoint = 0.5,oob=squish)
  696. #scale_x_discrete(breaks=c(0,5994,7712,9820,11428,11674,11836,11878))
  697. pp<-p+ylab("group")+xlab("genes")+theme(axis.text.x=element_text(size=20,colour = "black",angle = 40, vjust =0.6,face="bold"),axis.text.y=element_text(size=10, colour = "black",face="bold"),axis.title=element_blank(),plot.title = element_text(size=16),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  698. panel.background = element_blank(), axis.line = element_blank(),axis.ticks.x = element_blank())+ scale_x_discrete(labels=c("Esr1KOM" = "KOM", "Cast" = "Cast",
  699. "AM" = "AM"))
  700. print(pp)
  701. ggsave(file=paste(text="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/heatmap_male_common_3groups_",low,"_",high,"_mini_color_adjusted.pdf",sep=""),width=2.5,height=5,paper="letter")
  703. # %%
  704. size
  705. color
  707. # %%
  708. low<- -0.0
  709. high<-1
  710. p<-ggplot(Cell_number, aes(cluster, gene))+
  711. geom_tile(aes(fill =zscore))+ scale_fill_continuous(limits=c(low, high), breaks=seq(low,high,by=0.4),low = "black",high = "yellow", oob=squish)
  712. #scale_x_discrete(breaks=c(0,5994,7712,9820,11428,11674,11836,11878))
  713. pp<-p+ylab("group")+xlab("genes")+theme(axis.text.x=element_text(size=15,colour = "black",angle = 55, vjust =0.6,face="bold"),axis.text.y=element_text(size=0,colour = "black"),axis.title=element_blank(),plot.title = element_text(size=16),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  714. panel.background = element_blank(), axis.line = element_blank(),axis.ticks.y = element_blank(),axis.ticks.x = element_blank(), plot.margin = margin(10, 28, 10, 10))
  715. print(pp)
  716. ggsave(file=paste(text="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/heatmap_male_common_5groups_",low,"_",high,"_mini.pdf",sep=""),width=2.5,height=6)
  718. # %%
  719. high_C<-"#FCFADE"
  720. middle_C<-"#489588"
  721. low_C<-"#262E68"
  722. low<- -0.0
  723. high<-1
  724. p<-ggplot(data = Cell_number, mapping = aes(x = cluster, y = gene,fill =zscore,label=gene)) + scale_y_discrete(breaks=c(genes))+
  725. geom_tile() + scale_fill_gradient2(limits=c(low, high), breaks=seq(low,high,by=0.1),low = low_C,mid=middle_C,high =high_C, midpoint = 0.5,oob=squish)
  726. #scale_x_discrete(breaks=c(0,5994,7712,9820,11428,11674,11836,11878))
  727. pp<-p+ylab("group")+xlab("genes")+theme(axis.text.x=element_text(size=20,colour = "black",angle = 40, vjust =0.6,face="bold"),axis.text.y=element_text(size=10, colour = "black",face="bold"),axis.title=element_blank(),plot.title = element_text(size=16),panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
  728. panel.background = element_blank(), axis.line = element_blank(),axis.ticks.x = element_blank())+ scale_x_discrete(labels=c("Esr1KOM" = "KOM", "Cast" = "Cast",
  729. "AM" = "AM"))
  730. print(pp)
  731. ggsave(file=paste(text="/media/garret/New Volume/scRNAseq_data/Koichi/V3_analysis/MPOA/MPOA_Esr1KOM/Neuron/DEG/heatmap_male_common_3groups_hormonelow_",low,"_",high,"_mini.pdf",sep=""),width=2.5,height=5,paper="letter")

Integrating Esr1KOM and MPOA scRNAseq Fig4.ipynb at commit b46c819, under MIT · at the source

Overview

Authors: Koichi Hashikawa1, Yoshiko Hashikawa1, Brandy Briones1, Kentaro K Ishii1, Yuejia Liu1, Mark Rossi1, Marcus L Basiri1,2, Jane Y Chen3, Omar Ahmad1, Rishi Mukundan1, Nathan Johnston1, Rhiana Simon1, James Soetedjo1, Jason Siputro1, Jenna McHenry4, Richard D Palmiter3,5, David Rubinow6, Larry S Zweifel7,8, Garret D Stuber1
ORCID iDs: Koichi Hashikawa, Brandy Briones, Mark Rossi, Marcus L Basiri, Jane Y Chen, Jenna McHenry, Richard D Palmiter, Larry S Zweifel, Garret D Stuber
  1. Center for the Neurobiology of Addiction, Pain, and Emotion, Department of Anesthesiology and Pain Medicine, Department of Pharmacology, University of Washington Seattle United States
  2. University of North Carolina Chapel Hill United States
  3. Department of Biochemistry, University of Washington Seattle United States
  4. Department of Psychology & Neuroscience, Duke University Durham United States
  5. Howard Hughes Medical Institute, University of Washington Seattle United States
  6. Department of Psychiatry, University of North Carolina at Chapel Hill Chapel Hill United States
  7. Department of Psychiatry and Behavioral Sciences, University of Washington Seattle United States
  8. Department of Pharmacology, University of Washington Seattle United States
Institutions: University of Washington (United States); University of North Carolina at Chapel Hill (United States); Duke University (United States)
Journal: eLife, volume 14, article RP106347
Dates: published online 19 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.106347 · PMID 42318880 · PMCID PMC13282117 · OpenAlex W4409995543
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), mouse (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, Connectivity, fMRI & imaging
Keywords: development, sex differences, steroid hormones, Mouse
MeSH: Estrogen Receptor alpha*, Preoptic Area*, Sexual Behavior, Animal*, Signal Transduction*, Animals, Female, GABAergic Neurons, Gene Expression Regulation, Developmental, Male, Mice, Transcription, Genetic (* major topic)
Journal subjects: Neuroscience
Topic: Circadian rhythm and melatonin (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: NIH Office of Research Infrastructure Programs (DA038168, DA032750, DA054317, DK121883, P30DA048736); Foundation of Hope; National Alliance for Research on Schizophrenia and Depression
Citations: cited by 1 paper (Europe PMC); 77 references in the paper
Research resources: C57BL/6 J males RRID:IMSR_JAX:000664, VgatFlp RRID:IMSR_JAX:029591, Vglut2Flp RRID:IMSR_JAX:030212, Esr1lox/lox RRID:IMSR_JAX:032173, Enrichr RRID:SCR_001575, R (v3.6) RRID:SCR_001905, ImageJ (Fiji) RRID:SCR_002285, GraphPad Prism v9.0.2 RRID:SCR_002798, CateGOrizer RRID:SCR_005737, The Gene Ontology (GO) Project RRID:SCR_006447, Seurat v3 RRID:SCR_016341, Cell Ranger RRID:SCR_017344, Monocle (V3) RRID:SCR_018685

Abstract

Mating and other behaviors emerge during adolescence through the coordinated actions of steroid hormone signaling throughout the nervous system and periphery. In this study, we investigated the transcriptional dynamics of the medial preoptic area (MPOA), a critical region for reproductive behavior, using single-cell RNA sequencing (scRNA-seq) and in situ hybridization techniques in male and female mice throughout adolescence development. Our findings reveal that estrogen receptor 1 (Esr1) plays a pivotal role in the transcriptional maturation of GABAergic neurons within the MPOA during adolescence. Deletion of the estrogen receptor gene, Esr1, in GABAergic neurons (Vgat+) disrupted the developmental progression of mating behaviors in both sexes, while its deletion in glutamatergic neurons (Vglut2+) had no observable effect. In males and females, these neurons displayed distinct transcriptional trajectories, with hormone-dependent gene expression patterns emerging throughout adolescence and regulated by Esr1. Esr1 deletion in MPOA GABAergic neurons, prior to adolescence, arrested adolescent transcriptional progression of these cells and uncovered sex-specific gene-regulatory networks associated with Esr1 signaling. Our results underscore the critical role of Esr1 in orchestrating sex-specific transcriptional dynamics during adolescence, revealing gene regulatory networks implicated in the development of hypothalamic-controlled reproductive behaviors.

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 9 matches between paragraphs and lines of code.

theislab/paga

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d5688a38d9054c6fcea7302023afa0510cd3577a, 12 April 2019
Languages: Jupyter (20)
Size: 27 files, 20 scripts
Software Heritage: archived
Found in: the references
Holds: README, license file, 20 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (15 files), Scanpy (15 files), Matplotlib (14 files), pandas (14 files), SciPy (7 files), seaborn (5 files), Monocle 3 (4 files), NetworkX (3 files), scikit-learn (3 files), tidyverse (2 files), anndata (1 file), igraph (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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22 files
At the source: github.com/theislab/paga

stuberlab/Hashikawa-Hashikawa-eLife

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b46c819e2f231da93b0ccce6f367fd33f865571b, 18 June 2026
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Size: 31 files, 29 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 29 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (13 files), tidyverse (13 files), ggplot2 (11 files), reshape2 (4 files), cowplot (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
14 files
At the source: github.com/stuberlab/Hashikawa-Hashikawa-eLife

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

Tracing map

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What the map holds:

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

Datasets cited

Data availability

The NCBI Gene Expression Omnibus accession number for the scRNAseq data reported in this paper for the reviewers is GEO: GSE172177. All the codes used to analyze scRNAseq, and HM-HCR FISH are available at a GithubGitHub repository affiliated with Stuber Laboratory group and this manuscript title (https://github.com/stuberlab/Hashikawa-Hashikawa-eLife) copy archived at Hashikawa, 2022.

The following dataset was generated:

HashikawaK 2025scRNAseq to understand hormonal control on pubertal transcription in the medial preoptic areaNCBI Gene Expression OmnibusGSE172177

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 19 authors, 4 keywords, 11 MeSH terms, 3 funders, 70 references, 13 RRIDs.

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Hashikawa, K., Hashikawa, Y., Briones, B., Ishii, K. K., Liu, Y., Rossi, M., Basiri, M. L., Chen, J. Y., Ahmad, O., Mukundan, R., Johnston, N., Simon, R., Soetedjo, J., Siputro, J., McHenry, J., Palmiter, R. D., Rubinow, D., Zweifel, L. S., & Stuber, G. D. (2026). Esr1-dependent signaling and transcriptional maturation in the medial preoptic area of the hypothalamus shape the development of mating behavior during adolescence. eLife, 14, RP106347. https://doi.org/10.7554/elife.106347

BibTeX

@article{hashikawa2026esr1,
author = {Hashikawa, Koichi and Hashikawa, Yoshiko and Briones, Brandy and Ishii, Kentaro K and Liu, Yuejia and Rossi, Mark and Basiri, Marcus L and Chen, Jane Y and Ahmad, Omar and Mukundan, Rishi and Johnston, Nathan and Simon, Rhiana and Soetedjo, James and Siputro, Jason and McHenry, Jenna and Palmiter, Richard D and Rubinow, David and Zweifel, Larry S and Stuber, Garret D},
title = {{Esr1-dependent signaling and transcriptional maturation in the medial preoptic area of the hypothalamus shape the development of mating behavior during adolescence}},
journal = {eLife},
year = {2026},
month = jun,
volume = {14},
pages = {RP106347},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.106347},
url = {https://doi.org/10.7554/elife.106347},
pmid = {42318880},
pmcid = {PMC13282117}
}

RIS

TY - JOUR
AU - Hashikawa, Koichi
AU - Hashikawa, Yoshiko
AU - Briones, Brandy
AU - Ishii, Kentaro K
AU - Liu, Yuejia
AU - Rossi, Mark
AU - Basiri, Marcus L
AU - Chen, Jane Y
AU - Ahmad, Omar
AU - Mukundan, Rishi
AU - Johnston, Nathan
AU - Simon, Rhiana
AU - Soetedjo, James
AU - Siputro, Jason
AU - McHenry, Jenna
AU - Palmiter, Richard D
AU - Rubinow, David
AU - Zweifel, Larry S
AU - Stuber, Garret D
TI - Esr1-dependent signaling and transcriptional maturation in the medial preoptic area of the hypothalamus shape the development of mating behavior during adolescence
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/06/19
VL - 14
SP - RP106347
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.106347
UR - https://doi.org/10.7554/elife.106347
LA - en
ER -

CSL-JSON

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"author": [
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"volume": "14",
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"DOI": "10.7554/elife.106347",
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"issued": {
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