OSCR

Spatiotemporally resolved single-nucleus transcriptomics reveals cellular and regional vulnerability in the midbrain in Parkinson's disease.

Code ↔ Paper

5 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 5 matches
  1. [1] § Materials and methods › Cell-type annotation and differential expression analysis ↔ 02_Neuron/pseudobulk.R, lines 1 to 43 · score 0.88 · fitType, glmGamPoi, useT, DESeq2, LRT, Scran · Report a mismatch
  2. [2] § Results › Neuronal heterogeneity and vulnerable dopaminergic subtypes in the PD substantia nigra ↔ 02_Neuron/Step4_SNpc_PD_data_neuron_cluster_anz_norm.ipynb, lines 335 to 336 · score 0.72 · SLC17A7, SLC6A1, ALDH1A1, SLC6A3, PVALB, GAD1 · Report a mismatch
  3. [3] § Materials and methods › Gene regulatory network analysis ↔ 02_Neuron/SCENIC_process_and_comparison_plot.R, lines 65 to 147 · score 0.55 · regulon activity, SCENIC, Networks, TF, RSS, scores · Report a mismatch
  4. [4] § Results › Neuronal heterogeneity and vulnerable dopaminergic subtypes in the PD substantia nigra ↔ 02_Neuron/Step4_SNpc_PD_data_neuron_cluster_anz_norm.ipynb, lines 371 to 372 · score 0.54 · SEMA6D, SLC18A2, KLHL1, TTC6, neurons, PD · Report a mismatch
  5. [5] § Materials and methods › Gene ontology enrichment analysis ↔ 01_Preprocessing/zvalue_exp_heatmap.R, lines 1 to 72 · score 0.53 · log2 fold change, filtered, components, DEGs, clusters, genes · Report a mismatch

Paper

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The authors' code

Jupyter notebook · 791 lines · 35 KB · no license · 2 matches

  1. # %%
  2. library(Seurat)
  3. library(SeuratObject)
  4. library(ggplot2)
  5. library(magrittr)
  6. library(dplyr)
  8. # %%
  9. load('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step3_seurat_anz/SNU_PD_20221204_QC_END.RData')
  11. # %% [markdown]
  12. # ### Neuron
  14. # %%
  15. neuron <- subset(snu_pd_data,cluster_names==c("Neuron"))
  17. # %%
  18. DimPlot(neuron,cols = c("grey"))
  20. # %%
  21. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4/SNU_neuron_umap_raw.png',width=200,height=200,units="mm")
  23. # %% [markdown]
  24. # #### reclustering
  26. # %%
  27. DefaultAssay(neuron) <- 'integrated'
  29. # %%
  30. ElbowPlot(neuron,ndims = 50)
  32. # %%
  33. neuron <- RunPCA(neuron, features = VariableFeatures(object = neuron))
  35. # %%
  36. neuron <- FindNeighbors(neuron, dims = 1:20)
  37. neuron <- FindClusters(neuron, resolution = 1.5)
  38. neuron <- RunUMAP(neuron, reduction = "pca", dims = 1:20)
  39. neuron <- RunTSNE(neuron, dims = 1:20)
  41. # %%
  42. DimPlot(neuron)
  44. # %%
  45. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_umap.png',width=200,height=200,units="mm")
  47. # %%
  48. DimPlot(neuron,reduction = 'tsne',pt.size = 3,label.size = 6) + NoLegend()
  50. # %%
  51. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_tsne.png',width=200,height=200,units="mm")
  53. # %%
  54. DimPlot(neuron,label = T)
  56. # %%
  57. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_umap_label_na.png',width=200,height=200,units="mm")
  59. # %%
  60. DimPlot(neuron,reduction = 'tsne',label = T)
  62. # %%
  63. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_tsne_label_na.png',width=200,height=200,units="mm")
  65. # %% [markdown]
  66. # #### attach tmp name
  68. # %%
  69. P_levels <- c('3','5','9','2','7','10','4','0','8','6','1')
  71. # %%
  72. Idents(neuron) <- factor(Idents(neuron), levels= P_levels)
  74. # %%
  75. cluster_names <- c('NC1','NC2','NC3','NC4','NC5','NC6','NC7','NC8','NC9','NC10','NC11')
  77. # %%
  78. names(cluster_names) <- levels(neuron)
  80. # %%
  81. neuron <- RenameIdents(neuron, cluster_names)
  83. # %%
  84. tmp_neuron = neuron
  85. group = tibble(Idents(neuron))
  86. colnames(group) = c("cluster_names")
  87. [email hidden] <- [email hidden][, -which(colnames([email hidden]) %in% 'cluster_names')]
  88. [email hidden] <- cbind([email hidden],group)
  90. # %%
  91. rm(tmp_neuron)
  92. rm(cluster_names)
  93. rm(group)
  94. rm(P_levels)
  96. # %%
  97. DimPlot(neuron,label = T,pt.size = 2,label.size = 5) + NoLegend()
  99. # %%
  100. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_umap_label.png',width=200,height=200,units="mm")
  102. # %%
  103. DimPlot(neuron,pt.size = 2) + NoLegend()
  105. # %%
  106. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_umap_n_label.png',width=200,height=200,units="mm")
  108. # %%
  109. DimPlot(neuron,label = T,split.by = c('patient_state')) + NoLegend()
  111. # %%
  112. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_umap_label_sp.png',width=200,height=200,units="mm")
  114. # %%
  115. DimPlot(neuron,reduction = 'tsne',label = T,pt.size = 3,label.size = 6) + NoLegend()
  117. # %%
  118. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_tsne_label.png',width=200,height=200,units="mm")
  120. # %%
  121. DimPlot(neuron,reduction = 'tsne',label = T,split.by = c('sample')) + NoLegend()
  123. # %%
  124. DimPlot(neuron,reduction = 'tsne',label = T,split.by = c('patient_state')) + NoLegend()
  126. # %%
  127. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/SNU_neuron_tsne_label_sp.png',width=400,height=200,units="mm")
  129. # %%
  130. save(neuron,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/SNU_neuron_tmp_20230209.RData')
  132. # %%
  133. load('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/SNU_neuron_tmp_20230209.RData')
  135. # %% [markdown]
  136. # #### check neruon cluster
  138. # %%
  139. DefaultAssay(neuron) <- 'RNA'
  140. Idents(neuron) <- 'cluster_names'
  141. neuron.markers_sc <- FindAllMarkers(neuron, min.pct = 0.25, logfc.threshold = 0.25)
  142. neuron.markers_sc %>%
  143. group_by(cluster) %>%
  144. slice_max(n = 5, order_by = avg_log2FC)-> top10_neuron_sc
  146. # %%
  147. neuron <- ScaleData(neuron, features = rownames(neuron))
  148. DoHeatmap(neuron, features = top10_neuron_sc$gene) +theme(text = element_text(size = 15))
  150. # %%
  151. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/heatmap_top10_genes_sc.png',width=400,height=400,units="mm")
  153. # %%
  154. write.csv(neuron.markers_sc,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/neruon_gene_sc.csv')
  155. write.csv(top10_neuron_sc,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/neruon_top10_gene_sc.csv')
  157. # %%
  158. DefaultAssay(neuron) <- 'RNA'
  159. Idents(neuron) <- 'patient_state'
  161. # %%
  162. neuron.markers_sp <- FindAllMarkers(neuron, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  163. neuron.markers_sp %>%
  164. group_by(cluster) %>%
  165. slice_max(n = 10, order_by = avg_log2FC)-> top10_neuron_sp
  167. # %%
  168. neuron <- ScaleData(neuron, features = rownames(neuron))
  169. DoHeatmap(neuron, features = top10_neuron_sp$gene) +theme(text = element_text(size = 15))
  171. # %%
  172. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/heatmap_top10_genes_sp.png',width=200,height=200,units="mm")
  174. # %%
  175. write.csv(neuron.markers_sp,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/neruon_gene_sp.csv')
  176. write.csv(top10_neuron_sp,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/neruon_top10_gene_sp.csv')
  178. # %%
  179. library(metap)
  181. # %%
  182. DefaultAssay(neuron) <- 'RNA'
  183. Idents(neuron) <- 'cluster_names'
  185. neuron.cm.markers1 <- FindConservedMarkers(neuron, ident.1= "NC1", grouping.var = "patient_state", verbose = FALSE)
  186. neuron.cm.markers2 <- FindConservedMarkers(neuron, ident.1= "NC2", grouping.var = "patient_state", verbose = FALSE)
  187. neuron.cm.markers3 <- FindConservedMarkers(neuron, ident.1= "NC3", grouping.var = "patient_state", verbose = FALSE)
  188. neuron.cm.markers4 <- FindConservedMarkers(neuron, ident.1= "NC4", grouping.var = "patient_state", verbose = FALSE)
  189. neuron.cm.markers5 <- FindConservedMarkers(neuron, ident.1= "NC5", grouping.var = "patient_state", verbose = FALSE)
  190. neuron.cm.markers6 <- FindConservedMarkers(neuron, ident.1= "NC6", grouping.var = "patient_state", verbose = FALSE)
  191. neuron.cm.markers7 <- FindConservedMarkers(neuron, ident.1= "NC7", grouping.var = "patient_state", verbose = FALSE)
  192. neuron.cm.markers8 <- FindConservedMarkers(neuron, ident.1= "NC8", grouping.var = "patient_state", verbose = FALSE)
  193. neuron.cm.markers9 <- FindConservedMarkers(neuron, ident.1= "NC9", grouping.var = "patient_state", verbose = FALSE)
  194. neuron.cm.markers10 <- FindConservedMarkers(neuron, ident.1= "NC10", grouping.var = "patient_state", verbose = FALSE)
  195. neuron.cm.markers11 <- FindConservedMarkers(neuron, ident.1= "NC11", grouping.var = "patient_state", verbose = FALSE)
  197. write.csv(neuron.cm.markers1,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N1.csv')
  198. write.csv(neuron.cm.markers2,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N2.csv')
  199. write.csv(neuron.cm.markers3,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N3.csv')
  200. write.csv(neuron.cm.markers4,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N4.csv')
  201. write.csv(neuron.cm.markers5,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N5.csv')
  202. write.csv(neuron.cm.markers6,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N6.csv')
  203. write.csv(neuron.cm.markers7,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N7.csv')
  204. write.csv(neuron.cm.markers8,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N8.csv')
  205. write.csv(neuron.cm.markers9,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N9.csv')
  206. write.csv(neuron.cm.markers10,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N10.csv')
  207. write.csv(neuron.cm.markers11,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ConservedMarker_result_list/neuron_gene_fcm_N11.csv')
  209. # %%
  210. DefaultAssay(neuron) <- 'RNA'
  211. Idents(neuron) <- 'cluster_names'
  213. NC1 <- subset(neuron,cluster_names==c("NC1"))
  214. NC2 <- subset(neuron,cluster_names==c("NC2"))
  215. NC3 <- subset(neuron,cluster_names==c("NC3"))
  216. NC4 <- subset(neuron,cluster_names==c("NC4"))
  217. NC5 <- subset(neuron,cluster_names==c("NC5"))
  218. NC6 <- subset(neuron,cluster_names==c("NC6"))
  219. NC7 <- subset(neuron,cluster_names==c("NC7"))
  220. NC8 <- subset(neuron,cluster_names==c("NC8"))
  221. NC9 <- subset(neuron,cluster_names==c("NC9"))
  222. NC10 <- subset(neuron,cluster_names==c("NC10"))
  223. NC11 <- subset(neuron,cluster_names==c("NC11"))
  225. # %%
  226. Idents(NC1) <- "patient_state"
  227. Idents(NC2) <- "patient_state"
  228. Idents(NC3) <- "patient_state"
  229. Idents(NC4) <- "patient_state"
  230. Idents(NC5) <- "patient_state"
  231. Idents(NC6) <- "patient_state"
  232. Idents(NC7) <- "patient_state"
  233. Idents(NC8) <- "patient_state"
  234. Idents(NC9) <- "patient_state"
  235. Idents(NC10) <- "patient_state"
  236. Idents(NC11) <- "patient_state"
  238. # %%
  239. NC1.markers_sp <- FindAllMarkers(NC1, min.pct = 0.25, logfc.threshold = 0.25)
  240. NC2.markers_sp <- FindAllMarkers(NC2, min.pct = 0.25, logfc.threshold = 0.25)
  241. NC3.markers_sp <- FindAllMarkers(NC3, min.pct = 0.25, logfc.threshold = 0.25)
  242. NC4.markers_sp <- FindAllMarkers(NC4, min.pct = 0.25, logfc.threshold = 0.25)
  243. NC5.markers_sp <- FindAllMarkers(NC5, min.pct = 0.25, logfc.threshold = 0.25)
  244. NC6.markers_sp <- FindAllMarkers(NC6, min.pct = 0.25, logfc.threshold = 0.25)
  245. NC7.markers_sp <- FindAllMarkers(NC7, min.pct = 0.25, logfc.threshold = 0.25)
  246. NC8.markers_sp <- FindAllMarkers(NC8, min.pct = 0.25, logfc.threshold = 0.25)
  247. NC9.markers_sp <- FindAllMarkers(NC9, min.pct = 0.25, logfc.threshold = 0.25)
  248. NC10.markers_sp <- FindAllMarkers(NC10, min.pct = 0.25, logfc.threshold = 0.25)
  249. NC11.markers_sp <- FindAllMarkers(NC11, min.pct = 0.25, logfc.threshold = 0.25)
  251. # %%
  252. write.csv(NC1.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC1.csv')
  253. write.csv(NC2.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC2.csv')
  254. write.csv(NC3.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC3.csv')
  255. write.csv(NC4.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC4.csv')
  256. write.csv(NC5.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC5.csv')
  257. write.csv(NC6.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC6.csv')
  258. write.csv(NC7.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC7.csv')
  259. write.csv(NC8.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC8.csv')
  260. write.csv(NC9.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC9.csv')
  261. write.csv(NC10.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC10.csv')
  262. write.csv(NC11.markers_sp,file = '/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/cluster_sp_gene_list/neuron_gene_sp_NC11.csv')
  264. # %%
  265. load('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step7_SCENIC_neuron/tot/SNU_neuron_20230308.RData')
  267. # %%
  268. DimPlot(neuron)
  270. # %%
  271. DefaultAssay(neuron) <- 'RNA'
  273. # %%
  274. Neuron_AE_scsp <- AverageExpression(neuron)
  276. # %%
  277. head(Neuron_AE_scsp)
  279. # %%
  280. write.csv(Neuron_AE_scsp$RNA,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/neuron_RNA_AE_scsp.csv')
  281. write.csv(Neuron_AE_scsp$integrated,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/neuron_IT_AE_scsp.csv')
  283. # %% [markdown]
  284. # #### check neuron marker genes
  286. # %%
  287. DefaultAssay(neuron) <- 'RNA'
  288. Idents(neuron) <- 'cluster_names'
  290. # %%
  291. VlnPlot(neuron,features = c('SYT1','KCNJ6','SYNPR','UNC13C'),pt.size = 0)
  293. # %%
  294. VlnPlot(neuron,pt.size = 0,features = c('PAX6','TH','SLC10A4','ALDH1A1','SLC6A3','RORB','NR2F2','GAD1','GAD2','GRIN1','GRIN2A','GRIN2B'),stack = TRUE,flip = TRUE,fill.by = 'ident',) + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  296. # %%
  297. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check1.png',width=300,height=300,units="mm")
  299. # %%
  300. VlnPlot(neuron,pt.size = 0,features = c('GAD1','GAD2','HTR2C','TH','SLC6A3','MEIS2','SLC17A7'),stack = TRUE,flip = TRUE,fill.by = 'ident',) + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  302. # %%
  303. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check2.png',width=300,height=300,units="mm")
  305. # %%
  306. VlnPlot(neuron,pt.size = 0,features = c('MEIS2','SLC17A7','TH','SLC6A3','GAD1','GAD2','HTR2C'),stack = TRUE,flip = TRUE,fill.by = 'ident',) + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  308. # %%
  309. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check3.png',width=300,height=300,units="mm")
  311. # %%
  312. VlnPlot(neuron,pt.size = 0,features = c('GAD1','GAD2','PAX2','SLC32A1','SLC6A1','GABBR2','GADD45B','VIP','PVALB'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  314. # %%
  315. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check3.png',width=300,height=300,units="mm")
  317. # %%
  318. VlnPlot(neuron,pt.size = 0,features = c('TH','SLC6A3','SLC17A7','SLC17A6','PITX3','SMAD3','NEUROD6','SLC18A3','SLC18A2','DDC'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  320. # %%
  321. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check4.png',width=300,height=300,units="mm")
  323. # %%
  324. VlnPlot(neuron,pt.size = 0,features = c('SLC6A4','TPH2','DDC','SLC18A2','ESM1','SLC22A3'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  326. # %%
  327. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check5.png',width=300,height=300,units="mm")
  329. # %%
  330. VlnPlot(neuron,pt.size = 0,features = c('GAD1','PVALB','VIP','FGF12','NXPH1','EOMES','KCNC2','CALB1','CCK','VSX2','EN1','ETV1'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  332. # %%
  333. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check6.png',width=300,height=300,units="mm")
  335. # %%
  336. VlnPlot(neuron,pt.size = 0,features = c('SLC17A7','GAD1','GAD2','SLC6A1','TH','ALDH1A1','SLC6A3','SLC18A2','PVALB'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  338. # %%
  339. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check7.png',width=300,height=300,units="mm")
  341. # %%
  342. VlnPlot(neuron,pt.size = 0,features = c('SLC17A7','GAD1','GAD2','SLC6A1','TH','ALDH1A1','SLC6A3','SLC18A2','PVALB'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  344. # %%
  345. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check8.png',width=300,height=300,units="mm")
  347. # %%
  348. VlnPlot(neuron,pt.size = 0,features = c('PAX6','TH','SLC10A4','ALDH1A1','SLC6A3','RORB','NR2F2','GAD1','GAD2','GRIN1','GRIN2A','GRIN2B'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  350. # %%
  351. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check9.png',width=300,height=300,units="mm")
  353. # %%
  354. VlnPlot(neuron,pt.size = 0,features = c('FLT3', 'MBP', 'CMYA5', 'MLIP', 'MSR1', 'MAL', 'SLC30A10', 'SLC18A2', 'FOXP2'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  356. # %%
  357. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check10.png',width=300,height=300,units="mm")
  359. # %%
  360. VlnPlot(neuron, feature = c('FAT2','ETV1','FAM160A1','ZIC1','PAX6','ADAMTS16','FSTL5','NEUROD1','LINC01798','DLK1','SLC6A3','SLC18A2','VAT1','SLC10A4','TH','SNCG','ALDH1A1'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()
  362. # %%
  363. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check11.png',width=300,height=600,units="mm")
  365. # %%
  366. VlnPlot(neuron, feature = c('TF','DOCK5','CTNNA3','ST18','MOBP','TENM2','RMST','POU6F2','MATN2','TBR1','MLIP','SATB2','KIAA1211L','CBLN2','FOXG1','RMST','GABRG3','PCDH11X'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()
  368. # %%
  369. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check12.png',width=300,height=600,units="mm")
  371. # %%
  372. VlnPlot(neuron, feature = c('SYTL5','HTR2C','KLHL1','TTC6','SLC18A2','ST8SIA6','SEMA6D','KCNJ6','DRD2','SCGN','TFAP2B','BHLHE22','CLMP','OTX2-AS1','GRIK1','RORB','GRIK1','GULP1','MEIS2','NR2F2','OTX2','PRSS12','LEF1','SYNDIG1L'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend()
  374. # %%
  375. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check13.png',width=300,height=600,units="mm")
  377. # %%
  378. VlnPlot(neuron, feature = c('FAT2','FAM160A1','NEUROD1','PAX6','SLC6A3','SLC18A2','ALDH1A1','DLK1','VAT1','SLC10A4','TH','TF','ST18','MOBP','TENM2','RMST','POU6F2','MATN2','TBR1','MLIP','SATB2','KIAA1211L','GABRG3','PCDH11X','HTR2C','KLHL1','TTC6','SLC18A2','KCNJ6','SCGN','TFAP2B','BHLHE22','CLMP','OTX2-AS1','GRIK1','GULP1','NR2F2','OTX2','LEF1','SYNDIG1L'),stack = TRUE,flip = TRUE,fill.by = 'ident') + NoLegend() + theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  380. # %%
  381. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check14.png',width=300,height=600,units="mm")
  383. # %%
  384. png('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/neuron_vlnplot_check_gene_ss.png',width=800,height=400)
  385. VlnPlot(neuron, features = c('SLC18A2', 'NELL1'),split.by = 'sample',pt.size = 0,stack = TRUE,flip = TRUE,fill.by = 'ident') + FontSize(x.text = 15)
  386. dev.off()
  388. # %%
  389. VlnPlot(neuron,pt.size = 0,features = c('FLT3', 'MBP', 'CMYA5', 'MLIP', 'MSR1', 'MAL', 'SLC30A10', 'SLC18A2', 'FOXP2'),stack = TRUE,flip = TRUE,fill.by = 'ident',split.by = 'patient_state',split.plot = TRUE) + NoLegend()+ theme(axis.text.x = element_text(size = 20),axis.text.y = element_text(size = 20))
  391. # %%
  392. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_gene_check15_scp.png',width=300,height=300,units="mm")
  394. # %%
  395. VlnPlot(neuron, feature = c('PAH','TH','TPST1','MAOA','MAOB','COMT','ALDH1A1','ALDH1A2','SKP1'), pt.size = 0,stack = TRUE,flip = TRUE,fill.by = 'ident',split.by = 'patient_state',split.plot = TRUE) + FontSize(x.text = 15)
  397. # %%
  398. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/vlnplot_DA_pathway_genes.png',width=200,height=200,units="mm")
  400. # %%
  401. DotPlot(neuron,features = c('SLC17A7','GAD1','GAD2','SLC6A1','TH','ALDH1A1','SLC6A3','SLC18A2','PVALB'),dot.scale = 15) + theme(axis.text.x = element_text(size = 13),axis.text.y = element_text(size = 20))
  403. # %%
  404. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/dotplot_gene_check15.png',width=400,height=300,units="mm")
  406. # %%
  407. DotPlot(neuron,features = c('GABRA1','GABRB1','GABRB2','GABRB3','GABRG2','GABRG3','SLC17A7','GAD2','GAD1','SLC6A1','TH','ALDH1A1','SLC6A3','SLC18A2','PVALB'),dot.scale = 10,scale.max = 80) + theme(axis.text.x = element_text(size = 10),axis.text.y = element_text(size = 10),text = element_text(face = "bold"))+ coord_flip()
  409. # %%
  410. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/dotplot_gene_check15.5_2.png',width=220,height=150,units="mm")
  412. # %%
  413. FeaturePlot(neuron,features = c('BCAS1', 'PTPRK', 'SPOCK2' ),split.by = c('patient_state'),reduction = 'tsne')
  415. # %%
  416. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/featureplot_gene_check16_sp.png',width=300,height=300,units="mm")
  418. # %%
  419. FeaturePlot(neuron,features = c('PLCG2', 'COQ4', 'ANKFN1', 'EN2' ),split.by = c('patient_state'),reduction = 'tsne')
  421. # %%
  422. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/featureplot_gene_check17_sp.png',width=300,height=300,units="mm")
  424. # %%
  425. FeaturePlot(neuron, reduction = "tsne", features = c('TH','ALDH1A1','SLC6A3','SLC18A2','GAD1','GAD2','SLC6A1','GRM5'), label=F, max.cutoff = 3,ncol = 4) & NoAxes() & theme(plot.title = element_text(size = 25))
  427. # %%
  428. png('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/monocle_featureplot_neuron_marker_genes.png',width=1600,height=800)
  429. FeaturePlot(neuron, reduction = "tsne", features = c('TH','ALDH1A1','SLC6A3','SLC18A2','GAD1','GAD2','SLC6A1','GRM5'), label=F, max.cutoff = 3,ncol = 4) & NoAxes() & theme(plot.title = element_text(size = 25))
  430. dev.off()
  432. # %%
  435. # %%
  436. NEUAE <- AverageExpression(object = neuron)
  438. # %%
  439. write.csv(NEUAE$RNA,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/neuron_RNA_AE_sc.csv')
  440. write.csv(NEUAE$integrated,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/neuron_integrated_AE_sc.csv')
  442. # %%
  445. # %%
  448. # %% [markdown]
  449. # ### integration with public data
  451. # %%
  452. library(rliger)
  453. library(SeuratWrappers)
  454. library(ggalluvial)
  456. # %%
  457. load('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/SNU_neuron_tmp_20230209.RData')
  459. # %%
  460. SNU_PDN <- neuron
  461. rm(neuron)
  463. # %%
  464. load("/home/Data_Drive_8TB_2/BIOSYS/Pub_data/Pub_pd_DA.Rdata")
  465. load("/home/Data_Drive_8TB_2/BIOSYS/Pub_data/Pub_pd_NDA.Rdata")
  467. # %%
  468. [email hidden]$reference = rep("SNU", 722)
  469. [email hidden]$reference = rep("PU", 22048)
  470. [email hidden]$reference = rep("PU", 91479)
  472. # %%
  473. TN_2raw <- merge(SNU_PDN, y= c(Pub_PD_DA,Pub_PD_NDA))
  475. # %%
  476. DefaultAssay(TN_2raw) <- 'RNA'
  478. # %%
  479. TN_2raw <- NormalizeData(TN_2raw)
  480. TN_2raw <- FindVariableFeatures(TN_2raw)
  481. TN_2raw <- ScaleData(TN_2raw, split.by = "reference", do.center = FALSE)
  482. TN_2raw <- RunOptimizeALS(TN_2raw, k = 20, lambda = 5, split.by = "reference")
  483. TN_2raw <- RunQuantileNorm(TN_2raw, split.by = "reference")
  484. TN_2raw <- RunUMAP(TN_2raw, dims = 1:ncol(TN_2raw[["iNMF"]]), reduction = "iNMF")
  486. # %%
  487. save(TN_2raw, file = "/home/Data_Drive_8TB_2/BIOSYS/Pub_data/Liger_PD_TN_snu_20230822.Rdata")
  489. # %%
  490. DimPlot(TN_2raw,label = T,raster=FALSE) + NoLegend()
  492. # %%
  493. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/umap_ligar_PU_SNU_raw.png',width=200,height=200,units="mm")
  495. # %%
  496. Idents(TN_2raw) <- "cluster_names"
  498. # %%
  499. DimPlot(TN_2raw,raster=FALSE)
  501. # %%
  502. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4/umap_ligar_PU_SNU.png',width=400,height=200,units="mm")
  504. # %%
  505. DimPlot(TN_2raw,raster=FALSE,label=T) + NoLegend()
  507. # %%
  508. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4/umap_ligar_PU_SNU_label.png',width=200,height=200,units="mm")
  510. # %%
  511. DimPlot(TN_2raw, split.by = c("cluster_names"),ncol = 3,raster=FALSE) + NoLegend()
  513. # %%
  514. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4/umap_ligar_PU_SNU_sc_label.png',width=400,height=400,units="mm")
  516. # %%
  517. DimPlot(TN_2raw, split.by = c("reference"),ncol = 2,raster=FALSE)
  519. # %%
  520. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4/umap_ligar_PU_SNU_sp.png',width=500,height=200,units="mm")
  522. # %%
  523. DimPlot(TN_2raw, split.by = c("reference"),ncol = 2,raster=FALSE,label = T) + NoLegend()
  525. # %%
  526. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4/umap_ligar_PU_SNU_sp_label.png',width=400,height=200,units="mm")
  528. # %% [markdown]
  529. # ### Do alluvial plot
  531. # %%
  532. load("/home/Data_Drive_8TB_2/BIOSYS/Pub_data/Liger_PD_TN_snu_20230822.Rdata")
  534. # %%
  535. TN_2raw_snu <- subset(TN_2raw,reference==c("SNU"))
  536. TN_2raw_pu <- subset(TN_2raw,reference==c("PU"))
  538. # %%
  539. a <- [email hidden]$cluster_names
  540. b <- [email hidden]$clusters
  541. c <- [email hidden]$reference
  543. # %%
  544. matrix_raw = data.frame(a, b, c)
  545. colnames(matrix_raw) = c("cluster_names","clusters","reference")
  546. rm(a,b,c)
  548. # %%
  549. write.csv(matrix_raw,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ligar/matrix_raw.csv')
  551. # %%
  552. matrix_raw_snu <- subset(matrix_raw,reference==c("SNU"))
  553. matrix_raw_pu <- subset(matrix_raw,reference==c("PU"))
  555. # %%
  556. write.csv(matrix_raw_snu,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ligar/matrix_raw_snu.csv')
  557. write.csv(matrix_raw_pu,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ligar/matrix_raw_pu.csv')
  559. # %%
  560. TN_2raw<-RunPCA(TN_2raw)
  561. TN_NH<-FindNeighbors(TN_2raw,k.param = 100,return.neighbor=TRUE)
  562. TN_NH_P <- data.frame()
  564. # %%
  565. for(i in 1:722){
  566. TN_NH_P <- rbind(TN_NH_P ,TopNeighbors(TN_NH[['RNA.nn']],cell=colnames(TN_2raw_snu)[i],n=100))
  567. }
  569. # %%
  570. colnames(TN_NH_P) = c(1:100)
  572. # %%
  573. write.csv(TN_NH_P,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/TN_Neighbors_top.csv')
  575. # %%
  576. TN_NH_M<-data.frame()
  578. # %%
  579. for(i in 1:722){
  580. TN_NH_M <- rbind(TN_NH_M,grep('^pPD',TN_NH_P[i,],value=TRUE)[1])
  581. }
  583. # %%
  584. colnames(TN_NH_M) = c("1")
  586. # %%
  587. write.csv(TN_NH_M,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/TN_Neighbors_PU_top.csv')
  589. # %%
  590. TN_NH_M_list<-TN_NH_M[,1]
  592. # %%
  593. rm(TN_2raw_snu)
  594. rm(TN_2raw)
  596. # %%
  597. write.csv(TN_2raw_pu$cluster_names,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/test_cluster.csv')
  599. # %%
  600. TN_PU_CN <- read.csv('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/test_cluster.csv')
  602. # %%
  603. TN_PU_CN_list=data.frame()
  605. # %%
  606. for (i in 1:722) {
  607. for (j in 1:113527) {
  608. if(TN_NH_M_list[i] == TN_PU_CN[,1][j]){
  609. TN_PU_CN_list<-rbind(TN_PU_CN_list,TN_PU_CN[,2][j])}
  610. }
  611. }
  613. # %%
  614. write.csv(TN_PU_CN_list,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/TN_Neighbors_matching_cell_name.csv')
  616. # %% [markdown]
  617. # ##### in this time, make file for plot by excel. next time i ll try ro r
  619. # %%
  620. test <- c("NC1","NC2","NC3","NC4","NC5","NC6","NC7","NC8","NC9","NC10","NC11")
  621. test2 <- c("Ligar_1","Ligar_2","Ligar_3","Ligar_4","Ligar_5","Ligar_6","Ligar_7","Ligar_8","Ligar_9","Ligar_10","Ligar_11","Ligar_12","Ligar_13","Ligar_14","Ligar_15","Ligar_16","Ligar_17","Ligar_18","Ligar_19","Ligar_20")
  622. test3 <- c("SOX6_AGTR1","SOX6_DDT","SOX6_GFRA2","SOX6_PART1","CALB1_TRHR","CALB1_CALCR","CALB1_PPP1R17","CALB1_CRYM_CCDC68","CALB1_GEM","CALB1_RBP4" ,"Ex_PPP1R1C","Ex_CYP2J2","Ex_SATB2","Ex_LAMP5_NTNG2" ,"Ex_LAMP5_BAIAP3","Ex_VWA5B1_CALB1","Ex_POSTN","Ex_OPRD1","Ex_EBF2_CTC-552D5.1","Ex_MYO5B","Inh_PAX5_VCAN","Inh_OTX2_CASR","Inh_IGFBP5","Inh_PAX5_CCBE1","Inh_PRLR_RP11-384J4.2","Inh_SIX3","Inh_INHBA")
  624. # %%
  625. a<-data.frame()
  626. for (i in 1:11) {
  627. for (j in 1:540) {
  628. a <- rbind(a,test[i])
  629. }
  630. }
  632. # %%
  633. b<-data.frame()
  634. for (i in 1:11) {
  635. for (l in 1:20) {
  636. for (j in 1:27) {
  637. b <- rbind(b,test2[l])
  638. }
  639. }
  640. }
  642. # %%
  643. c<-data.frame()
  644. for (j in 1:220) {
  645. for (i in 1:27) {
  646. c <- rbind(c,test3[i])
  647. }
  648. }
  650. # %%
  651. liger_list <- cbind(a,b,c)
  652. colnames(liger_list) = c("Snu_cluster","Ligar_cluster","PU_cluster")
  654. # %% [markdown]
  655. # #### cluster_number_check
  657. # %%
  658. TN_FN<-read.csv('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ligar/matrix_raw_snu_pu_for_ligar.csv')
  660. # %%
  661. TN_FN_cnsnu <- TN_FN$cluster_names_snu
  662. TN_FN_lc <- TN_FN$clusters
  663. TN_FN_cnpu <- TN_FN$cluster_names_pu
  665. # %%
  666. matrix_TNF = data.frame(TN_FN_cnsnu, TN_FN_lc, TN_FN_cnpu)
  667. rm(TN_FN_cnsnu, TN_FN_lc, TN_FN_cnpu)
  669. # %%
  670. TR<-data.frame()
  671. for(i in 1:5940){
  672. TR<-rbind(TR,paste(liger_list$Snu_cluster[i],liger_list$Ligar_cluster[i],liger_list$PU_cluster[i]))
  673. }
  675. # %%
  676. TR2<-data.frame()
  677. for(i in 1:722){
  678. TR2<-rbind(TR2,paste(matrix_TNF$TN_FN_cnsnu[i],matrix_TNF$TN_FN_lc[i],matrix_TNF$TN_FN_cnpu[i]))
  679. }
  681. # %%
  682. d<-data.frame()
  683. for(i in 1:5940){
  684. d<-rbind(d,length(which(matrix_TNF$TN_FN_cnsnu==liger_list$Snu_cluster[i]&matrix_TNF$TN_FN_lc==liger_list$Ligar_cluster[i]&matrix_TNF$TN_FN_cnpu==liger_list$PU_cluster[i])))
  685. }
  687. # %%
  688. liger_list <- cbind(liger_list,d)
  689. colnames(liger_list) = c("Snu_cluster","Ligar_cluster","PU_cluster","Freq")
  690. write.csv(liger_list,file = '/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ligar/End_matrix_file_for_ligar.csv')
  692. # %% [markdown]
  693. # #### draw plot
  695. # %%
  696. matrix_rpf_snu<-liger_list
  697. #matrix_rpf_snu<-read.csv('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/SC_ANZ_Result_data/step4_neuron/gene_list/ligar/End_matrix_file_for_ligar.csv')
  699. # %%
  700. ggplot(matrix_rpf_snu,aes(y = Freq, axis1 = Snu_cluster, axis2 = Ligar_cluster, axis3 = PU_cluster)) +
  701. geom_alluvium(aes(fill=Ligar_cluster),width = 1/20,show.legend = FALSE) +
  702. geom_stratum(width = 1/12, color = 'grey') +
  703. geom_text(stat = "stratum", aes(label = after_stat(stratum)), size= 8) +
  704. scale_x_discrete(limits = c("Snu_cluster","Ligar_cluster","PU_cluster"), expand = c(.1, .1))+
  705. theme_void()
  707. # %%
  708. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/riverplot_ligar_pu_snu_neuron.png',width=1200,height=1000,units="mm")
  710. # %% [markdown]
  711. # #### neuron cluster proporation
  713. # %%
  714. length(which([email hidden]$patient_state=='CON'))
  715. length(which([email hidden]$patient_state=='PD'))
  717. # %%
  718. length(which([email hidden]$cluster_names=='NC1'&[email hidden]$patient_state=='CON'))
  719. length(which([email hidden]$cluster_names=='NC2'&[email hidden]$patient_state=='CON'))
  720. length(which([email hidden]$cluster_names=='NC3'&[email hidden]$patient_state=='CON'))
  721. length(which([email hidden]$cluster_names=='NC4'&[email hidden]$patient_state=='CON'))
  722. length(which([email hidden]$cluster_names=='NC5'&[email hidden]$patient_state=='CON'))
  723. length(which([email hidden]$cluster_names=='NC6'&[email hidden]$patient_state=='CON'))
  724. length(which([email hidden]$cluster_names=='NC7'&[email hidden]$patient_state=='CON'))
  725. length(which([email hidden]$cluster_names=='NC8'&[email hidden]$patient_state=='CON'))
  726. length(which([email hidden]$cluster_names=='NC9'&[email hidden]$patient_state=='CON'))
  727. length(which([email hidden]$cluster_names=='NC10'&[email hidden]$patient_state=='CON'))
  728. length(which([email hidden]$cluster_names=='NC11'&[email hidden]$patient_state=='CON'))
  730. # %%
  731. length(which([email hidden]$cluster_names=='NC1'&[email hidden]$patient_state=='PD'))
  732. length(which([email hidden]$cluster_names=='NC2'&[email hidden]$patient_state=='PD'))
  733. length(which([email hidden]$cluster_names=='NC3'&[email hidden]$patient_state=='PD'))
  734. length(which([email hidden]$cluster_names=='NC4'&[email hidden]$patient_state=='PD'))
  735. length(which([email hidden]$cluster_names=='NC5'&[email hidden]$patient_state=='PD'))
  736. length(which([email hidden]$cluster_names=='NC6'&[email hidden]$patient_state=='PD'))
  737. length(which([email hidden]$cluster_names=='NC7'&[email hidden]$patient_state=='PD'))
  738. length(which([email hidden]$cluster_names=='NC8'&[email hidden]$patient_state=='PD'))
  739. length(which([email hidden]$cluster_names=='NC9'&[email hidden]$patient_state=='PD'))
  740. length(which([email hidden]$cluster_names=='NC10'&[email hidden]$patient_state=='PD'))
  741. length(which([email hidden]$cluster_names=='NC11'&[email hidden]$patient_state=='PD'))
  743. # %%
  744. total_file <- read.table("/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/proportion_data/neuron_cell_proportion_2.txt", sep="\t", header = T, as.is = T, check.names = F)
  746. # %%
  747. ofp <- ggplot(data=total_file, aes(x=cohort,y=cell_population,fill=factor(cluster_name)),colo) + theme(text = element_text(size = 20)) + geom_bar(colour="black",stat='identity', width = 0.5,position="fill")+ scale_fill_manual("legend", values = c('NC1'= '#F8766D','NC2' = '#DB8E00','NC3' = '#AEA200','NC4' = '#64B200','NC5' = '#00BD5C','NC6' = '#00C1A7','NC7' = '#00BADE','NC8' = '#00A6FF','NC9' = '#B385FF','NC10' = '#EF67EB','NC11' = '#FF63B6')) + ggtitle(" ") + xlab(" ") + ylab(" ") + labs(fill = " ")+ theme_bw()+ theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  748. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) + scale_y_continuous(expand = c(0, 0)) + theme(text = element_text(size = 15,face = 'bold')) #+ theme(legend.position="top")
  749. ofp
  751. # %%
  752. P_levels <- c('NC1','NC2','NC3','NC4','NC5','NC6','NC7','NC8','NC9','NC10','NC11')
  754. # %%
  755. ofp$data$cluster_name <- factor(ofp$data$cluster_name, levels = P_levels)
  756. ofp
  758. # %%
  759. ggsave('/home/Data_Drive_8TB_2/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/cell_proporation_tmp_graph.png',width=200,height=200,units="mm")
  761. # %%
  762. ofp <- ggplot(data=total_file, aes(x=cluster_name,y=cell_population,fill=factor(cohort))) + geom_bar(colour="black",stat='identity', width = 0.7,position="fill") + ggtitle(" ") + xlab(" ") + ylab(" ") + labs(fill = " ") + theme_bw()+ theme(panel.border = element_blank(), panel.grid.major = element_blank(),
  763. panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) + scale_y_continuous(expand = c(0, 0)) + theme(text = element_text(size = 6)) #+ theme(legend.position="top")
  764. ofp
  766. # %%
  767. factor(ofp$data$cluster_name)
  769. # %%
  770. P_levels <- c('NC1','NC2','NC3','NC4','NC5','NC6','NC7','NC8','NC9','NC10','NC11')
  772. # %%
  773. ofp$data$cluster_name <- factor(ofp$data$cluster_name, levels = P_levels)
  774. ofp
  776. # %%
  777. ggsave('/Volumes/BIOSYS/SNUH_PD_Raw/SubNa_SNUH/Result_image/step4_neuron/cell_proporation_graph_total.png',width=100,height=60,units="mm")
  779. # %% [markdown]
  780. # #### neuron cluster colors
  782. # %%
  783. require(scales)
  785. identities <- levels(neuron$seurat_clusters)
  786. my_color_palette <- hue_pal()(length(identities))
  788. # %%
  789. identities <- levels(neuron$seurat_clusters)
  790. my_color_palette <- hue_pal()(length(identities))
  791. my_color_palette

Step4_SNpc_PD_data_neuron_cluster_anz_norm.ipynb at commit e99fd06, no license · at the source

Overview

Authors: Sooyeon Yoo1,2, Kwanghoon Lee1,2, Junseo Seo3, Hwisoo Choi4, Seong-Ik Kim1,2, Junyoung Chang4, Yu-Mi Shim1,2, Junil Kim3,4, Jae-Kyung Won1,2, Sung-Hye Park1,2,5
ORCID iDs: Sooyeon Yoo, Kwanghoon Lee, Junseo Seo, Hwisoo Choi, Seong-Ik Kim, Junyoung Chang, Yu-Mi Shim, Junil Kim, Jae-Kyung Won, Sung-Hye Park
  1. Department of Pathology, Seoul National University Hospital, College of Medicine,Seoul, Republic of Korea
  2. Brain Bank, Seoul National University Hospital,Seoul, Republic of Korea
  3. School of Systems Biomedical Science, Soongsil University,Seoul, Republic of Korea
  4. Department of Bioinformatics, Soongsil University,Seoul, Republic of Korea
  5. Neuroscience Research Institute, Seoul National University, College of Medicine,Seoul, Republic of Korea
Institutions: Seoul National University Hospital (South Korea); Soongsil University (South Korea); Seoul National University (South Korea)
Journal: Signal transduction and targeted therapy, volume 11, issue 1, article 468
Dates: received 12 August 2025; accepted 11 May 2026; published online 26 October 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41392-026-02794-2 · PMID 42928249 · PMCID PMC13696728 · OpenAlex W7223560604
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), Parkinson's (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Molecular neuroscience, Diseases of the nervous system
MeSH: Mesencephalon*, Parkinson Disease*, Transcriptome*, Aged, alpha-Synuclein, Astrocytes, Dopaminergic Neurons, Female, Gene Regulatory Networks, Genome-Wide Association Study, Humans, Lewy Body Disease, Male, Microglia, Middle Aged, Substantia Nigra (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Research of Korea Disease Control and Prevention Agency (grant number: 2023-ER1005-01), Korean Ministry of Health and Welfare's Program for Institution of Corpse-derived substance supply (grant number: 2024-437); Ministry of Health & Welfare and Ministry of Science and ICT (MSIT), Republic of Korea (grant number: RS-2022-KH127042); National Research Foundation of Korea (NRF) grant funded by the Korea government MSIT (grant number: RS-2022-NR072380)
Citations: not cited yet (Europe PMC); 65 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.

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whistle-ch0i/SNUH_PD_analysis

License: none: the authors keep all their rights
State: the link answers, verified on 27 October 2026
Evidence: files inventoried
Commit: e99fd06b025ff019d197d80a9a5857b769dfeca1, 23 December 2025
Languages: Jupyter (21), R (18), Python (4)
Size: 57 files, 43 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, 21 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), Seurat (7 files), ggplot2 (6 files), igraph (3 files), Monocle 3 (3 files), pheatmap (3 files), pandas (2 files), patchwork (2 files), anndata (1 file), circlize (1 file), ComplexHeatmap (1 file), DESeq2 (1 file), NumPy (1 file), Scanpy (1 file), scVelo (1 file)
Availability: 1 check, the latest on 27 October 2026: the link answers
  • 27 October 2026: the link answers
10 files
At the source: github.com/whistle-ch0i/SNUH_PD_analysis

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

Tracing map

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Version 1, 27 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 16 MeSH terms, 3 funders, 64 references.

Cite

This paper

Yoo, S., Lee, K., Seo, J., Choi, H., Kim, S.-I., Chang, J., Shim, Y.-M., Kim, J., Won, J.-K., & Park, S.-H. (2026). Spatiotemporally resolved single-nucleus transcriptomics reveals cellular and regional vulnerability in the midbrain in Parkinson's disease. Signal transduction and targeted therapy, 11(1), 468. https://doi.org/10.1038/s41392-026-02794-2

BibTeX

@article{yoo2026spatiotemporally,
author = {Yoo, Sooyeon and Lee, Kwanghoon and Seo, Junseo and Choi, Hwisoo and Kim, Seong-Ik and Chang, Junyoung and Shim, Yu-Mi and Kim, Junil and Won, Jae-Kyung and Park, Sung-Hye},
title = {{Spatiotemporally resolved single-nucleus transcriptomics reveals cellular and regional vulnerability in the midbrain in Parkinson's disease}},
journal = {Signal transduction and targeted therapy},
year = {2026},
month = oct,
volume = {11},
number = {1},
pages = {468},
publisher = {Nature Publishing Group},
issn = {2095-9907},
doi = {10.1038/s41392-026-02794-2},
url = {https://doi.org/10.1038/s41392-026-02794-2},
pmid = {42928249},
pmcid = {PMC13696728}
}

RIS

TY - JOUR
AU - Yoo, Sooyeon
AU - Lee, Kwanghoon
AU - Seo, Junseo
AU - Choi, Hwisoo
AU - Kim, Seong-Ik
AU - Chang, Junyoung
AU - Shim, Yu-Mi
AU - Kim, Junil
AU - Won, Jae-Kyung
AU - Park, Sung-Hye
TI - Spatiotemporally resolved single-nucleus transcriptomics reveals cellular and regional vulnerability in the midbrain in Parkinson's disease
T2 - Signal transduction and targeted therapy
J2 - Signal Transduct Target Ther
PY - 2026
DA - 2026/10/26
VL - 11
IS - 1
SP - 468
SN - 2095-9907
PB - Nature Publishing Group
DO - 10.1038/s41392-026-02794-2
UR - https://doi.org/10.1038/s41392-026-02794-2
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41392-026-02794-2",
"type": "article-journal",
"title": "Spatiotemporally resolved single-nucleus transcriptomics reveals cellular and regional vulnerability in the midbrain in Parkinson's disease",
"container-title": "Signal transduction and targeted therapy",
"author": [
{
"family": "Yoo",
"given": "Sooyeon"
},
{
"family": "Lee",
"given": "Kwanghoon"
},
{
"family": "Seo",
"given": "Junseo"
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{
"family": "Choi",
"given": "Hwisoo"
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{
"family": "Kim",
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{
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{
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"container-title-short": "Signal Transduct Target Ther",
"volume": "11",
"issue": "1",
"page": "468",
"DOI": "10.1038/s41392-026-02794-2",
"PMID": "42928249",
"PMCID": "PMC13696728",
"ISSN": "2095-9907",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41392-026-02794-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
26
]
]
}
}

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