OSCR

A single-nucleus RNA-seq dataset of the colon in Pink1-deficient and wild-type mice.

Code ↔ Paper

7 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 7 matches
  1. [1] § Technical Validation › Anchor-based label transfer and validation ↔ Code.R, lines 361–448 · score 0.89 · FindTransferAnchors, MapQuery, reference.reduction, wt_pink1ko, LogNormalize, pcaproject
  2. [2] § Methods › Single-nucleus RNA-seq data processing and analysis ↔ Code.R, lines 1–63 · score 0.84 · FindNeighbors, IntegrateLayers, variable features, Seurat, QC, resolution
  3. [3] § Background & Summary ↔ Code.R, lines 201–258 · score 0.67 · intestinal epithelial cells, goblet cells, stem cells, colonocytes, clustering, genes
  4. [4] § Technical Validation › Anchor-based label transfer and validation ↔ Code.R, lines 260–310 · score 0.59 · Col1a1, Acta2, Lyz2, Pdgfra, Ptprc, fibroblasts
  5. [5] § Technical Validation › Anchor-based label transfer and validation ↔ Code.R, lines 739–816 · score 0.57 · decrease Gini, decrease accuracy, MDA, MDG, class, predicted
  6. [6] § Technical Validation › Anchor-based label transfer and validation ↔ Code.R, lines 739–816 · score 0.56 · Decrease Gini, Decrease Accuracy, Random forest, MDA, MDG, predicted cell
  7. [7] § Background & Summary ↔ Code.R, lines 201–258 · score 0.55 · intestinal epithelium, stem cells, DEGs, colonocytes, immune, gene

Paper

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The authors' code

R · 816 lines · 28 KB · CC-BY-4.0 · 7 matches

  1. library(Seurat)
  2. library(SeuratObject)
  3. library(dplyr)
  4. library(Matrix)
  5. library(ggplot2)
  6. library(patchwork)
  8. # Loading datasets
  9. WT_data <- Read10X(data.dir = "WT")
  10. WT <- CreateSeuratObject(counts = WT_data, project = "WT")
  12. PINK1_data <- Read10X(data.dir = "PINK1")
  13. PINK1 <- CreateSeuratObject(counts = PINK1_data, project = "PINK1")
  15. # Assigning the dataset metadata
  16. WT$dataset <- "WT"
  17. PINK1$dataset <- "PINK1"
  18. WT <- JoinLayers(WT)
  19. PINK1 <- JoinLayers(PINK1)
  21. # Merging datasets
  22. merged <- merge(WT, y = PINK1, add.cell.ids = c("WT", "PINK1"), project = "Gut_tissue")
  23. Idents(object = merged) <- "Gut_tissue"
  24. merged[["percent.mt"]] <- PercentageFeatureSet(merged, pattern = "^mt-")
  26. # QC metrics
  27. VlnPlot(merged, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
  28. merged <- subset(merged, subset = nFeature_RNA > 200 & nFeature_RNA < 3000 &
  29. nCount_RNA > 500 & nCount_RNA < 10000 &
  30. percent.mt < 10)
  31. FeatureScatter(merged, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", raster = FALSE)
  32. dim(WT)
  33. dim(merged)
  34. head(merged)
  35. # preprocessing
  36. merged <- NormalizeData(merged)
  37. merged <- FindVariableFeatures(merged)
  38. merged <- ScaleData(merged)
  39. merged <- RunPCA(merged)
  40. DimPlot(merged, reduction = "pca", group.by = "orig.ident") + ggtitle("PCA After Integration")
  41. ElbowPlot(merged)
  42. merged <- FindNeighbors(merged, dims = 1:20, reduction = "pca")
  43. merged <- FindClusters(merged, resolution = 2, cluster.name = "unintegrated_clusters")
  44. merged <- RunUMAP(merged, dims = 1:30, reduction = "pca", reduction.name = "umap.unintegrated")
  45. merged <- RunTSNE(merged, dims = 1:30, reduction = "pca", reduction.name = "tsne.unintegrated")
  47. DimPlot(merged, reduction = "umap.unintegrated", group.by = "orig.ident") + ggtitle("umap Before Integration")
  48. DimPlot(merged, reduction = "tsne.unintegrated", group.by = "orig.ident") +
  49. ggtitle("t-SNE Before Integration")
  51. merged <- IntegrateLayers(
  52. object = merged, method = CCAIntegration,
  53. orig.reduction = "pca", new.reduction = "integrated.cca",
  54. verbose = FALSE
  55. )
  58. merged <- FindNeighbors(merged, reduction = "integrated.cca", dims = 1:20)
  59. merged <- FindClusters(merged, resolution = 0.2, cluster.name = "cca_clusters")
  60. merged <- RunUMAP(merged, reduction = "integrated.cca", dims = 1:20, reduction.name = "umap.cca")
  61. DimPlot(merged, reduction = "umap.cca", group.by = "orig.ident") + ggtitle("umap After Integration")
  62. merged <- RunTSNE(merged, reduction = "integrated.cca", dims = 1:20, reduction.name = "tsne.cca")
  63. DimPlot(merged, reduction = "tsne.cca", group.by = "orig.ident") + ggtitle("tsne After Integration")
  65. # Create a vector of different resolutions
  66. #resolutions <- c(0.1, 0.15, 0.2, 0.25, 0.3, 0.5)
  68. # Loop through resolutions and find clusters at each level
  69. #for (res in resolutions) {
  70. #merged <- FindClusters(merged, resolution = res, cluster.name = paste0("res_", res))
  71. #}
  73. #library(patchwork) # For combining plots
  75. # Generate UMAP plots for different resolutions
  76. #plots <- lapply(resolutions, function(res) {
  77. #DimPlot(merged, reduction = "umap.cca", group.by = paste0("res_", res)) + ggtitle(paste("Resolution:", res))
  78. #})
  80. # Combine all plots
  81. #wrap_plots(plots, ncol = 2)
  83. #table(merged$seurat_clusters) # Default clustering (last applied resolution)
  84. #for (res in resolutions) {
  85. #print(table([email hidden][[paste0("res_", res)]]))
  86. #}
  89. DimPlot(merged, reduction = "umap.cca", label = TRUE)
  90. DimPlot(merged, reduction = "tsne.cca", label = TRUE)
  92. merged<-JoinLayers(merged)
  93. [email hidden]
  96. Idents(merged) <- "cca_clusters"
  98. # colors selection
  99. cluster_colors <- c(
  100. "#1f78b4", "#33a02c", "#e31a1c", "#ff7f00", "#6a3d9a",
  101. "#b15928", "#b2df8a", "#fb9a99", "#fdbf6f", "#cab2d6",
  102. "#a6cee3", "#ffff99", "#8c510a", "#d73027", "#4575b4",
  103. "#f46d43", "#74add1", "#d53e4f"
  104. )
  106. # UMAP for WT (Keeping Cluster Colors)
  107. p1 <- DimPlot(
  108. merged,
  109. reduction = "tsne.cca",
  110. group.by = "cca_clusters",
  111. cells = WhichCells(merged, expression = orig.ident == "WT"),
  112. cols = cluster_colors,
  113. pt.size = 0.1
  114. ) +
  115. ggtitle("WT") +
  116. theme_minimal() +
  117. theme(
  118. plot.title = element_text(size = 18, face = "bold"),
  119. legend.position = "right",
  120. axis.text = element_text(size = 14),
  121. axis.title = element_text(size = 16)
  122. )
  124. # UMAP for PINK1 KO (Keeping Cluster Colors)
  125. p2 <- DimPlot(
  126. merged,
  127. reduction = "tsne.cca",
  128. group.by = "cca_clusters",
  129. cells = WhichCells(merged, expression = orig.ident == "PINK1"),
  130. cols = cluster_colors,
  131. pt.size = 0.1
  132. ) +
  133. ggtitle("PINK1 KO") +
  134. theme_minimal() +
  135. theme(
  136. plot.title = element_text(size = 18, face = "bold"),
  137. legend.position = "right",
  138. axis.text = element_text(size = 14),
  139. axis.title = element_text(size = 16)
  140. )
  143. # Combining both plots side by side
  144. final_plot <- p1 + p2
  146. table(merged$cca_clusters, merged$orig.ident)
  148. DEGs <- FindAllMarkers(merged, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  149. View(DEGs)
  151. # Count total cells per condition (WT & PINK1 KO)
  152. total_cells <- table(merged$orig.ident)
  153. table(merged$orig.ident)
  155. # Count the number of cells in each cluster per condition
  156. cluster_counts <- as.data.frame(table(merged$cca_clusters, merged$orig.ident))
  158. # Rename columns
  159. colnames(cluster_counts) <- c("cca_clusters", "dataset", "cluster_counts")
  161. # Calculate relative abundance (% of total cells in each condition)
  162. cluster_counts <- cluster_counts %>%
  163. group_by(dataset) %>%
  164. mutate(Relative_Abundance = (cluster_counts / sum(cluster_counts)) * 100)
  167. head(cluster_counts)
  169. # Plotting the relative abundance of each cluster in WT vs. PINK1 KO
  170. ggplot(cluster_counts, aes(x = cca_clusters, y = Relative_Abundance, fill = dataset)) +
  171. geom_bar(stat = "identity", position = "dodge", width = 0.7) +
  172. theme_minimal() +
  173. labs(title = "",
  174. x = "Cluster",
  175. y = "Relative Abundance (%)") +
  176. theme(
  177. axis.text.x = element_text(angle = 45, hjust = 1, size = 14, face = "bold"),
  178. axis.text.y = element_text(size = 12, face = "bold"),
  179. axis.title = element_text(size = 14, face = "bold"),
  180. plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
  181. legend.title = element_text(size = 14, face = "bold"),
  182. legend.text = element_text(size = 12)
  183. ) +
  184. scale_fill_manual(values = c("WT" = "#1f78b4", "PINK1" = "#e31a1c"))
  186. VlnPlot(merged, features = c("Pink1", "Map2", "Syt1"), raster = FALSE, pt.size = 0, group.by = "cca_clusters")
  189. top_DEGs <- DEGs %>%
  190. group_by(cluster) %>%
  191. top_n(n = 3, wt = avg_log2FC) %>%
  192. pull(gene)
  194. print(top_DEGs)
  196. View(DEGs)
  197. # color palette
  198. custom_colors <- c("#66c2a5", "#fc8d62", "#8da0cb", "#e78ac3", "#a6d854",
  199. "#ffd92f", "#e5c494", "#b3b3b3", "#1f78b4", "#33a02c")
  201. # dot plot
  202. dot_plot <- DotPlot(merged, features = top_DEGs, cols = custom_colors, dot.scale = 6) +
  203. theme_minimal() +
  204. labs(title = "Top DEGs Across Clusters", x = "Clusters", y = "Genes") +
  205. theme(
  206. axis.text.x = element_text(angle = 45, hjust = 1, size = 9, face = "bold"),
  207. axis.text.y = element_text(size = 9, face = "bold"),
  208. plot.title = element_text(size = 14, face = "bold"),
  209. legend.position = "right"
  210. )
  212. print(dot_plot)
  214. ############ cell type assingnment
  215. merged$cell_type <- as.character(Idents(merged))
  217. merged$cell_type[merged$seurat_clusters == 0] <- "Stem cell"
  218. merged$cell_type[merged$seurat_clusters == 1] <- "Stem cell"
  219. merged$cell_type[merged$seurat_clusters == 2] <- "Goblet cells"
  220. merged$cell_type[merged$seurat_clusters == 3] <- "Goblet cells"
  221. merged$cell_type[merged$seurat_clusters == 4] <- "Colonocytes"
  222. merged$cell_type[merged$seurat_clusters == 5] <- "Myofibroblasts"
  223. merged$cell_type[merged$seurat_clusters == 6] <- "Pericytes"
  224. merged$cell_type[merged$seurat_clusters == 7] <- "Stem cell"
  225. merged$cell_type[merged$seurat_clusters == 8] <- "Intestinal epithelial cells"
  226. merged$cell_type[merged$seurat_clusters == 9] <- "Immune cells"
  227. merged$cell_type[merged$seurat_clusters == 10] <- "Immune cells"
  228. merged$cell_type[merged$seurat_clusters == 11] <- "Colonocytes"
  229. merged$cell_type[merged$seurat_clusters == 12] <- "Enteroendocrine cells"
  230. merged$cell_type[merged$seurat_clusters == 13] <- "Endothelial cells"
  231. merged$cell_type[merged$seurat_clusters == 14] <- "Endothelial cells"
  232. merged$cell_type[merged$seurat_clusters == 15] <- "Schwann cells"
  233. merged$cell_type[merged$seurat_clusters == 16] <- "Stem cell"
  234. merged$cell_type[merged$seurat_clusters == 17] <- "Schwann cells"
  236. Idents(merged) <- merged$cell_type
  238. my_colors <- c(
  239. "#E64B35", "#4DBBD5", "#00A087", "#3C5488", "#F39B7F", "#8491B4", "#91D1C2",
  240. "#DC0000", "#7E6148", "#B09C85", "#FFDC91", "#00A6D6", "#FF61CC", "#1B9E77",
  241. "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#A6CEE3",
  242. "#FB9A99", "#FDBF6F", "#CAB2D6", "#B3DE69", "#BC80BD", "#9E9AC8",
  243. "#8DD3C7", "#FCCDE5", "#D9D9D9", "#FFB3AB", "#A1D99B", "#B3CDE3", "#C49C94",
  244. "#F2B447", "#C7CEEA", "#FF6F91", "#88CCEE", "#DDCC77", "#A594F9" # NEW FINAL COLOR
  245. )
  247. umap_plot <- DimPlot(merged, reduction = "tsne.cca", label = FALSE, cols = cluster_colors) +
  248. theme_classic(base_size = 14) +
  249. theme(
  250. axis.text = element_blank(),
  251. axis.ticks = element_blank(),
  252. axis.title = element_blank(),
  253. legend.title = element_text(size = 12, face = "bold"),
  254. legend.text = element_text(size = 10)
  255. ) +
  256. ggtitle("")
  258. ggsave("wt_vs_pink1.png", plot = umap_plot, width = 5.1, height = 3.1, units = "in", dpi = 300, bg = "transparent")
  260. # Define Ordered Marker Genes
  261. features_ordered <- c(
  262. "Fcgbp", "Zg16", "Muc2", # Goblet Cells
  263. "Ptprc", "Lyz2", # Myeloid Immune Cells
  264. "Cd19", "Ms4a1", # Lymphoid Immune Cells
  265. "Plp1", "Ngfr", "Sox10", # Schwann Cells
  266. "Pecam1", "Lyve1", "Flt4", # Endothelial Cells
  267. "Chga", "Chgb", "Tph1", "Neurod1", # Enteroendocrine Cells
  268. "Col1a1", "Pdgfra", "Acta2", "Vim", # Fibroblasts
  269. "Mki67", "Pcna", # Proliferating Cells
  270. "Prom1", # Adult Stem Cell
  271. "Tfrc", "Clock", "Ccnd2", "Fut9", # Stem Cells
  272. "mt-Nd4", "Cox6a1", "Rplp2", # Mitochondria-Rich Cells
  273. "Tex9", "Cluh", # Migrating Cells
  274. "Slc26a3", "Slc15a1", "Epcam" # Colonocytes/Enterocytes
  275. )
  277. library(Seurat)
  278. library(ggplot2)
  280. # Define ordered marker genes
  281. features_ordered <- c(
  282. "Chga", "Neurod1", # Enteroendocrine
  283. "Lgr5", "Axin2", # Stem Cells
  284. "Prom1", "Car1", # Colonocytes
  285. "Slc15a1", "Epcam", # Enterocytes
  286. "Col1a1", "Pdgfra", # Fibroblasts
  287. "Zg16", "Muc2", # Goblet Cells
  288. "Pecam1", "Lyve1", # Endothelial
  289. "Ptprc", "Lyz2", # Immune Cells
  290. "Acta2", "Vim", # Myofibroblasts
  291. "Plp1", "Ngfr" # Schwann Cells
  292. )
  295. # Create DotPlot
  296. dot_plot <- DotPlot(merged, features = features_ordered) +
  297. RotatedAxis() +
  298. ggtitle("Marker Genes Across Gut Cell Types") +
  299. scale_color_gradientn(
  300. colors = c("#e0ecf4", "#9ecae1", "#fdae61", "#f46d43", "#a50026"), # Blue to red custom palette
  301. name = "Expression"
  302. ) +
  303. scale_size(range = c(1, 6), name = "% Cells") + # Adjust dot sizes
  304. theme_classic(base_size = 14) +
  305. theme(
  306. axis.text.x = element_text(angle = 45, hjust = 1, size = 10, face = "bold"),
  307. axis.text.y = element_text(size = 10, face = "bold"),
  308. plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
  309. panel.border = element_rect(color = "black", fill = NA, size = 1) # Add box line
  310. )
  313. # UMAP for WT
  314. p1 <- DimPlot(
  315. merged,
  316. reduction = "tsne.cca",
  317. cells = WhichCells(merged, expression = orig.ident == "WT"),
  318. cols = cluster_colors,
  319. pt.size = 0.1
  320. ) +
  321. ggtitle("WT") +
  322. theme_classic(base_size = 14) + # Clean base theme
  323. theme(
  324. plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
  325. axis.text = element_text(size = 12),
  326. axis.title = element_text(size = 14),
  327. legend.position = "none", # Hide legend
  328. panel.border = element_rect(color = "black", fill = NA, size = 1), # Add box line
  329. panel.grid = element_blank() # Remove grid lines
  330. )
  332. # UMAP for PINK1 KO
  333. p2 <- DimPlot(
  334. merged,
  335. reduction = "tsne.cca",
  336. cells = WhichCells(merged, expression = orig.ident == "PINK1"),
  337. cols = cluster_colors,
  338. pt.size = 0.1
  339. ) +
  340. ggtitle("PINK1 KO") +
  341. theme_classic(base_size = 14) +
  342. theme(
  343. plot.title = element_text(size = 18, face = "bold", hjust = 0.5),
  344. axis.text = element_text(size = 12),
  345. axis.title = element_text(size = 14),
  346. legend.position = "none",
  347. panel.border = element_rect(color = "black", fill = NA, size = 1),
  348. panel.grid = element_blank()
  349. )
  351. # Combine plots
  352. library(patchwork)
  353. final_plot <- p1 + p2
  354. final_plot
  356. ggsave("wt_vs_pink1_diff.png", plot = final_plot, width = 5.1, height = 2.8, units = "in", dpi = 300, bg = "transparent")
  358. library(dplyr)
  359. library(ggplot2)
  361. # Creating a data frame of counts
  362. metadata_df <- as.data.frame([email hidden])
  364. summary_df <- metadata_df %>%
  365. group_by(orig.ident, cell_type) %>%
  366. summarize(count = n(), .groups = "drop")
  368. # Calculating proportions
  369. summary_df <- summary_df %>%
  370. group_by(orig.ident) %>%
  371. mutate(proportion = count / sum(count))
  373. # colors for cell types
  374. my_colors <- cluster_colors
  376. # Stacked bar plot
  377. ggplot(summary_df, aes(x = orig.ident, y = proportion, fill = cell_type)) +
  378. geom_col(color = "black", size = 0.3) + # Thin black outline
  379. scale_y_continuous(labels = scales::percent_format()) +
  380. scale_fill_manual(values = my_colors) +
  381. labs(
  382. title = "",
  383. x = "Condition",
  384. y = "Cell Type Proportion",
  385. fill = "Cell Type"
  386. ) +
  387. theme_classic(base_size = 14) +
  388. theme(
  389. axis.text.x = element_text(angle = 45, hjust = 1, size = 12, face = "bold"),
  390. axis.text.y = element_text(size = 12),
  391. plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
  392. panel.border = element_rect(color = "black", fill = NA, size = 1) # Add box line
  393. )
  395. #####################################label transfer validation######################
  398. reference <- mouse_colon_male
  399. query <- wt_pink1ko
  402. reference <- NormalizeData(reference)
  403. reference <- FindVariableFeatures(reference)
  405. query <- NormalizeData(query)
  406. query <- FindVariableFeatures(query)
  408. anchors <- FindTransferAnchors(
  409. reference = reference,
  410. query = query,
  411. normalization.method = "LogNormalize",
  412. reduction = "pcaproject",
  413. reference.reduction = "pca"
  414. )
  416. reference <- RunUMAP(reference, reduction = "pca", dims = 1:20, return.model = T)
  418. reference[["umap.new"]] <- CreateDimReducObject(
  419. embeddings = reference[["umap"]]@cell.embeddings,
  420. key = "UMAPnew_", # Set the key with an underscore
  421. assay = "RNA"
  422. )
  424. Validation <- MapQuery(
  425. anchorset = anchors,
  426. query = query,
  427. reference = reference,
  428. refdata = list(celltype = reference$Annotation),
  429. reference.reduction = "pca",
  430. reduction.model = "umap"
  431. )
  433. colors <- c(
  434. "#e6194B", "#3cb44b", "#ffe119", "#4363d8", "#f58231", "#911eb4",
  435. "#46f0f0", "#f032e6", "#bcf60c", "#fabebe", "#008080", "#e6beff",
  436. "#9A6324", "#fffac8", "#800000", "#aaffc3", "#808000", "#ffd8b1",
  437. "#0000FF", "#FF1493", "#00FF7F", "#FF4500", "#1E90FF", "#FF00FF",
  438. "#ADFF2F", "#FF6347", "#20B2AA"
  439. )
  442. table(Validation$predicted.celltype) # See label distribution
  443. p1 <- DimPlot(Validation, group.by = "cell_type", reduction = "tsne.cca", label = FALSE, cols = colors) +
  444. ggtitle("Actual cell type")
  445. p2 <- DimPlot(Validation, group.by = "predicted.celltype", reduction = "tsne.cca", label = FALSE, cols = colors) +
  446. ggtitle("Predicted cell type")
  448. p1 + p2
  450. ########### validation dataet to show predictic score distrtibution in density plot
  451. library(ggplot2)
  452. library(viridis)
  453. # Create a data frame of all predicted cell type scores
  454. scores <- [email hidden]$predicted.celltype.score
  455. cell_types <- [email hidden]$predicted.celltype
  456. data <- data.frame(cell_type = cell_types, score = scores)
  458. # Set the y-axis scale limits to be between 0 and the maximum density value
  459. max_density <- max(density(data$score)$y)
  460. ylim_max <- max_density + max_density*0.2 # Set a little extra space at the top
  461. ggplot(data, aes(x = score, fill = cell_type)) +
  462. geom_density(alpha = 0.5) +
  463. xlab("Predicted cell type score") +
  464. ylab("Density") +
  465. ggtitle("Distribution of predicted cell type scores") +
  466. ylim(0, ylim_max)
  468. # Generate a slightly darker palette
  469. num_types <- length(unique(data$cell_type))
  470. palette_colors <- viridis(num_types, option = "C")
  472. # Create a histogram of all predicted cell type scores
  473. validation_score <- ggplot(data, aes(x = score, fill = cell_type)) +
  474. geom_histogram(alpha = 0.5, position = "identity", bins = 30) +
  475. xlab("Predicted cell type score") +
  476. ylab("Count") +
  477. scale_fill_manual(values = palette_colors) +
  478. labs(
  479. title = "Distribution of Predicted Cell Type Scores",
  480. x = "Predicted Cell Type Score",
  481. y = "Cell Count",
  482. fill = "Predicted Cell Type"
  483. ) +
  484. theme_classic(base_size = 14) + # No background, no grid
  485. theme(
  486. plot.title = element_text(face = "bold", size = 14, hjust = 0.5),
  487. axis.title = element_text(face = "bold"),
  488. axis.text = element_text(color = "black"),
  489. legend.title = element_text(face = "bold"),
  490. legend.key.size = unit(0.4, "cm"),
  491. legend.text = element_text(size = 10),
  492. legend.position = "right",
  493. panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.7) # Add outer box
  494. )
  495. ggsave("validation_score.png", plot = validation_score, width = 6.19, height = 3.42, units = "in", dpi = 300, bg = "transparent")
  498. ggplot(data, aes(x = score, fill = cell_type)) +
  499. geom_histogram(bins = 30, color = "black", fill = "#FFB3BA") +
  500. facet_wrap(~ cell_type, scales = "free_y", ncol = 5) +
  501. theme_minimal() +
  502. labs(
  503. title = "Predicted Cell Type Score Distribution per Cell Type",
  504. x = "Predicted Score", y = "Cell Count"
  505. ) +
  506. theme(strip.text = element_text(face = "bold"))
  508. table(Validation$predicted.celltype)
  510. table(Validation_high$predicted.celltype)
  512. Validation_high <- subset(Validation, subset = predicted.celltype.score > 0.5)
  513. table(Validation$predicted.celltype.score > 0.5)
  514. DimPlot(Validation_high, group.by = "predicted.celltype", reduction = "tsne.cca", label = FALSE, cols = colors) +
  515. ggtitle("Predicted cell type")
  517. p1 <- DimPlot(Validation_high, group.by = "cell_type", reduction = "tsne.cca", label = FALSE, cols = colors) +
  518. ggtitle("Actual cell type")
  519. p2 <- DimPlot(Validation_high, group.by = "predicted.celltype", reduction = "tsne.cca", label = TRUE, cols = colors) +
  520. ggtitle("Predicted cell type")
  522. p1 + p2
  524. table(Validation_high$predicted.celltype)
  525. table(Validation$predicted.celltype)
  528. library(randomForest)
  529. library(caret)
  530. library(pheatmap)
  531. library(ggplot2)
  532. library(multiROC)
  534. #1. data
  535. expr_mat <- GetAssayData(Validation_high, slot = "data") %>% as.matrix() %>% t()
  536. labels <- as.factor([email hidden]$predicted.celltype) # Use predicted label
  539. num_features <- 2000
  540. var_genes <- FindVariableFeatures(Validation_high, nfeatures = num_features) %>% VariableFeatures()
  541. expr_mat_reduced <- expr_mat[, var_genes]
  543. # 2. Build ML dataframe
  544. ml_data <- as.data.frame(expr_mat_reduced)
  545. ml_data$cell_type <- labels
  546. colnames(ml_data) <- make.names(colnames(ml_data))
  549. # Train/Test Split
  551. set.seed(123)
  552. trainIndex <- createDataPartition(ml_data$cell_type, p = 0.7, list = FALSE)
  553. train_data <- ml_data[trainIndex, ]
  554. test_data <- ml_data[-trainIndex, ]
  557. # Train RF Classifier
  559. set.seed(123)
  560. rf_model <- randomForest(cell_type ~ ., data = train_data, ntree = 500, importance = TRUE)
  561. # Predict on test set
  562. test_predictions <- predict(rf_model, newdata = test_data)
  563. test_probs <- predict(rf_model, newdata = test_data, type = "prob")
  565. # ----------------------------
  566. # Confusion Matrix & Enhanced Visualization
  567. # ----------------------------
  568. conf_mat <- confusionMatrix(test_predictions, test_data$cell_type)
  569. print(conf_mat)
  571. # Convert to matrix for heatmap
  572. cm <- conf_mat$table
  573. cm_df <- as.data.frame(cm)
  574. colnames(cm_df) <- c("Prediction", "Reference", "Freq")
  575. cm_df$Freq_log <- log1p(cm_df$Freq)
  577. # ggplot2 heatmap (log scale for low counts)
  578. ggplot(cm_df, aes(x = Reference, y = Prediction, fill = Freq_log)) +
  579. geom_tile(color = "grey90", linewidth = 0.3) +
  580. geom_text(aes(label = Freq), size = 3.5, color = "black") +
  581. scale_fill_gradientn(colors = c("white", "lightyellow", "orange", "red", "darkred"),
  582. name = "log(Freq+1)",
  583. guide = guide_colorbar(barwidth = 10, barheight = 1)) +
  584. labs(title = "Random Forest Confusion Matrix",
  585. x = "Actual Cell Type", y = "Predicted Cell Type") +
  586. theme_minimal(base_size = 13) +
  587. theme(
  588. axis.text.x = element_text(angle = 45, hjust = 1),
  589. plot.title = element_text(hjust = 0.5, face = "bold"),
  590. legend.position = "top"
  591. )
  593. # ----------------------------
  594. # Filter Confusion Matrix: remove all-zero rows/columns
  595. # ----------------------------
  597. # Extract raw confusion matrix
  598. cm <- conf_mat$table
  600. # Keep only classes that appear in both predicted and actual
  601. keep_classes <- intersect(rownames(cm)[rowSums(cm) > 0],
  602. colnames(cm)[colSums(cm) > 0])
  604. cm_filtered <- cm[keep_classes, keep_classes]
  606. # Convert to dataframe for ggplot
  607. cm_df <- as.data.frame(cm_filtered)
  608. colnames(cm_df) <- c("Prediction", "Reference", "Freq")
  609. cm_df$Freq_log <- log1p(cm_df$Freq)
  612. ggplot(cm_df, aes(x = Reference, y = Prediction, fill = Freq_log)) +
  613. geom_tile(color = "grey90", linewidth = 0.3) +
  614. geom_text(aes(label = Freq), size = 3.5, color = "black") +
  615. scale_fill_gradientn(colors = c("white", "grey90", "lightyellow","red", "darkred"),
  616. name = "log(Freq+1)",
  617. guide = guide_colorbar(barwidth = 10, barheight = 1)) +
  618. labs(title = "Random Forest Confusion Matrix",
  619. x = "Actual Cell Type", y = "Predicted Cell Type") +
  620. theme_minimal(base_size = 13) +
  621. theme(
  622. axis.text.x = element_text(angle = 45, hjust = 1),
  623. plot.title = element_text(hjust = 0.5, face = "bold"),
  624. legend.position = "top"
  625. )
  628. # Filtered cm_df is already prepared
  629. cm_df$Freq_log <- log1p(cm_df$Freq)
  631. random <- ggplot(cm_df, aes(x = Reference, y = Prediction, fill = Freq_log)) +
  632. # Draw tiles only where Freq > 0
  633. geom_tile(data = subset(cm_df, Freq > 0), color = "grey90", linewidth = 0.3) +
  634. # Add text labels only for nonzero cells
  635. geom_text(data = subset(cm_df, Freq > 0),
  636. aes(label = Freq), size = 3.5, color = "black") +
  637. scale_fill_gradientn(colors = c("white", "grey90", "lightyellow","red", "darkred"),
  638. name = "log(Freq+1)",
  639. guide = guide_colorbar(barwidth = 10, barheight = 1),
  640. na.value = "white") + # Make zero cells blank
  641. labs(title = "Random Forest Confusion Matrix",
  642. x = "Actual Cell Type", y = "Predicted Cell Type") +
  643. theme_minimal(base_size = 13) +
  644. theme(
  645. axis.text.x = element_text(angle = 45, hjust = 1),
  646. plot.title = element_text(hjust = 0.5, face = "bold"),
  647. legend.position = "top"
  648. )
  650. # assumes: cm_df with columns Reference, Prediction, Freq
  651. cm_df$Freq_log <- log1p(cm_df$Freq)
  653. random <- ggplot(subset(cm_df, Freq > 0),
  654. aes(x = Reference, y = Prediction, fill = Freq_log)) +
  655. geom_tile(color = "grey90", linewidth = 0.3) +
  656. scale_fill_gradientn(
  657. colors = c("white", "grey90", "lightyellow","red", "darkred"),
  658. name = "log(Freq+1)",
  659. guide = guide_colorbar(barwidth = 10, barheight = 1),
  660. na.value = "white"
  661. ) +
  662. coord_fixed(expand = FALSE) +
  663. labs(
  664. title = "Random Forest Confusion Matrix",
  665. x = "Actual Cell Type", y = "Predicted Cell Type"
  666. ) +
  667. theme_classic(base_size = 13) +
  668. theme(
  669. legend.position = "top",
  670. plot.title = element_text(hjust = 0.5, face = "bold"),
  671. axis.text.x = element_text(angle = 45, hjust = 1),
  672. panel.border = element_rect(color = "black", fill = NA, linewidth = 0.7) # boxed frame
  673. # no panel.grid in theme_classic(), so gridlines are gone
  674. )
  676. random
  678. ggsave("rf.png", plot = random, width = 6.00, height = 6.13, units = "in", dpi = 300, bg = "transparent")
  679. getwd()
  680. # Extract confusion matrix
  681. cm <- conf_mat$table
  683. # Calculate per-class accuracy (Recall)
  684. class_accuracy <- diag(cm) / rowSums(cm)
  686. # Remove NA (where row sum = 0, i.e., no true cells of that type)
  687. class_accuracy <- class_accuracy[!is.na(class_accuracy)]
  689. # Make dataframe
  690. acc_df <- data.frame(
  691. CellType = names(class_accuracy),
  692. Accuracy = class_accuracy
  693. )
  695. # Plot as horizontal barplot
  696. library(ggplot2)
  697. ggplot(acc_df, aes(x = reorder(CellType, Accuracy), y = Accuracy)) +
  698. geom_col(fill = "#9ABB50") +
  699. geom_text(aes(label = sprintf("%.2f", Accuracy)),
  700. hjust = -0.2, size = 3.5, color = "black") +
  701. scale_y_continuous(limits = c(0, 1.05), expand = c(0,0)) +
  702. coord_flip() +
  703. labs(title = "Per-Class prediction accuracy (random forest)",
  704. x = "Cell Type", y = "Accuracy (recall)") +
  705. theme_minimal(base_size = 14) +
  706. theme(
  707. axis.text.x = element_text(size = 12),
  708. axis.text.y = element_text(size = 12),
  709. plot.title = element_text(hjust = 0.5, face = "bold")
  710. )
  712. library(ggplot2)
  714. library(ggplot2)
  716. ggplot(acc_df, aes(x = reorder(CellType, Accuracy), y = Accuracy)) +
  717. geom_col(fill = "#9ABB50", width = 0.6) + # Sleek bar width
  718. scale_y_continuous(limits = c(0, 1.05), expand = c(0,0)) +
  719. coord_flip() +
  720. labs(
  721. title = "Per-Class Prediction Accuracy",
  722. x = NULL,
  723. y = "Accuracy (Recall)"
  724. ) +
  725. theme_classic(base_size = 10) +
  726. theme(
  727. axis.text.x = element_text(size = 10, color = "black"),
  728. axis.text.y = element_text(size = 10, color = "black"),
  729. axis.ticks.y = element_blank(),
  730. plot.title = element_text(hjust = 0.5, face = "bold", size = 10),
  731. panel.grid.major.y = element_blank(),
  732. panel.grid.minor = element_blank()
  733. )
  736. library(dplyr)
  737. library(ggplot2)
  739. # Extract importance from trained RF
  740. imp_mat <- randomForest::importance(rf_model)
  741. imp_df <- data.frame(Gene = rownames(imp_mat), imp_mat, row.names = NULL)
  743. ## ---- Top 20 by MeanDecreaseAccuracy (MDA) ----
  744. top20_mda <- imp_df %>%
  745. filter(!is.na(MeanDecreaseAccuracy)) %>%
  746. arrange(desc(MeanDecreaseAccuracy)) %>%
  747. slice_head(n = 20)
  749. p_mda <- ggplot(top20_mda,
  750. aes(x = MeanDecreaseAccuracy,
  751. y = reorder(Gene, MeanDecreaseAccuracy))) +
  752. geom_point(size = 4, color = "#E69F00") + # orange dots
  753. labs(title = "Top 20 Features by Mean Decrease Accuracy",
  754. x = "Mean Decrease Accuracy", y = NULL) +
  755. theme_classic(base_size = 12) +
  756. theme(
  757. axis.text.y = element_text(size = 9),
  758. plot.title = element_text(hjust = 0.5, face = "bold"),
  759. panel.border = element_rect(color = "black", fill = NA, linewidth = 0.8)
  760. )
  762. ggsave("rf_top20_MDA_dotplot.png", p_mda, width = 7, height = 6, dpi = 300, bg = "white")
  764. ## ---- Top 20 by MeanDecreaseGini (MDG) ----
  765. top20_mdg <- imp_df %>%
  766. filter(!is.na(MeanDecreaseGini)) %>%
  767. arrange(desc(MeanDecreaseGini)) %>%
  768. slice_head(n = 20)
  770. p_mdg <- ggplot(top20_mdg,
  771. aes(x = MeanDecreaseGini,
  772. y = reorder(Gene, MeanDecreaseGini))) +
  773. geom_point(size = 4, color = "#56B4E9") + # blue dots
  774. labs(title = "Top 20 Features by Mean Decrease Gini",
  775. x = "Mean Decrease Gini", y = NULL) +
  776. theme_classic(base_size = 12) +
  777. theme(
  778. axis.text.y = element_text(size = 9),
  779. plot.title = element_text(hjust = 0.5, face = "bold"),
  780. panel.border = element_rect(color = "black", fill = NA, linewidth = 0.8)
  781. )
  783. ggsave("rf_top20_MDG_dotplot.png", p_mdg, width = 7, height = 6, dpi = 300, bg = "white")
  785. ########################
  786. # build a tiny data frame of diagonal positions (all class levels)
  787. lev <- levels(factor(cm_df$Reference, levels = unique(cm_df$Reference)))
  788. diag_df <- data.frame(
  789. Reference = factor(lev, levels = lev),
  790. Prediction = factor(lev, levels = lev)
  791. )
  793. diag_color <- "#1f1f1f" # <- change this to any accent (e.g., "#9C27B0", "#FF5722")
  795. random <- ggplot(subset(cm_df, Freq > 0),
  796. aes(x = Reference, y = Prediction, fill = log1p(Freq))) +
  797. geom_tile(color = "grey90", linewidth = 0.3) +
  798. # make the diagonal pop: thin white halo + colored stroke
  799. geom_tile(data = diag_df, fill = NA, color = "white", linewidth = 2.0) +
  800. geom_tile(data = diag_df, fill = NA, color = diag_color, linewidth = 1.1) +
  801. scale_fill_gradientn(
  802. colors = c("#FFFFFF", "#EEEEEE", "#F4F1D0", "#E57373", "#B71C1C"),
  803. name = "log(Freq+1)",
  804. guide = guide_colorbar(barwidth = 10, barheight = 1),
  805. na.value = "white"
  806. ) +
  807. coord_fixed(expand = FALSE) +
  808. labs(title = "Random Forest Confusion Matrix",
  809. x = "Actual Cell Type", y = "Predicted Cell Type") +
  810. theme_classic(base_size = 13) +
  811. theme(
  812. legend.position = "top",
  813. plot.title = element_text(hjust = 0.5, face = "bold"),
  814. axis.text.x = element_text(angle = 45, hjust = 1),
  815. panel.border = element_rect(color = "black", fill = NA, linewidth = 0.7)
  816. )

Code.R, under CC-BY-4.0 · at the source

Overview

Authors: Muhammad Junaid1,2,3, Soo Jung Park4, Yiseul Bae2,3,4, Eun Jeong Lee2,3,4, Su Bin Lim1,2,3
  1. Department of Biochemistry & Molecular Biology, Ajou University School of Medicine,Suwon, 16499 South Korea
  2. Department of Biomedical Science, Graduate School of Ajou University,Suwon, 16499 South Korea
  3. BK21 R&E initiative for Advanced Precision Medicine, Ajou University School of Medicine,Suwon, 16499 South Korea
  4. Department of Brain Science, Ajou University School of Medicine,Suwon, 16499 South Korea
Institutions: Ajou University (South Korea)
Journal: Scientific data, volume 13, issue 1, article 819
Dates: received 8 December 2025; accepted 31 March 2026; published online 6 April 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07193-4 · PMID 41942478 · PMCID PMC13230531 · OpenAlex W7150806569
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), Parkinson's (population), methods / tools (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
MeSH: Colon*, Protein Kinases*, Animals, Cell Nucleus, Mice, Mice, Knockout, PTEN-Induced Putative Kinase, RNA-Seq, Sequence Analysis, RNA (* major topic)
Journal subjects: Data Descriptor
Topic: Genetic factors in colorectal cancer (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: National Research Foundation of Korea (2022R1C1C1005741, RS-2023-00217595, RS-2025-02217836, RS-2020-NR049588, RS-2020-NR046270, 2022R1C1C1004756); Korea Health Industry Development Institute (HR22C1734, RS-2025-02310331)
Citations: not cited yet (Europe PMC); 22 references in the paper

Abstract

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

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

figshare 30702425

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 3 files, 1 script
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: caret (1 file), ggplot2 (1 file), patchwork (1 file), pheatmap (1 file), randomForest (1 file), Seurat (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
1 file
  • Code.R, R, 816 lines, 7 matches
At the source:

Code availability statement

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  • it points to the authors' code: figshare 30702425

Read it in the paper: doi.org/10.1038/s41597-026-07193-4.

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41597-026-07193-4.

Versions

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

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 9 MeSH terms, 2 funders, 21 references.

Cite

This paper

Junaid, M., Park, S. J., Bae, Y., Lee, E. J., & Bin Lim, S. (2026). A single-nucleus RNA-seq dataset of the colon in Pink1-deficient and wild-type mice. Scientific data, 13(1), 819. https://doi.org/10.1038/s41597-026-07193-4

BibTeX

@article{junaid2026single,
author = {Junaid, Muhammad and Park, Soo Jung and Bae, Yiseul and Lee, Eun Jeong and Bin Lim, Su},
title = {{A single-nucleus RNA-seq dataset of the colon in Pink1-deficient and wild-type mice}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {819},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07193-4},
url = {https://doi.org/10.1038/s41597-026-07193-4},
pmid = {41942478},
pmcid = {PMC13230531}
}

RIS

TY - JOUR
AU - Junaid, Muhammad
AU - Park, Soo Jung
AU - Bae, Yiseul
AU - Lee, Eun Jeong
AU - Bin Lim, Su
TI - A single-nucleus RNA-seq dataset of the colon in Pink1-deficient and wild-type mice
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/06
VL - 13
IS - 1
SP - 819
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07193-4
UR - https://doi.org/10.1038/s41597-026-07193-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41597-026-07193-4",
"type": "article-journal",
"title": "A single-nucleus RNA-seq dataset of the colon in Pink1-deficient and wild-type mice",
"container-title": "Scientific data",
"author": [
{
"family": "Junaid",
"given": "Muhammad"
},
{
"family": "Park",
"given": "Soo Jung"
},
{
"family": "Bae",
"given": "Yiseul"
},
{
"family": "Lee",
"given": "Eun Jeong"
},
{
"family": "Bin Lim",
"given": "Su"
}
],
"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "819",
"DOI": "10.1038/s41597-026-07193-4",
"PMID": "41942478",
"PMCID": "PMC13230531",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-07193-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
6
]
]
}
}

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

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