OSCR

Optics-free spatial genomics for mapping mammalian brain aging by IRISeq.

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] § Methods › EasySci sequencing data preprocessing ↔ EasySci_pipeline.sh, lines 1–47 · score 0.77 · shortdT, randomN, EasySci, barcode information, pipeline, exon
  2. [2] § Results › Overview of IRISeq ↔ Analysis_Scripts/2_Fig2_plotting.ipynb, lines 173–203 · score 0.74 · LGN, STN, Unk, Vip, interneurons, DG
  3. [3] § Results › A spatially resolved transcriptome atlas of the mouse brain ↔ Analysis_Scripts/3_Fig1_plotting.ipynb, lines 324–367 · score 0.67 · hippocampal dentate gyrus, caudate putamen, habenula, amygdala, hypothalamus, cortex
  4. [4] § Methods › cDNA data matrix processing ↔ Bead_interaction_pipeline/UMI_barcode_extraction.py, lines 9–78 · score 0.66 · r1 fastq, r2 fastq, bead barcode, Read1, Read2, filtered
  5. [5] § Methods › Single-cell clustering and annotation analysis ↔ Analysis_Scripts/2_Fig2_plotting.ipynb, lines 21–36 · score 0.64 · FindClusters, variable features, SelectIntegrationFeatures, vst, Seurat, clustering
  6. [6] § Methods › cDNA data matrix processing ↔ script_folder/barcoding_reads_paired.py, lines 18–137 · score 0.61 · r1 fastq, r2 fastq, UMIs, trimmed, Read2, barcode
  7. [7] § Methods › EasySci sequencing data preprocessing ↔ script_folder/post_processing_exons.py, lines 69–129 · score 0.59 · shortdT, randomN, doublet, exon, PCR, matrix
  8. [8] § Methods › Differential abundance analysis for IRISeq ↔ Analysis_Scripts/5_Beads_deconvolution_cell_abundance_analysis.ipynb, lines 27–95 · score 0.57 · differentialGeneTest, cell abundance, model, RTCD, gene expression, matrix
  9. [9] § Methods › Computational procedures for processing IRISeq libraries › Bead-connection processing ↔ Sample_reconstruction_code_GPUipynb.ipynb, lines 120–155 · score 0.55 · sender bead, receiver bead, density, row, matrix, filtered

Paper

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The authors' code

Jupyter notebook · 588 lines · 19 KB · no license · 2 matches

  1. # %%
  3. library(Matrix)
  4. library(Seurat)
  5. library(Matrix)
  7. gene_count <- readMM("//genecount.mtx")
  8. df_cell <- read.csv("//df_cell.csv")
  9. df_gene <- read.csv("//df_gene.csv")
  11. colnames(gene_count) = df_cell$cell_name_temp #your cell name column
  12. rownames(gene_count) = df_gene$Gene_name
  13. #must do this
  14. rownames(df_cell) <- df_cell$cell_name_temp
  15. # Now, proceed with creating the Seurat object
  16. Iris_Seurat <- CreateSeuratObject(counts = gene_count, meta.data = df_cell, project = "Spatial", assay = "RNA")
  17. Iris_Seurat <- subset(Iris_Seurat, subset = nCount_RNA >= 400)
  21. # %%
  22. Iris_Seurat <- NormalizeData(Iris_Seurat)
  23. Iris_Seurat <- FindVariableFeatures(Iris_Seurat, selection.method = "vst", nfeatures = 10000)
  26. # plot variable features with and without labels
  27. plot1 <- VariableFeaturePlot(Iris_Seurat)
  28. plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
  29. plot1 + plot2
  30. all.genes <- rownames(Iris_Seurat)
  31. Iris_Seurat <- ScaleData(Iris_Seurat, features = all.genes)
  32. Iris_Seurat <- RunPCA(Iris_Seurat, features = VariableFeatures(object = Iris_Seurat),npcs=25)
  33. Iris_Seurat <- FindNeighbors(Iris_Seurat, dims = 1:50)
  34. Iris_Seurat <- FindClusters(Iris_Seurat, resolution = 2)
  35. Iris_Seurat <- RunUMAP(Iris_Seurat, dims = 1:40)
  36. DimPlot(Iris_Seurat, reduction = "umap")
  39. # %%
  40. #used this for figure
  41. Iris_Seurat <- FindNeighbors(Iris_Seurat, dims = 1:20)
  42. Iris_Seurat <- FindClusters(Iris_Seurat, resolution = 2)
  43. Iris_Seurat <- RunUMAP(Iris_Seurat, dims = 1:20)
  44. DimPlot(Iris_Seurat, reduction = "umap")
  48. # %%
  49. #used this for figure
  51. Iris_Seurat <- RunUMAP(Iris_Seurat, dims = 1:20,min.dist = 0.1)
  55. # %%
  58. # %%
  61. # %%
  64. # %%
  65. DimPlot(Iris_Seurat, reduction = "umap")+NoLegend()
  68. # %%
  69. Iris_Seurat <- FindClusters(Iris_Seurat, resolution = 2.2)
  72. # %%
  73. Iris_Seurat <- FindClusters(Iris_Seurat, resolution = 1)
  76. # %%
  77. DimPlot(Iris_Seurat, reduction = "umap")
  80. # %%
  81. library(Matrix)
  82. library(Seurat)
  83. library(spacexr)
  84. library(Matrix)
  85. library(doParallel)
  88. # %%
  89. RTCD = readRDS("../10um_RDS.rds")
  91. # %%
  92. weights <- RTCD@results$weights
  93. norm_weights <- normalize_weights(weights)
  94. barcodes <- colnames(RTCD@spatialRNA@counts)
  95. weights <- RTCD@results$weights
  96. RTCD <- as.data.frame(norm_weights)
  99. # %%
  100. #adding umap coords
  101. # Extract UMAP coordinates
  102. umap_coords <- [email hidden][, c("UMAP1", "UMAP2")]
  104. # Set UMAP embeddings in the Seurat object
  105. Iris_Seurat[["umapSpatial"]] <- CreateDimReducObject(embeddings = as.matrix(umap_coords), key = "UMAP_")
  108. # %%
  109. # Assuming 'your_seurat_object' is your Seurat object
  110. # Extract the relevant columns into a matrix
  111. columns_of_interest <- c('Astro_AMY', 'Astro_AMY_CTX', 'Astro_CTX', 'Astro_HPC',
  112. 'Astro_HYPO', 'Astro_STR', 'Astro_THAL_hab', 'Astro_THAL_lat',
  113. 'Astro_THAL_med', 'Astro_WM', 'Endo', 'Ext_Amy_1', 'Ext_Amy_2',
  114. 'Ext_ClauPyr', 'Ext_Hpc_CA1', 'Ext_Hpc_CA2', 'Ext_Hpc_CA3',
  115. 'Ext_Hpc_DG1', 'Ext_Hpc_DG2', 'Ext_L23', 'Ext_L25', 'Ext_L5_1',
  116. 'Ext_L5_2', 'Ext_L5_3', 'Ext_L56', 'Ext_L6', 'Ext_L6B', 'Ext_Med',
  117. 'Ext_Pir', 'Ext_Thal_1', 'Ext_Thal_2', 'Ext_Unk_1', 'Ext_Unk_2',
  118. 'Ext_Unk_3', 'Inh_1', 'Inh_2', 'Inh_3', 'Inh_4', 'Inh_5', 'Inh_6',
  119. 'Inh_Lamp5', 'Inh_Meis2_1', 'Inh_Meis2_2', 'Inh_Meis2_3',
  120. 'Inh_Meis2_4', 'Inh_Pvalb', 'Inh_Sst', 'Inh_Vip', 'LowQ_1',
  121. 'LowQ_2', 'Micro', 'Nb_1', 'Nb_2', 'Oligo_1', 'Oligo_2',
  122. 'OPC_1', 'OPC_2', 'Unk_1', 'Unk_2')
  124. # %%
  127. # %%
  128. DimPlot(Iris_Seurat,reduction="umapSpatial", group.by = "Idents", cols = c("OPC_1"="red"))
  129. DimPlot(Iris_Seurat, group.by = "Idents", cols = c("OPC_1"="red"))
  131. DimPlot(Iris_Seurat,reduction="umapSpatial", group.by = "Idents", cols = c("OPC_2"="red"))
  132. DimPlot(Iris_Seurat, group.by = "Idents", cols = c("OPC_2"="red"))
  135. [email hidden]$Idents
  136. #Astro_AMY, Astro_AMY_CTX, Astro_CTX, , Astro_STR, Astro_THAL_hab,Astro_THAL_lat,Astro_THAL_med=Astrocytes
  137. #Astro_WM=Astrocytes_WM
  138. #Astro_HYPO=Astrocytes_Hypo
  139. #Endo=Endo
  140. #Ext_Amy_1=Ext_Amy_1
  141. #Ext_Amy_2=Ext_Amy_2
  142. #Inh_1=Inh_Amy
  143. #Ext_ClauPyr=Ext_ClauPyr
  144. #Ext_Hpc_CA1=Ext_Hpc_CA1
  145. #Ext_Hpc_CA2=Ext_Hpc_CA2
  146. #Ext_Hpc_CA3=Ext_Hpc_CA3
  147. #Ext_Hpc_DG1,Ext_Hpc_DG2=Ext_Hpc_DG
  148. #Ext_L23=Ext_L23
  149. #Ext_L25=Ext_L25
  150. #Ext_L5_1,Ext_L5_2,Ext_L5_3=Ext_L5
  151. #Ext_L56,Ext_L6B=Ext_L56
  152. #Ext_Med=Ext_Med
  153. #Ext_Pir,Ext_Unk_1=Ext_Pir
  154. #Ext_Thal_1', 'Ext_Thal_2,Ext_Unk_3=Thalamus
  156. #Ext_Unk_2=Ext_Unk_2
  157. #Inh_2,Inh_3,Inh=Hypothalamus
  158. #Inh_5=STN
  159. #Inh_6=Habenuela
  160. #Inh_Lamp5=Inh_Hippocampus
  161. ##Inh_Meis2_1,Inh_Meis2_2,Inh_Meis2_3=Striatal
  162. #Inh_Meis2_4=LGN
  163. #Inh_Sst=Inh_Sst
  164. #Inh_Pvalb=Inh_Pvalb
  165. #Inh_Vip=Inh_Vip
  166. #LowQ_1=Choroid_Plexus
  167. #Micro=Micro
  168. #Nb_1,Nb_2=Nb
  169. #Oligo_1', 'Oligo_2=Oligo
  170. #OPC_2,OPC_1=OPC
  171. #Unk_1', 'Unk_2-interneurons
  173. # %%
  174. # Define the mapping dictionary
  175. idents_mapping <- c(
  176. "Astro_AMY" = "Astrocytes", "Astro_AMY_CTX" = "Astrocytes", "Astro_CTX" = "Astrocytes",
  177. "Astro_STR" = "Astrocytes", "Astro_THAL_hab" = "Astrocytes", "Astro_THAL_lat" = "Astrocytes",
  178. "Astro_THAL_med" = "Astrocytes", "Astro_WM" = "Astrocytes_WM", "Astro_HYPO" = "Astrocytes_Hypo",
  179. "Endo" = "Endo", "Ext_Amy_1" = "Ext_Amy_1", "Ext_Amy_2" = "Ext_Amy_2",
  180. "Inh_1" = "Inh_Amy", "Ext_ClauPyr" = "Ext_ClauPyr", "Ext_Hpc_CA1" = "Ext_Hpc_CA1",
  181. "Ext_Hpc_CA2" = "Ext_Hpc_CA2", "Ext_Hpc_CA3" = "Ext_Hpc_CA3", "Ext_Hpc_DG1" = "Ext_Hpc_DG",
  182. "Ext_Hpc_DG2" = "Ext_Hpc_DG", "Ext_L23" = "Ext_L23", "Ext_L25" = "Ext_L25",
  183. "Ext_L5_1" = "Ext_L5", "Ext_L5_2" = "Ext_L5", "Ext_L5_3" = "Ext_L5",
  184. "Ext_L56" = "Ext_L56", "Ext_L6B" = "Ext_L56", "Ext_Med" = "Ext_Med",
  185. "Ext_Pir" = "Ext_Pir", "Ext_Unk_1" = "Ext_Pir", "Ext_Thal_1" = "Thalamus",
  186. "Ext_Thal_2" = "Thalamus", "Ext_Unk_3" = "Thalamus", "Ext_Unk_2" = "Ext_Unk_2",
  187. "Inh_2" = "Hypothalamus", "Inh_3" = "Hypothalamus", "Inh" = "Hypothalamus",
  188. "Inh_5" = "STN", "Inh_6" = "Habenuela", "Inh_Lamp5" = "Inh_Hippocampus",
  189. "Inh_Meis2_1" = "Striatal", "Inh_Meis2_2" = "Striatal", "Inh_Meis2_3" = "Striatal",
  190. "Inh_Meis2_4" = "LGN", "Inh_Sst" = "Inh_Sst", "Inh_Pvalb" = "Inh_Pvalb",
  191. "Inh_Vip" = "Inh_Vip", "LowQ_1" = "Choroid_Plexus", "Micro" = "Micro",
  192. "Nb_1" = "Nb", "Nb_2" = "Nb", "Oligo_1" = "Oligo", "Oligo_2" = "Oligo",
  193. "OPC_1" = "OPC", "OPC_2" = "OPC", "Unk_1" = "interneurons", "Unk_2" = "interneurons"
  194. )
  196. # Create a new column with mapped values
  197. [email hidden]$NewIdents <- idents_mapping[as.character([email hidden]$Idents)]
  199. # Ensure any NA values (unmapped identities) remain unchanged
  200. [email hidden]$NewIdents[is.na([email hidden]$NewIdents)] <- [email hidden]$Idents[is.na([email hidden]$NewIdents)]
  202. # Verify the changes
  203. table([email hidden]$NewIdents)
  206. # %%
  209. # %%
  210. DimPlot(Iris_Seurat,reduction="umapSpatial", group.by = "NewIdents")+NoLegend()
  211. DimPlot(Iris_Seurat, group.by = "NewIdents")+NoLegend()
  216. # %%
  219. # %%
  222. # %%
  225. # %%
  228. # %%
  231. # %%
  234. # %%
  237. # %%
  240. # %%
  243. # %%
  246. # %%
  249. # %%
  252. # %%
  255. # %%
  258. # %%
  259. FeaturePlot(Iris_Seurat, "Ttr")
  260. DimPlot(Iris_Seurat,reduction="umapSpatial", group.by = "Idents", cols = c("Inh_Meis2_1"="red"))
  261. DimPlot(Iris_Seurat, group.by = "Idents", cols = c("Inh_Meis2_1"="red"))
  264. # %%
  267. # %%
  268. [email hidden] = RTCD[,columns_of_interest]
  270. # %%
  273. # %%
  276. # Get the matrix of values for the specified columns
  277. data_matrix <- FetchData(Iris_Seurat, vars = columns_of_interest)
  279. # Identify the column with the highest value for each cell
  280. max_id <- apply(data_matrix, 1, function(x) columns_of_interest[which.max(x)])
  282. # Add this information as a new column in the Seurat metadata
  283. Iris_Seurat$Idents <- max_id
  285. # Optionally set the Seurat object identity to this new column
  286. Idents(Iris_Seurat) <- "Idents"
  288. # View the updated Seurat object metadata
  289. head([email hidden])
  292. # %%
  295. # %%
  298. # %%
  301. # %%
  304. # %%
  305. # Fetch cell names from Seurat object metadata
  306. metadata_cell_names <- rownames([email hidden])
  308. # Ensure data_matrix includes all metadata cells in the same order
  309. data_matrix <- FetchData(Iris_Seurat, vars = columns_of_interest, cells = metadata_cell_names)
  311. # Use max.col to quickly find the index of the max value for each row
  312. max_indices <- max.col(data_matrix, ties.method = "first")
  314. # Map the indices back to column names
  315. identities <- setNames(columns_of_interest[max_indices], rownames(data_matrix))
  317. # Add the new identities as a metadata column
  318. [email hidden]$Idents <- identities
  322. # %%
  325. # %%
  328. # %%
  329. library(Seurat)
  330. library(ggplot2)
  331. library(RColorBrewer)
  332. library(cowplot)
  334. # Extract unique identities
  335. unique_idents <- unique([email hidden]$NewIdents)
  337. # Generate visually distinct colors
  338. num_clusters <- length(unique_idents)
  339. palette_colors <- colorRampPalette(brewer.pal(12, "Paired"))(num_clusters)
  341. # Assign colors to each identity
  342. names(palette_colors) <- unique_idents
  344. # Plot UMAP with distinct colors
  345. DimPlot(Iris_Seurat, group.by = "NewIdents", cols = palette_colors) +
  346. theme_minimal() +
  347. ggtitle("UMAP with Distinct Clusters")
  350. # %%
  351. DimPlot(Iris_Seurat, group.by = "NewIdents", cols = palette_colors, reduction = "umapSpatial", pt.size = 0.5) +
  352. theme_void() + # Removes background, grid, and axis lines
  353. ggtitle("UMAP with Distinct Clusters") +
  354. theme(
  355. axis.text = element_blank(), # Remove axis text
  356. axis.ticks = element_blank(), # Remove axis ticks
  357. panel.border = element_blank(), # Remove panel border
  358. legend.text = element_text(family = "Helvetica", size = 14), # Set legend font to Helvetica
  359. legend.title = element_text(family = "Helvetica", size = 14)+NoLegend() # Ensure title is also Helvetica
  360. )+NoLegend()
  363. # %%
  364. DimPlot(Iris_Seurat, group.by = "NewIdents", cols = palette_colors, reduction = "umapSpatial", pt.size = 0.5) +
  365. theme_void() + # Removes background, grid, and axis lines
  366. ggtitle("UMAP with Distinct Clusters") +
  367. theme(
  368. axis.text = element_blank(), # Remove axis text
  369. axis.ticks = element_blank(), # Remove axis ticks
  370. panel.border = element_blank(), # Remove panel border
  371. legend.text = element_text(family = "Helvetica", size = 14), # Set legend font to Helvetica
  372. legend.title = element_text(family = "Helvetica", size = 14)+NoLegend() # Ensure title is also Helvetica
  373. )
  376. # %%
  377. # Automatically update colors for all clusters containing "Thal"
  378. palette_colors[grep("Thal", names(palette_colors))] <- "#FCE7C8"
  379. # Automatically update colors for all clusters containing "Thal"
  380. palette_colors[grep("Oligo", names(palette_colors))] <- "#FADA7A"
  382. # Automatically update colors for all clusters containing "Thal"
  383. palette_colors[grep("Striatal", names(palette_colors))] <- "#CBA35C"
  387. # %%
  388. DimPlot(Iris_Seurat, group.by = "NewIdents", cols = palette_colors, reduction = "umapSpatial", pt.size = .5) +
  389. theme_void() + # Removes background, grid, and axis lines
  390. ggtitle("UMAP with Distinct Clusters") +
  391. theme(
  392. axis.text = element_blank(), # Remove axis text
  393. axis.ticks = element_blank(), # Remove axis ticks
  394. panel.border = element_blank(), # Remove panel border
  395. legend.text = element_text(family = "Helvetica", size = 14), # Set legend font to Helvetica
  396. legend.title = element_text(family = "Helvetica", size = 14)+NoLegend() # Ensure title is also Helvetica
  397. )+NoLegend()
  400. # %%
  403. # %%
  404. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("Hypothalamus" = "#A94A4A"))
  405. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("Ext_Amy_2" = "red"))
  406. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("interneurons" = "red"))
  407. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("Ext_L56" = "red"))
  408. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("Ext_Pir" = "red"))
  409. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("Oligo" = "red"))
  410. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("Choroid_Plexus" = "red"))
  411. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("Thalamus" = "red"))
  412. DimPlot(Iris_Seurat, reduction = "umapSpatial",group.by = "NewIdents", cols = c("Ext_Med" = "red"))
  416. # %%
  417. DimPlot(Iris_Seurat, group.by = "NewIdents", cols = palette_colors, reduction = "umapSpatial", pt.size = .5) +
  418. theme_void() + # Removes background, grid, and axis lines
  419. ggtitle("UMAP with Distinct Clusters") +
  420. theme(
  421. axis.text = element_blank(), # Remove axis text
  422. axis.ticks = element_blank(), # Remove axis ticks
  423. panel.border = element_blank(), # Remove panel border
  424. legend.text = element_text(family = "Helvetica", size = 14), # Set legend font to Helvetica
  425. legend.title = element_text(family = "Helvetica", size = 14)+NoLegend() # Ensure title is also Helvetica
  426. )+NoLegend()
  429. # %%
  432. # %%
  435. # %%
  436. library(ggplot2)
  437. library(reshape2)
  439. # Convert metadata to a data frame for ggplot
  440. metadata_df <- [email hidden]
  442. # Rename columns for readability
  443. colnames(metadata_df)[colnames(metadata_df) %in% c("nCount_RNA", "nFeature_RNA")] <- c("UMI_Counts", "Gene_Counts")
  445. # Reshape data to long format for side-by-side boxplots
  446. melted_data <- melt(metadata_df, measure.vars = c("UMI_Counts", "Gene_Counts"))
  448. # Custom colors for each box plot
  449. box_colors <- c("UMI_Counts" = "#8B5A2B", "Gene_Counts" = "#1F497D") # Brown & Blue
  451. # Set max y-axis limit
  452. max_y <- 2000 # Adjust based on data range
  454. # Generate Box Plot
  455. pdf("Umi.pdf")
  456. ggplot(melted_data, aes(x = variable, y = value, fill = variable)) +
  457. geom_boxplot(color = "black", outlier.shape = 16, outlier.colour = "black", outlier.size = 1) +
  458. scale_fill_manual(values = box_colors) +
  459. theme_minimal() +
  460. theme(
  461. text = element_text(family = "Helvetica"),
  462. axis.title.x = element_blank(),
  463. axis.title.y = element_blank(),
  464. axis.text.x = element_text(size = 14, face = "bold"),
  465. axis.text.y = element_text(size = 12),
  466. legend.position = "none", # Remove legend for a cleaner look
  467. panel.grid = element_blank(), # Remove grid
  468. panel.border = element_blank(),
  469. axis.line = element_line(color = "black", size = 1), # Add x and y axis lines
  470. axis.ticks = element_line(color = "black", size = 1) # Add ticks to both axes
  471. ) +
  472. coord_cartesian(ylim = c(0, max_y)) # Set y-axis max limit
  473. dev.off()
  476. # %%
  477. pdf("Plotcells.pdf")
  478. DimPlot(Iris_Seurat, group.by = "Idents", cols = palette_colors, reduction ="umapSpatial", pt.size = 0.5) +
  479. theme_void() + # Removes background, grid, and axis lines
  480. ggtitle("UMAP with Distinct Clusters") +
  481. NoLegend() +
  482. theme(
  483. axis.text = element_blank(), # Remove axis text
  484. axis.ticks = element_blank(), # Remove axis ticks
  485. panel.border = element_blank() # Remove panel border
  486. )
  490. # Define custom colors as a named vector
  491. custom_colors <- c(
  492. "Ext_Hpc_CA1" = "#1f77b4",
  493. "Ext_Hpc_CA2" = "#ff7f0e",
  494. "Ext_Hpc_CA3" = "#2ca02c",
  495. "Ext_Hpc_DG1" = "orange",
  496. "Ext_Hpc_DG2" = "orange",
  497. "Ext_L23" = "#8c564b",
  498. "Ext_L25" = "#e377c2",
  499. "Ext_L5_1" = "#7f7f7f",
  500. "Astro_THAL_med" = "#bcbd22",
  501. "Ext_L56" = "#17becf","Astro_THAL_med" = "#D99D81","Astro_THAL_lat" ="#DF9755" , "Astro_THAL_hab"= "#C890A7",
  502. "Oligo_1"="#A31D1D","Oligo_2"="#A31D1D","OPC_1"="#809D3C","OPC_2"="#F4FFC3"
  504. )
  506. # Plot with custom colors
  507. DimPlot(
  508. Iris_Seurat,
  509. group.by = "Idents",
  510. reduction = "umapSpatial",
  511. cols = custom_colors, pt.size = .5
  512. ) + NoLegend()
  515. custom_colors <- c(
  516. "Ext_Hpc_CA1" = "#1f77b4",
  517. "Ext_Hpc_CA2" = "#ff7f0e",
  518. "Ext_Hpc_CA3" = "#2ca02c",
  519. "Ext_Hpc_DG1" = "orange",
  520. "Ext_Hpc_DG2" = "orange",
  521. "Ext_L23" = "#8c564b",
  522. "Ext_L25" = "#e377c2",
  523. "Ext_L5_1" = "#7f7f7f",
  524. "Astro_THAL_med" = "#bcbd22",
  525. "Ext_L56" = "#17becf","Astro_THAL_med" = "#D99D81","Astro_THAL_lat" ="#DF9755" , "Astro_THAL_hab"= "#C890A7",
  526. "Oligo_1"="#A31D1D","Oligo_2"="#A31D1D","OPC_1"="#809D3C","OPC_2"="#F4FFC3"
  528. )
  530. # Plot with custom colors
  531. DimPlot(
  532. Iris_Seurat,
  533. group.by = "Idents",
  534. reduction = "umapSpatial",
  535. cols = custom_colors, pt.size = .5
  536. )
  541. dev.off()
  544. # %%
  547. # %%
  548. FeaturePlot(Iris_Seurat,c("Ext_Hpc_CA1","Ext_Hpc_CA2","Ext_Hpc_CA3"), reduction = "umapSpatial",raster=FALSE)+NoLegend()
  552. # %%
  553. pdf("pdf_subtype.png")
  554. FeaturePlot(Iris_Seurat,"Ext_Hpc_CA1", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  555. FeaturePlot(Iris_Seurat,"Ext_Hpc_CA2", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  556. FeaturePlot(Iris_Seurat,"Ext_Hpc_CA3", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  557. FeaturePlot(Iris_Seurat,"Ext_ClauPyr", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  558. FeaturePlot(Iris_Seurat,"Endo", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  559. FeaturePlot(Iris_Seurat,"Ext_Thal_1", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  560. FeaturePlot(Iris_Seurat,"Ext_Thal_2", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  561. FeaturePlot(Iris_Seurat,"Ext_Hpc_DG1", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  562. FeaturePlot(Iris_Seurat,"Ext_Hpc_DG2", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  563. FeaturePlot(Iris_Seurat,"Ext_L23", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  564. FeaturePlot(Iris_Seurat,"Ext_L25", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  565. FeaturePlot(Iris_Seurat,"Ext_L5_1", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  566. FeaturePlot(Iris_Seurat,"Ext_L5_2", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  567. FeaturePlot(Iris_Seurat,"Ext_L5_3", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  568. FeaturePlot(Iris_Seurat,"Ext_L56", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  569. FeaturePlot(Iris_Seurat,"Ext_L6", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  570. FeaturePlot(Iris_Seurat,"Ext_L6B", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  571. FeaturePlot(Iris_Seurat,"Ext_Med", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  572. FeaturePlot(Iris_Seurat,"Ext_Pir", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  573. FeaturePlot(Iris_Seurat,"Micro", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  574. FeaturePlot(Iris_Seurat,"Oligo_1", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  575. FeaturePlot(Iris_Seurat,"Oligo_2", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  576. FeaturePlot(Iris_Seurat,"OPC_1", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  577. FeaturePlot(Iris_Seurat,"OPC_2", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  578. FeaturePlot(Iris_Seurat,"Astro_AMY", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  579. FeaturePlot(Iris_Seurat,"Astro_AMY_CTX", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  580. FeaturePlot(Iris_Seurat,"Astro_CTX", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  581. FeaturePlot(Iris_Seurat,"Astro_HPC", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  582. FeaturePlot(Iris_Seurat,"Astro_HYPO", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  584. FeaturePlot(Iris_Seurat,"Astro_STR", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  585. FeaturePlot(Iris_Seurat,"Astro_THAL_hab", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  586. FeaturePlot(Iris_Seurat,"Astro_THAL_lat", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  587. FeaturePlot(Iris_Seurat,"Astro_THAL_med", reduction = "umapSpatial",raster=FALSE)+NoLegend()
  588. dev.off()

2_Fig2_plotting.ipynb at commit da8bf40, no license · at the source

Overview

Authors: Abdulraouf Abdulraouf1,2,3, Weirong Jiang1, Zehao Zhang1,3, Zihan Xu1,3, Ziyu Lu1,3, Tiffany Merlinsky2, Andrew Liao1,2,3, Ahmet Doymaz1,2,3, Samuel Isakov1, Tanvir Raihan1, Wei Zhou1, Junyue Cao1
ORCID iDs: Zehao Zhang, Zihan Xu, Ziyu Lu, Ahmet Doymaz, Wei Zhou, Junyue Cao
  1. Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY USA
  2. The Tri-Institutional M.D-Ph.D Program, New York, NY USA
  3. The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY USA
Institutions: Rockefeller University (United States)
Journal: Nature neuroscience, volume 29, issue 7, pages 1762-1773
Dates: received 5 April 2025; accepted 6 April 2026; published online 12 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02293-1 · PMID 42120609 · PMCID PMC13337494 · OpenAlex W7160958222
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: RNA sequencing, Molecular neuroscience, Neuroimmunology
MeSH: Aging*, Brain*, Brain Mapping*, Genomics*, Animals, Lymphocytes, Mice, Mice, Inbred C57BL, Single-Cell Gene Expression Analysis, Spatial Transcriptomics, Transcriptome (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (RM1HG011014, 1R01AG076932); NHGRI NIH HHS (RM1 HG011014); NIGMS NIH HHS (T32 GM152349)
Citations: cited by 4 papers (Europe PMC); 57 references in the paper

Abstract

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

JunyueCaoLab/EasySci

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: dc7ab035fc6a15a1cac02e60e208b5e965fa6471, 17 January 2024
Languages: Python (10), Shell (1)
Size: 20 files, 11 scripts
Software Heritage: not archived
Found in: the text, “EasySci sequencing data preprocessing”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), pandas (6 files), Biopython (2 files), SciPy (2 files), SAMtools (1 file), STAR (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
13 files
At the source: github.com/JunyueCaoLab/EasySci

AbdulAbdulRU/IRISeq

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: da8bf4072343c4fc5c04460ce71cd5b90a02bd22, 8 November 2025
Languages: Python (18), Jupyter (7)
Size: 43 files, 25 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 7 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (10 files), NumPy (6 files), Matplotlib (3 files), Scanpy (3 files), scikit-learn (3 files), Seurat (3 files), SciPy (2 files), seaborn (2 files), cowplot (1 file), ggplot2 (1 file), Monocle 3 (1 file), Numba (1 file), reshape2 (1 file), tidyverse (1 file), UMAP (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
26 files
At the source: github.com/AbdulAbdulRU/IRISeq

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41593-026-02293-1.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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;
  • 36 scripts, each with its path and the digest of its content;
  • 9 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

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41593-026-02293-1.

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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 11 MeSH terms, 3 funders, 56 references.

Cite

This paper

Abdulraouf, A., Jiang, W., Zhang, Z., Xu, Z., Lu, Z., Merlinsky, T., Liao, A., Doymaz, A., Isakov, S., Raihan, T., Zhou, W., & Cao, J. (2026). Optics-free spatial genomics for mapping mammalian brain aging by IRISeq. Nature neuroscience, 29(7), 1762-1773. https://doi.org/10.1038/s41593-026-02293-1

BibTeX

@article{abdulraouf2026optics,
author = {Abdulraouf, Abdulraouf and Jiang, Weirong and Zhang, Zehao and Xu, Zihan and Lu, Ziyu and Merlinsky, Tiffany and Liao, Andrew and Doymaz, Ahmet and Isakov, Samuel and Raihan, Tanvir and Zhou, Wei and Cao, Junyue},
title = {{Optics-free spatial genomics for mapping mammalian brain aging by IRISeq}},
journal = {Nature neuroscience},
year = {2026},
month = may,
volume = {29},
number = {7},
pages = {1762--1773},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02293-1},
url = {https://doi.org/10.1038/s41593-026-02293-1},
pmid = {42120609},
pmcid = {PMC13337494}
}

RIS

TY - JOUR
AU - Abdulraouf, Abdulraouf
AU - Jiang, Weirong
AU - Zhang, Zehao
AU - Xu, Zihan
AU - Lu, Ziyu
AU - Merlinsky, Tiffany
AU - Liao, Andrew
AU - Doymaz, Ahmet
AU - Isakov, Samuel
AU - Raihan, Tanvir
AU - Zhou, Wei
AU - Cao, Junyue
TI - Optics-free spatial genomics for mapping mammalian brain aging by IRISeq
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/05/12
VL - 29
IS - 7
SP - 1762
EP - 1773
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02293-1
UR - https://doi.org/10.1038/s41593-026-02293-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02293-1",
"type": "article-journal",
"title": "Optics-free spatial genomics for mapping mammalian brain aging by IRISeq",
"container-title": "Nature neuroscience",
"author": [
{
"family": "Abdulraouf",
"given": "Abdulraouf"
},
{
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{
"family": "Zhang",
"given": "Zehao"
},
{
"family": "Xu",
"given": "Zihan"
},
{
"family": "Lu",
"given": "Ziyu"
},
{
"family": "Merlinsky",
"given": "Tiffany"
},
{
"family": "Liao",
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},
{
"family": "Doymaz",
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},
{
"family": "Isakov",
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},
{
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"container-title-short": "Nat Neurosci",
"volume": "29",
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"DOI": "10.1038/s41593-026-02293-1",
"PMID": "42120609",
"PMCID": "PMC13337494",
"ISSN": "1097-6256",
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"language": "en",
"issued": {
"date-parts": [
[
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12
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}
}

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

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