OSCR

Enrichment of Neural Crest Cells by Antibody Labeling and Flow Cytometry for Single-Cell Transcriptomics in a Lizard.

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] § Material and Methods › Single Cell Transcriptomic Analysis › Assessment of NCC Enrichment ↔ 02_Analysis/08_scmapSmrtSOnCao_old.R, lines 2–44 · score 0.76 · likelihood threshold, variable features, Gene orthology, downloaded, Ensembl, scmap
  2. [2] § Material and Methods › Single Cell Transcriptomic Analysis › 10X Chromium ↔ 02_Analysis/04_IntAnalysis10XAndSmrtS.Rmd, lines 116–152 · score 0.75 · cell cycle scores, cell cycle state, SCTransform, v2, regression, human
  3. [3] § Results › Differential Expression Analysis in Unsorted Cells Reveals Putative Novel NCC Markers ↔ 02_Analysis/04_IntAnalysis10XAndSmrtS.Rmd, lines 564–581 · score 0.74 · TFAP2B, TFAP2A, Ets1, Sox8, Acbd7, Ednrb
  4. [4] § Material and Methods › Single Cell Transcriptomic Analysis › Assessment of NCC Enrichment ↔ 02_Analysis/04_IntAnalysis10XAndSmrtS.Rmd, lines 349–369 · score 0.72 · AddModuleScore, TFAP2A, Zic1, Pax3, Sox5, Pax7
  5. [5] § Material and Methods › Single Cell Transcriptomic Analysis › Smart‐seq3 ↔ 02_Analysis/04_IntAnalysis10XAndSmrtS.Rmd, lines 116–152 · score 0.69 · cell cycle scores, cell cycle state, SCTransform, regressed, predicted, genes
  6. [6] § Results › Characterization of HNK‐1 Positive Cells Using scRNA‐Seq ↔ 02_Analysis/04_IntAnalysis10XAndSmrtS.Rmd, lines 349–369 · score 0.68 · TFAP2A, median, leniently sorted, strictly sorted, Zic1, Pax3
  7. [7] § Material and Methods › Single Cell Transcriptomic Analysis › Assessment of NCC Enrichment ↔ 02_Analysis/04_IntAnalysis10XAndSmrtS.Rmd, lines 251–275 · score 0.65 · IntegrateLayers, SCTransform, UMAP, harmony
  8. [8] § Material and Methods › Single Cell Transcriptomic Analysis › 10X Chromium ↔ 02_Analysis/03_CompileAndFilter10X.Rmd, lines 355–373 · score 0.62 · reduced dimension, SCTransform, PCA, regression, space, UMAP
  9. [9] § Material and Methods › Single Cell Transcriptomic Analysis › Smart‐seq3 ↔ 02_Analysis/03_CompileAndFilter10X.Rmd, lines 355–373 · score 0.62 · reduced dimension, SCTransform, PCA, regressed, space, UMAP

Paper

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The authors' code

R Markdown · 680 lines · 24 KB · no license · 6 matches

  1. ---
  2. title: ''
  3. output: html_document
  4. date: "2025-12-03"
  5. ---
  7. ```{r setup, include=FALSE}
  8. knitr::opts_chunk$set(echo = TRUE)
  9. ```
  11. # Set up
  12. ```{r}
  13. options(future.globals.maxSize = 120000 * 1024^2) # Change this to fit your
  14. Sys.setenv(R_MAX_VSIZE = "120Gb") # computer
  16. rm(list=ls());gc()
  17. start_time <- Sys.time()
  19. library(Seurat, lib.loc = "/usr/local/lib/R/site-library") #Change these to
  20. library(glmGamPoi, lib.loc = "/usr/local/lib/R/site-library") #mach the location
  21. library(dplyr, lib.loc = "/usr/local/lib/R/site-library") #of your libraries
  22. library(ggplot2, lib.loc = "/usr/local/lib/R/site-library")
  23. library(patchwork, lib.loc = "/usr/local/lib/R/site-library")
  24. library(latex2exp, lib.loc = "/home/pranter/R/x86_64-pc-linux-gnu-library/4.5")
  25. library(ggrepel, lib.loc = "/usr/local/lib/R/site-library")
  26. library(ggbreak, lib.loc = "/home/pranter/R/x86_64-pc-linux-gnu-library/4.5")
  27. library(ggridges, lib.loc = "/usr/local/lib/R/site-library")
  28. library(stringr, lib.loc = "/usr/local/lib/R/site-library")
  30. path <- "~/WorkDirs/PmurNCCPilot_10X_rerun/"
  31. ```
  33. # Read filtered 10X data
  34. ```{r}
  35. filename <- list.files(paste0(path, "02_Analysis/"),
  36. pattern = "03_CompileAndFilter10X_out_")
  37. if (length(filename) == 1) {
  38. TenX.SO <- readRDS(file=paste0(path, "02_Analysis/", filename))
  39. } else {
  40. print(paste0("You have two files matching the pattern.",
  41. "Choose one to read and remove the other from the directory."))
  42. print(filename)
  43. }
  44. rm(filename)
  45. TenX.SO
  46. ```
  48. # Read filtered SmartSeq data
  49. ```{r}
  50. filename <- list.files(paste0(path,"02_Analysis/"),
  51. pattern = "02_FilterSmrtS_output_")
  52. if (length(filename) == 1) {
  53. SmrtS.SO <- readRDS(file = paste0(path, "02_Analysis/", filename))
  54. } else {
  55. print(paste0("You have two files matching the pattern. ",
  56. "Choose one to read and remove the other from the directory."))
  57. print(filename)
  58. }
  59. rm(filename)
  60. SmrtS.SO
  61. ```
  63. # Merge objects
  64. ```{r}
  65. # Select metadata columns
  66. met.cols <- c("orig.ident", "type", "nCount_RNA","nFeature_RNA",
  67. "percent.mt", "percent.rb")
  68. SmrtS.SO$type <- "Strictly sorted"
  69. [email hidden] <- [email hidden] %>% select(all_of(met.cols))
  70. [email hidden] <- [email hidden] %>% select(all_of(met.cols))
  71. rm(met.cols)
  73. #Restor seurat objects to non-normalized form
  74. DefaultAssay(SmrtS.SO) <- "RNA"
  75. SmrtS.SO <- DietSeurat(object = SmrtS.SO,
  76. layers = "counts",
  77. assays = "RNA")
  78. DefaultAssay(TenX.SO) <- "RNA"
  79. TenX.SO <- DietSeurat(object = TenX.SO,
  80. layers = "counts",
  81. assays = "RNA")
  83. #Merge
  84. full.SO <- merge(TenX.SO, c(SmrtS.SO))
  85. full.SO[["RNA"]] <- JoinLayers(full.SO[["RNA"]])
  87. # Fix the type column in meta data (mostly for plotting)
  88. [email hidden]$type <- factor([email hidden]$type,
  89. levels = c("Unsort", "Sorted",
  90. "Strictly sorted"))
  91. [email hidden] <- [email hidden] %>%
  92. mutate(type = recode(type,
  93. "Unsort" = "Unsorted",
  94. "Sorted" = "Leniently sorted"),
  95. type.short = recode(type,
  96. "Unsorted" = "U",
  97. "Leniently sorted" = "L",
  98. "Strictly sorted" = "S"))
  100. full.SO
  101. [email hidden] %>% colnames()
  102. ```
  104. # Basic stats
  105. ```{r}
  106. [email hidden] %>%
  107. group_by(type) %>%
  108. summarize(n = n(),
  109. av_count = mean(nCount_RNA) %>% round(),
  110. sd_count = sd(nCount_RNA) %>% round(),
  111. av_genes = mean(nFeature_RNA) %>% round(),
  112. sd_genes = sd(nFeature_RNA) %>% round()) %>%
  113. t()
  114. ```
  116. # Calc Cell Cycl
  117. ```{r}
  118. # temporary normalisation for CC-scoring
  119. full.SO <- SCTransform(full.SO,
  120. assay = 'RNA',
  121. new.assay.name = 'temp_SCT',
  122. vars.to.regress = c('percent.mt'),
  123. vst.flavor = "v2",
  124. variable.features.n = 3000) #3000 is default
  125. # Load ortholog table
  126. Ortho.df <- read.csv(file=paste0(path, "01_Data/",
  127. "Pmur1.0Human_OrthoTable_BioMartManDL20241206.txt"))
  128. # Save S and G2M markers in Pmur
  129. G2M.genes <- Ortho.df[Ortho.df$Human.gene.name %in%
  130. cc.genes.updated.2019$g2m.genes, ]$Gene.name
  131. S.genes <- Ortho.df[Ortho.df$Human.gene.name %in%
  132. cc.genes.updated.2019$s.genes, ]$Gene.name
  134. #Check that the markers are present in the data
  135. print(paste0(full.SO %>% Features() %in% G2M.genes %>% sum(), "/",
  136. G2M.genes %>% length(),
  137. " G2M-markers are present in the data"))
  138. print(paste0(full.SO %>% Features() %in% S.genes %>% sum(), "/",
  139. S.genes %>% length(),
  140. " S-markers are present in the data"))
  142. #Subset only the genes that are present
  143. G2M.genes <- G2M.genes[G2M.genes %in% Features(full.SO)]
  144. S.genes <- S.genes[S.genes %in% Features(full.SO)]
  145. # Predict cell cycle state (make sure to specify the "assay" parameter)
  146. full.SO <- CellCycleScoring(full.SO,
  147. s.features = S.genes,
  148. g2m.features = G2M.genes,
  149. assay = 'temp_SCT',
  150. set.ident = TRUE)
  151. rm(G2M.genes, S.genes); gc()
  152. ```
  154. # Plot QCs
  155. ```{r}
  156. PQC <- c("nCount_RNA", "nFeature_RNA", "percent.mt", "percent.rb") %>%
  157. lapply(function(feats){
  158. ggplot([email hidden], aes(x = type, y = .data[[feats]], fill = type)) +
  159. geom_violin() +
  160. scale_fill_manual(values = c("Unsorted" = "#BDBDBD",
  161. "Leniently sorted" = "#53AC90",
  162. "Strictly sorted" = "#64EC09")) +
  163. labs(y = c("nCount_RNA" = "n counts",
  164. "nFeature_RNA" = "n genes",
  165. "percent.mt" = "% mitochondrial",
  166. "percent.rb" = "% ribosomal")[feats], fill = "") +
  167. theme_classic() +
  168. theme(plot.margin = margin(t=1.7, r=1, b=0, l=0.4, unit = "mm"),
  169. axis.text.y = element_text(size = 8, angle = 45),
  170. axis.title.y = element_text(size = 11),
  172. axis.text.x = element_blank(),
  173. axis.title.x = element_blank(),
  174. axis.ticks.x = element_blank(),
  176. legend.text = element_text(size = 8),
  177. legend.key.size = unit(3, "mm"),
  178. legend.title = element_text(size = 11),
  180. plot.title = element_blank())
  181. }) %>%
  182. wrap_plots(ncol = 4) +
  183. plot_layout(guides = "collect") &
  184. theme(legend.position = "bottom")
  185. PQC
  186. ggsave(filename = paste0(path, "03_Results/QCPlot.svg"),
  187. plot = PQC, height = 50, width = 180, units = "mm")
  188. ```
  190. # Table for manuscript
  191. ```{r}
  192. [email hidden] %>% select("type", "nCount_RNA", "nFeature_RNA") %>%
  193. group_by(type) %>%
  194. summarize(n = n(),
  195. av_count = mean(nCount_RNA) %>% round(),
  196. sd_count = sd(nCount_RNA) %>% round(),
  197. av_genes = mean(nFeature_RNA) %>% round(),
  198. sd_genes = sd(nFeature_RNA) %>% round()) %>%
  199. t()
  200. ```
  202. # Standard processing
  203. ## Without integration
  204. ```{r}
  205. #DefaultAssay(full.SO) <- "RNA"
  206. #full.SO[["temp_SCT"]] <- NULL
  207. ## Normalize
  208. #full.SO <- SCTransform(full.SO,
  209. # assay = 'RNA',
  210. # new.assay.name = 'SCT',
  211. # vars.to.regress =c("percent.mt", "S.Score", "G2M.Score"),
  212. # vst.flavor = "v2",
  213. # variable.features.n = 3000)
  214. #
  215. ## Inspect the normalization
  216. #cbind(Matrix::rowSums(full.SO[["RNA"]]$counts)[
  217. # rownames(full.SO[["RNA"]]$counts) %in%
  218. # rownames(full.SO[["SCT"]]$data)],
  219. # Matrix::rowSums(full.SO[["SCT"]]$data)) %>%
  220. # as.data.frame() %>%
  221. # dplyr::rename(raw = V1, norm = V2) %>%
  222. # ggplot(aes(raw, norm)) +
  223. # geom_point() +
  224. # labs(title = "Normalized data vs raw counts (each dot is a gene)") +
  225. # theme(text = element_text(size = 6))
  226. #
  227. ## Inspect HVG selection
  228. #VariableFeaturePlot(full.SO, assay = "SCT") %>%
  229. # LabelPoints(points = VariableFeatures(full.SO, assay = "SCT") %>% head(20),
  230. # repel = TRUE,
  231. # xnudge = 0, ynudge = 0, max.overlaps = 22) +
  232. # theme(text = element_text(size = 6))
  233. #
  234. ## Lin dim reduc
  235. #full.SO <- RunPCA(full.SO, assay = "SCT")
  236. #ElbowPlot(full.SO, reduction = "pca", ndims = 50)
  237. #
  238. #Non-lin dim reduc
  239. #full.SO <- RunUMAP(full.SO, dims = 1:20)#, spread = 0.4, min.dist = 0.4)
  240. # # dims = 11 seems good judgeing by the elbowplot and PC-heatmaps
  241. # # Changing the min.dist dramatically changes the umap. This requires some
  242. # # optimaization. Come back to this later.
  243. #
  244. ##Plot
  245. #DimPlot(full.SO, reduction = "umap", group.by = "type",
  246. # shuffle = TRUE, alpha = 0.5) + ggtitle("No integration")
  247. ##DimPlot(full.SO, group.by = "orig.ident", shuffle = TRUE, alpha = 0.5,
  248. ## cells = Cells(subset(full.SO, subset = type.short == "L")))
  249. ```
  251. ## With integration
  252. See insturctions here: https://github.com/satijalab/seurat/issues/7542
  253. and here: https://satijalab.org/seurat/articles/seurat5_integration
  254. ```{r}
  255. # Normalize each sample separately
  256. full.SO[["RNA"]] <- split(full.SO[["RNA"]], f = full.SO$orig.ident)
  257. DefaultAssay(full.SO) <- "RNA"
  258. full.SO <- SCTransform(full.SO,
  259. assay = "RNA",
  260. new.assay.name = "SCT_BySample",
  261. vars.to.regress =c("percent.mt", "G2M.Score", "S.Score"),
  262. vst.flavor = "v2",
  263. variable.features.n = 3000) %>%
  264. # Calculate PCA
  265. RunPCA(assay = "SCT_BySample", reduction.name = "pca_SCT_BySample") %>%
  266. # Integrate data sets
  267. IntegrateLayers(method = HarmonyIntegration,
  268. orig.reduction = "pca_SCT_BySample",
  269. new.reduction = 'harmony',
  270. assay = "SCT_BySample",
  271. normalization.method = "SCT",
  272. verbose = FALSE) %>%
  273. # Calc umap
  274. RunUMAP(dims = 1:30, reduction = "harmony", reduction.name = "umap_harmony")
  275. ```
  276. # Plot sort types in umap
  277. ```{r}
  278. P01 <- DimPlot(full.SO,
  279. reduction = "umap_harmony", group.by = "type",
  280. order = c("Strictly sorted", "Leniently sorted", "Unsorted"),
  281. alpha = 0.5, stroke.size = 0, pt.size = 1) +
  282. scale_color_manual(values = c("Unsorted" = "#BDBDBD",
  283. "Leniently sorted" = "#53AC90",
  284. "Strictly sorted" = "#64EC09")) +
  285. labs(y = "umap 2", x = "umap 1") +
  286. theme(plot.margin = margin(t=1.7, r=1, b=1.7, l=0.4, unit = "mm"),
  287. axis.text.y = element_text(size = 8, angle = 45),
  288. axis.title.y = element_text(size = 11),
  290. axis.text.x = element_text(size = 8, angle = 45),
  291. axis.title.x = element_text(size = 11),
  293. legend.text = element_text(size = 8),
  294. legend.key.size = unit(2.5, "mm"),
  295. legend.position = "inside",
  297. plot.title = element_blank())
  298. P01
  300. ggsave(paste0(path, "03_Results/UMAP_SortType.svg"),
  301. P01, height = 89, width = 89, units = "mm")
  302. ```
  304. # Plot marker exp in umap
  305. ```{r}
  306. # First normalize across samples to correct for sequencing depth
  307. full.SO[["RNA"]] <- JoinLayers(full.SO[["RNA"]])
  308. full.SO <- SCTransform(full.SO,
  309. assay = "RNA",
  310. new.assay.name = "SCT",
  311. vars.to.regress =c("percent.mt", "G2M.Score", "S.Score"),
  312. vst.flavor = "v2",
  313. variable.features.n = 3000)
  315. # It also seems to work to do PrepSCTFindMarkers
  316. ```
  318. ```{r}
  319. # Then plor using FeaturePlot
  320. P02 <- FeaturePlot(full.SO, c("SOX10", "TFAP2A",
  321. "SNAI2", "FOXD3"#,
  322. #"PAX7", "SOX9", "TWIST1", "PAX3", "GAPDH"
  323. ),
  324. order = TRUE, pt.size = 1, alpha = 0.5, keep.scale = "all") &
  325. NoAxes() &
  326. theme(legend.key.size = unit(3, "mm"),
  327. legend.text = element_text(size = 8))
  329. #Remove stroke arround points
  330. P02 <- lapply(P02, function(p) {
  331. p$layers[[1]]$aes_params$stroke <- 0
  332. old_title <- p$labels$title
  333. #Chance title font
  334. p <- p + ggtitle(str_to_sentence(old_title)) +
  335. theme(plot.title = element_text(face = "italic"))
  336. p
  337. })
  339. #Tidy up
  340. P02 <- wrap_plots(P02, ncol = 2, guides = "collect") &
  341. FontSize(main = 11) +
  342. NoAxes()
  344. P02
  346. ggsave(paste0(path, "03_Results/UMAP_MrkrExp.svg"),
  347. P02, height = 89, width = 89, units = "mm")
  348. ```
  349. # Calc NCC-ness score
  350. ```{r}
  351. full.SO <- AddModuleScore(object = full.SO,
  352. features = list(c("PAX3", "PAX7", "FOXD3", "SOX10",
  353. "SOX9", "WNT1", "TFAP2A", "SOX5",
  354. "SNAI2", "ZIC1")),
  355. name = "NCC_AMS",
  356. ctrl = 100)
  357. [email hidden] %>%
  358. select(type, NCC_AMS1) %>%
  359. group_by(type) %>%
  360. summarize(avg_NCCAMS = median(NCC_AMS1)) %>%
  361. t()
  363. wilcox.test(x = subset(full.SO, subset = type == "Strictly sorted")$NCC_AMS1,
  364. y = subset(full.SO, subset = type == "Leniently sorted")$NCC_AMS1)
  365. wilcox.test( x = subset(full.SO, subset = type == "Strictly sorted")$NCC_AMS1,
  366. y = subset(full.SO, subset = type == "Unsorted")$NCC_AMS1)
  367. wilcox.test( x = subset(full.SO, subset = type == "Leniently sorted")$NCC_AMS1,
  368. y = subset(full.SO, subset = type== "Unsorted")$NCC_AMS1)
  369. ```
  371. # NCC markers
  372. ## Def NCCs in unsorted samp
  373. ```{r}
  374. unsort.SO <- full.SO %>%
  375. subset(cells = Cells(subset(full.SO, subset = type == "Unsorted")))
  377. # Extract SOX10 expression (RNA assay, default data slot)
  378. sox10_expr <- GetAssayData(unsort.SO, assay = "SCT", layer = "data")["SOX10", ]
  380. # Create NCC classification
  381. unsort.SO$NCC <- ifelse(sox10_expr > 0, "NCC", "Other")
  382. rm(sox10_expr); gc()
  384. # Optionally make it a factor with defined order
  385. unsort.SO$NCC <- factor(unsort.SO$NCC, levels = c("Other", "NCC"))
  387. unsort.SO$NCC %>% table()
  388. ```
  390. ## Plot unsort samp in umap
  391. ```{r}
  392. Idents(unsort.SO) <- unsort.SO$type
  393. P03 <- DimPlot(unsort.SO,
  394. reduction = "umap_harmony", group.by = "NCC",
  395. order = c("NCC", "Other"),
  396. alpha = 0.7, stroke.size = 0, pt.size = 1) +
  397. scale_color_manual(values = c("Other" = "#BDBDBD",
  398. "NCC" = "#ffc64fff")) +
  399. labs(y = "umap 2", x = "umap 1") +
  400. theme(plot.margin = margin(t=1.7, r=1, b=1.7, l=0.4, unit = "mm"),
  401. axis.text.y = element_text(size = 8, angle = 45),
  402. axis.title.y = element_text(size = 11),
  404. axis.text.x = element_text(size = 8, angle = 45),
  405. axis.title.x = element_text(size = 11),
  407. legend.text = element_text(size = 11),
  408. legend.key.size = unit(2.5, "mm"),
  409. legend.title = element_text(size = 11),
  410. legend.position = "inside",
  412. plot.title = element_blank())
  413. P03
  415. ggsave(paste0(path, "03_Results/UMAP_Unsort.svg"),
  416. P03, height = 89, width = 89, units = "mm")
  417. ```
  419. ```{r}
  420. P03_inset <- FetchData(unsort.SO,
  421. vars = c("SOX10", "nCount_RNA"),
  422. assay = "RNA,",
  423. layer = "count") %>%
  424. mutate(Sox10_PM = SOX10 / (nCount_RNA / 1e6),
  425. logSox10_PM = log1p(Sox10_PM)) %>%
  426. ggplot(aes(x = logSox10_PM, fill = SOX10 == 0)) +
  427. geom_histogram(binwidth = 1) +
  429. scale_x_continuous(breaks = log1p(c(0, 200, 800)),
  430. labels = c("0", "200", "800")) +
  432. scale_y_break(breaks = c(150, 10450)) +
  433. scale_y_continuous(breaks = c(0, 100, 10500, 10600),
  434. labels = c("0", "100", "10500", "10600"),
  435. limits = c(0, 10610)) +
  437. scale_fill_manual(values = c(`TRUE` = "#BDBDBD", # bar at 0
  438. `FALSE` = "#ffc64fff"), # non-zero bars
  439. guide = "none") +
  440. #labs(x = "Sox10 per 1M", y = "Freq.") +
  441. theme_classic() +
  442. theme(plot.margin = margin(t=0, r=0, b=0, l=0),
  444. axis.text.y.right = element_blank(),
  445. axis.ticks.y.right = element_blank(),
  446. axis.line.y.right = element_blank(),
  448. axis.title = element_blank(),
  449. axis.text.x = element_text(size = 8, angle = 45, hjust = 1),
  450. axis.text.y = element_text(size = 8, angle = 45, vjust = 0.5))
  452. ggsave(paste0(path, "03_Results/UMAP_Unsort_hist.svg"),
  453. P03_inset, height = 35, width = 40, units = "mm")
  454. P03_inset
  455. ```
  457. ## Calc diff. exp.
  458. This does differential expression testing between NCCs (Sox10 positive cells)
  459. and all other cells in the unsorted samples.
  460. ```{r}
  461. Idents(unsort.SO) <- unsort.SO$NCC
  462. DEG.df <- FindMarkers(unsort.SO,
  463. ident.1 = "NCC",
  464. only.pos = FALSE,
  465. logfc.threshold = 0,
  466. min.pct = 0)
  467. DEG.df$gene <- rownames(DEG.df)
  469. #Add average normalized expression in NCCs to the dataframe
  470. avg_NCC_exp <- unsort.SO %>%
  471. subset(subset = NCC == "NCC") %>%
  472. GetAssayData(assay = "SCT", layer = "counts") %>%
  473. rowMeans()
  474. DEG.df <- DEG.df %>% mutate(avg_NCC_exp = avg_NCC_exp[rownames(DEG.df)])
  475. rm(avg_NCC_exp); gc()
  477. # Add negative log10 p column
  478. DEG.df$neg_log10p <- log10(DEG.df$p_val)*-1
  480. DEG.df %>% head()
  481. DEG.df %>% str()
  482. ```
  484. ## Def NCC markers
  485. ```{r}
  486. #Subset DE table
  487. NCCmrkrs.df <- DEG.df %>% #Use only:
  488. subset(avg_log2FC > 0.1) %>% # -Genes with relevant effect size
  489. subset(p_val_adj <= 0.05) %>% # -Significantly different genes
  490. subset(avg_NCC_exp > 0.05) #%>% # -Genes with informative mean exp in NCCs
  491. #subset(pct.1 >= 0.01)
  493. #Add column to label the NCC markers in DEG.df
  494. DEG.df$NCCmrkrs <- ifelse(DEG.df$gene %in% NCCmrkrs.df$gene, "Yes", "No")
  496. ##Save table for supplementary as .csv
  497. #DEG.df %>%
  498. # #Select the rellevant columns
  499. # select(gene, avg_log2FC, avg_NCC_exp, p_val_adj, NCCmrkrs) %>%
  500. # #Reorder the rows to make it easier to find the markers at a glance
  501. # arrange(factor(NCCmrkrs, levels = c("Yes", "No")),
  502. # p_val_adj,
  503. # desc(avg_log2FC)
  504. # ) %>%
  505. # # Round off all values
  506. # mutate(avg_log2FC = round(avg_log2FC, 3),
  507. # avg_NCC_exp = round(avg_NCC_exp, 3),
  508. # p_val_adj = case_when(
  509. # p_val_adj < 0.001 ~ "<0.001",
  510. # p_val_adj > 0.001 ~ as.character(round(p_val_adj, 3)))) %>%
  511. # # rename the columns for readability
  512. # rename("Gene" = gene,
  513. # "Average log2-fold change" = avg_log2FC,
  514. # "Average SCT counts in NCCs" = avg_NCC_exp,
  515. # "Adjusted p-value" = p_val_adj,
  516. # "NCC markers" = NCCmrkrs) %>%
  517. # write.csv(paste0(path, "03_Results/",
  518. # "DiffExpAll_", Sys.Date(), ".csv"),
  519. # row.names = FALSE)
  521. ```
  523. ## Plotting
  524. Here i plot differential expression in a few different ways for one of the
  525. figures in the paper.
  527. ## MA plot
  528. ```{r}
  529. #Plot
  530. P04 <- DEG.df %>%
  531. mutate(avg_log2FC = ifelse(avg_log2FC > 5, 5, avg_log2FC)) %>%
  532. mutate(avg_log2FC = ifelse(avg_log2FC < -5, -5, avg_log2FC)) %>%
  533. mutate(avg_NCC_exp = ifelse(avg_NCC_exp > 10, 10, avg_NCC_exp)) %>%
  534. arrange(NCCmrkrs == "Yes") %>%
  536. ggplot(aes(y = avg_log2FC, x = avg_NCC_exp, color = NCCmrkrs)) +
  537. geom_point(shape = 19, size = .7, alpha = .7) +
  538. scale_x_continuous(trans = scales::log1p_trans()) +
  539. scale_color_manual(values = c("Yes" = "#ffc64fff", "No" = "#BDBDBD")) +
  540. ylab(label = TeX('$log_{2}$(fold change)')) +
  541. xlab(label = "Mean expressin in putative NCCs, log(x+1)")+
  542. labs(title = "Differential expression",
  543. color = "NCC markers") +
  544. theme(plot.title = element_blank(),
  545. axis.title.y = element_text(size = 11),
  546. axis.title.x = element_text(size = 11),
  547. axis.text.y = element_text(size = 8),
  548. axis.text.x = element_text(size = 8),
  549. legend.title = element_text(size = 8),
  550. legend.key.size = unit(2.5, "mm"),
  551. legend.position = "none",
  552. #Background
  553. plot.background = element_rect(fill = "white", color = "white"),
  554. panel.background = element_rect(fill = "white", color = "white"),
  555. panel.border = element_rect(colour = "black", fill = NA)) +
  556. geom_vline(xintercept = .05, linetype = "dashed") +
  557. geom_hline(yintercept = .2, linetype = "dashed")
  558. P04
  560. ggsave(filename = paste0(path, "03_Results/MAPlot.svg"),
  561. plot = P04, height = 89, width = 89, units = "mm")
  562. ```
  564. ## Volcano plot
  565. ### Lebels
  566. ```{r}
  567. # Manually set some selected genes to label
  568. Volcano_labels <- c("SOX10", "EDNRB", "TFAP2A", "TFAP2B", "ZEB2",
  569. "PAX7", "ETS1", "SOX8", "SOX5",
  571. "GYPC", "ACBD7", "RAB19")
  573. Volcano_labels <- NCCmrkrs.df[Volcano_labels, ]
  575. Volcano_labels <- Volcano_labels %>%
  576. mutate(avg_log2FC = ifelse(avg_log2FC > 7, 7, avg_log2FC)) %>%
  577. mutate(avg_log2FC = ifelse(avg_log2FC < -7, -7, avg_log2FC)) %>%
  578. mutate(neg_log10p = ifelse(neg_log10p > 100, 100, neg_log10p))
  580. Volcano_labels
  581. ```
  583. ### Plot
  584. ```{r}
  585. # Set line for p value
  586. sig_line <- DEG.df %>%
  587. arrange(NCCmrkrs == "Yes") %>% {log10(0.05/nrow(.))*-1}
  589. #Construct the plot
  590. P05 <- DEG.df %>%
  591. mutate(avg_log2FC = ifelse(avg_log2FC > 7, 7, avg_log2FC)) %>%
  592. mutate(avg_log2FC = ifelse(avg_log2FC < -7, -7, avg_log2FC)) %>%
  593. mutate(neg_log10p = ifelse(neg_log10p > 100, 100, neg_log10p)) %>%
  594. arrange(NCCmrkrs == "Yes") %>%
  596. ggplot(aes(x = avg_log2FC, y = neg_log10p, color = NCCmrkrs)) +
  597. geom_point(shape = 19, size = .7, alpha = .7) +
  598. geom_vline(xintercept = 0.2, linetype = "dashed") +
  599. geom_hline(yintercept = sig_line, linetype = "dashed") +
  600. scale_color_manual(values = c("Yes" = "#ffc64fff", "No" = "#BDBDBD")) +
  601. ylab(label = TeX('$-log_{10}$(adj. p-value)')) +
  602. xlab(label = TeX('$log_{2}$(fold change)')) +
  603. labs(title = "NCC markers",
  604. color = "NCC markers") +
  605. geom_label_repel(data = Volcano_labels,
  606. #Italic lowercase gene names
  607. aes(label = paste0("italic(", str_to_sentence(gene), ")")),
  608. parse = TRUE,
  609. color = "black",
  610. max.overlaps = 20,
  611. nudge_x = -4,
  612. size = 3,
  613. label.padding = 0.1,
  614. label.r = .1) +
  615. theme(plot.title = element_blank(),
  616. axis.title.y = element_text(size = 11),
  617. axis.title.x = element_text(size = 11),
  618. axis.text.y = element_text(size = 8),
  619. axis.text.x = element_text(size = 8),
  620. #Background
  621. plot.background = element_rect(fill = "white", color = "white"),
  622. panel.background = element_rect(fill = "white", color = "white"),
  623. panel.border = element_rect(colour = "black", fill = NA),
  624. legend.position = "none")
  625. P05
  626. ggsave(filename = paste0(path, "03_Results/VolcanoPlot.svg"),
  627. plot = P05, height = 89, width = 89, units = "mm")
  628. ```
  631. ```{r}
  632. P05 + geom_point(
  633. data = DEG.df %>% filter(gene == "GYPC"), # Change gene to label the point
  634. aes(x = avg_log2FC, y = neg_log10p), # of your choice
  635. color = "red", size = 2)
  636. ```
  638. # Module score new NCC markers
  639. ```{r}
  640. full.SO <- AddModuleScore(full.SO,
  641. list(NCCmrkrs.df$gene),
  642. #Or, excluding Sox10:
  643. #list(NCCmrkrs.df$gene[2:length(NCCmrkrs.df$gene)]),
  644. name = "NewNCCmrkrsAMD")
  645. ```
  647. ```{r}
  648. P06 <- [email hidden] %>%
  649. ggplot(aes(x = type, fill = type, y = NewNCCmrkrsAMD1)) +
  650. geom_violin() +
  651. scale_fill_manual(values = c("Unsorted" = "#BDBDBD",
  652. "Leniently sorted" = "#53AC90",
  653. "Strictly sorted" = "#64EC09")) +
  654. labs(y = "NCC marker module score") +
  655. theme_classic() +
  656. theme(axis.title.x = element_blank(),
  657. axis.title.y = element_text(size = 11),
  658. axis.text = element_text(size = 8),
  659. legend.position = "none")
  661. ggsave(filename = paste0(path, "03_Results/NCCmrkrVln.svg"),
  662. plot = P06, height = 89, width = 89, units = "mm")
  663. P06
  664. ```
  666. # Save data
  667. Save the filtered data and meta data in an RDS file to be read by the next script in the pipeline (04_IntAnalysis10XAndSmrtS.Rmd)
  668. ```{r}
  669. full.SO
  670. saveRDS(full.SO, paste0(path,
  671. "02_Analysis/04_IntAnalysis_output_",
  672. Sys.Date(),
  673. ".Rds"))
  674. ```
  676. Calculated runtime:
  677. ```{r}
  678. end_time <- Sys.time()
  679. paste0("Elapsed time: ", end_time - start_time)
  680. ```

04_IntAnalysis10XAndSmrtS.Rmd at commit 11c8df9, no license · at the source

Overview

Authors: Robin Pranter1,2, Cedric Patthey3, Nathalie Feiner1,2
ORCID iDs: Robin Pranter, Nathalie Feiner
  1. Department of Biology, Lund University, Lund, Sweden
  2. Current address: Max Planck Institute for Evolutionary Biology, Plön, Germany
  3. Department of Diagnostic and Intervention, Umeå University, Umeå, Sweden
Institutions: Lund University (Sweden); Max Planck Institute for Evolutionary Biology (Germany); Umeå University (Sweden)
Journal: Evolution & development, volume 28, issue 1, article e70030
Dates: received 20 May 2025; accepted 4 February 2026; published online 18 February 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/ede.70030 · PMID 41709476 · PMCID PMC12917300 · OpenAlex W7130504013
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other (modality), other (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: cell types, fluorescence‐activated cell sorting, HNK‐1 antibody, non‐model organism, scRNA‐seq, transcriptional profiles
MeSH: Flow Cytometry*, Lizards*, Neural Crest*, Single-Cell Analysis*, Transcriptome*, Animals, Antibodies, Single-Cell Gene Expression Analysis (* major topic)
Topic: Developmental Biology and Gene Regulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Vetenskapsrådet (2020‐03650, 2020-03650); European Research Council (948126); Kungliga Fysiografiska Sällskapet i Lund (42010); Jörgen Lindströms stipendiefond
Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Neural crest cells (NCCs) are a key component of the vertebrate body plan and contribute to a variety of different traits. Recent advances in single‐cell transcriptomics (scRNA‐seq) have significantly improved our understanding of NCC biology. However, their dynamic migratory behavior and spatiotemporal heterogeneity in the developing embryo pose significant challenges for their identification and isolation. Consequently, most studies of NCCs have been confined to model organisms with established transgenic tools or established methods for in ovo manipulation. To overcome this limitation, we present a novel approach that combines antibody labeling with fluorescence activated cell sorting to enrich for NCCs and we demonstrate the approach in the common wall lizard (Podarcis muralis). Through microscopy, reverse transcription quantitative polymerase chain reaction and single‐cell RNA sequencing, we show that the method enriches for NCCs as efficiently as methods relying on transgenic animals. Using this technique, we successfully characterize transcriptional profiles of NCCs in wall lizard embryos. We anticipate that this method can be applied to a wide range of vertebrates that lack transgenic tools, enabling deeper insights into the diverse roles of neural crest cells in development and evolution.

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

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Zenodo 18544211

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source:

robinpranter/enrichment-of-neural-crest-cells---supplementary-code

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 11c8df99b76d3231d3fb5ba905b1e4adea11c4c7, 6 February 2026
Languages: R (12)
Size: 128 files, 12 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, 8 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (12 files), Seurat (9 files), ggplot2 (7 files), SingleCellExperiment (4 files), patchwork (3 files), car (1 file), emmeans (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
13 files
At the source: github.com/RobinPranter/Enrichment-of-neural-crest-cells---Supplementary-Code/tree/SupCode

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

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Data

Datasets cited

Data Availability Statement

All sequence data generated in this study have been deposited in NCBI GEO with accession number GSE319069 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE319069). Code to reproduce the results presented in this study are deposited at Zenodo together with qPCR and flow cytometry data DOI:10.5281/zenodo.18544211.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 8 MeSH terms, 4 funders, 56 references.

Cite

This paper

Pranter, R., Patthey, C., & Feiner, N. (2026). Enrichment of Neural Crest Cells by Antibody Labeling and Flow Cytometry for Single-Cell Transcriptomics in a Lizard. Evolution & development, 28(1), e70030. https://doi.org/10.1111/ede.70030

BibTeX

@article{pranter2026enrichment,
author = {Pranter, Robin and Patthey, Cedric and Feiner, Nathalie},
title = {{Enrichment of Neural Crest Cells by Antibody Labeling and Flow Cytometry for Single-Cell Transcriptomics in a Lizard}},
journal = {Evolution \& development},
year = {2026},
month = mar,
volume = {28},
number = {1},
pages = {e70030},
publisher = {Wiley},
issn = {1520-541X},
doi = {10.1111/ede.70030},
url = {https://doi.org/10.1111/ede.70030},
pmid = {41709476},
pmcid = {PMC12917300}
}

RIS

TY - JOUR
AU - Pranter, Robin
AU - Patthey, Cedric
AU - Feiner, Nathalie
TI - Enrichment of Neural Crest Cells by Antibody Labeling and Flow Cytometry for Single-Cell Transcriptomics in a Lizard
T2 - Evolution & development
J2 - Evol Dev
PY - 2026
DA - 2026/03/01
VL - 28
IS - 1
SP - e70030
SN - 1520-541X
PB - Wiley
DO - 10.1111/ede.70030
UR - https://doi.org/10.1111/ede.70030
LA - en
ER -

CSL-JSON

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"family": "Pranter",
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"container-title-short": "Evol Dev",
"volume": "28",
"issue": "1",
"page": "e70030",
"DOI": "10.1111/ede.70030",
"PMID": "41709476",
"PMCID": "PMC12917300",
"ISSN": "1520-541X",
"publisher": "Wiley",
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"language": "en",
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"date-parts": [
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