OSCR

The alx gene family confers segmental identity to frontonasal cranial neural crest cells.

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  1. [1] § Results › Frontonasal cells gain arch one gene expression and lose frontonasal gene expression in alx mutants ↔ processing_4_17_25.Rmd, lines 171–236 · score 0.61 · pax3a, dlx2a, prrx1a, Lhx6, gata3, gene expressed
  2. [2] § Methods › Zebrafish husbandry, strains, and mutagenesis › 48 hpf FACS isolation and scRNA-seq ↔ alx_sc_analysis.Rmd, lines 166–228 · score 0.60 · FeaturePlot, Cell cycle, phase, G2M, PCA, resolution
  3. [3] § Methods › Zebrafish husbandry, strains, and mutagenesis › Zebrafish in situ hybridization chain reaction (HCR), imaging, and Imaris quantification ↔ processing_4_17_25.Rmd, lines 7–127 · score 0.54 · dlx2a, cells expressing, prrx1a, alx4a, genotypes, thresholds

Paper

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The authors' code

R Markdown · 766 lines · 27 KB · no license · 2 matches

  1. ---
  2. title: "Alx_for_paper"
  3. output: html_document
  4. date: "2025-04-09"
  5. ---
  7. ```{r setup, include=FALSE}
  8. knitr::opts_chunk$set(echo = TRUE)
  10. library(dplyr)
  12. library(Seurat)
  13. library(patchwork)
  15. alx_so <- readRDS(file = "alx_regressed_so.rds")
  17. DimPlot(alx_so, reduction = "umap", split.by = "orig.ident", group.by = "seurat_clusters")
  21. number_perCluster.alx<- table([email hidden]$seurat_clusters,
  22. [email hidden]$orig.ident)
  24. View(number_perCluster.alx)
  25. write.csv(number_perCluster.alx, 'cell_counts.csv')
  28. alx.markers.sat <- FindAllMarkers(alx_so, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  30. alx_so <- SetIdent(alx_so, value = [email hidden]$seurat_clusters)
  31. alx.markers <- FindAllMarkers(alx_so, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  33. write.csv(alx.markers, 'alx_markers.csv')
  36. sum(GetAssayData(object = [email hidden]$orig.ident == "WT", slot = "data")["prrx1a",]>0)
  38. ## create subset SOs for each genotype and for fn clusters
  40. wt_object <- subset(alx_so, orig.ident == "WT")
  41. wt_objectfn <- subset(wt_object, idents = c("Frontonasal 1","Frontonasal 2"))
  42. wt_arch <- subset(wt_object, idents = c("Anterior Arches"))
  44. threeM_obj <- subset(alx_so, orig.ident == "alx3M")
  45. threeM_objectfn <- subset(threeM_obj, idents = c("Frontonasal 1","Frontonasal 2"))
  46. threeM_arch <- subset(threeM_obj, idents = c("Anterior Arches"))
  48. threefourM <- subset(alx_so, orig.ident == "alx3Malx4aM")
  49. threefourMfn <- subset(threefourM, idents = c("Frontonasal 1","Frontonasal 2"))
  50. threefourM_arch <- subset(threefourM, idents = c("Anterior Arches"))
  53. #how many cells express prrx1a in each genotype?
  54. sum(GetAssayData(object = wt_object, slot = "data")["prrx1a",]>0)
  55. sum(GetAssayData(object = threeM_obj, slot = "data")["prrx1a",]>0)
  56. sum(GetAssayData(object = threefourM, slot = "data")["prrx1a",]>0)
  58. #how many cells express dlx2a in each genotype?
  59. sum(GetAssayData(object = wt_object, slot = "data")["dlx2a",]>0)
  60. sum(GetAssayData(object = threeM_obj, slot = "data")["dlx2a",]>0)
  61. sum(GetAssayData(object = threefourM, slot = "data")["dlx2a",]>0)
  63. #how many fn cells express prrx1a in each genotype?
  64. sum(GetAssayData(object = wt_objectfn, slot = "data")["prrx1a",]>0)
  65. sum(GetAssayData(object = threeM_objectfn, slot = "data")["prrx1a",]>0)
  66. sum(GetAssayData(object = threefourMfn, slot = "data")["prrx1a",]>0)
  68. #how many fn cells express prrx1a in each genotype?
  69. sum(GetAssayData(object = wt_objectfn, slot = "data")["dlx2a",]>0)
  70. sum(GetAssayData(object = threeM_objectfn, slot = "data")["dlx2a",]>0)
  71. sum(GetAssayData(object = threefourMfn, slot = "data")["dlx2a",]>0)
  73. #how many fn cells express alx3 in each genotype?
  74. sum(GetAssayData(object = wt_objectfn, slot = "data")["alx3",]>0)
  75. sum(GetAssayData(object = threeM_objectfn, slot = "data")["alx3",]>0)
  76. sum(GetAssayData(object = threefourMfn, slot = "data")["alx3",]>0)
  78. #how many fn cells express alx4a in each genotype?
  79. sum(GetAssayData(object = wt_objectfn, slot = "data")["ALX4",]>0)
  80. sum(GetAssayData(object = threeM_objectfn, slot = "data")["ALX4",]>0)
  81. sum(GetAssayData(object = threefourMfn, slot = "data")["ALX4",]>0)
  84. #how many fn cells express prrx1a in each genotype?
  85. sum(GetAssayData(object = wt_arch, slot = "data")["prrx1a",]>0)
  86. sum(GetAssayData(object = threeM_arch, slot = "data")["prrx1a",]>0)
  87. sum(GetAssayData(object = threefourM_arch, slot = "data")["prrx1a",]>0)
  89. #how many arch cells express dlx2a in each genotype?
  90. sum(GetAssayData(object = wt_arch, slot = "data")["dlx2a",]>0)
  91. sum(GetAssayData(object = threeM_arch, slot = "data")["dlx2a",]>0)
  92. sum(GetAssayData(object = threefourM_arch, slot = "data")["dlx2a",]>0)
  94. ##Cells in FN
  95. number_wt_fn<- table([email hidden]$seurat_clusters,
  96. [email hidden]$orig.ident)
  98. View(number_wt_fn)
  100. number_single_fn<- table([email hidden]$seurat_clusters,
  101. [email hidden]$orig.ident)
  103. View(number_single_fn)
  105. number_double_fn<- table([email hidden]$seurat_clusters,
  106. [email hidden]$orig.ident)
  108. View(number_double_fn)
  110. #number cells in arch
  111. number_wt_arch<- table([email hidden]$seurat_clusters,
  112. [email hidden]$orig.ident)
  114. View(number_wt_arch)
  116. number_single_arch<- table([email hidden]$seurat_clusters,
  117. [email hidden]$orig.ident)
  119. View(number_single_arch)
  121. number_double_arch<- table([email hidden]$seurat_clusters,
  122. [email hidden]$orig.ident)
  124. View(number_double_arch)
  127. ```
  129. ## R Markdown
  131. This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
  133. When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
  135. ```{r cars}
  139. DimPlot(`SCTfinal 1`, reduction = "umap",group.by = "seurat_clusters", split.by = "orig.ident")
  141. FeaturePlot(`SCTfinal 1`, features = c('alx3', "prrx1a"), pt.size = 1.4, blend = TRUE, blend.threshold = 0.05)
  144. FeaturePlot(`SCTfinal 1`, features = 'alx3', pt.size = 1, cols = c("grey", "#FF0000"))
  145. FeaturePlot(`SCTfinal 1`, features = 'alx4a', pt.size = 1, cols = c("grey", "#FF00FF"))
  146. FeaturePlot(`SCTfinal 1`, features = 'alx1', pt.size = 1, cols = c("grey", "#3EFF00"))
  147. FeaturePlot(`SCTfinal 1`, features = c('alx3', "prrx1a"), pt.size = 1, blend = TRUE, cols = c('red', 'purple'), blend.threshold = 0.05)
  149. FeaturePlot(`SCTfinal 1`, features = c('alx4a', "alx1"), pt.size = .5, blend = TRUE, cols = c('purple', 'green'), blend.threshold = 0.05)
  151. FeaturePlot(`SCTfinal 1`, features = c('alx3', "alx1"), pt.size = .5, blend = TRUE, cols = c('red', 'green'), blend.threshold = 0.05)
  153. FeaturePlot(`SCTfinal 1`, features = c('alx3', "alx4a"), pt.size = .5, blend = TRUE, cols = c('red', 'purple'), blend.threshold = 0.05)
  155. VlnPlot(alx_so, features = c('shox'), same.y.lims = TRUE)
  157. DimPlot(`SCTfinal 1`, reduction = "umap",group.by = "seurat_clusters")
  160. #number of cells expressing prrx in frontonasal in 24hpf wt
  161. number_perCluster.wt<- table(`SCTfinal 1`@meta.data$seurat_clusters,
  162. `SCTfinal 1`@meta.data$orig.ident)
  163. wt24hpf <- subset(`SCTfinal 1`, orig.ident == "alx3_WT_24h")
  164. number_perCluster.wt24<- table([email hidden]$seurat_clusters,
  165. [email hidden]$orig.ident)
  167. View(number_perCluster.wt24)
  168. #write.csv(number_perCluster.alx, 'cell_counts.csv')
  171. #alx.markers.sat <- FindAllMarkers(alx_so, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  173. #alx_so <- SetIdent(alx_so, value = [email hidden]$seurat_clusters)
  174. #alx.markers <- FindAllMarkers(alx_so, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  176. #write.csv(alx.markers, 'alx_markers.csv')
  179. sum(GetAssayData(object = [email hidden]$orig.ident == "WT", slot = "data")["prrx1a",]>0)
  181. ## create subset SOs for each genotype and for fn clusters
  184. wt_24fn <- subset(wt24hpf, idents = c("1"))
  185. wt_24arch <- subset(wt24hpf, idents = c("0"))
  187. DimPlot(wt24hpf, reduction = "umap",group.by = "seurat_clusters")
  188. DimPlot(wt_24arch, reduction = "umap",group.by = "seurat_clusters")
  189. DimPlot(wt_24fn, reduction = "umap",group.by = "seurat_clusters")
  191. sum(GetAssayData(object = wt_24arch, slot = "data")["alx3",]>0)
  192. sum(GetAssayData(object = wt_24fn, slot = "data")["alx3",]>0)
  193. sum(GetAssayData(object = wt_24arch, slot = "data")["dlx2a",]>0)
  194. sum(GetAssayData(object = wt_24fn, slot = "data")["dlx2a",]>0)
  195. sum(GetAssayData(object = wt_24arch, slot = "data")["lhx8a",]>0)
  196. sum(GetAssayData(object = wt_24fn, slot = "data")["lhx8a",]>0)
  197. sum(GetAssayData(object = wt_24arch, slot = "data")["lhx6",]>0)
  198. sum(GetAssayData(object = wt_24fn, slot = "data")["lhx6",]>0)
  199. sum(GetAssayData(object = wt_24arch, slot = "data")["pax3a",]>0)
  200. sum(GetAssayData(object = wt_24fn, slot = "data")["pax3a",]>0)
  201. sum(GetAssayData(object = wt_24arch, slot = "data")["pax7a",]>0)
  202. sum(GetAssayData(object = wt_24fn, slot = "data")["pax7a",]>0)
  203. sum(GetAssayData(object = wt_24arch, slot = "data")["alx4a",]>0)
  204. sum(GetAssayData(object = wt_24fn, slot = "data")["alx4a",]>0)
  205. sum(GetAssayData(object = wt_24arch, slot = "data")["alx1",]>0)
  206. sum(GetAssayData(object = wt_24fn, slot = "data")["alx1",]>0)
  207. sum(GetAssayData(object = wt_24arch, slot = "data")["gata3",]>0)
  208. sum(GetAssayData(object = wt_24fn, slot = "data")["gata3",]>0)
  210. FeaturePlot(wt24hpf, features = c('gata3'))
  212. length(which(GetAssayData(object = wt_24fn, slot = "data")["alx3",]>0 & GetAssayData(object = wt_24fn, slot = "data")["prrx1a",]>0))
  216. DimPlot(alx_so, reduction = "umap",group.by = "seurat_clusters")
  217. FeaturePlot(alx_so, features = c('alx3'))
  218. FeaturePlot(alx_so, features = c('alx3', "prrx1a"), pt.size = .5, blend = TRUE, cols = c('red', 'purple'), blend.threshold = 0.05)
  220. Idents(alx_so) <- "seurat_clusters"
  221. #Finding differentially expressed genes in frontonasal comparing WT and double M
  222. alx_so_fn <- subset(alx_so, idents = c("Frontonasal 1","Frontonasal 2"))
  223. DimPlot(alx_so_fn, reduction = "umap", group.by = "seurat_clusters", split.by = "orig.ident")
  225. Idents(alx_so_fn) <- "orig.ident"
  226. de_genes_fn <- FindMarkers(
  227. alx_so_fn,
  228. ident.1 = "alx3M", # condition of interest
  229. ident.2 = "WT", # reference condition
  230. logfc.threshold = 0.1, # minimum log2 fold change to test
  231. min.pct = 0.01, # genes expressed in at least 10% of cells
  232. test.use = "wilcox" # default test (can use "MAST", "DESeq2", etc.)
  233. )
  234. head(de_genes_fn)
  235. library(ggplot2)
  236. library(ggrepel)
  239. de_genes_fn$gene <- rownames(de_genes_fn)
  240. head(de_genes_fn)
  241. top_genes_fn <- de_genes_fn %>%
  242. arrange(p_val_adj) %>%
  243. head(20)
  245. head(top_genes_fn)
  246. write.csv(de_genes_fn, 'degs_fn_correct.csv')
  247. ggplot(de_genes_fn, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  248. geom_point(aes(color = p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)) +
  249. scale_color_manual(values = c("grey", "red")) +
  250. theme_minimal() +
  251. geom_text_repel(
  252. data = top_genes_fn,
  253. aes(label = gene),
  254. size = 3,
  255. max.overlaps = 10
  256. ) +
  257. labs(
  258. x = "Average log2 fold change",
  259. y = "-log10(Adjusted p-value)",
  260. title = "Volcano plot: alx3Malx4aM vs WT"
  261. )
  262. ggplot(de_genes_fn, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  263. geom_point(aes(color = p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)) +
  264. scale_color_manual(values = c("grey", "red")) +
  265. theme_minimal() +
  266. geom_text_repel(
  267. data = top_genes_fn,
  268. aes(label = gene),
  269. size = 3,
  270. max.overlaps = 10
  271. ) +
  272. labs(
  273. x = "Average log2 fold change",
  274. y = "-log10(Adjusted p-value)",
  275. title = "Volcano plot: alx3Malx4aM vs WT"
  276. )
  279. #Finding differentially expressed genes in Arches comparing WT and double M
  280. alx_so_arch <- subset(alx_so, idents = c("Anterior Arches"))
  281. DimPlot(alx_so_arch, reduction = "umap", group.by = "seurat_clusters", split.by = "orig.ident")
  283. Idents(alx_so_arch) <- "orig.ident"
  284. de_genes_arch <- FindMarkers(
  285. alx_so_arch,
  286. ident.1 = "alx3M", # condition of interest
  287. ident.2 = "WT", # reference condition
  288. logfc.threshold = 0.25, # minimum log2 fold change to test
  289. min.pct = 0.1, # genes expressed in at least 10% of cells
  290. test.use = "wilcox" # default test (can use "MAST", "DESeq2", etc.)
  291. )
  293. write.csv(de_genes_arch, 'degs_arch_correct.csv')
  294. head(de_genes_arch)
  295. library(ggplot2)
  296. library(ggrepel)
  299. de_genes_arch$gene <- rownames(de_genes_arch)
  300. head(de_genes_arch)
  301. top_genes_arch <- de_genes_arch %>%
  302. arrange(p_val_adj) %>%
  303. head(20)
  305. head(top_genes_arch)
  306. write.csv(de_genes_arch, 'degs_arch_correct.csv')
  308. ggplot(de_genes_arch, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  309. geom_point(aes(color = p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)) +
  310. scale_color_manual(values = c("grey", "red")) +
  311. theme_minimal() +
  312. geom_text_repel(
  313. data = top_genes_fn,
  314. aes(label = gene),
  315. size = 3,
  316. max.overlaps = 10
  317. ) +
  318. labs(
  319. x = "Average log2 fold change",
  320. y = "-log10(Adjusted p-value)",
  321. title = "Volcano plot: alx3Malx4aM vs WT"
  322. )
  324. #Finding differentially expressed genes in frontonasaland arch comparing WT and double M
  325. alx_so_fn_arch <- subset(alx_so, idents = c("Frontonasal 1","Frontonasal 2", "Anterior Arches"))
  326. DimPlot(alx_so_fn_arch, reduction = "umap", group.by = "seurat_clusters", split.by = "orig.ident")
  328. Idents(alx_so_fn_arch) <- "orig.ident"
  329. de_genes_fn_arch <- FindMarkers(
  330. alx_so_fn_arch,
  331. ident.1 = "alx3M", # condition of interest
  332. ident.2 = "WT", # reference condition
  333. logfc.threshold = 0.15, # minimum log2 fold change to test
  334. min.pct = 0.1, # genes expressed in at least 10% of cells
  335. test.use = "wilcox" # default test (can use "MAST", "DESeq2", etc.)
  336. )
  337. head(de_genes_fn_arch)
  338. library(ggplot2)
  339. library(ggrepel)
  342. de_genes_fn_arch$gene <- rownames(de_genes_fn_arch)
  343. head(de_genes_fn_arch)
  344. top_genes_fn_arch <- de_genes_fn_arch %>%
  345. arrange(p_val_adj) %>%
  346. head(20)
  348. head(top_genes_fn_arch)
  349. write.csv(de_genes_fn_arch, 'degs_fn_arch.csv')
  351. ggplot(de_genes_fn_arch, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  352. geom_point(aes(color = p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)) +
  353. scale_color_manual(values = c("grey", "red")) +
  354. theme_minimal() +
  355. geom_text_repel(
  356. data = top_genes_fn,
  357. aes(label = gene),
  358. size = 3,
  359. max.overlaps = 20
  360. ) +
  361. labs(
  362. x = "Average log2 fold change",
  363. y = "-log10(Adjusted p-value)",
  364. title = "Volcano plot: alx3Malx4aM vs WT frontonasal and arch"
  365. )
  367. FeaturePlot(alx_so, features = c('gpc6a'), split.by = "orig.ident")
  368. FeaturePlot(alx_so, features = c('COL5A1'), split.by = "orig.ident")
  370. VlnPlot(alx_so, features = c('ALX4'), same.y.lims = TRUE, split.by = "orig.ident")
  373. FeaturePlot(alx_so, features = c('gata3'), split.by = "orig.ident")
  375. de_genes_overall <- FindMarkers(
  376. alx_so,
  377. ident.1 = "alx3M", # condition of interest
  378. ident.2 = "WT", # reference condition
  379. logfc.threshold = 0.25, # minimum log2 fold change to test
  380. min.pct = 0.1, # genes expressed in at least 10% of cells
  381. test.use = "wilcox" # default test (can use "MAST", "DESeq2", etc.)
  382. )
  384. write.csv(de_genes_overall, 'degs_correct.csv')
  387. #Finding differentially expressed genes in frontonasal 1 comparing WT and double M
  388. alx_so_fn1 <- subset(alx_so, idents = c("Frontonasal 1"))
  389. DimPlot(alx_so_fn1, reduction = "umap", group.by = "seurat_clusters", split.by = "orig.ident")
  391. Idents(alx_so_fn1) <- "orig.ident"
  392. de_genes_fn1 <- FindMarkers(
  393. alx_so_fn1,
  394. ident.1 = "alx3M", # condition of interest
  395. ident.2 = "WT", # reference condition
  396. logfc.threshold = 0.05, # minimum log2 fold change to test
  397. min.pct = 0.001, # genes expressed in at least 10% of cells
  398. test.use = "wilcox" # default test (can use "MAST", "DESeq2", etc.)
  399. )
  400. head(de_genes_fn1)
  401. library(ggplot2)
  402. library(ggrepel)
  405. de_genes_fn1$gene <- rownames(de_genes_fn1)
  406. head(de_genes_fn1)
  407. top_genes_fn1 <- de_genes_fn1 %>%
  408. arrange(p_val_adj) %>%
  409. head(20)
  411. head(top_genes_fn1)
  412. write.csv(de_genes_fn1, 'degs_fn1_correct.csv')
  413. ggplot(de_genes_fn1, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  414. geom_point(aes(color = p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)) +
  415. scale_color_manual(values = c("grey", "red")) +
  416. theme_minimal() +
  417. geom_text_repel(
  418. data = top_genes_fn1,
  419. aes(label = gene),
  420. size = 3,
  421. max.overlaps = 10
  422. ) +
  423. labs(
  424. x = "Average log2 fold change",
  425. y = "-log10(Adjusted p-value)",
  426. title = "Volcano plot: alx3Malx4aM vs WT fn1"
  427. )
  428. ggplot(de_genes_fn1, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  429. geom_point(aes(color = p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)) +
  430. scale_color_manual(values = c("grey", "red")) +
  431. theme_minimal() +
  432. geom_text_repel(
  433. data = top_genes_fn1,
  434. aes(label = gene),
  435. size = 3,
  436. max.overlaps = 10
  437. ) +
  438. labs(
  439. x = "Average log2 fold change",
  440. y = "-log10(Adjusted p-value)",
  441. title = "Volcano plot: alx3Malx4aM vs WT fn1"
  442. )
  444. ## FN2
  445. alx_so_fn2 <- subset(alx_so, idents = c("Frontonasal 2"))
  446. DimPlot(alx_so_fn2, reduction = "umap", group.by = "seurat_clusters", split.by = "orig.ident")
  448. Idents(alx_so_fn2) <- "orig.ident"
  449. de_genes_fn2 <- FindMarkers(
  450. alx_so_fn2,
  451. ident.1 = "alx3M", # condition of interest
  452. ident.2 = "WT", # reference condition
  453. logfc.threshold = 0.15, # minimum log2 fold change to test
  454. min.pct = 0.025, # genes expressed in at least 10% of cells
  455. test.use = "wilcox" # default test (can use "MAST", "DESeq2", etc.)
  456. )
  457. head(de_genes_fn2)
  458. library(ggplot2)
  459. library(ggrepel)
  462. de_genes_fn2$gene <- rownames(de_genes_fn2)
  463. head(de_genes_fn2)
  464. top_genes_fn2 <- de_genes_fn2 %>%
  465. arrange(p_val_adj) %>%
  466. head(20)
  468. head(top_genes_fn2)
  469. write.csv(de_genes_fn2, 'degs_fn2_correct.csv')
  470. ggplot(de_genes_fn2, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  471. geom_point(aes(color = p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)) +
  472. scale_color_manual(values = c("grey", "red")) +
  473. theme_minimal() +
  474. geom_text_repel(
  475. data = top_genes_fn1,
  476. aes(label = gene),
  477. size = 3,
  478. max.overlaps = 10
  479. ) +
  480. labs(
  481. x = "Average log2 fold change",
  482. y = "-log10(Adjusted p-value)",
  483. title = "Volcano plot: alx3Malx4aM vs WT fn2"
  484. )
  485. ggplot(de_genes_fn2, aes(x = avg_log2FC, y = -log10(p_val_adj))) +
  486. geom_point(aes(color = p_val_adj < 0.05 & abs(avg_log2FC) > 0.5)) +
  487. scale_color_manual(values = c("grey", "red")) +
  488. theme_minimal() +
  489. geom_text_repel(
  490. data = top_genes_fn2,
  491. aes(label = gene),
  492. size = 3,
  493. max.overlaps = 10
  494. ) +
  495. labs(
  496. x = "Average log2 fold change",
  497. y = "-log10(Adjusted p-value)",
  498. title = "Volcano plot: alx3Malx4aM vs WT fn2"
  499. )
  502. #Finding differentially expressed genes in Arches comparing WT and double M
  503. alx_so_arch <- subset(alx_so, idents = c("Anterior Arches"))
  504. DimPlot(alx_so_arch, reduction = "umap", group.by = "seurat_clusters", split.by = "orig.ident")
  506. Idents(alx_so_arch) <- "orig.ident"
  507. de_genes_arch <- FindMarkers(
  508. alx_so_arch,
  509. ident.1 = "alx3M", # condition of interest
  510. ident.2 = "WT", # reference condition
  511. logfc.threshold = 0.25, # minimum log2 fold change to test
  512. min.pct = 0.1, # genes expressed in at least 10% of cells
  513. test.use = "wilcox" # default test (can use "MAST", "DESeq2", etc.)
  514. )
  516. write.csv(de_genes_arch, 'degs_arch_correct.csv')
  518. ```
  521. ```{r}
  522. threeM_obj <- subset(alx_so, orig.ident == "alx3M")
  523. threeM_objectfn <- subset(threeM_obj, idents = c("Frontonasal 1","Frontonasal 2"))
  524. threeM_arch <- subset(threeM_obj, idents = c("Anterior Arches"))
  526. threefourM <- subset(alx_so, orig.ident == "alx3Malx4aM")
  527. threefourMfn <- subset(threefourM, idents = c("Frontonasal 1","Frontonasal 2"))
  528. threefourM_arch <- subset(threefourM, idents = c("Anterior Arches"))
  531. #how many cells express prrx1a in each genotype?
  532. sum(GetAssayData(object = wt_object, slot = "data")["prrx1a",]>0)
  533. sum(GetAssayData(object = threeM_obj, slot = "data")["prrx1a",]>0)
  534. sum(GetAssayData(object = threefourM, slot = "data")["prrx1a",]>0)
  536. #how many cells express dlx2a in each genotype?
  537. sum(GetAssayData(object = wt_object, slot = "data")["dlx2a",]>0)
  538. sum(GetAssayData(object = threeM_obj, slot = "data")["dlx2a",]>0)
  539. sum(GetAssayData(object = threefourM, slot = "data")["dlx2a",]>0)
  541. #how many fn cells express prrx1a in each genotype?
  542. sum(GetAssayData(object = wt_objectfn, slot = "data")["prrx1a",]>0)
  543. sum(GetAssayData(object = threeM_objectfn, slot = "data")["prrx1a",]>0)
  544. sum(GetAssayData(object = threefourMfn, slot = "data")["prrx1a",]>0)
  546. #how many fn cells express prrx1a in each genotype?
  547. sum(GetAssayData(object = wt_objectfn, slot = "data")["dlx2a",]>0)
  548. sum(GetAssayData(object = threeM_objectfn, slot = "data")["dlx2a",]>0)
  549. sum(GetAssayData(object = threefourMfn, slot = "data")["dlx2a",]>0)
  551. #how many fn cells express alx3 in each genotype?
  552. sum(GetAssayData(object = wt_objectfn, slot = "data")["alx3",]>0)
  553. sum(GetAssayData(object = threeM_objectfn, slot = "data")["alx3",]>0)
  554. sum(GetAssayData(object = threefourMfn, slot = "data")["alx3",]>0)
  556. #how many fn cells express alx4a in each genotype?
  557. sum(GetAssayData(object = wt_objectfn, slot = "data")["ALX4",]>0)
  558. sum(GetAssayData(object = threeM_objectfn, slot = "data")["ALX4",]>0)
  559. sum(GetAssayData(object = threefourMfn, slot = "data")["ALX4",]>0)
  562. #how many fn cells express prrx1a in each genotype?
  563. sum(GetAssayData(object = wt_arch, slot = "data")["prrx1a",]>0)
  564. sum(GetAssayData(object = threeM_arch, slot = "data")["prrx1a",]>0)
  565. sum(GetAssayData(object = threefourM_arch, slot = "data")["prrx1a",]>0)
  567. #how many arch cells express dlx2a in each genotype?
  568. sum(GetAssayData(object = wt_arch, slot = "data")["dlx2a",]>0)
  569. sum(GetAssayData(object = threeM_arch, slot = "data")["dlx2a",]>0)
  570. sum(GetAssayData(object = threefourM_arch, slot = "data")["dlx2a",]>0)
  572. ##Cells in FN
  573. number_wt_fn<- table([email hidden]$seurat_clusters,
  574. [email hidden]$orig.ident)
  576. View(number_wt_fn)
  578. number_single_fn<- table([email hidden]$seurat_clusters,
  579. [email hidden]$orig.ident)
  581. View(number_single_fn)
  583. number_double_fn<- table([email hidden]$seurat_clusters,
  584. [email hidden]$orig.ident)
  586. View(number_double_fn)
  588. #number cells in arch
  589. number_wt_arch<- table([email hidden]$seurat_clusters,
  590. [email hidden]$orig.ident)
  592. View(number_wt_arch)
  594. number_single_arch<- table([email hidden]$seurat_clusters,
  595. [email hidden]$orig.ident)
  597. View(number_single_arch)
  599. number_double_arch<- table([email hidden]$seurat_clusters,
  600. [email hidden]$orig.ident)
  602. View(number_double_arch)
  603. ```
  607. ```{r}
  609. Idents(alx_so_fn) <- "orig.ident"
  610. ### Make df of all genes expressed in fn
  611. expression_fn <- AverageExpression(alx_so_fn, return.seurat = FALSE, group.by = "orig.ident", layer = "counts")
  613. expression_df <- as.data.frame(expression_fn)
  614. expression_df$gene <- rownames(expression_df)
  616. ### make df of arch markers
  617. arch_genes_df <- alx.markers[alx.markers$cluster == "Anterior Arches", c('cluster', "gene")]
  619. ### make df of arch marker expression in fn
  620. arch_in_fn <- expression_df[expression_df$gene %in% arch_genes_df$gene,]
  622. write.csv(arch_in_fn, 'archgenesinfn.csv')
  624. ### how many arch genes are higher, lower, equal in M than WT
  625. higher_arch_inM_fn <- sum(arch_in_fn$RNA.alx3M > arch_in_fn$RNA.WT)
  626. print(higher_arch_inM_fn)
  628. lower_arch_inM_fn <- sum(arch_in_fn$RNA.alx3M < arch_in_fn$RNA.WT)
  629. print(lower_arch_inM_fn)
  631. equal_arch_inM_fn <- sum(arch_in_fn$RNA.alx3M == arch_in_fn$RNA.WT)
  632. print(equal_arch_inM_fn)
  634. values_arch <- c(331, 62, 0)
  635. archpercent <- round(100 * values_arch / sum(values_arch), 1)
  636. labels_arch <- c("higher in mutants", "lower in mutants", "equal")
  637. colors <- c("red", "blue", "yellow")
  639. pie(values_arch, labels = paste(archpercent,"%"), main = "Changes in anterior arch marker expression in frontonasal populations", col = colors)
  640. legend("topright", labels_arch, fill = colors, cex = 0.8)
  643. ### how many fn genes are higher in M than WT
  645. ### make df of fn markers
  646. fn_genes_df <- alx.markers[alx.markers$cluster == c("Frontonasal 1","Frontonasal 2"), c('cluster', "gene")]
  648. ### make df of arch marker expression in fn
  649. fn_in_fn <- expression_df[expression_df$gene %in% fn_genes_df$gene,]
  651. write.csv(fn_in_fn, 'fngenesinfn.csv')
  652. ### how many fn genes are higher, lower, equal in M than WT
  653. higher_fn_inM_fn <- sum(fn_in_fn$RNA.alx3M > fn_in_fn$RNA.WT)
  654. print(higher_fn_inM_fn)
  656. lower_fn_inM_fn <- sum(fn_in_fn$RNA.alx3M < fn_in_fn$RNA.WT)
  657. print(lower_fn_inM_fn)
  659. equal_fn_inM_fn <- sum(fn_in_fn$RNA.alx3M == fn_in_fn$RNA.WT)
  660. print(equal_fn_inM_fn)
  662. values_fn <- c(209, 425, 0)
  663. fnpercent <- round(100 * values_fn / sum(values_fn), 1)
  664. labels_fn <- c("higher in mutants", "lower in mutants", "equal")
  665. colors <- c("red", "blue", "yellow")
  667. pie(values_fn, labels = paste(fnpercent,"%"), main = "Changes in frontonasal marker expression in frontonasal populations", col = colors)
  668. legend("topright", labels_fn, fill = colors, cex = 0.8)
  670. ### how many total genes are higher, lower, equal in M than WT
  671. higher_inM_fn_all <- sum(expression_df$RNA.alx3M > expression_df$RNA.WT)
  672. print(higher_inM_fn_all)
  674. lower_inM_fn_all <- sum(expression_df$RNA.alx3M < expression_df$RNA.WT)
  675. print(lower_inM_fn_all)
  677. equal_inM_fn_all <- sum(expression_df$RNA.alx3M == expression_df$RNA.WT)
  678. print(equal_inM_fn_all)
  680. values_all <- c(10283, 13529, 1458)
  681. allpercent <- round(100 * values_all / sum(values_all), 1)
  682. labels_all <- c("higher in mutants", "lower in mutants", "equal")
  683. colors <- c("red", "blue", "yellow")
  685. pie(values_all, labels = paste(allpercent,"%"), main = "Changes in all gene expression in frontonasal populations", col = colors)
  686. legend("topright", labels_all, fill = colors, cex = 0.8)
  692. ### Plotting interesting changed genes
  693. FeaturePlot(alx_so, features = c('lhx6a'), split.by = "orig.ident")
  694. VlnPlot(alx_so, features = c('lhx6a'), same.y.lims = TRUE, split.by = "orig.ident", group.by = "seurat_clusters")
  696. FeaturePlot(alx_so, features = c('msx1b'), split.by = "orig.ident")
  697. VlnPlot(alx_so, features = c('COL6A3'), same.y.lims = TRUE, split.by = "orig.ident", group.by = "seurat_clusters")
  699. FeaturePlot(alx_so, features = c('col5a2a'), split.by = "orig.ident")
  700. VlnPlot(alx_so, features = c('col5a2a'), same.y.lims = TRUE, split.by = "orig.ident", group.by = "seurat_clusters")
  702. FeaturePlot(alx_so, features = c('barx1'), split.by = "orig.ident")
  703. VlnPlot(alx_so, features = c('gata3'), same.y.lims = TRUE, split.by = "orig.ident", group.by = "seurat_clusters")
  705. number_perCluster.alx<- table([email hidden]$seurat_clusters,
  706. [email hidden]$orig.ident)
  708. View(number_perCluster.alx)
  709. ```
  713. ``` {r}
  714. Idents(alx_so_fn) <- "orig.ident"
  715. expression_fn <- AverageExpression(alx_so_fn, return.seurat = FALSE, group.by = "orig.ident", layer = "counts")
  717. expression_df <- as.data.frame(expression_fn)
  718. expression_df$gene <- rownames(expression_df)
  720. arch_genes_df <- alx.markers[alx.markers$cluster == "Anterior Arches", c('cluster', "gene")]
  722. arch_in_fn <- expression_df[expression_df$gene %in% arch_genes_df$gene,]
  724. higher_inM_fn <- sum(arch_in_fn$RNA.alx3Malx4aM > arch_in_fn$RNA.alx3M)
  725. print(higher_inM_fn)
  727. higher_inM_all_fn <- sum(expression_df$RNA.alx3M == expression_df$RNA.alx3Malx4aM)
  728. print(higher_inM_all_fn)
  732. fn_reclustered <- FindNeighbors(alx_so_fn, dims = 1:10)
  734. fn_reclustered <- FindClusters(fn_reclustered, resolution = 0.4)
  736. fn_reclustered <- RunUMAP(fn_reclustered, dims = 1:10)
  738. new_fn_markers <- FindAllMarkers(fn_reclustered, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
  740. DimPlot(fn_reclustered, reduction = "umap", split.by = "orig.ident")
  742. new_fn_markers %>% group_by(cluster) %>% slice_max(n = 20, order_by = avg_log2FC)
  745. ```
  747. ## Including Plots
  749. You can also embed plots, for example:
  751. ```{r pressure, echo=FALSE}
  753. `SCTfinal 1` <- readRDS("SCTfinal 1.rds")
  755. FeaturePlot(`SCTfinal 1`, features = c('alx3', "prrx1a"), pt.size = 1, blend = TRUE, cols = c('#FF0000', '#9400D3'), blend.threshold = 0.05)
  757. FeaturePlot(`SCTfinal 1`, features = c('alx3', "alx4a", "alx1"))
  759. FeaturePlot(`SCTfinal 1`, features = 'alx3', pt.size = 1, cols = c("grey", "#FF0000"))
  760. FeaturePlot(`SCTfinal 1`, features = 'alx4a', pt.size = 1, cols = c("grey", "#FF00FF"))
  761. FeaturePlot(`SCTfinal 1`, features = 'alx1', pt.size = 1, cols = c("grey", "#3EFF00"))
  764. ```
  766. Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.

processing_4_17_25.Rmd at commit b84f2f0, no license · at the source

Overview

Authors: Abigail Mumme-Monheit1, Jennyfer M Mitchell1, Raisa Bailon-Zambrano1, Nadia Wright1, Lindsey A Neukirch1, Margaret K Keating1, Colette A Hopkins1, Kent Riemondy1, Grace E Gustafson1, Nicole D Moss1, Daniel M Medeiros2, James T Nichols1
ORCID iDs: Abigail Mumme-Monheit, Jennyfer M Mitchell, Raisa Bailon-Zambrano, Margaret K Keating, Colette A Hopkins, Grace E Gustafson, Daniel M Medeiros, James T Nichols
  1. Department of Craniofacial Biology, University of Colorado Anschutz Medical Campus, Aurora, CO USA
  2. Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO USA
Institutions: University of Colorado Anschutz (United States); University of Colorado Boulder (United States)
Journal: Nature communications, volume 17, issue 1, article 7713
Dates: received 5 September 2025; accepted 4 June 2026; published online 18 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74434-w · PMID 42315835 · PMCID PMC13433946 · OpenAlex W7165195162
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (organism), zebrafish (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: Body patterning, Bone development, Evolutionary developmental biology
MeSH: Homeodomain Proteins*, Neural Crest*, Transcription Factors*, Zebrafish*, Zebrafish Proteins*, Animals, Branchial Region, Gene Expression Regulation, Developmental, Lampreys, Multigene Family, Mutation, Skull (* major topic)
Topic: Developmental Biology and Gene Regulation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health &amp; Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR) (R01DE030448, R01DE029193); NIDCR NIH HHS (R01 DE029193, R01 DE030448)
Citations: not cited yet (Europe PMC); 79 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

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AbiRMM/alx3-alx4-scRNA-seq-Analysis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b84f2f03d023ebb145b313008131118b5ec4adab, 31 March 2026
Languages: R (2)
Size: 10 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: patchwork (2 files), Seurat (2 files), tidyverse (2 files), ggplot2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/AbiRMM/alx3-alx4-scRNA-seq-Analysis

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 3 keywords, 12 MeSH terms, 2 funders, 74 references.

Cite

This paper

Mumme-Monheit, A., Mitchell, J. M., Bailon-Zambrano, R., Wright, N., Neukirch, L. A., Keating, M. K., Hopkins, C. A., Riemondy, K., Gustafson, G. E., Moss, N. D., Medeiros, D. M., & Nichols, J. T. (2026). The alx gene family confers segmental identity to frontonasal cranial neural crest cells. Nature communications, 17(1), 7713. https://doi.org/10.1038/s41467-026-74434-w

BibTeX

@article{mummemonheit2026alx,
author = {Mumme-Monheit, Abigail and Mitchell, Jennyfer M and Bailon-Zambrano, Raisa and Wright, Nadia and Neukirch, Lindsey A and Keating, Margaret K and Hopkins, Colette A and Riemondy, Kent and Gustafson, Grace E and Moss, Nicole D and Medeiros, Daniel M and Nichols, James T},
title = {{The alx gene family confers segmental identity to frontonasal cranial neural crest cells}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7713},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74434-w},
url = {https://doi.org/10.1038/s41467-026-74434-w},
pmid = {42315835},
pmcid = {PMC13433946}
}

RIS

TY - JOUR
AU - Mumme-Monheit, Abigail
AU - Mitchell, Jennyfer M
AU - Bailon-Zambrano, Raisa
AU - Wright, Nadia
AU - Neukirch, Lindsey A
AU - Keating, Margaret K
AU - Hopkins, Colette A
AU - Riemondy, Kent
AU - Gustafson, Grace E
AU - Moss, Nicole D
AU - Medeiros, Daniel M
AU - Nichols, James T
TI - The alx gene family confers segmental identity to frontonasal cranial neural crest cells
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/18
VL - 17
IS - 1
SP - 7713
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74434-w
UR - https://doi.org/10.1038/s41467-026-74434-w
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74434-w",
"type": "article-journal",
"title": "The alx gene family confers segmental identity to frontonasal cranial neural crest cells",
"container-title": "Nature communications",
"author": [
{
"family": "Mumme-Monheit",
"given": "Abigail"
},
{
"family": "Mitchell",
"given": "Jennyfer M"
},
{
"family": "Bailon-Zambrano",
"given": "Raisa"
},
{
"family": "Wright",
"given": "Nadia"
},
{
"family": "Neukirch",
"given": "Lindsey A"
},
{
"family": "Keating",
"given": "Margaret K"
},
{
"family": "Hopkins",
"given": "Colette A"
},
{
"family": "Riemondy",
"given": "Kent"
},
{
"family": "Gustafson",
"given": "Grace E"
},
{
"family": "Moss",
"given": "Nicole D"
},
{
"family": "Medeiros",
"given": "Daniel M"
},
{
"family": "Nichols",
"given": "James T"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7713",
"DOI": "10.1038/s41467-026-74434-w",
"PMID": "42315835",
"PMCID": "PMC13433946",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74434-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
18
]
]
}
}

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Transcriptional and cellular maturation of the chick spinal cord in the context of distinct neuromuscular circuits.
Journal: iScience
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[10] doi:10.1038/s41467-026-70232-6 [code]
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Journal: Nature communications
In common: Seurat, patchwork, ggplot2, 1 other tool, 1 reference

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