OSCR

Gene expression dynamics of human and mouse craniofacial development at the single-cell level.

Code ↔ Paper

38 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 38 matches
  1. [1] § Methods › Processing of snRNA and identification of major cell types ↔ old/main_figures.R, lines 171–230 · score 0.98 · sex determination genes, DDX3Y, KDM5D, NLGN4Y, logfc.threshold, min.cells.feature
  2. [2] § Results › Single nuclei gene-expression atlas of the developing human face ↔ clustering/human/Craniofacial-scRNAseq-manuscript-Rmarkdown.Rmd, lines 1777–1783 · score 0.93 · malar flattening, neural crest tumors, Epidermolysis Bullosa, brain tumors, Palate fistula, cleft palate
  3. [3] § Methods › Processing of snRNA and identification of major cell types ↔ clustering/mouse/mouse_ect_mes_subclustering.R, lines 395–469 · score 0.91 · logfc.threshold, min.cells.feature, min.pct, return.thresh, G2M score, FindAllMarkers
  4. [4] § Results › Characterization of human mesenchymal cell subtypes ↔ clustering/mouse/mouse_ect_mes_subclustering.R, lines 344–393 · score 0.85 · palatalShelf2, pLNP2, palatal.fusion, muscle.mes, palatal shelf1, fusion.mes2
  5. [5] § Results › Characterization of human mesenchymal cell subtypes ↔ old/02_annotate_mes_ect_subtypes.R, lines 190–240 · score 0.84 · palatalShelf2, pLNP2, palatal.fusion, muscle.mes, palatal shelf1, fusion.mes2
  6. [6] § Results › Conservation of cell types in craniofacial development ↔ main figures/spatial_expression.R, lines 416–463 · score 0.81 · NR2F2, NR2F1, Tfap2b, Tfap2a, human CNCC subtypes, SOX5
  7. [7] § Methods › Gene list enrichments per cell type ↔ magma_celltyping/ewce_analysis.R, lines 292–339 · score 0.79 · oneClosest, geneSizeControl, bootstrap enrichment, HARs, EWCE, Neanderthal
  8. [8] § Methods › Processing of spatial transcriptomics ↔ clustering/mouse/mouse_ect_mes_subclustering.R, lines 50–124 · score 0.78 · FindVariableFeatures, FindTransferAnchors, TransferData, log normalization, subtype annotations, cca
  9. [9] § Methods › Processing of spatial transcriptomics ↔ main figures/spatial_expression.R, lines 65–152 · score 0.74 · SCTransform, SpatialFeaturePlot, FindAllMarkers, variable features, Harmony, resolution
  10. [10] § Results › Conservation of cell types in craniofacial development ↔ old/mouse_figures.R, lines 672–736 · score 0.71 · NR2F2, NR2F1, Tfap2b, Tfap2a, SOX5, Foxd3
  11. [11] § Results › Characterization of human mesenchymal cell subtypes ↔ old/02_annotate_mes_ect_subtypes.R, lines 190–240 · score 0.69 · palatalShelf1, palatalShelf2, fusion.mes2, Chondrodysplasia, osteoblasts, mandibulararch
  12. [12] § Results › Characterization of putative human cranial neural crest ↔ main figures/spatial_expression.R, lines 416–463 · score 0.69 · NR2F2, NR2F1, Tfap2b, Tfap2a, SOX9, CNCC subtypes
  13. [13] § Results › Characterization of human mesenchymal cell subtypes ↔ clustering/mouse/mouse_ect_mes_subclustering.R, lines 344–393 · score 0.69 · palatalShelf1, palatalShelf2, fusion.mes2, Chondrodysplasia, osteoblasts, mandibulararch
  14. [14] § Methods › Marker gene comparisons across species ↔ clustering/mouse/mouse_ect_mes_subclustering.R, lines 1–48 · score 0.69 · convert_orthologs, non121_strategy, HGNC symbol, gprofiler, orthogene, species
  15. [15] § Methods › Subclustering of major cell types ↔ clustering/human/Ectoderm_subclustering_and_annotation.Rmd, lines 337–355 · score 0.68 · FindTransferAnchors, log normalization, transfer mouse, rpca, orthogene, Predicted
  16. [16] § Methods › Subclustering of major cell types ↔ clustering/human/Mesenchyme_subclustering_and_annotation.Rmd, lines 477–496 · score 0.68 · FindTransferAnchors, log normalization, transfer mouse, rpca, orthogene, Predicted
  17. [17] § Methods › Marker gene comparisons across species ↔ main figures/Species_Comparison.Rmd, lines 914–942 · score 0.68 · convert_orthologs, non121_strategy, HGNC symbol, species, orthogene, Conserved
  18. [18] § Results › Characterization of human mesenchymal cell subtypes ↔ clustering/mouse/Mouse_Spatial_Expression.Rmd, lines 766–797 · score 0.68 · MxP2, palatal shelf, fusion.mes2, mouse spatial, LNP, cluster
  19. [19] § Results › Cell type specific contributions to craniofacial phenotypes ↔ old/magma_celltyping_finngen.R, lines 542–590 · score 0.67 · LVOTO Narrow, Cleft Lip, Cleft Palate, tongue, tract, heart
  20. [20] § Methods › Subclustering of major cell types ↔ clustering/mouse/Mouse_Spatial_Expression.Rmd, lines 681–733 · score 0.67 · neural stem, early.CNCC, int.CNCC, Schwann, mesenchymal, clusters
  21. [21] § Results › Cell type specific contributions to craniofacial phenotypes ↔ magma_celltyping/Magma_Celltyping.R, lines 581–649 · score 0.65 · lip thickness, brow, chin, jaw, protrusion, nostril
  22. [22] § Results › Cell type specific contributions to craniofacial phenotypes ↔ old/magma_celltyping_finngen.R, lines 592–641 · score 0.65 · lip thickness, brow, chin, jaw, protrusion, nostril
  23. [23] § Results › Cell type specific contributions to craniofacial phenotypes ↔ main figures/Spatial_Expression.Rmd, lines 159–180 · score 0.65 · LVOTO Narrow, gsMap, CS13 human, B4GALNT3, trait, mapping
  24. [24] § Methods › Single-nucleus multiomics ↔ clustering/human/Craniofacial-scRNAseq-manuscript-Rmarkdown.Rmd, lines 45–68 · score 0.65 · CellRanger ARC, identify cell, CS16, CS14, CS12, CS17
  25. [25] § Methods › Subclustering of major cell types ↔ clustering/mouse/mouse_ect_mes_subclustering.R, lines 473–494 · score 0.64 · scToppR, toppFun, ToppGene, subclustering, mouse, ectodermal
  26. [26] § Methods › Phenotype-cell type association tests ↔ main figures/phenomix_analysis.R, lines 28–86 · score 0.62 · MSTExplorer, run_phenomix, HPO, phenotypes, EWCE, CellTypeData
  27. [27] § Methods › Processing of snRNA and identification of major cell types › RNA velocity analyses ↔ clustering/mouse/Mouse_clustering_and_annotation.Rmd, lines 21–78 · score 0.59 · Read10X_h5, mouse cluster, e13, Seurat, e10, e15
  28. [28] § Results › Characterization of putative human cranial neural crest ↔ clustering/mouse/Mouse_SOX2-_SOX10+_subclustering_and_annotation.Rmd, lines 442–496 · score 0.58 · early.CNCC, int.CNCC, melanocyte, CNCC subtypes, sensory, Schwann
  29. [29] § Results › Characterization of human ectodermal cell subtypes ↔ old/02_subclustering_and_annotation.R, lines 84–125 · score 0.57 · fusion.zone, ectodermal subtypes, thyroid, periderm, auditory, pituitary
  30. [30] § Methods › Facial variation and congenital abnormality GWAS enrichments ↔ magma_celltyping/Magma_Celltyping.R, lines 1747–1807 · score 0.57 · facial variation, MAGMA.Celltyping, linear, kb, trait, seq
  31. [31] § Results › Single nuclei gene-expression atlas of the developing human face ↔ clustering/human/Craniofacial-scRNAseq-manuscript-Rmarkdown.Rmd, lines 1057–1085 · score 0.57 · CD86, FLT1, MYH3, MYOG, PAX6, RHAG
  32. [32] § Results › Cell type specific contributions to craniofacial phenotypes ↔ main figures/Spatial_Expression.Rmd, lines 99–122 · score 0.55 · gsMap, Cleft Lip, Cleft Palate, MAB21L1, traits, CS13
  33. [33] § Results › Cell type specific contributions to craniofacial phenotypes ↔ magma_celltyping/FinnGen/retrieve_studies.sh, lines 1–60 · score 0.55 · musculoskeletal system, nervous system, growth, neck, eye, abnormality
  34. [34] § Results › Cell type specific contributions to craniofacial phenotypes ↔ old/magma_celltyping_finngen.R, lines 542–590 · score 0.55 · musculoskeletal system, nervous system, growth, neck, eye, abnormality
  35. [35] § Results › Characterization of putative human cranial neural crest ↔ old/mouse_spatial_plots.R, lines 95–164 · score 0.53 · Tfap2b, Tfap2a, HAND2, ALX4, module scores, PAX3
  36. [36] § Results › Characterization of putative human cranial neural crest ↔ clustering/mouse/Mouse_Spatial_Expression.Rmd, lines 681–733 · score 0.52 · Early CNCCs, ncl4, ncl2, ncl3, ncl1, melanocytes
  37. [37] § Methods › Spatial GWAS enrichments ↔ main figures/Spatial_Expression.Rmd, lines 99–122 · score 0.51 · gsMap, SpatialFeaturePlot, traits, CS13, mapped, score
  38. [38] § Results › Cell type specific contributions to craniofacial phenotypes ↔ main figures/phenomix_analysis.R, lines 28–86 · score 0.51 · MSTExplorer, run_phenomix, HPO, phenotypes, magma, abnormalities

Paper

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The authors' code

R · 581 lines · 30 KB · CC0-1.0 · 6 matches

  1. library(scToppR)
  2. library(Seurat)
  3. library(tidyverse)
  4. library(harmony)
  5. library(patchwork)
  6. library(openxlsx)
  7. library(orthogene)
  9. # Mouse ectoderm and mesenchyme subclustering
  11. #subset E11 matching TW E11 data
  12. setwd("/scr1/users/manchela/Data")
  13. cds <- readRDS("face_mouse_final.rds")
  14. Idents(cds) <- "stage"
  15. E11 <- subset(cds, idents = "E11")
  16. Idents(E11) <- "cell_type"
  18. require(biomaRt)
  19. mart_m <- useMart("ENSEMBL_MART_ENSEMBL", host = "useast.ensembl.org")
  20. mart_m <- useDataset("mmusculus_gene_ensembl", mart_m)
  21. mGenes <- getBM(
  22. mart = mart_m,
  23. attributes = c(
  24. "mgi_symbol","description",
  25. "entrezgene_id",
  26. "ensembl_gene_id",
  27. "gene_biotype"),
  28. filter = "mgi_symbol",
  29. values = rownames(cds),
  30. uniqueRows=TRUE)
  32. tf_table <- read.table("human_tf_Lambertetal_PMID29425488.txt",sep="\t",header = T)
  33. mart <- useMart("ENSEMBL_MART_ENSEMBL") #, host = "https://useast.ensembl.org")
  34. mart <- useDataset("hsapiens_gene_ensembl", mart)
  35. annotLookup <- getBM(
  36. mart = mart,
  37. attributes = c(
  38. "hgnc_symbol","description",
  39. "entrezgene_id","ensembl_gene_id",
  40. "gene_biotype"),
  41. filter = "ensembl_gene_id",
  42. values = tf_table$Human_TF,
  43. uniqueRows=TRUE)
  44. annotLookup_ortho_one2one <- orthogene::convert_orthologs(unique(annotLookup$hgnc_symbol),input_species="human",output_species = "mouse",
  45. non121_strategy = "drop_both_species", method="gprofiler")
  46. annotLookup$mgi_symbol <- rownames(annotLookup_ortho_one2one)[match(annotLookup$hgnc_symbol,annotLookup_ortho_one2one$input_gene)]
  47. mGenes$TF <- "No"
  48. mGenes$TF[which(mGenes$mgi_symbol%in%na.omit(annotLookup$mgi_symbol))] <- "Yes"
  50. ### MESENCHYME ###
  52. #subset E11 mesenchyme and transfer subtype annotation from TW
  53. mes <- subset(E11, idents = "mesenchyme")
  54. TW_mes <- readRDS("/Volumes/Extreme SSD/Mouse_cellranger/figures_all/TW/mm.rds")
  55. anchors <- FindTransferAnchors(reference = TW_mes, query = mes, reduction = 'cca', normalization.method = "LogNormalize", dims = 1:30)
  56. predicted.id <- TransferData(anchorset = anchors, refdata = TW_mes$RNA_snn_res.1, weight.reduction = mes[['harmony']],dims = 1:30)
  57. mes$cellType1 <- predicted.id$predicted.id
  58. predicted.id <- TransferData(anchorset = anchors, refdata = TW_mes$cellType2, weight.reduction = mes[['harmony']],dims = 1:30)
  59. mes$cellType2 <- predicted.id$predicted.id
  60. DimPlot(mes, group.by = "cellType2")
  62. #pre-processing
  63. mes <- NormalizeData(mes) %>%
  64. FindVariableFeatures() %>%
  65. ScaleData(vars.to.regress = c("S.Score", "G2M.Score")) %>%
  66. RunPCA(verbose = FALSE) %>%
  67. RunHarmony(group.by.vars = "orig.ident")
  68. mes <- RunUMAP(mes, reduction = "harmony", dims = 1:30, min.dist = 0.3) %>%
  69. FindNeighbors(reduction = "harmony", dims = 1:30, verbose = FALSE) %>%
  70. FindClusters(resolution = c(0.1,0.2,0.4,0.6,0.8,1), verbose = FALSE)
  71. Idents(mes) <- "RNA_snn_res.0.8"
  73. #plotting
  74. library(clustree)
  75. p4 <- clustree(mes, prefix = "RNA_snn_res.")
  76. p1 <- DimPlot(mes, label = T)+NoAxes()+NoLegend()
  77. p2 <- DimPlot(mes, group.by = "cellType2")+NoAxes()
  78. p3 <- DimPlot(mes,split.by = "stage",label = T)+NoAxes()+NoLegend()
  79. pdf("figures/mes_E11_TWtransferID.pdf",width = 16,height = 8)
  80. p1+p2
  81. p4
  82. dev.off()
  84. # find markers
  85. sig_genes <- FindAllMarkers(mes, assay = "RNA" ,logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1,
  86. min.diff.pct = -Inf, node = NULL, verbose = TRUE,
  87. only.pos = TRUE, max.cells.per.ident = Inf, random.seed = 1,
  88. latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3,
  89. pseudocount.use = 1, return.thresh = 0.01)
  90. sig_genes <- mutate(sig_genes,pct.diff=pct.1-pct.2)
  91. sig_genes$TF <- "no"
  92. id <- intersect(mGenes$mgi_symbol,sig_genes$gene)
  93. x <- sig_genes[sig_genes$gene %in% id,]
  94. sig_genes$TF[sig_genes$gene %in% id] <- mGenes$TF[match(x$gene,mGenes$mgi_symbol)]
  95. sig_genes$Entrez <- mGenes$entrezgene_id[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
  96. sig_genes$description <- mGenes$description[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
  97. sig_genes <- sig_genes[, c("gene","TF","Entrez","description","p_val_adj","avg_log2FC","pct.1","pct.2","pct.diff","cluster")]
  98. head(sig_genes)
  99. #remove "Rik" and "Gm" genes from markers lists
  100. RikFeatures <- grep("Rik",rownames(count))
  101. GmFeatures <- grep("Gm",rownames(count))
  102. count1 <- count[-RikFeatures,]
  103. count1 <- count1[-GmFeatures,]
  104. sig_genes2 <- filter(sig_genes, gene %in% row.names(count1))
  105. top <- sig_genes2 %>% group_by(cluster) %>% top_n(5, avg_log2FC) #%>% top_n(5, pct.diff);top
  106. p <- DotPlot(object = mes, features = unique(top$gene))+ coord_flip()+ RotatedAxis()
  107. df <- data.frame(Gene = p$data$features.plot, avg.exp = p$data$avg.exp.scaled, pct.exp = p$data$pct.exp, cluster = p$data$id)
  109. p2 <- df %>%
  110. filter(avg.exp > 0, pct.exp > 1) %>%
  111. ggplot(aes(x=cluster, y = Gene, color = avg.exp, size = pct.exp)) +
  112. geom_point() +
  113. scale_color_viridis_c() +
  114. cowplot::theme_cowplot() +
  115. theme(axis.line = element_blank()) +
  116. theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  117. ylab('') + xlab("")+
  118. theme(axis.ticks = element_blank())
  119. p2
  121. heatmap(table(mes$cellType1, mes$RNA_snn_res.0.8))
  122. mes <- RenameIdents(mes,c("0"="surface.fusion","1"="pLNP1","2"="palatal shelf1","3"="pLNP2","4"="chondroprogenitors",
  123. "5"="palatal shelf2","6"="MxP1","7"="MxP2","8"="MxP.aLNP","9"="ambiguous","10"="fusion.mes2",
  124. "11"="surface.mes","12"="palatal.fusion","13"="aLNP","14"="hrmn","15"="oxy","16"="cyc"))
  127. #use scToppR for validating annotations
  128. setwd("/scr1/users/manchela/Data")
  129. mes_degs <- openxlsx::read.xlsx("Craniofacial-scRNAseqManuscript/final-tables/Mouse_SigGenes_mes_SubTypes.xlsx")
  130. mes_toppData <- toppFun(mes_degs,
  131. gene_col = "gene",
  132. cluster_col = "cluster",
  133. p_val_col = "p_val_adj",
  134. logFC_col = "avg_log2FC")
  135. head(mes_toppData)
  137. p <- toppBalloon(mes_toppData,
  138. categories = "MousePheno",x_axis_text_size = 10) + coord_flip() +
  139. viridis::scale_color_viridis(option = "C",direction=-1) +
  140. ggtitle("Mouse Mesenchyme Subtypes: Mouse Phenotype ToppGene") +
  141. theme(panel.border = element_rect(color = "black", fill = NA, linewidth = 0.5),
  142. axis.text = element_text(color="black"),
  143. plot.title = element_text(color="black",size=14,hjust=0.5))
  145. pdf("Craniofacial-scRNAseqManuscript/final-figs/Mouse_mes_subtypes_scToppR_baloonplot.pdf",height=14,width=8.5)
  146. p
  147. dev.off()
  150. # functional annotation
  151. mmes1 <- readRDS("mmes1_final.rds")
  152. mart <- useMart("ENSEMBL_MART_ENSEMBL")#, host = "https://asia.ensembl.org")
  153. mart <- useDataset("hsapiens_gene_ensembl", mart)
  154. mes_annotLookup <- getBM(
  155. mart = mart,
  156. attributes = c(
  157. "hgnc_symbol","description",
  158. "entrezgene_id", "ensembl_gene_id",
  159. "gene_biotype"),
  160. filter = "hgnc_symbol",
  161. values = rownames(mmes1),
  162. uniqueRows=TRUE)
  164. mes_degs$entrez <- mes_annotLookup$entrezgene_id[match(mes_degs$gene,mes_annotLookup$hgnc_symbol)]
  165. mes_degs$biotype <- mes_annotLookup$gene_biotype[match(mes_degs$gene,mes_annotLookup$hgnc_symbol)]
  167. top_mes <- mes_degs %>% filter(p_val_adj < 0.05) %>% group_by(cluster) %>% top_n(10, avg_log2FC)
  168. top100_mes <- mes_degs %>% filter(p_val_adj < 0.05) %>% group_by(cluster) %>% top_n(100, avg_log2FC)
  169. top100pval_mes <- subset(top100_mes, rowSums(top100_mes[5] < 0.05) > 0)
  171. df1 <- top100pval_mes[,c(6,10)]
  172. df1$cluster <- factor(df1$cluster,levels=unique(df1$cluster))
  173. df1sample <- split(df1$entrez,df1$cluster)
  174. length(df1sample)
  175. initial_types <- names(df1sample)
  177. genelist <- as.list(df1sample)
  178. background_genes <- unique(mes_degs$entrez)
  180. BPclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichGO", OrgDb = "org.Hs.eg.db", pvalueCutoff=0.05, pAdjustMethod = "BH", ont = 'BP', universe = background_genes)
  181. BPclusterplot <- pairwise_termsim(BPclusterplot)
  183. CCclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichGO", OrgDb = "org.Hs.eg.db", pvalueCutoff=0.05, pAdjustMethod = "BH", ont = 'CC', universe = background_genes)
  184. CCclusterplot <- pairwise_termsim(CCclusterplot)
  186. MFclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichGO", OrgDb = "org.Hs.eg.db", pvalueCutoff=0.05, pAdjustMethod = "BH", ont = 'MF', universe = background_genes)
  187. MFclusterplot <- pairwise_termsim(MFclusterplot)
  189. Pathwayclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichPathway", pvalueCutoff=0.05, pAdjustMethod = "BH", universe = background_genes)
  190. Pathwayclusterplot <- pairwise_termsim(Pathwayclusterplot)
  192. KEGGclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichKEGG", pvalueCutoff=0.05, pAdjustMethod = "BH", universe = background_genes)
  193. KEGGclusterplot <- pairwise_termsim(KEGGclusterplot)
  195. DOclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichDGN", pvalueCutoff=0.05, pAdjustMethod = "BH", universe = background_genes)
  196. DOclusterplot <- pairwise_termsim(DOclusterplot)
  199. options(enrichplot.colours = c("blue", "grey"))
  200. p1 <- dotplot(BPclusterplot, showCategory = 5, font.size = 6, title = "GO: Biological Process", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  201. options(enrichplot.colours = c("orange", "grey"))
  202. p2 <- dotplot(CCclusterplot, showCategory = 5, font.size = 6, title = "GO: Cellular Component", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  203. options(enrichplot.colours = c("green", "grey"))
  204. p3 <- dotplot(MFclusterplot, showCategory = 5, font.size = 6, title = "GO: Molecular Function", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  205. options(enrichplot.colours = c("brown", "grey"))
  206. p4 <- dotplot(Pathwayclusterplot, showCategory = 5, font.size = 6, title = "Reactome Pathway", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  207. options(enrichplot.colours = c("yellow", "grey"))
  208. p5 <- dotplot(KEGGclusterplot, showCategory = 5, font.size = 6, title = "KEGG Pathway", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  209. options(enrichplot.colours = c("red", "grey"))
  210. p6 <- dotplot(DOclusterplot, showCategory = 5, font.size = 6, title = "DisGeNet", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  212. p1
  213. p2
  214. p3
  215. p4
  216. p5
  217. p6
  219. mes_annot_list <- list(BP =BPclusterplot, CC=CCclusterplot, MF = MFclusterplot, Reactome=Pathwayclusterplot,
  220. KEGG=KEGGclusterplot, DisGeNet=DOclusterplot)
  221. saveRDS(mes_annot_list, "mouse_mes_subtype_functional-annotation_lists.rds")
  223. mes_annot_list <- readRDS("mouse_mes_subtype_functional-annotation_lists.rds")
  224. mes_annot_list$BP <- mes_annot_list$BP@compareClusterResult
  225. mes_annot_list$CC <- mes_annot_list$CC@compareClusterResult
  226. mes_annot_list$MF <- mes_annot_list$MF@compareClusterResult
  227. mes_annot_list$Reactome <- mes_annot_list$Reactome@compareClusterResult
  228. mes_annot_list$KEGG <- mes_annot_list$KEGG@compareClusterResult
  229. mes_annot_list$DisGeNet <- mes_annot_list$DisGeNet@compareClusterResult
  230. openxlsx::write.xlsx(mes_annot_list, file = "Craniofacial-scRNAseqManuscript/final-tables/mouse_mes_subtype_functional-annotation.xlsx",
  231. sheetName = names(mes_annot_list), rowNames = FALSE)
  233. ### ECTODERM ###
  235. #subset E11 ectoderm and transfer subtype annotation from TW
  236. ect <- subset(E11, idents = "ectoderm")
  237. TW_ect <- readRDS("/Volumes/Extreme SSD/Mouse_cellranger/figures_all/TW/ee.rds")
  238. anchors <- FindTransferAnchors(reference = TW_ect, query = ect, reduction = 'cca', normalization.method = "LogNormalize", dims = 1:30)
  239. predicted.id <- TransferData(anchorset = anchors, refdata = TW_ect$cellType2, weight.reduction = ect[['harmony']],dims = 1:30)
  240. ect$cellType2 <- predicted.id$predicted.id
  241. DimPlot(ect, group.by = "cellType2")
  242. ect <- NormalizeData(ect) %>%
  243. FindVariableFeatures() %>%
  244. ScaleData(vars.to.regress = c("S.Score", "G2M.Score")) %>%
  245. RunPCA(verbose = FALSE) %>%
  246. RunHarmony(group.by.vars = "orig.ident")
  248. ect <- RunUMAP(ect, reduction = "harmony", dims = 1:30, min.dist = 0.3) %>%
  249. FindNeighbors(reduction = "harmony", dims = 1:30, verbose = FALSE) %>%
  250. FindClusters(resolution = c(0.1,0.2,0.4,0.5,0.6,0.8,1), verbose = FALSE)
  251. clustree(ect, prefix = "RNA_snn_res.")
  252. Idents(ect) <- "RNA_snn_res.0.8"
  253. p1 <- DimPlot(ect, label = T)+NoAxes()+NoLegend()
  254. p2 <- DimPlot(ect, group.by = "cellType2")+NoAxes()
  255. p3 <- DimPlot(ect,split.by = "stage",label = T)+NoAxes()+NoLegend()
  256. p4 <- clustree(ect, prefix = "RNA_snn_res.")
  258. pdf("figures/ect_E11_TWtransferID.pdf",width = 16,height = 8)
  259. p1+p2
  260. p4
  261. dev.off()
  263. sig_genes <- FindAllMarkers(ect, assay = "RNA" ,logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1,
  264. min.diff.pct = -Inf, node = NULL, verbose = TRUE,
  265. only.pos = TRUE, max.cells.per.ident = Inf, random.seed = 1,
  266. latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3,
  267. pseudocount.use = 1, return.thresh = 0.01)
  268. sig_genes <- mutate(sig_genes,pct.diff=pct.1-pct.2)
  269. sig_genes$TF <- "no"
  270. id <- intersect(mGenes$mgi_symbol,sig_genes$gene)
  271. x <- sig_genes[sig_genes$gene %in% id,]
  272. sig_genes$TF[sig_genes$gene %in% id] <- mGenes$TF[match(x$gene,mGenes$mgi_symbol)]
  273. sig_genes$Entrez <- mGenes$entrezgene_id[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
  274. sig_genes$description <- mGenes$description[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
  275. sig_genes <- sig_genes[, c("gene","TF","Entrez","description","p_val_adj","avg_log2FC","pct.1","pct.2","pct.diff","cluster")]
  276. head(sig_genes)
  277. #remove "Rik" and "Gm" genes from markers lists
  278. # RikFeatures <- grep("Rik",rownames(count))
  279. # GmFeatures <- grep("Gm",rownames(count))
  280. # count1 <- count[-RikFeatures,]
  281. # count1 <- count1[-GmFeatures,]
  282. sig_genes2 <- filter(sig_genes, gene %in% row.names(count1))
  283. top <- sig_genes2 %>% group_by(cluster) %>% top_n(5, avg_log2FC) #%>% top_n(5, pct.diff);top
  284. p <- DotPlot(object = ect, features = unique(top$gene))+ coord_flip()+ RotatedAxis()
  285. df <- data.frame(Gene = p$data$features.plot, avg.exp = p$data$avg.exp.scaled, pct.exp = p$data$pct.exp, cluster = p$data$id)
  287. p2 <- df %>%
  288. filter(avg.exp > 0, pct.exp > 1) %>%
  289. ggplot(aes(x=cluster, y = Gene, color = avg.exp, size = pct.exp)) +
  290. geom_point() +
  291. scale_color_viridis_c() +
  292. cowplot::theme_cowplot() +
  293. theme(axis.line = element_blank()) +
  294. theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  295. ylab('') + xlab("")+
  296. theme(axis.ticks = element_blank())
  297. p2
  298. heatmap(table(ect$cellType2, ect$RNA_snn_res.0.8))
  299. ect <- RenameIdents(ect,c("0"="fusion.surface","1"="palate","2"="OE.NaP","3"="ambiguous","4"="surface","5"="OE1",
  300. "6"="dental1","7"="periderm2","8"="OE3","9"="periderm","10"="OE2","11"="dental2","12"="NaP","13"="blood"))
  301. ect <- subset(ect,idents = "blood",invert=T)
  302. DimPlot(ect, label = T)
  303. ect$cellType3 <- Idents(ect)
  304. mes$cellType3 <- Idents(mes)
  306. E11_mes_ect <- merge(mes,ect)
  307. Idents(E11_mes_ect) <- "cellType3"
  308. E11$cell_type3 <- E11_mes_ect$cellType3
  309. Idents(cds) <- "cell_type"
  311. cds$E11ectmes1 <- E11_mes_ect$cellType3
  313. for (i in levels(E11_mes_ect)) {
  314. id <- rownames([email hidden][[email hidden]$cellType3 == i,])
  315. cds <- SetIdent(cds,id,value = i)
  316. }
  317. cds$E11ectmes <- Idents(cds)
  319. DimPlot(cds, group.by = "E11ectmes")+NoLegend()+NoAxes()
  320. #expand the annotation from E11 mesenchyme to combined mesenchyme
  321. Idents(cds) <- "cell_type"
  322. mes_all <- subset(cds, idents = "mesenchyme")
  323. mes_all <- NormalizeData(mes_all) %>%
  324. FindVariableFeatures() %>%
  325. ScaleData(vars.to.regress = c("S.Score", "G2M.Score")) %>%
  326. RunPCA(verbose = FALSE) %>%
  327. RunHarmony(group.by.vars = "orig.ident")
  328. ElbowPlot(ect,ndims = 50)
  329. mes_all <- RunUMAP(mes_all, reduction = "harmony", dims = 1:30, min.dist = 0.3) %>%
  330. FindNeighbors(reduction = "harmony", dims = 1:30, verbose = FALSE) %>%
  331. FindClusters(resolution = c(0.1,0.2,0.4,0.6,0.8,1), verbose = FALSE)
  332. p4 <- clustree(mes_all, prefix = "RNA_snn_res.")
  333. Idents(mes_all) <- "RNA_snn_res.0.8"
  334. p1 <- DimPlot(mes_all, label = T)+NoAxes()+NoLegend()
  335. p2 <- DimPlot(mes_all, group.by = "orig.ident")+NoAxes()
  336. p3 <- DimPlot(mes_all,split.by = "stage",label = T)+NoAxes()+NoLegend()
  337. p5 <- DimPlot(mes_all,group.by = "RNA_snn_res.0.8", label = T)+NoAxes()+NoLegend()
  338. p1+p2
  339. pdf("figures/mes_all_subtypes.pdf",width = 16,height = 8)
  340. p1+p2
  341. p5+p4
  342. dev.off()
  344. Idents(mes_all) <- "stage"
  345. mes1 <- subset(mes_all, idents = "E11")
  346. p1 <- DimPlot(mes_all, group.by = "RNA_snn_res.0.8",label = T)+NoAxes()+NoLegend()
  347. p2 <- DimPlot(mes1, group.by = "E11ectmes",label = T)+NoAxes()
  348. p1+p2
  349. heatmap(table(mes1$E11ectmes1, mes1$RNA_snn_res.0.8))
  350. barplot(prop.table(table(mes_all$stage,mes_all$RNA_snn_res.0.8),margin = 2),col = c("navy","blue","purple","brown","red","orange", "yellow"),legend.text = T)
  351. Idents(mes_all) <- "RNA_snn_res.0.8"
  352. mes_all <- RenameIdents(mes_all,c("0"="palatalShelf1","1"="MxP.surface","2"="LNP","3"="MxP.aLNP","4"="MandibularArch",
  353. "5"="pLNP.fusion","6"="e.osteoblast","7"="palatalShelf2","8"="MxP2","9"="cycling",
  354. "10"="pLNP2","11"="muscle.mes","12"="fusion.mes2","13"="palatal.fusion","14"="palatalShelf2",
  355. "15"="chondrocyte","16"="cartilage","17"="neural","18"="l.Osteoblast","19"="immune"))
  356. mes_all$cellType4 <- Idents(mes_all)
  357. heatmap(table(mes_all$E11ectmes1, mes_all$cellType4))
  358. sig_genes <- FindAllMarkers(mes_all, assay = "RNA" ,logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1,
  359. min.diff.pct = -Inf, node = NULL, verbose = TRUE,
  360. only.pos = TRUE, max.cells.per.ident = Inf, random.seed = 1,
  361. latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3,
  362. pseudocount.use = 1, return.thresh = 0.01)
  363. sig_genes <- mutate(sig_genes,pct.diff=pct.1-pct.2)
  364. sig_genes$TF <- "no"
  365. id <- intersect(mGenes$mgi_symbol,sig_genes$gene)
  366. x <- sig_genes[sig_genes$gene %in% id,]
  367. sig_genes$TF[sig_genes$gene %in% id] <- mGenes$TF[match(x$gene,mGenes$mgi_symbol)]
  368. sig_genes$Entrez <- mGenes$entrezgene_id[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
  369. sig_genes$description <- mGenes$description[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
  370. sig_genes <- sig_genes[, c("gene","TF","Entrez","description","p_val_adj","avg_log2FC","pct.1","pct.2","pct.diff","cluster")]
  371. head(sig_genes)
  372. #remove "Rik" and "Gm" genes from markers lists
  373. # RikFeatures <- grep("Rik",rownames(count))
  374. # GmFeatures <- grep("Gm",rownames(count))
  375. # count1 <- count[-RikFeatures,]
  376. # count1 <- count1[-GmFeatures,]
  377. sig_genes2 <- filter(sig_genes, gene %in% row.names(count1))
  378. top <- sig_genes2 %>% group_by(cluster) %>% top_n(10, avg_log2FC) #%>% top_n(5, pct.diff);top
  379. p <- DotPlot(object = mes_all, features = unique(top$gene))+ coord_flip()+ RotatedAxis()
  380. df <- data.frame(Gene = p$data$features.plot, avg.exp = p$data$avg.exp.scaled, pct.exp = p$data$pct.exp, cluster = p$data$id)
  382. p2 <- df %>%
  383. filter(avg.exp > 0, pct.exp > 1) %>%
  384. ggplot(aes(x=cluster, y = Gene, color = avg.exp, size = pct.exp)) +
  385. geom_point() +
  386. scale_color_viridis_c() +
  387. cowplot::theme_cowplot() +
  388. theme(axis.line = element_blank()) +
  389. theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  390. ylab('') + xlab("")+
  391. theme(axis.ticks = element_blank())
  392. p2
  393. openxlsx::write.xlsx(sig_genes,"tables/SigGenes_mes_all_res0.8.xlsx")
  395. #expand the annotation from E11 ectoderm to combined ectoderm
  396. ect_all <- subset(cds, idents = "ectoderm")
  397. ect_all <- NormalizeData(ect_all) %>%
  398. FindVariableFeatures() %>%
  399. ScaleData(vars.to.regress = c("S.Score", "G2M.Score")) %>%
  400. RunPCA(verbose = FALSE) %>%
  401. RunHarmony(group.by.vars = "orig.ident")
  402. ElbowPlot(ect,ndims = 50)
  403. ect_all <- RunUMAP(ect_all, reduction = "harmony", dims = 1:30, min.dist = 0.3) %>%
  404. FindNeighbors(reduction = "harmony", dims = 1:30, verbose = FALSE) %>%
  405. FindClusters(resolution = c(0.1,0.2,0.4,0.6,0.8,1), verbose = FALSE)
  406. p4 <- clustree(ect_all, prefix = "RNA_snn_res.")
  407. Idents(ect_all) <- "RNA_snn_res.0.8"
  409. p1 <- DimPlot(ect_all, label = T)+NoAxes()+NoLegend()
  410. p2 <- DimPlot(ect_all, group.by = "orig.ident")+NoAxes()
  411. p3 <- DimPlot(ect_all,split.by = "stage",label = T)+NoAxes()+NoLegend()
  412. p5 <- DimPlot(ect_all,group.by = "RNA_snn_res.0.8", label = T)+NoAxes()+NoLegend()
  413. p1+p2
  414. pdf("figures/ect_all_subtypes.pdf",width = 16,height = 8)
  415. p1+p2
  416. p5+p4
  417. dev.off()
  419. Idents(ect_all) <- "stage"
  420. ect1 <- subset(ect_all, idents = "E11")
  421. p1 <- DimPlot(ect_all, group.by = "RNA_snn_res.0.8",label = T)+NoAxes()+NoLegend()
  422. p2 <- DimPlot(ect1, group.by = "E11ectmes",label = T)+NoAxes()
  423. p1+p2
  424. heatmap(table(ect1$E11ectmes1, ect1$RNA_snn_res.0.8))
  425. barplot(prop.table(table(ect_all$stage,ect_all$RNA_snn_res.0.8),margin = 2),col = c("navy","blue","purple","brown","red","orange", "yellow"),legend.text = T)
  426. Idents(ect_all) <- "RNA_snn_res.0.8"
  427. sig_genes <- FindAllMarkers(ect_all, assay = "RNA" ,logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1,
  428. min.diff.pct = -Inf, node = NULL, verbose = TRUE,
  429. only.pos = TRUE, max.cells.per.ident = Inf, random.seed = 1,
  430. latent.vars = NULL, min.cells.feature = 3, min.cells.group = 3,
  431. pseudocount.use = 1, return.thresh = 0.01)
  432. sig_genes <- mutate(sig_genes,pct.diff=pct.1-pct.2)
  433. sig_genes$TF <- "no"
  434. id <- intersect(mGenes$mgi_symbol,sig_genes$gene)
  435. x <- sig_genes[sig_genes$gene %in% id,]
  436. sig_genes$TF[sig_genes$gene %in% id] <- mGenes$TF[match(x$gene,mGenes$mgi_symbol)]
  437. sig_genes$Entrez <- mGenes$entrezgene_id[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
  438. sig_genes$description <- mGenes$description[match(as.character(sig_genes$gene),mGenes$mgi_symbol)]
  439. sig_genes <- sig_genes[, c("gene","TF","Entrez","description","p_val_adj","avg_log2FC","pct.1","pct.2","pct.diff","cluster")]
  440. head(sig_genes)
  441. #remove "Rik" and "Gm" genes from markers lists
  442. # RikFeatures <- grep("Rik",rownames(count))
  443. # GmFeatures <- grep("Gm",rownames(count))
  444. # count1 <- count[-RikFeatures,]
  445. # count1 <- count1[-GmFeatures,]
  446. sig_genes2 <- filter(sig_genes, gene %in% row.names(count1))
  447. top <- sig_genes2 %>% group_by(cluster) %>% top_n(10, avg_log2FC) #%>% top_n(5, pct.diff);top
  448. p <- DotPlot(object = ect_all, features = unique(top$gene))+ coord_flip()+ RotatedAxis()
  449. df <- data.frame(Gene = p$data$features.plot, avg.exp = p$data$avg.exp.scaled, pct.exp = p$data$pct.exp, cluster = p$data$id)
  451. p2 <- df %>%
  452. filter(avg.exp > 0, pct.exp > 1) %>%
  453. ggplot(aes(x=cluster, y = Gene, color = avg.exp, size = pct.exp)) +
  454. geom_point() +
  455. scale_color_viridis_c() +
  456. cowplot::theme_cowplot() +
  457. theme(axis.line = element_blank()) +
  458. theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  459. ylab('') + xlab("")+
  460. theme(axis.ticks = element_blank())
  461. p2
  463. ect_all <- RenameIdents(ect_all,c("0"="fusion.surface","1"="ambiguous","2"="palate","3"="periderm2","4"="dental1",
  464. "5"="OE2","6"="dental2","7"="periderm","8"="surface.ect1","9"="NaP",
  465. "10"="OE.NaP","11"="OE1.Pax7","12"="OE3","13"="surface.ect2","14"="ciliated",
  466. "15"="collagen","16"="cardiac","17"="auditory","18"="pituitary","19"="myeloid"))
  467. ect_all$cellType4 <- Idents(ect_all)
  468. heatmap(table(ect_all$E11ectmes1, ect_all$cellType4))
  469. openxlsx::write.xlsx(sig_genes,"tables/SigGenes_ect_all_res0.8.xlsx")
  473. #use scToppR for validating annotations
  474. ect_degs <- openxlsx::read.xlsx("Craniofacial-scRNAseqManuscript/final-tables/Mouse_SigGenes_ect_SubTypes.xlsx")
  475. ect_toppData <- toppFun(ect_degs,
  476. gene_col = "gene",
  477. cluster_col = "cluster",
  478. p_val_col = "p_val_adj",
  479. logFC_col = "avg_log2FC")
  480. head(ect_toppData)
  482. p <- toppBalloon(ect_toppData,
  483. categories = "MousePheno",x_axis_text_size = 10) + coord_flip() +
  484. viridis::scale_color_viridis(option = "C",direction=-1) +
  485. ggtitle("Mouse Ectoderm Subtypes: Mouse Phenotype ToppGene") +
  486. theme(panel.border = element_rect(color = "black", fill = NA, linewidth = 0.5),
  487. axis.text = element_text(color="black"),
  488. plot.title = element_text(color="black",size=14,hjust=0.5))
  490. p
  492. pdf("Craniofacial-scRNAseqManuscript/final-figs/Mouse_ect_subtypes_scToppR_baloonplot.pdf",height=14,width=8.5)
  493. p
  494. dev.off()
  496. ## functional annotation
  497. mect1 <- readRDS("mect1_final.rds")
  499. ect_annotLookup <- getBM(
  500. mart = mart,
  501. attributes = c(
  502. "hgnc_symbol","description",
  503. "entrezgene_id", "ensembl_gene_id",
  504. "gene_biotype"),
  505. filter = "hgnc_symbol",
  506. values = rownames(mect1),
  507. uniqueRows=TRUE)
  509. ect_degs$entrez <- ect_annotLookup$entrezgene_id[match(ect_degs$gene,ect_annotLookup$hgnc_symbol)]
  510. ect_degs$biotype <- ect_annotLookup$gene_biotype[match(ect_degs$gene,ect_annotLookup$hgnc_symbol)]
  512. top_mes <- ect_degs %>% filter(p_val_adj < 0.05) %>% group_by(cluster) %>% top_n(10, avg_log2FC)
  513. top100_mes <- ect_degs %>% filter(p_val_adj < 0.05) %>% group_by(cluster) %>% top_n(100, avg_log2FC)
  514. top100pval_mes <- subset(top100_mes, rowSums(top100_mes[5] < 0.05) > 0)
  516. df1 <- top100pval_mes[,c(6,10)]
  517. df1$cluster <- factor(df1$cluster,levels=unique(df1$cluster))
  518. df1sample <- split(df1$entrez,df1$cluster)
  519. length(df1sample)
  520. initial_types <- names(df1sample)
  522. genelist <- as.list(df1sample)
  523. background_genes <- unique(ect_degs$entrez)
  525. BPclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichGO", OrgDb = "org.Hs.eg.db", pvalueCutoff=0.05, pAdjustMethod = "BH", ont = 'BP', universe = background_genes)
  526. BPclusterplot <- pairwise_termsim(BPclusterplot)
  528. CCclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichGO", OrgDb = "org.Hs.eg.db", pvalueCutoff=0.05, pAdjustMethod = "BH", ont = 'CC', universe = background_genes)
  529. CCclusterplot <- pairwise_termsim(CCclusterplot)
  531. MFclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichGO", OrgDb = "org.Hs.eg.db", pvalueCutoff=0.05, pAdjustMethod = "BH", ont = 'MF', universe = background_genes)
  532. MFclusterplot <- pairwise_termsim(MFclusterplot)
  534. Pathwayclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichPathway", pvalueCutoff=0.05, pAdjustMethod = "BH", universe = background_genes)
  535. Pathwayclusterplot <- pairwise_termsim(Pathwayclusterplot)
  537. KEGGclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichKEGG", pvalueCutoff=0.05, pAdjustMethod = "BH", universe = background_genes)
  538. KEGGclusterplot <- pairwise_termsim(KEGGclusterplot)
  540. DOclusterplot <- compareCluster(geneCluster = genelist, fun = "enrichDGN", pvalueCutoff=0.05, pAdjustMethod = "BH", universe = background_genes)
  541. DOclusterplot <- pairwise_termsim(DOclusterplot)
  544. options(enrichplot.colours = c("blue", "grey"))
  545. p1 <- dotplot(BPclusterplot, showCategory = 5, font.size = 6, title = "GO: Biological Process", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  546. options(enrichplot.colours = c("orange", "grey"))
  547. p2 <- dotplot(CCclusterplot, showCategory = 5, font.size = 6, title = "GO: Cellular Component", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  548. options(enrichplot.colours = c("green", "grey"))
  549. p3 <- dotplot(MFclusterplot, showCategory = 5, font.size = 6, title = "GO: Molecular Function", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  550. options(enrichplot.colours = c("brown", "grey"))
  551. p4 <- dotplot(Pathwayclusterplot, showCategory = 5, font.size = 6, title = "Reactome Pathway", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  552. options(enrichplot.colours = c("yellow", "grey"))
  553. p5 <- dotplot(KEGGclusterplot, showCategory = 5, font.size = 6, title = "KEGG Pathway", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  554. options(enrichplot.colours = c("red", "grey"))
  555. p6 <- dotplot(DOclusterplot, showCategory = 5, font.size = 6, title = "DisGeNet", color = "p.adjust", label_format = 50) + theme( legend.text=element_text(size=5),legend.position = "right",legend.box = "vertical",text = element_text(size=6), plot.title = element_text(hjust= 0.5, size = 12))
  557. p1
  558. p2
  559. p3
  560. p4
  561. p5
  562. p6
  564. ect_annot_list <- list(BP =BPclusterplot, CC=CCclusterplot, MF = MFclusterplot, Reactome=Pathwayclusterplot,
  565. KEGG=KEGGclusterplot, DisGeNet=DOclusterplot)
  566. saveRDS(ect_annot_list, "mouse_ect_subtype_functional-annotation_lists.rds")
  568. ect_annot_list <- readRDS("mouse_ect_subtype_functional-annotation_lists.rds")
  569. ect_annot_list$BP <- ect_annot_list$BP@compareClusterResult
  570. ect_annot_list$CC <- ect_annot_list$CC@compareClusterResult
  571. ect_annot_list$MF <- ect_annot_list$MF@compareClusterResult
  572. ect_annot_list$Reactome <- ect_annot_list$Reactome@compareClusterResult
  573. ect_annot_list$KEGG <- ect_annot_list$KEGG@compareClusterResult
  574. ect_annot_list$DisGeNet <- ect_annot_list$DisGeNet@compareClusterResult
  575. openxlsx::write.xlsx(ect_annot_list, file = "Craniofacial-scRNAseqManuscript/final-tables/mouse_ect_subtype_functional-annotation.xlsx",
  576. sheetName = names(ect_annot_list), rowNames = FALSE)

mouse_ect_mes_subclustering.R at commit 368d608, under CC0-1.0 · at the source

Overview

Authors: Nagham Khouri-Farah1, Alexandra Manchel2, Emma Wentworth Winchester1, Brian M. Schilder3,4, Kelsey Robinson5, Sarah W. Curtis5, Nathan G. Skene3,4, Elizabeth J. Leslie-Clarkson5, Justin Cotney2,6
ORCID iDs: Nagham Khouri-Farah, Alexandra Manchel, Brian M. Schilder, Nathan G. Skene, Elizabeth J. Leslie-Clarkson, Justin Cotney
  1. Graduate Program in Genetics and Developmental Biology, UConn Health,Farmington, CT USA
  2. Department of Surgery, Children’s Hospital of Philadelphia,Philadelphia, PA USA
  3. Department of Brain Sciences, Faculty of Medicine, Imperial College London,London, UK
  4. UK Dementia Research Institute at Imperial College London,London, UK
  5. Department of Human Genetics, Emory University School of Medicine,Atlanta, GA USA
  6. Department of Genetics, Perelman School of Medicine, University of Pennsylvania,Philadelphia, PA USA
Institutions: UConn Health (United States); Children's Hospital of Philadelphia (United States); Imperial College London (United Kingdom); UK Dementia Research Institute (United Kingdom); Emory University (United States); University of Pennsylvania (United States)
Journal: Nature communications, volume 17, issue 1, article 3714
Dates: received 23 January 2025; accepted 19 February 2026; published online 9 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-70232-6 · PMID 41803119 · PMCID PMC13102946 · OpenAlex W7134836733
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Development, Musculoskeletal development, Sequencing
MeSH: Gene Expression Regulation, Developmental*, Skull*, Animals, Cleft Lip, Cleft Palate, Ectoderm, Humans, Mesoderm, Mice, Neural Crest, Single-Cell Analysis, Single-Cell Gene Expression Analysis, Spatial Transcriptomics (* major topic)
Topic: Cleft Lip and Palate Research (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Dental and Craniofacial Research (NIDCR) (1R01DE028945, 1R03DE028588); NIGMS (5R35GM119465)
Citations: cited by 2 papers (Europe PMC); 163 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 38 matches between paragraphs and lines of code.

cotneylab/craniofacial_snrna

License: CC0-1.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 368d60853543935043f47b9894f742af90263e14, 11 September 2025
Languages: R (30), Shell (9)
Size: 70 files, 39 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README, license file, 10 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (27 files), Seurat (24 files), patchwork (21 files), cowplot (20 files), Harmony (20 files), clusterProfiler (13 files), pheatmap (12 files), limma (11 files), reshape2 (11 files), ggplot2 (7 files), data.table (5 files), ggpubr (5 files), SingleCellExperiment (3 files), DESeq2 (2 files), edgeR (1 file), reticulate (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
41 files
At the source: github.com/cotneylab/craniofacial_snrna

Zenodo 18246448

License: CC0-1.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (27 files), Seurat (24 files), patchwork (21 files), cowplot (20 files), Harmony (20 files), clusterProfiler (13 files), pheatmap (12 files), limma (11 files), reshape2 (11 files), ggplot2 (7 files), data.table (5 files), ggpubr (5 files), SingleCellExperiment (3 files), DESeq2 (2 files), edgeR (1 file), reticulate (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
41 files
At the source:

Code availability statement

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Tracing map

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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;
  • 78 scripts, each with its path and the digest of its content;
  • 38 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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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:

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 13 MeSH terms, 2 funders, 155 references.

Cite

This paper

Khouri-Farah, N., Manchel, A., Wentworth Winchester, E., Schilder, B. M., Robinson, K., Curtis, S. W., Skene, N. G., Leslie-Clarkson, E. J., & Cotney, J. (2026). Gene expression dynamics of human and mouse craniofacial development at the single-cell level. Nature communications, 17(1), 3714. https://doi.org/10.1038/s41467-026-70232-6

BibTeX

@article{khourifarah2026gene,
author = {Khouri-Farah, Nagham and Manchel, Alexandra and Wentworth Winchester, Emma and Schilder, Brian M. and Robinson, Kelsey and Curtis, Sarah W. and Skene, Nathan G. and Leslie-Clarkson, Elizabeth J. and Cotney, Justin},
title = {{Gene expression dynamics of human and mouse craniofacial development at the single-cell level}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3714},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70232-6},
url = {https://doi.org/10.1038/s41467-026-70232-6},
pmid = {41803119},
pmcid = {PMC13102946}
}

RIS

TY - JOUR
AU - Khouri-Farah, Nagham
AU - Manchel, Alexandra
AU - Wentworth Winchester, Emma
AU - Schilder, Brian M.
AU - Robinson, Kelsey
AU - Curtis, Sarah W.
AU - Skene, Nathan G.
AU - Leslie-Clarkson, Elizabeth J.
AU - Cotney, Justin
TI - Gene expression dynamics of human and mouse craniofacial development at the single-cell level
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/09
VL - 17
IS - 1
SP - 3714
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70232-6
UR - https://doi.org/10.1038/s41467-026-70232-6
LA - en
ER -

CSL-JSON

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