OSCR

Whole-genome duplication shaped cell-type evolution in the vertebrate brain.

Code ↔ Paper

26 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 26 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Clustering and annotation ↔ 0.atlas/5.iterative_clustering/Run.iterative_clustering.R, the whole file · a weak match · score 1.00 · de.score.th, de_param, iter_clust, max.dim, merge_cl, min.genes
  2. [2] § Methods › Identification of cell-type-specific TFs and conserved sets for cell-type families ↔ 3.markers/3.nsforest/find_minimum_combinations_of_TFs.py, lines 39–75 · score 0.96 · BinaryFirst_high, gene_selection, n_binary_genes, n_top_genes, n_trees, nsforesting.NSForest
  3. [3] § Methods › Identification of cell-type-specific TFs and conserved sets for cell-type families ↔ 3.markers/3.nsforest/find_minimum_combinations_of_markers.py, lines 35–71 · score 0.96 · BinaryFirst_high, gene_selection, n_binary_genes, n_top_genes, n_trees, nsforesting.NSForest
  4. [4] § Methods › Cell-type nonspecific dominant expression ↔ 5.paralog_expression_evolution/Dominant expression/s2.check_dominant_within_species_paralogs.R, lines 55–105 · score 0.75 · friedman_test, wilcox_test, paralogue families, Bonferroni, pairwise, dominant
  5. [5] § Methods › Identification of marker genes at the cell-type family and cluster level ↔ 0.atlas/7.final_Bflo/step2.harmony.R, lines 113–183 · score 0.73 · logfc.threshold, min.pct, FindAllMarkers, pos, clusters, genes
  6. [6] § Methods › Atlas integration and cross-species mapping ↔ 0.atlas/6.SAMap/SAMap_run_vertebrate_neurons.py, lines 1–42 · score 0.72 · crossK, hom_edge_mode, Pearson, SAMap, pairwise, Atlas
  7. [7] § Methods › Atlas integration and cross-species mapping ↔ 0.atlas/6.SAMap/SAMap_run_vertebrate_nonneurons.py, lines 1–42 · score 0.72 · crossK, hom_edge_mode, Pearson, SAMap, pairwise, Atlas
  8. [8] § Methods › Subfunctionalization and neofunctionalization ↔ 5.paralog_expression_evolution/Paralog shift sub_vs_neo/s2.get_vertebrate_ancestral_states_and_check_sub_neo_loss.latest_gene_family_reconsider.ipynb, lines 84–153 · score 0.68 · vertebrate ancestral states, gene families, loss, binarized, amniote, homologous
  9. [9] § Methods › Clustering and annotation ↔ cytograph/clustering/polished_louvain.py, lines 128–241 · score 0.64 · Louvain clustering, min cells, resolution, iterative, component, max
  10. [10] § Genome duplication and regional identity ↔ 10.case_studies/1.macroglia/AST/5.regionalisation_genes/s1.get_matrix.R, the whole file · a weak match · score 0.63 · GABAergic, Glutamatergic neurons, regional genes, De, location, AST
  11. [11] § Genome duplication and regional identity ↔ 10.case_studies/1.macroglia/AST/5.regionalisation_genes/s1.get_matrix.R, the whole file · a weak match · score 0.63 · GABAergic, glutamatergic neurons, Regional genes, telencephalon, sum, astrocytes
  12. [12] § Subfunctionalization and neofunctionalization ↔ 5.paralog_expression_evolution/Paralog shift sub_vs_neo/s2.get_vertebrate_ancestral_states_and_check_sub_neo_loss.latest_gene_family_reconsider.ipynb, lines 84–153 · score 0.63 · Ancestral state, neo, sub, gene family, Binarized, amniotes
  13. [13] § Methods › Subfunctionalization and neofunctionalization ↔ 5.paralog_expression_evolution/Paralog shift sub_vs_neo/s3.calculate_divergence_across_species.ipynb, lines 14–30 · score 0.61 · retained orthogroups, OrthoFinder, Gene relationships, copy, SSDs, paralogue
  14. [14] § Methods › Gene regulatory network analysis ↔ 8.GRNs_detection/1.mouse/pyscenic_run.py, lines 28–50 · score 0.60 · motif annotation, target gene, pySCENIC, ranking, mouse, TFs
  15. [15] § Methods › Gene regulatory network analysis ↔ 8.GRNs_detection/1.human/pyscenic_run.py, lines 28–51 · score 0.60 · motif annotation, target gene, pySCENIC, ranking, TFs, Human
  16. [16] § Methods › RNA velocity and multipotency analyses in amphioxus ↔ cytograph/preprocessing/doublet_finder.py, lines 46–173 · score 0.58 · n_neighbors, Dimensionality reduction, variance, log, transformation, preprocessed
  17. [17] § Methods › GO annotations and enrichment analyses ↔ 10.case_studies/1.macroglia/AST/5.regionalisation_genes/plot_and_check_regionalisation.v2.ipynb, lines 324–366 · score 0.57 · enrichGO, db, cutoff, hs, mm, enriched
  18. [18] § Genome duplication and regional identity ↔ 10.case_studies/1.macroglia/AST/5.regionalisation_genes/plot_and_check_regionalisation.v2.ipynb, lines 45–99 · score 0.57 · Comparison matrix, regional genes, AST, gradient, log10, Variance
  19. [19] § Methods › Cell-type nonspecific dominant expression ↔ 5.paralog_expression_evolution/Dominant expression/s3.plot_dominate_expression_frequency_for_individual_species.by_pairwise_wilcox.ipynb, lines 110–132 · score 0.55 · dominant copy, wilcox, pairwise, pct, exp, SSD
  20. [20] § Methods › Identifying gene relationships for orthologues, paralogues, ohnologues and SSD paralogues ↔ 5.paralog_expression_evolution/Paralog shift sub_vs_neo/s3.calculate_divergence_across_species.ipynb, lines 14–30 · score 0.55 · OrthoFinder, gene relationships, matched, duplicated, orthogroups, WGD
  21. [21] § Methods › GO annotations and enrichment analyses ↔ cytograph/species/species.py, the whole file · a weak match · score 0.54 · Danio rerio, musculus, sapiens, mouse, human, species
  22. [22] § Lasting effect of WGD on cell types ↔ cytograph/species/mouse.py, lines 961–1020 · score 0.54 · Nr2f1, Nr2f2, mice, species
  23. [23] § Methods › Identifying gene relationships for orthologues, paralogues, ohnologues and SSD paralogues ↔ 5.paralog_expression_evolution/Paralog shift sub_vs_neo/s4.paralog_switching_calculation.updated.ipynb, lines 12–46 · score 0.54 · OrthoFinder, gene relationships, orthology, duplicated, WGD, paralogues
  24. [24] § Methods › Identification of marker genes at the cell-type family and cluster level ↔ 3.markers/DEGs_Seurat/FindMarkers.R, the whole file · a weak match · score 0.52 · FindAllMarkers, ROC, Seurat, wilcox, genes, cell
  25. [25] § Dosage selection across cell types ↔ 5.paralog_expression_evolution/Dominant expression/s3.plot_dominate_expression_frequency_for_individual_species.by_pairwise_wilcox.ipynb, lines 110–132 · score 0.51 · dominant copy, individual species, insign, pct, exp, SSD
  26. [26] § Linking WGD to cell-type evolution ↔ 10.case_studies/3.amphi_glia_evo_devo/3.subset_glia/subset_glia.ipynb, lines 10–14 · score 0.51 · N4 stage, neural tube, RNA, glia, brain, adult

Paper

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The authors' code

Jupyter notebook · 340 lines · 16 KB · GPL-3.0 · 2 matches

  1. # %%
  2. suppressPackageStartupMessages({
  3. require(igraph)
  4. library(dplyr)
  5. library(ggvenn)
  6. library(tidyr)
  7. library(ggpubr)
  8. library(rstatix)
  9. library(stringr)
  10. })
  12. # %%
  13. markers <- readRDS('/mnt/data01/yuanzhen/01.Vertebrate_cell_evo/01.data/03.markers/allmarkers.wilcox.FDR001_FC1.5.rds')
  15. # %%
  16. # expressed genes for each species at cell type family level
  17. #markers <- readRDS('exp_domain/combined.exp_tri_score.rds')
  19. # %%
  20. Amniote_homologous_celltypes <- read.delim('/mnt/data01/yuanzhen/01.Vertebrate_cell_evo/01.data/02.atlas_final/3.plotting/8.plot_dotplot_cross_species/celltype_ordering.v2.txt', header = F)$V1
  21. Vertebrate_homologous_celltypes <- Amniote_homologous_celltypes[!Amniote_homologous_celltypes %in%
  22. c('Oligodendrocyte precursor cells','Oligodendrocytes')]
  24. # %%
  25. duplicate_pairs <- readRDS("Combined.SSD_WGD.rds")
  27. # %%
  28. orthogroups <- read.delim('/mnt/data01/yuanzhen/01.Vertebrate_cell_evo/02.gene_relationships/run4/results/Ortho_pipeline/OrthoFinder/Orthogroups/Orthogroups.tsv')
  29. # at least one copy for 4 species
  30. orthogroups <- orthogroups %>% select(c('Orthogroup', 'Pmar', 'Pvit', 'Mmus', 'Hsap')) %>%
  31. filter(Pmar != '' & Pvit != '' & Mmus != '' & Hsap != '')
  33. # %%
  34. # calculate number of genes by calcualting commas in it
  35. count_commas <- function(x) {
  36. sapply(gregexpr(",", x), function(match) ifelse(match[1] == -1, 0, length(match)))
  37. }
  38. number_genes <- data.frame(apply(orthogroups, c(1,2), count_commas))
  40. # retain orthogroups with only 5 copies max for each four species
  41. orthogroups <- orthogroups[which(number_genes$Pmar <= 5 & number_genes$Pvit <= 5 & number_genes$Mmus <= 5 & number_genes$Hsap <= 5), ]
  43. # %%
  44. dim(orthogroups)
  46. # %%
  47. WGD_genes <- unique(c(duplicate_pairs[duplicate_pairs$type == 'WGD', 'dup1'], duplicate_pairs[duplicate_pairs$type == 'WGD', 'dup2']))
  48. SSD_genes <- unique(c(duplicate_pairs[duplicate_pairs$type == 'SSD', 'dup1'], duplicate_pairs[duplicate_pairs$type == 'SSD', 'dup2']))
  49. paralog_genes <- unique(c(duplicate_pairs[,'dup1'], duplicate_pairs[,'dup2']))
  51. # pay attention! at least a pair (since it's in the same gene family:orthogroup)
  52. # a pair of ohnologues or SSD paralogues
  53. WGD_orthogroups <- orthogroups[apply(orthogroups, 1, function(row) {
  54. sum(sapply(row, function(cell) {
  55. sum(WGD_genes %in% unlist(strsplit(as.character(cell), ", "))) >= 2
  56. })) >= 3
  57. }), ]
  59. SSD_orthogroups <- orthogroups[apply(orthogroups, 1, function(row) {
  60. sum(sapply(row, function(cell) {
  61. sum(SSD_genes %in% unlist(strsplit(as.character(cell), ", "))) >= 2
  62. })) >= 3
  63. }), ]
  65. Paralog_orthogroups <- orthogroups[apply(orthogroups, 1, function(row) {
  66. sum(sapply(row, function(cell) {
  67. sum(paralog_genes %in% unlist(strsplit(as.character(cell), ", "))) >= 2
  68. })) >= 3
  69. }), ]
  71. # %%
  72. #check the number of orthogroups being classified as both WGD_orthogroups and SSD_orthogroups
  73. shared <- semi_join(WGD_orthogroups, SSD_orthogroups, by = colnames(SSD_orthogroups))
  74. dim(Paralog_orthogroups)
  75. dim(WGD_orthogroups)
  76. dim(SSD_orthogroups)
  77. dim(shared)
  79. # %%
  80. get_gene_ID <- function(row, species){
  81. return(paste(species, unlist(strsplit(as.character(row[[species]]), ", ")), sep = '_'))
  82. }
  84. # %%
  85. # for only amniotes
  86. get_results_amniotes <- function(orthogroup){
  87. results <- Reduce(rbind, apply(orthogroup, 1, FUN = function(row){
  88. # get gene IDs for each species
  89. Hsap = get_gene_ID(row, 'Hsap')
  90. Mmus = get_gene_ID(row, 'Mmus')
  91. Pvit = get_gene_ID(row, 'Pvit')
  92. # construct exp matrix
  93. mtx1 = data.frame(matrix(0, nrow = length(c(Hsap, Mmus, Pvit)), ncol = length(Amniote_homologous_celltypes)))
  94. rownames(mtx1) <- c(Hsap, Mmus, Pvit)
  95. colnames(mtx1) <- Amniote_homologous_celltypes
  96. # use markers to binarize mtx
  97. for (g in rownames(mtx1)){
  98. c <- markers[match(g, markers$species_gene), "cluster"]
  99. if (!is.na(c)){
  100. mtx1[g, colnames(mtx1) %in% c] <- 1
  101. }
  102. }
  104. Hsap_tmp <- colSums(mtx1[grepl('Hsap', rownames(mtx1)),])
  105. Mmus_tmp <- colSums(mtx1[grepl('Mmus', rownames(mtx1)),])
  106. Pvit_tmp <- colSums(mtx1[grepl('Pvit', rownames(mtx1)),])
  107. Ancestral_states1 <- ifelse(
  108. (Hsap_tmp > 0 & Mmus_tmp > 0) | (Hsap_tmp > 0 & Pvit_tmp > 0) | (Mmus_tmp > 0 & Pvit_tmp > 0),1,0)
  110. if (sum(Ancestral_states1[Ancestral_states1 == 1]) > 0){
  111. # calculate sub- and neo- for species Human compared to ancestral state of amniotes and lamprey
  112. info_mtx <- sweep(mtx1, 2, Ancestral_states1, FUN = "-")
  113. # calculate sub- number for human (I also considered loss-of-function for all paralogs in that family, in that case, wouldn't be count as sub)
  114. Hsap_sub <- sum(info_mtx[grepl('Hsap', rownames(mtx1)), ] == -1)
  115. Hsap_loss <- (length(Hsap)*sum(colSums(info_mtx[grepl('Hsap', rownames(mtx1)), ]) == length(Hsap)))
  116. Hsap_sub <- Hsap_sub - Hsap_loss
  117. # calculate neo- number for human
  118. Hsap_neo <- sum(info_mtx[grepl('Hsap', rownames(mtx1)), ] == 1)
  119. # calculate sub- number for mouse
  120. Mmus_sub <- sum(info_mtx[grepl('Mmus', rownames(mtx1)), ] == -1)
  121. Mmus_loss <- (length(Mmus)*sum(colSums(info_mtx[grepl('Mmus', rownames(mtx1)), ]) == length(Mmus)))
  122. Mmus_sub <- Mmus_sub - Mmus_loss
  123. # calculate neo- number for mouse
  124. Mmus_neo <- sum(info_mtx[grepl('Mmus', rownames(mtx1)), ] == 1)
  125. # calculate sub- number for lizard
  126. Pvit_sub <- sum(info_mtx[grepl('Pvit', rownames(mtx1)), ] == -1)
  127. Pvit_loss <- (length(Pvit)*sum(colSums(info_mtx[grepl('Pvit', rownames(mtx1)), ]) == length(Pvit)))
  128. Pvit_sub <- Pvit_sub - Pvit_loss
  129. # calculate neo- number for lizard
  130. Pvit_neo <- sum(info_mtx[grepl('Pvit', rownames(mtx1)), ] == 1)
  132. info <- rbind(c(row[['Orthogroup']], Hsap_sub, Hsap_sub/length(grepl('Hsap', rownames(mtx1))), "sub", "Hsap"),
  133. c(row[['Orthogroup']], Hsap_neo, Hsap_neo/length(grepl('Hsap', rownames(mtx1))), "neo", "Hsap"),
  134. c(row[['Orthogroup']], Hsap_loss, Hsap_loss/length(grepl('Hsap', rownames(mtx1))), "loss", "Hsap"),
  135. c(row[['Orthogroup']], Mmus_sub, Mmus_sub/length(grepl('Mmus', rownames(mtx1))), "sub", "Mmus"),
  136. c(row[['Orthogroup']], Mmus_neo, Mmus_neo/length(grepl('Mmus', rownames(mtx1))), "neo", "Mmus"),
  137. c(row[['Orthogroup']], Mmus_loss, Mmus_loss/length(grepl('Mmus', rownames(mtx1))), "loss", "Mmus"),
  138. c(row[['Orthogroup']], Pvit_sub, Pvit_sub/length(grepl('Pvit', rownames(mtx1))), "sub", "Pvit"),
  139. c(row[['Orthogroup']], Pvit_neo, Pvit_neo/length(grepl('Pvit', rownames(mtx1))), "neo", "Pvit"),
  140. c(row[['Orthogroup']], Pvit_loss, Pvit_loss/length(grepl('Pvit', rownames(mtx1))), "loss", "Pvit"))
  141. } else {
  142. info <- NULL
  143. #info_mtx <- NULL
  144. }
  145. return(info)
  146. }))
  147. results <- data.frame(results)
  148. colnames(results) <- c("orthogroups", "number", "ratio", "fun", "species")
  149. results$ratio <- as.double(results$ratio)
  150. results$number <- as.numeric(results$number)
  151. results$species <- factor(results$species, levels = c('Hsap', 'Mmus', 'Pvit'))
  152. return(results)
  153. }
  155. # %%
  156. # for all vertebrates, if 3/4 used as marker then 1
  157. get_results_vertebrates <- function(orthogroup){
  158. results <- Reduce(rbind, apply(orthogroup, 1, FUN = function(row){
  159. # get gene IDs for each species
  160. Hsap = get_gene_ID(row, 'Hsap')
  161. Mmus = get_gene_ID(row, 'Mmus')
  162. Pvit = get_gene_ID(row, 'Pvit')
  163. Pmar = get_gene_ID(row, 'Pmar')
  164. # construct exp matrix
  165. mtx1 = data.frame(matrix(0, nrow = length(c(Hsap, Mmus, Pvit, Pmar)), ncol = length(Vertebrate_homologous_celltypes)))
  166. rownames(mtx1) <- c(Hsap, Mmus, Pvit, Pmar)
  167. colnames(mtx1) <- Vertebrate_homologous_celltypes
  168. # use markers to binarize mtx
  169. for (g in rownames(mtx1)){
  170. c <- markers[match(g, markers$species_gene), "cluster"]
  171. if (!is.na(c)){
  172. mtx1[g, colnames(mtx1) %in% c] <- 1
  173. }
  174. }
  176. Hsap_tmp <- colSums(mtx1[grepl('Hsap', rownames(mtx1)),])
  177. Mmus_tmp <- colSums(mtx1[grepl('Mmus', rownames(mtx1)),])
  178. Pvit_tmp <- colSums(mtx1[grepl('Pvit', rownames(mtx1)),])
  179. Pmar_tmp <- colSums(mtx1[grepl('Pmar', rownames(mtx1)),])
  180. Ancestral_states1 <- ifelse(
  181. (Hsap_tmp > 0 & Mmus_tmp > 0 & Pvit_tmp > 0) | (Hsap_tmp > 0 & Mmus_tmp > 0 & Pmar_tmp > 0) |
  182. (Hsap_tmp > 0 & Pmar_tmp > 0 & Pvit_tmp > 0) | (Mmus_tmp > 0 & Pvit_tmp > 0 & Pmar_tmp > 0),1,0)
  184. if (sum(Ancestral_states1[Ancestral_states1 == 1]) > 0){
  185. # calculate sub- and neo- for species Human compared to ancestral state of amniotes and lamprey
  186. info_mtx <- sweep(mtx1, 2, Ancestral_states1, FUN = "-")
  187. # calculate sub- number for human
  188. Hsap_sub <- sum(info_mtx[grepl('Hsap', rownames(mtx1)), ] == -1)
  189. Hsap_loss <- (length(Hsap)*sum(colSums(info_mtx[grepl('Hsap', rownames(mtx1)), ]) == length(Hsap)))
  190. Hsap_sub <- Hsap_sub - Hsap_loss
  191. # calculate neo- number for human
  192. Hsap_neo <- sum(info_mtx[grepl('Hsap', rownames(mtx1)), ] == 1)
  193. # calculate sub- number for mouse
  194. Mmus_sub <- sum(info_mtx[grepl('Mmus', rownames(mtx1)), ] == -1)
  195. Mmus_loss <- (length(Mmus)*sum(colSums(info_mtx[grepl('Mmus', rownames(mtx1)), ]) == length(Mmus)))
  196. Mmus_sub <- Mmus_sub - Mmus_loss
  197. # calculate neo- number for mouse
  198. Mmus_neo <- sum(info_mtx[grepl('Mmus', rownames(mtx1)), ] == 1)
  199. # calculate sub- number for lizard
  200. Pvit_sub <- sum(info_mtx[grepl('Pvit', rownames(mtx1)), ] == -1)
  201. Pvit_loss <- (length(Pvit)*sum(colSums(info_mtx[grepl('Pvit', rownames(mtx1)), ]) == length(Pvit)))
  202. Pvit_sub <- Pvit_sub - Pvit_loss
  203. # calculate neo- number for lizard
  204. Pvit_neo <- sum(info_mtx[grepl('Pvit', rownames(mtx1)), ] == 1)
  205. # calculate sub- number for lamprey
  206. Pmar_sub <- sum(info_mtx[grepl('Pmar', rownames(mtx1)), ] == -1)
  207. Pmar_loss <- (length(Pmar)*sum(colSums(info_mtx[grepl('Pmar', rownames(mtx1)), ]) == length(Pmar)))
  208. Pmar_sub <- Pmar_sub - Pmar_loss
  209. # calculate neo- number for lamprey
  210. Pmar_neo <- sum(info_mtx[grepl('Pmar', rownames(mtx1)), ] == 1)
  213. info <- rbind(c(row[['Orthogroup']], Hsap_sub, Hsap_sub/length(Hsap), "sub", "Hsap"),
  214. c(row[['Orthogroup']], Hsap_neo, Hsap_neo/length(Hsap), "neo", "Hsap"),
  215. c(row[['Orthogroup']], Hsap_loss, Hsap_loss/length(Hsap), "loss", "Hsap"),
  216. c(row[['Orthogroup']], Mmus_sub, Mmus_sub/length(Mmus), "sub", "Mmus"),
  217. c(row[['Orthogroup']], Mmus_neo, Mmus_neo/length(Mmus), "neo", "Mmus"),
  218. c(row[['Orthogroup']], Mmus_loss, Mmus_loss/length(Mmus), "loss", "Mmus"),
  219. c(row[['Orthogroup']], Pvit_sub, Pvit_sub/length(Pvit), "sub", "Pvit"),
  220. c(row[['Orthogroup']], Pvit_neo, Pvit_neo/length(Pvit), "neo", "Pvit"),
  221. c(row[['Orthogroup']], Pvit_loss, Pvit_loss/length(Pvit), "loss", "Pvit"),
  222. c(row[['Orthogroup']], Pmar_sub, Pmar_sub/length(Pmar), "sub", "Pmar"),
  223. c(row[['Orthogroup']], Pmar_neo, Pmar_neo/length(Pmar), "neo", "Pmar"),
  224. c(row[['Orthogroup']], Pmar_loss, Pmar_loss/length(Pmar), "loss", "Pmar"))
  225. } else {
  226. info <- NULL
  227. #info_mtx <- NULL
  228. }
  229. return(info)
  230. }))
  231. results <- data.frame(results)
  232. colnames(results) <- c("orthogroups", "number", "ratio", "fun", "species")
  233. results$ratio <- as.double(results$ratio)
  234. results$number <- as.numeric(results$number)
  235. results$species <- factor(results$species, levels = c('Hsap', 'Mmus', 'Pvit', 'Pmar'))
  236. return(results)
  237. }
  239. # %%
  240. WGD_res_vertebrates <- get_results_vertebrates(WGD_orthogroups)
  241. SSD_res_vertebrates <- get_results_vertebrates(SSD_orthogroups)
  242. Paralog_res_vertebrates <- get_results_vertebrates(Paralog_orthogroups)
  244. # %%
  245. WGD_res_vertebrates$type <- 'WGD'
  246. SSD_res_vertebrates$type <- 'SSD'
  247. results <- rbind(WGD_res_vertebrates, SSD_res_vertebrates)
  248. results$type <- factor(results$type, levels = c('WGD', 'SSD'))
  250. # %%
  251. head(results)
  253. # %%
  254. # print the total number of changes explained by each way
  255. a1 = sum(results[results$fun == "sub" & results$type == 'WGD', "number"])
  256. a2 = sum(results[results$fun == "neo" & results$type == 'WGD', "number"])
  257. a3 = sum(results[results$fun == "loss" & results$type == 'WGD', "number"])
  259. b1 = sum(results[results$fun == "sub" & results$type == 'SSD', "number"])
  260. b2 = sum(results[results$fun == "neo" & results$type == 'SSD', "number"])
  261. b3 = sum(results[results$fun == "loss" & results$type == 'SSD', "number"])
  263. c1 = sum(Paralog_res_vertebrates[Paralog_res_vertebrates$fun == "sub", "number"])
  264. c2 = sum(Paralog_res_vertebrates[Paralog_res_vertebrates$fun == "neo", "number"])
  265. c3 = sum(Paralog_res_vertebrates[Paralog_res_vertebrates$fun == "loss", "number"])
  267. # %%
  268. a1/(a1+a2+a3)
  269. a2/(a1+a2+a3)
  270. a3/(a1+a2+a3)
  272. b1/(b1+b2+b3)
  273. b2/(b1+b2+b3)
  274. b3/(b1+b2+b3)
  276. c1/(c1+c2+c3)
  277. c2/(c1+c2+c3)
  278. c3/(c1+c2+c3)
  280. # %%
  281. results %>% filter(fun == 'neo' & species == 'Hsap') %>% arrange(orthogroups, fun) %>% filter(orthogroups %in% shared$Orthogroup) %>% filter(number > 3)
  283. # %%
  284. sum(table(ops$orthogroups) == 2)
  286. # %%
  287. my_comparisons <- list(c("sub", "neo"))
  288. p1 <- results %>% filter(fun != 'loss') %>% ggboxplot(x = "fun", y = "ratio", color = "fun", palette = "npg") +
  289. stat_compare_means(comparisons = my_comparisons, method = "wilcox", paired = TRUE) +
  290. facet_grid(vars(type), vars(species))
  292. p2 <- results %>% filter(fun != 'loss') %>% ggboxplot(x = "fun", y = "number", color = "fun", palette = "npg") +
  293. stat_compare_means(comparisons = my_comparisons, method = "wilcox", paired = TRUE) +
  294. facet_grid(vars(type), vars(species))
  296. # %%
  297. ggsave("vertebrates.marker.ratio.sub_neo.pdf", p1, width = 5, height = 6)
  298. ggsave("vertebrates.marker.number.sub_neo.pdf", p2, width = 5, height = 6)
  300. #ggsave("vertebrates.exp_Triscore.ratio.sub_neo.pdf", p1, width = 5, height = 6)
  301. #ggsave("vertebrates.exp_Triscore.number.sub_neo.pdf", p2, width = 5, height = 6)
  303. # %%
  304. WGD_res_amniotes <- get_results_amniotes(WGD_orthogroups)
  305. SSD_res_amniotes <- get_results_amniotes(SSD_orthogroups)
  306. WGD_res_amniotes$type <- 'WGD'
  307. SSD_res_amniotes$type <- 'SSD'
  308. results <- rbind(WGD_res_amniotes, SSD_res_amniotes)
  309. results$type <- factor(results$type, levels = c('WGD', 'SSD'))
  311. # %%
  312. my_comparisons <- list(c("sub", "neo"))
  313. p3 <- results %>% filter(fun != 'loss') %>% ggboxplot(x = "fun", y = "ratio", color = "fun", palette = "npg") +
  314. stat_compare_means(comparisons = my_comparisons, method = "wilcox", paired = TRUE) +
  315. facet_grid(vars(type), vars(species))
  317. p4 <- results %>% filter(fun != 'loss')%>% ggboxplot(x = "fun", y = "number", color = "fun", palette = "npg") +
  318. stat_compare_means(comparisons = my_comparisons, method = "wilcox", paired = TRUE) +
  319. facet_grid(vars(type), vars(species))
  321. ggsave("amniotes.marker.ratio.sub_neo.pdf", p3, width = 5, height = 6)
  322. ggsave("amniotes.marker.number.sub_neo.pdf", p4, width = 5, height = 6)
  323. #ggsave("amniotes.exp_Triscore.ratio.sub_neo.latest.pdf", p3, width = 5, height = 6)
  324. #ggsave("amniotes.exp_Triscore.number.sub_neo.latest.pdf", p4, width = 5, height = 6)
  326. # %%
  329. # %%
  332. # %%
  333. # not included in paper, pls ignore
  334. my_comparisons <- list(c("WGD", "SSD"))
  335. results %>% ggboxplot(x = "type", y = "ratio", color = "type", palette = "npg") +
  336. stat_compare_means(comparisons = my_comparisons, method = "wilcox") +
  337. facet_grid(vars(fun), vars(species))
  339. # %%

s2.get_vertebrate_ancestral_states_and_check_sub_neo_loss.latest_gene_family_reconsider.ipynb at commit 808269e, under GPL-3.0 · at the source

Overview

Authors: Yuanzhen Zhu1, Shuai Zhang2, Jiankai Wei1,3,4, Diego Dolgetta-Garcia1, Katia Jindrich1, Huimin Liu2, Chenggang Shi2,5,6, Rongrong Pan2,7, Yuwei Chen2, Yan Xu2, Qiye Li8,9, Günter P Wagner10,11, Peter W H Holland1, Guang Li2, Sebastian M Shimeld1
ORCID iDs: Yuanzhen Zhu, Qiye Li, Günter P Wagner, Peter W H Holland, Guang Li, Sebastian M Shimeld
  1. Department of Biology, University of Oxford, Oxford, UK
  2. State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
  3. Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, China
  4. MoE Key Laboratory of Evolution and Marine Biodiversity, Institute of Evolution and Marine Biodiversity, Ocean University of China, Qingdao, China
  5. Mountain Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
  6. China–Croatia Belt and Road Joint Laboratory on Biodiversity and Ecosystem Services, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
  7. Department of Basic Medical Sciences, Qiannan Medical College for Nationalities, Duyun, China
  8. State Key Laboratory of Genome and Multi-Omics Technologies and Shenzhen Key Laboratory of Forensics, BGI Research, Shenzhen, China
  9. BGI Research, Wuhan, China
  10. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT USA
  11. Department of Evolutionary Biology, University of Vienna, Vienna, Austria
Institutions: University of Oxford (United Kingdom); Xiamen University (China); Ocean University of China (China); Chinese Academy of Sciences (China); Chengdu Institute of Biology (China); Qiannan Medical College for Nationalities (China); BGI Research (China); BGI Research, Wuhan (China); University of Vienna (Austria); Yale University (United States)
Journal: Nature, volume 656, issue 8128, pages 659-669
Dates: received 24 June 2025; accepted 6 May 2026; published online 10 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41586-026-10629-x · PMID 42271056 · PMCID PMC13489949 · OpenAlex W7164165509
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), other (organism), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Molecular evolution, Genomics, Evolutionary developmental biology, Cellular neuroscience
MeSH: Brain*, Evolution, Molecular*, Gene Duplication*, Genome*, Lancelets*, Vertebrates*, Animals, Humans, Lampreys, Lizards, Mice, Phylogeny, Single-Cell Analysis, Transcription Factors, Transcriptome (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council (BB/X015203/1)
Citations: not cited yet (Europe PMC); 108 references in the paper

Abstract

The complex brains of vertebrates have more cell types than those of their closest relatives. Whole-genome duplications (WGDs) occurred during early vertebrate evolution1, but it is unclear whether the duplicated genes (ohnologues) facilitated cell-type evolution. Here using brain single-cell transcriptomes from five chordates—human2, mouse3, lizard4, lamprey5 and amphioxus—we report that many cell-type families with conserved core transcription factors in vertebrates do not show one-to-one homology with amphioxus. Moreover, ohnologues, particularly those from the first WGD, were more important than small-scale duplication paralogues for vertebrate cell-type evolution. To explore whether ohnologues are mechanistically important for this process, we predicted ancestral cell-type states and compared them to amphioxus and experimentally investigated macroglia. The findings indicate that ohnologues had a role in early vertebrate cell-type diversification. Moreover, by examining paralogue expression across cell types and species, we show that expression changes were mainly driven by dosage selection and subfunctionalization. We also link ohnologues to cellular diversity at different anatomical and cell-type scales. Our findings demonstrate the importance of WGDs for the evolution of early vertebrate brain complexity and highlight that the resultant ohnologues continued to capacitate cell-type evolution long after they were formed.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 26 matches between paragraphs and lines of code.

DiracZhu1998/WGD2celltype_evolution

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 808269e1c0c13331cf19a21284904ccbb26bf54d, 30 March 2026
Languages: Jupyter (47), R (22), Python (18), Shell (1)
Size: 388 files, 88 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 47 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (41 files), Seurat (38 files), pandas (23 files), ggpubr (18 files), Scanpy (18 files), ggplot2 (17 files), igraph (17 files), NumPy (13 files), anndata (12 files), Matplotlib (8 files), rstatix (6 files), circlize (4 files), cowplot (4 files), reshape2 (4 files), reticulate (3 files), SciPy (3 files), DESeq2 (2 files), seaborn (2 files), clusterProfiler (1 file), Harmony (1 file), lme4 (1 file), patchwork (1 file), scVelo (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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90 files
At the source: github.com/DiracZhu1998/WGD2celltype_evolution

fmarletaz/hagfish

License: none: the authors keep all their rights
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Commit: 68842e98e6515a12869c951fc1b82026fa0df103, 14 November 2023
Languages: R (8), Python (8), Jupyter (3), Shell (1), Julia (1)
Size: 22,165 files, 21 scripts
Software Heritage: archived
Found in: the text, “Identifying gene relationships for orthologues, ”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), NumPy (4 files), pandas (3 files), ggplot2 (2 files), ggpubr (2 files), pheatmap (2 files), SciPy (2 files), BEDTools (1 file), DataFrames.jl (1 file), Distributions.jl (1 file), Matplotlib (1 file), Pingouin (1 file), Plots.jl (1 file), seaborn (1 file), statsmodels (1 file), Turing.jl (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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22 files
At the source: raw.githubusercontent.com/fmarletaz/hagfish/refs/heads/main/Paralogons/Vert_Evt_OGrrA.txt

linnarsson-lab/adult-human-brain

License: BSD-2-Clause
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Evidence: files inventoried
Commit: 2b5aa12dffb8cc1bcb3979c409cb6ac907546b2e, 5 June 2026
Languages: Python (96), Jupyter (17)
Size: 130 files, 113 scripts
Software Heritage: archived
Found in: “Data availability”
Holds: README, license file, environment (setup.cfg, setup.py), 17 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (74 files), SciPy (35 files), Matplotlib (34 files), scikit-learn (26 files), pandas (12 files), NetworkX (8 files), seaborn (7 files), Numba (6 files), igraph (3 files), statsmodels (1 file), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
97 files
At the source: github.com/linnarsson-lab/adult-human-brain

figshare 29327111

License: GPL 3.0+
State: the link answers, verified on 27 September 2026
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Size: 6 files
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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At the source:

Code availability

All code and scripts associated with this analysis are publicly available from the GitHub repository (https://github.com/DiracZhu1998/WGD2celltype_evolution).

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

Tracing map

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

What the map holds:

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

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

Data

Datasets cited

Data Availability Statement

The reference genome, gene models, functional annotations of protein-coding genes, full marker gene list of each cell cluster and important intermediate files are deposited in a Figshare repository (10.6084/m9.figshare.29327111)108. All amphioxus bulk RNA-seq, scRNA-seq and snRNA-seq data, as well as lamprey embryonic neural tube scRNA-seq data, have been deposited in the CNCB database under BioProject accession number PRJCA059549 (https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA059549). Data analysed from publicly available sources are described in the paper, available from GitHub and Figshare repositories and other online resources, and are listed as follows: (1) human brain atlas (human_adult_GRCh38-3.0.0.h5ad) from https://github.com/linnarsson-lab/adult-human-brain; (2) mouse brain atlas (l5_all.loom) from http://mousebrain.org/adolescent/downloads.html; (3) lizard brain atlas (Pogona_vitticeps_cells_Science_2022.rds) from https://datashare.mpcdf.mpg.de/s/WBX59YhJKKebizb#editor; (4) lamprey brain atlases (adult_diencephalon.rds, adult_rhombencephalon.rds, adult_mesencephalon.rds, adult_telencephalon.rds and lamprey_adult_whole_brain.rds) from https://downloads.kaessmannlab.org/lamprey/; (5) human eye and lung scRNA, preprocessed by the Human Cell Atlas, from https://datasets.cellxgene.cziscience.com/64175889-d600-4b58-97ea-e74be80206e5.rds and https://datasets.cellxgene.cziscience.com/b351804c-293e-4aeb-9c4c-043db67f4540.rds; (6) three species of CN datasets (GSM4873765_mouse_data.RData.gz, GSM4873766_human_data.RData.gz and GSM4873767_chicken_data.RData.gz) from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160471; and (7) Ohnologs (v.2) database (http://ohnologs.curie.fr).

All code and scripts associated with this analysis are publicly available from the GitHub repository (https://github.com/DiracZhu1998/WGD2celltype_evolution).

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

Versions

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

Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 4 keywords, 15 MeSH terms, 1 funder, 100 references.

Cite

This paper

Zhu, Y., Zhang, S., Wei, J., Dolgetta-Garcia, D., Jindrich, K., Liu, H., Shi, C., Pan, R., Chen, Y., Xu, Y., Li, Q., Wagner, G. P., Holland, P. W. H., Li, G., & Shimeld, S. M. (2026). Whole-genome duplication shaped cell-type evolution in the vertebrate brain. Nature, 656(8128), 659-669. https://doi.org/10.1038/s41586-026-10629-x

BibTeX

@article{zhu2026whole,
author = {Zhu, Yuanzhen and Zhang, Shuai and Wei, Jiankai and Dolgetta-Garcia, Diego and Jindrich, Katia and Liu, Huimin and Shi, Chenggang and Pan, Rongrong and Chen, Yuwei and Xu, Yan and Li, Qiye and Wagner, Günter P and Holland, Peter W H and Li, Guang and Shimeld, Sebastian M},
title = {{Whole-genome duplication shaped cell-type evolution in the vertebrate brain}},
journal = {Nature},
year = {2026},
month = jun,
volume = {656},
number = {8128},
pages = {659--669},
publisher = {Nature Portfolio},
issn = {0028-0836},
doi = {10.1038/s41586-026-10629-x},
url = {https://doi.org/10.1038/s41586-026-10629-x},
pmid = {42271056},
pmcid = {PMC13489949}
}

RIS

TY - JOUR
AU - Zhu, Yuanzhen
AU - Zhang, Shuai
AU - Wei, Jiankai
AU - Dolgetta-Garcia, Diego
AU - Jindrich, Katia
AU - Liu, Huimin
AU - Shi, Chenggang
AU - Pan, Rongrong
AU - Chen, Yuwei
AU - Xu, Yan
AU - Li, Qiye
AU - Wagner, Günter P
AU - Holland, Peter W H
AU - Li, Guang
AU - Shimeld, Sebastian M
TI - Whole-genome duplication shaped cell-type evolution in the vertebrate brain
T2 - Nature
J2 - Nature
PY - 2026
DA - 2026/06/10
VL - 656
IS - 8128
SP - 659
EP - 669
SN - 0028-0836
PB - Nature Portfolio
DO - 10.1038/s41586-026-10629-x
UR - https://doi.org/10.1038/s41586-026-10629-x
LA - en
ER -

CSL-JSON

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