Interspecific diversity in the neuronal composition of the mammalian cortex arises from heterochrony in neurogenesis.

Code ↔ Paper

16 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 16 matches

[1] § Methods › Methods and protocols › Single-cell transcriptomics ↔ five-mammalian-species-gene-expression-dynamics/Five animals WGCNA.R, lines 127–187 · score 0.97 · soft thresholding power, signed Nowick, TOMType, blockwiseModules, minModuleSize, Co expression networks
[2] § Methods › Methods and protocols › Single-cell transcriptomics ↔ five-mammalian-species-gene-expression-dynamics/Five animals WGCNA with unnormalized expression.R, lines 186–227 · score 0.90 · soft thresholding power, TOMType, blockwiseModules, minModuleSize, Module eigengenes, WGCNA
[3] § Methods › Methods and protocols › Single-cell transcriptomics ↔ mouse-and-rat-temporal-alignment/Rat cells integration and pseudotime calculation.R, lines 63–111 · score 0.84 · glmGamPoi, nCount_RNA, nFeature_RNA, percent.mt, cc, SCT
[4] § Methods › Methods and protocols › Single-cell transcriptomics ↔ mouse-and-rat-temporal-alignment/Preparation and filtering for murine datasets.R, lines 190–230 · score 0.76 · nCount_RNA, nFeature_RNA, percent.mt, log normalized, SCT, Seurat
[5] § Results › Wnt ligand genes exhibit protracted expression in rat progenitor cells ↔ five-mammalian-species-gene-expression-dynamics/Five animals WGCNA.R, lines 437–514 · score 0.69 · Co expression network, module membership, module genes, top Gene, edge, Expression dynamics
[6] § Results › Wnt ligand genes exhibit protracted expression in rat progenitor cells ↔ five-mammalian-species-gene-expression-dynamics/Five animals WGCNA with unnormalized expression.R, lines 365–441 · score 0.68 · Co expression network, module genes, top Gene, module membership, edge, Expression dynamics
[7] § Methods › Methods and protocols › Single-cell transcriptomics ↔ mouse-and-rat-temporal-alignment/Preparation and filtering for murine datasets.R, lines 190–230 · score 0.63 · SCTransform, log10GenesPerUMI, filtered, Seurat, variable, species
[8] § Results › Wnt ligand genes exhibit protracted expression in rat progenitor cells ↔ five-mammalian-species-gene-expression-dynamics/Five animals WGCNA.R, lines 1–43 · score 0.61 · mTOR, receptor genes, FGF, Notch, ligands, Wnt
[9] § Results › Wnt ligand genes exhibit protracted expression in rat progenitor cells ↔ five-mammalian-species-gene-expression-dynamics/Five animals WGCNA with unnormalized expression.R, lines 1–47 · score 0.61 · mTOR, receptor genes, FGF, Notch, ligands, Wnt
[10] § Methods › Methods and protocols › Single-cell transcriptomics ↔ mouse-and-rat-temporal-alignment/Rodent temporal scaling_cellalign.R, lines 55–111 · score 0.58 · winSz, cellAlign, alignment, weighting, temporal, genes
[11] § Results › Wnt ligand genes exhibit protracted expression in rat progenitor cells ↔ five-mammalian-species-gene-expression-dynamics/Five animals WGCNA with unnormalized expression.R, lines 1–47 · score 0.53 · IGF2BP2, Wls, Hmga2, Axin2, ligands, single cell
[12] § Methods › Methods and protocols › Single-cell transcriptomics ↔ five-mammalian-species-gene-expression-dynamics/Five animals temporal scaling_cellalign.R, lines 53–92 · score 0.53 · winSz, cellAlign, weighting, Dynamic, genes, temporal
[13] § Results › Non-uniform temporal progression of cortical RG progenitors revealed by scRNAseq ↔ mouse-and-rat-temporal-alignment/Ordinal logistic regression for scoring murine.R, lines 1–55 · score 0.52 · neuroepithelial cells, NEC, overlap, profiles, ordinal, regression
[14] § Results › Wnt ligand genes exhibit protracted expression in rat progenitor cells ↔ five-mammalian-species-gene-expression-dynamics/Five animals WGCNA.R, lines 1–43 · score 0.52 · receptor genes, Positive regulation, retrieved, ligand, gene expression, Wnt
[15] § Methods › Methods and protocols › Single-cell transcriptomics ↔ mouse-and-rat-temporal-alignment/Rat cells integration and pseudotime calculation.R, lines 348–434 · score 0.52 · n_folds, coefficients, rank, age, psupertime, Pseudotime
[16] § Results › Wnt ligand genes exhibit protracted expression in rat progenitor cells ↔ five-mammalian-species-gene-expression-dynamics/Five animals double gene expression.R, lines 144–214 · score 0.51 · Lmx1a, Gene expression, Emx2, Lhx2, double, mammalian species

Paper

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The authors' code

R · 666 lines · 30 KB · no license · 4 matches

library(WGCNA)
library(Seurat)
library(dplyr)
library(ggplot2)
library(ComplexHeatmap)
library(igraph)
library(ggraph)
library(clusterProfiler)
library(org.Hs.eg.db)
setwd("F:/Single cell analysis/Mouse and rat/Network and trend/objects")
human_mean_expr <- read_rds("Human_vRG_expr_mean.rds")
macaque_mean_expr <- read_rds("Macaque_vRG_expr_mean.rds")
mouse_mean_expr <- read_rds("Mouse_vRG_expr_mean.rds")
ferret_mean_expr <- read_rds("Ferret_vRG_expr_mean.rds")
rat_mean_expr <- read_rds("Rat_vRG_expr_mean.rds")
human_mean_expr <- read_rds("Human_ExN_IPC_expr_mean.rds")
macaque_mean_expr <- read_rds("Macaque_ExN_IPC_expr_mean.rds")
mouse_mean_expr <- read_rds("Mouse_ExN_IPC_expr_mean.rds")
ferret_mean_expr <- read_rds("Ferret_ExN_IPC_expr_mean.rds")
rat_mean_expr <- read_rds("Rat_ExN_IPC_expr_mean.rds")
GOI <- c("AXIN2", "WNT3","WNT4","WNT5A","WNT5B","WNT7A","WNT7B", "WNT8B", "FRZB", "WLS", "LMO2")
GOI <- c("HMGA2","CCND1", "HES1","NOTCH2")
GOI <- GO_retrieve("GO:0030177") #positive regulation of WNT
intersect(sort(read.csv("module_v2blue.csv")$x), GO_retrieve("GO:0016055"))
GOI <- read.csv("module_blue.csv")$x
GOI <- read.csv("module_brown.csv")$x
ligand_gene <- c("WNT1", "WNT2", "WNT2B", "WNT3", "WNT3A", "WNT4", "WNT5A", "WNT5B", "WNT6", "WNT7A", "WNT7B", "WNT8A", "WNT8B", "WNT9A", "WNT9B", "WNT10A", "WNT10B", "WNT11", "WNT16", "PORCN", "WLS", "PGAP1", "GPC3")
ligand_gene <- c( "WNT5A", "WNT5B", "WNT7B", "WLS", "PGAP1", "GPC3")
receptor_gene <- c("FZD1", "FZD3", "FZD4", "FZD5", "FZD6", "FZD8", "FZD9", "FZD10", "LRP5", "LRP6", "ROR2", "PTK7", 'PKD1')
GOI <- ligand_gene
GOI <- receptor_gene
GOI <- unique(GO_retrieve("GO:0045880")) #SHH positive
GOI <- unique(c(GO_retrieve("GO:0030177"))) #Wnt positive
GOI <- unique(c(GO_retrieve("GO:0032008"))) #mTOR positive regulation
GOI <- unique(c(GO_retrieve("GO:0045747"))) #NOTCH positive
GOI <- unique(c(GO_retrieve("GO:0045743"))) #FGF positive
GOI <- unique(c(GO_retrieve("GO:0008543"))) #FGF
GOI <- unique(c(GO_retrieve("GO:0030513"))) #BMP positive
# Helper function to interpolate expression to a common scaled grid
# This function scales stages from 0 to 1 and interpolates gene expression onto a fixed number of bins.
get_scaled_binned_expr <- function(mean_expr, species, num_bins = 30) {
# Convert rownames to numeric stages and sort them
stages <- as.numeric(rownames(mean_expr))
sorted_idx <- order(stages)
stages <- stages[sorted_idx]
mean_expr <- mean_expr[sorted_idx, , drop = FALSE]
# Compute min and max stages for scaling
min_stage <- min(stages)
max_stage <- max(stages)
scaled_stages <- (stages - min_stage) / (max_stage - min_stage)
# Create a regular grid from 0 to 1 with num_bins points
grid <- seq(0, 1, length.out = num_bins)
# Interpolate each gene's expression onto the grid using linear approximation
expr_grid <- apply(mean_expr, 2, function(y) {
approx(scaled_stages, y[sorted_idx], xout = grid, method = "linear")$y
})
# Replace any NA values (from extrapolation) with 0
expr_grid[is.na(expr_grid)] <- 0
# Transpose: rows = genes, columns = bins; add species prefix to column names
expr_grid <- t(expr_grid)
colnames(expr_grid) <- paste(species, seq_len(num_bins), sep = "_")
return(expr_grid)
}
# Function to retrieve expression matrix across species (expanded to five species, no cell types)
# This prepares a combined matrix of interpolated expressions for genes of interest.
# Handles genes that may not be present in all species by setting missing expressions to 0.
Retrive_expr_mat <- function(genes = NULL, num_bins = 30) {
species <- c("human", "macaque", "mouse", "ferret", "rat")
mean_expr_list <- list(
human = human_mean_expr,
macaque = macaque_mean_expr,
mouse = mouse_mean_expr,
ferret = ferret_mean_expr,
rat = rat_mean_expr
)
# Determine all genes to include (union if genes=NULL, or provided genes)
if (is.null(genes)) {
all_genes <- unique(unlist(lapply(mean_expr_list, colnames)))
} else {
all_genes <- genes
# Warn if some genes are not found in any species
found_genes <- unique(unlist(lapply(mean_expr_list, colnames)))
missing <- setdiff(genes, found_genes)
if (length(missing) > 0) {
warning(paste("Genes not found in any species:", paste(missing, collapse = ", ")))
}
}
if (length(all_genes) == 0) {
stop("No genes available.")
}
# Process each species
expr_grids <- list()
for (sp in species) {
mean_expr <- mean_expr_list[[sp]]
if (is.null(mean_expr)) {
warning(paste("No expression data for", sp, ". Setting to 0."))
full_mean_expr <- matrix(0, nrow = 1, ncol = length(all_genes), dimnames = list("dummy", all_genes))
} else {
present_genes <- intersect(all_genes, colnames(mean_expr))
full_mean_expr <- matrix(0, nrow = nrow(mean_expr), ncol = length(all_genes), dimnames = list(rownames(mean_expr), all_genes))
full_mean_expr[, present_genes] <- as.matrix(mean_expr[, present_genes])
}
expr_grids[[sp]] <- get_scaled_binned_expr(full_mean_expr, sp, num_bins)
}
# Combine matrices side-by-side
expr_mat <- do.call(cbind, expr_grids)
return(expr_mat)
}
# WGCNA Analysis Setup
# Prepare data for WGCNA: transpose expression matrix so rows are "samples" (bins), columns are genes.
#GOI_expr <- Retrive_expr_mat(setdiff(colnames(datExpr), names(net$colors)[net$colors %in% c("brown","green","grey")])) # Use all common genes; optionally pass genes as argument
GOI_expr <- Retrive_expr_mat(GOI)
datExpr <- as.matrix(t(GOI_expr)) # Samples (species_bin) as rows, genes as columns
samples <- rownames(datExpr)
colnames(datExpr)
# Parse sample names to extract species and bin numbers
split_samples <- strsplit(samples, "_")
species_list <- sapply(split_samples, `[`, 1)
bin_list <- as.numeric(sapply(split_samples, `[`, 2))
# Define scaled stages based on the common grid
num_bins <- length(unique(bin_list)) # Should be 30 or whatever was used
grid <- seq(0, 1, length.out = num_bins)
stage_list <- rep(grid, length(unique(species_list))) # Repeat grid for each species
# Create traits data frame
datTraits <- data.frame(
species = species_list,
stage = stage_list,
row.names = samples
)
# Choose soft-thresholding power for WGCNA network construction
powers <- 1:20
sft <- pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)
power <- sft$powerEstimate
if (is.na(power)) power <- 6 # Default power if no good fit
# Construct co-expression network and identify modules using blockwiseModules
net <- blockwiseModules(
datExpr,
power = power,
TOMType = "signed Nowick 2", # Allows negative TOM for opposites; stricter than "signed Nowick 2"
minModuleSize = 100, # Smaller for finer separation (with 300 genes)
mergeCutHeight = 0.15, # Lower to merge less, preserving distinct dynamics
#networkType = "signed", # Preserves signs for dynamics
#consensusQuantile = 0, # Minimum TOM across sets (strict consensus)
verbose = 3
)
# Compute module eigengenes (summaries of module expression profiles)
MEs <- moduleEigengenes(datExpr, net$colors)$eigengenes
colnames(MEs)
plot_me_trends("grey", gene_wise = TRUE, alpha = 0.2, alphacurve = 0.2, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
plot_me_trends("grey", gene_wise = F, alpha = 0.15, alphacurve = 0.2, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
plot_dendrogram(net)
plot_me_trends(NULL, colnumber = 2, MEs = MEs, datTraits = datTraits, species_colors = species_colors)
# Create species-specific stage traits for correlation analysis
# Example module queries and operations (adapt as needed)
module <- "brown"
module <- "grey"
module <- "yellow"
module <- "turquoise"
module <- "blue"
module <- "green"
module <- "red"
sort(names(net$colors)[net$colors == module])
length(names(net$colors)[net$colors == module])
mouse_tf <- readLines("mouse TFs.txt")
intersect(names(net$colors)[net$colors == module], mouse_tf)
write.csv(names(net$colors)[net$colors == module], paste0("module_", module, ".csv"))
##############################################################################
# Function to get the module a gene belongs to
# Given a gene name, returns the module color it belongs to (or NA if not found)
get_module_for_gene <- function(gene, net) {
if (!gene %in% names(net$colors)) {
warning(paste("Gene", gene, "not found in the network."))
return(NA)
}
module_label <- net$colors[gene]
return(module_label)
}
# Usage example
get_module_for_gene("Ccnd1", net)
###############################################################################
# Plot module eigengene trends
# This function plots smoothed trends of module eigengenes over scaled stages for each species.
# If module is NULL (default), it will plot trends for all modules in MEs, arranged in a grid using cowplot.
plot_me_trends <- function(module = NULL, colnumber = 3, gene_wise = FALSE, alpha = 0.05, highlight_gene = NULL, MEs, datTraits, species_colors, net = NULL, datExpr = NULL, alphacurve = 1) {
unique_species <- unique(datTraits$species)
if (gene_wise) {
if (is.null(module)) {
stop("gene_wise=TRUE requires a specific module to be provided (module cannot be NULL).")
}
if (is.null(net) || is.null(datExpr)) {
stop("gene_wise=TRUE requires net and datExpr to be provided.")
}
# Make module lowercase for case-insensitivity
module <- tolower(module)
available_modules <- gsub("^ME", "", colnames(MEs))
if (!module %in% available_modules) {
stop(paste("Module", module, "not found in MEs. Available modules:", paste(sort(available_modules), collapse = ", ")))
}
# Get module genes
module_genes <- names(net$colors)[net$colors == module]
if (length(module_genes) == 0) {
stop(paste("No genes found in module", module))
}
# Extract expression data for module genes
expr_data <- datExpr[, module_genes, drop = FALSE]
# Prepare long format data: stage, species, gene, expression
plot_data <- data.frame(
stage = datTraits$stage,
species = datTraits$species
)
plot_data <- cbind(plot_data, as.data.frame(expr_data))
plot_data_long <- reshape2::melt(plot_data, id.vars = c("stage", "species"), variable.name = "gene", value.name = "expression")
# Reorder species as factors
plot_data_long$species <- factor(plot_data_long$species, levels = unique_species)
# Determine y-axis limits from data range
y_limits <- range(plot_data_long$expression, na.rm = TRUE)
# Define color codes
background_colors <- species_colors
highlight_color <- "#FAD32F"
highlight_colors <- setNames(rep(highlight_color, length(unique_species)), unique_species)
# Base plot (no data to allow separate layers)
p <- ggplot() +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
plot.subtitle = element_text(hjust = 0.5, size = 12),
axis.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom",
legend.title = element_text(face = "bold"),
strip.background = element_rect(fill = "grey90", color = "black"),
strip.text = element_text(face = "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
) +
coord_cartesian(ylim = c(-3.5,3.5)) +
labs(
title = paste("Gene Expression Trends for Module", module),
subtitle = paste("Across Species (Individual Genes)"),
x = "Scaled Developmental Stage (0 to 1)",
y = "Gene Expression",
color = "Species",
fill = "Species"
)
# Add background genes (non-highlighted) with lighter colors and low alpha
background_data <- plot_data_long[!plot_data_long$gene %in% highlight_gene, ]
if (nrow(background_data) > 0) {
for (sp in unique_species) {
bd_sp <- background_data[background_data$species == sp, ]
if (nrow(bd_sp) > 0) {
p <- p + geom_smooth(data = bd_sp, aes(x = stage, y = expression, group = gene),
color = alpha(background_colors[sp], alphacurve), fill = alpha(background_colors[sp], alphacurve),
method = "loess", se = TRUE, alpha = alpha, size = 0.5, span = 1.5, level = 0.5)
}
}
}
# Add highlighted genes with darker colors, full alpha, thicker lines, and labels
if (!is.null(highlight_gene)) {
highlight_data <- plot_data_long[plot_data_long$gene %in% highlight_gene, ]
if (nrow(highlight_data) == 0) {
warning("No highlight genes found in the module.")
} else {
p <- p + geom_smooth(data = highlight_data, aes(x = stage, y = expression, group = gene, color = species, fill = species),
method = "loess", se = TRUE, alpha = 0.5, size = 1.2, span = 1.5, level = 0.5) +
scale_color_manual(values = highlight_colors) +
scale_fill_manual(values = highlight_colors)
# Add labels at the end of the curves (stage = 1)
label_data <- highlight_data[highlight_data$stage == 1, ]
label_data$label <- paste(label_data$species, label_data$gene)
p <- p + geom_text(data = label_data, aes(x = stage, y = expression, label = label, color = species),
hjust = 1.05, vjust = 0.5, size = 4, fontface = "bold")
}
}
return(p)
}
# Non-gene_wise mode (original behavior)
if (is.null(module)) {
modules <- gsub("^ME", "", colnames(MEs))
plot_list <- lapply(modules, function(mod) {
# Get the base plot for the module
me_col <- paste0("ME", mod)
plot_data <- data.frame(
ME = MEs[, me_col],
species = datTraits$species,
stage = datTraits$stage
)
# Reorder species as factors for consistent plotting
plot_data$species <- factor(plot_data$species, levels = unique_species)
# Determine y-axis limits from data range
y_limits <- range(plot_data$ME, na.rm = TRUE)
p <- ggplot(plot_data, aes(x = stage, y = ME, color = species, group = species)) +
geom_smooth(aes(fill = species), method = "loess", se = TRUE, alpha = 0.2, size = 0.8, span = 1.5, level = 0.5) +
scale_color_manual(values = species_colors) +
scale_fill_manual(values = species_colors) +
coord_cartesian(ylim = y_limits) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 12),
plot.subtitle = element_blank(),
axis.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom",
legend.title = element_text(face = "bold"),
strip.background = element_rect(fill = "grey90", color = "black"),
strip.text = element_text(face = "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
# Modify for grid: remove x ticks, x title, y title, legend; set title to module name
p <- p + theme(
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "none") +
coord_cartesian(ylim = c(-0.3,0.3)) +
labs(title = mod)
return(p)
})
# Arrange plots in a grid
arranged_plot <- plot_grid(plotlist = plot_list, ncol = colnumber)
return(arranged_plot)
}
# Proceed with single module eigengene plotting
module <- tolower(module)
available_modules <- gsub("^ME", "", colnames(MEs))
if (!module %in% available_modules) {
stop(paste("Module", module, "not found in MEs. Available modules:", paste(sort(available_modules), collapse = ", ")))
}
me_col <- paste0("ME", module)
plot_data <- data.frame(
ME = MEs[, me_col],
species = datTraits$species,
stage = datTraits$stage
)
# Reorder species as factors for consistent plotting
plot_data$species <- factor(plot_data$species, levels = unique_species)
# Determine y-axis limits from data range
y_limits <- range(plot_data$ME, na.rm = TRUE)
p <- ggplot(plot_data, aes(x = stage, y = ME, color = species, group = species)) +
geom_smooth(aes(fill = species), method = "loess", se = TRUE, alpha = 0.05, size = 0.8, span = 1.5, level = 0.5) +
scale_color_manual(values = species_colors) +
scale_fill_manual(values = species_colors) +
coord_cartesian(ylim = y_limits) +
labs(
title = paste("Module Eigengene Trends for Module", module),
subtitle = "Across Species",
x = "Scaled Developmental Stage (0 to 1)",
y = "Module Eigengene Expression",
color = "Species",
fill = "Species"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
plot.subtitle = element_text(hjust = 0.5, size = 12),
axis.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom",
legend.title = element_text(face = "bold"),
strip.background = element_rect(fill = "grey90", color = "black"),
strip.text = element_text(face = "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
return(p)
}
# Usage examples
species_colors <- c("human" = "#9467BD", "macaque" = "#A87B20", "mouse" = "#2091A8", "ferret" = "#2CA02C", "rat" = "#A83720")
# Plot for a single module
plot_me_trends("turquoise", MEs = MEs, datTraits = datTraits, species_colors = species_colors)
# Plot for a single module with all gene being plotted
plot_me_trends("turquoise", gene_wise = TRUE, alpha = 0.03, alphacurve = 0.01, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
plot_me_trends("blue", gene_wise = TRUE, alpha = 0.03, alphacurve = 0.03, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
plot_me_trends("brown", gene_wise = TRUE, alpha = 0.03, alphacurve = 0.3, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
plot_me_trends("yellow", gene_wise = TRUE, alpha = 0.03, alphacurve = 0.1, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
plot_me_trends("green", gene_wise = TRUE, alpha = 0.03, alphacurve = 0.1, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
plot_me_trends("grey", gene_wise = TRUE, alpha = 0.08, alphacurve = 0.2, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
plot_me_trends("grey", gene_wise = F, alpha = 0.08, alphacurve = 0.2, highlight_gene = NULL, MEs = MEs, datTraits = datTraits, species_colors = species_colors, net = net, datExpr = datExpr)
# Plot for all modules arranged in a grid with 6 columns
plot_me_trends(NULL, colnumber = 2, MEs = MEs, datTraits = datTraits, species_colors = species_colors)
#########################################################################################
# Redesigned plot_coexpression_network function (adapted to new data structure, uses mouse_tf)
# This function visualizes the top connections in a module's co-expression network, highlighting TFs.
plot_coexpression_network <- function(module, datExpr, power, mouse_tf, top_connections = 50, label_top_n = 10) {
module_genes <- names(net$colors)[net$colors == module]
TOM <- TOMsimilarityFromExpr(datExpr[, module_genes], power = power)
# Select top connections based on TOM values
TOM_upper <- TOM[upper.tri(TOM)]
threshold <- quantile(TOM_upper, 1 - (top_connections / choose(length(module_genes), 2)))
links <- which(TOM > threshold, arr.ind = TRUE)
links <- links[links[,1] < links[,2], ]
links_df <- data.frame(from = module_genes[links[,1]], to = module_genes[links[,2]])
if (nrow(links_df) == 0) {
stop("No connections above the threshold. Try increasing top_connections.")
}
# Create igraph network object
network <- graph_from_data_frame(links_df, directed = FALSE)
# Get genes in the network
network_genes <- V(network)$name
# Calculate module eigengenes and membership (kME)
colors <- rep(module, length(module_genes))
MEs <- moduleEigengenes(datExpr[, module_genes], colors)$eigengenes
MM <- cor(datExpr[, module_genes], MEs, use = "p")
# Subset membership to network genes
MM_network <- MM[network_genes, , drop = FALSE]
# Identify TFs in the module and network
module_tfs <- intersect(module_genes, mouse_tf)
tfs_in_network <- module_tfs[module_tfs %in% network_genes]
# Set node attributes: size and color based on kME, labels for TFs and top genes
V(network)$kME <- MM_network[, 1]
V(network)$size <- 2 + 10 * (V(network)$kME - min(V(network)$kME)) / (max(V(network)$kME) - min(V(network)$kME))
V(network)$color <- scales::col_numeric("Blues", domain = V(network)$kME)(V(network)$kME)
V(network)$label <- NA
# Highlight and label TFs
if (length(tfs_in_network) > 0) {
tf_indices <- which(V(network)$name %in% tfs_in_network)
V(network)$color[tf_indices] <- "red"
V(network)$label[tf_indices] <- V(network)$name[tf_indices]
}
# Label top non-TF genes by kME
top_genes <- module_genes[order(MM[, 1], decreasing = TRUE)]
top_genes_in_network <- top_genes[top_genes %in% network_genes & !(top_genes %in% tfs_in_network)][1:label_top_n]
top_idx <- which(V(network)$name %in% top_genes_in_network)
V(network)$label[top_idx] <- V(network)$name[top_idx]
# Create plot using ggraph
p <- ggraph(network, layout = "fr") +
geom_edge_link(alpha = 0.4, color = "grey50", edge_width = 0.5) +
geom_node_point(aes(size = size, color = color)) +
geom_node_text(aes(label = label), repel = TRUE, size = 3, fontface = "bold", color = "black") +
scale_color_identity() +
scale_size_identity() +
theme_void() +
labs(
title = paste("Co-expression Network for Module", module),
subtitle = paste("TFs in red | Top", top_connections, "Connections"),
caption = "Node size and color intensity reflect kME (module membership)"
) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
plot.subtitle = element_text(hjust = 0.5, size = 12),
plot.caption = element_text(hjust = 0.5, size = 10, color = "grey50"),
plot.background = element_rect(fill = "white", color = NA),
panel.background = element_rect(fill = "white", color = NA)
)
return(p)
}
# For mouse_tf data: Download the list from https://resources.aertslab.org/cistarget/tf_lists/allTFs_mm.txt (one gene symbol per line).
# Alternatively, use AnimalTFDB[](http://bioinfo.life.hust.edu.cn/AnimalTFDB4/#/download) for mouse TF gene symbols.
# Usage example (after downloading and saving the file):
mouse_tf <- readLines("mouse TFs.txt")
plot_coexpression_network("red", datExpr, power, mouse_tf, top_connections = 500, label_top_n = 100)
plot_coexpression_network("turquoise", datExpr, power, mouse_tf, top_connections = 400, label_top_n = 100)
plot_coexpression_network("blue", datExpr, power, mouse_tf, top_connections = 500, label_top_n = 100)
plot_coexpression_network("brown", datExpr, power, mouse_tf, top_connections = 500, label_top_n = 100)
plot_coexpression_network("yellow", datExpr, power, mouse_tf, top_connections = 500, label_top_n = 100)
plot_coexpression_network("green", datExpr, power, mouse_tf, top_connections = 500, label_top_n = 100)
##################################################################
library(dendextend)
plot_dendrogram <- function(net, label_module = NULL) {
# Get the dendrogram and module assignments (assuming single block for simplicity)
dend <- net$dendrograms[[1]]
groups <- net$colors[net$blockGenes[[1]]]
moduleColors <- groups
# If label_module is provided, modify only the color bar for highlighting
if (!is.null(label_module)) {
# Update the color bar to pink for highlighted modules
moduleColors[moduleColors %in% label_module] <- "black"
}
# Plot the dendrogram with module colors (branches remain original)
plotDendroAndColors(
dend,
moduleColors,
"Module Colors",
dendroLabels = FALSE,
hang = 0.2,
addGuide = TRUE,
guideHang = 0.05,
main = "Gene Dendrogram and Module Assignments",
cex.main = 1.2,
font.main = 2
)
}
# Usage examples
plot_dendrogram(net) # Standard plot
plot_dendrogram(net, label_module = c("brown"))
###################################################################
###################################################################
# New function to plot expression trends for specified genes
# Plots smoothed curves over scaled stages for each gene in mouse and rat.
# Labels each curve with "species Gene" at the end of the curve.
# Uses species_colors for curve colors.
# Parameters: genes (vector of gene names), datExpr (expression matrix), datTraits (traits with species and stage), species_colors (named vector for mouse and rat).
plot_specified_genes <- function(genes, datExpr, datTraits, species_colors, set_normalize = FALSE) {
unique_species <- unique(datTraits$species)
if (length(genes) == 0) {
stop("No genes provided.")
}
# Check if all genes are in datExpr
missing_genes <- setdiff(genes, colnames(datExpr))
if (length(missing_genes) > 0) {
warning(paste("Missing genes:", paste(missing_genes, collapse = ", ")))
}
genes <- intersect(genes, colnames(datExpr))
if (length(genes) == 0) {
stop("None of the provided genes found in datExpr.")
return()
}
# Extract expression data for specified genes
expr_data <- datExpr[, genes, drop = FALSE]
# Normalize per species per gene if requested (z-score)
if (set_normalize) {
for (sp in unique_species) {
sp_rows <- which(datTraits$species == sp)
for (g in genes) {
vals <- expr_data[sp_rows, g]
if (sd(vals, na.rm = TRUE) == 0 || all(is.na(vals))) {
expr_data[sp_rows, g] <- 0 # Handle constant or all-NA cases
} else {
expr_data[sp_rows, g] <- (vals - mean(vals, na.rm = TRUE)) / sd(vals, na.rm = TRUE)
}
}
}
}
# Prepare long format data: stage, species, gene, expression
plot_data <- data.frame(
stage = datTraits$stage,
species = datTraits$species
)
plot_data <- cbind(plot_data, as.data.frame(expr_data))
plot_data_long <- reshape2::melt(plot_data, id.vars = c("stage", "species"), variable.name = "gene", value.name = "expression")
# Reorder species as factors
plot_data_long$species <- factor(plot_data_long$species, levels = unique_species)
# Determine y-axis limits from data range
y_limits <- range(plot_data_long$expression, na.rm = TRUE)
# Set y-label based on normalization
y_label <- ifelse(set_normalize, "Normalized Gene Expression (Z-score)", "Gene Expression")
# Base plot
p <- ggplot(plot_data_long, aes(x = stage, y = expression, color = species, fill = species, group = interaction(species, gene))) +
geom_smooth(method = "loess", se = TRUE, alpha = 0.05, size = 1, span = 3, level = 0.5) +
scale_color_manual(values = species_colors) +
scale_fill_manual(values = species_colors) +
coord_cartesian(ylim = y_limits) +
labs(
title = "Gene Expression Trends for Specified Genes",
subtitle = "Across Species",
x = "Scaled Developmental Stage (0 to 1)",
y = y_label,
color = "Species",
fill = "Species"
) +
theme_minimal(base_size = 14) +
coord_cartesian(ylim = c(-3,3)) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
plot.subtitle = element_text(hjust = 0.5, size = 12),
axis.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom",
legend.title = element_text(face = "bold"),
strip.background = element_rect(fill = "grey90", color = "black"),
strip.text = element_text(face = "bold"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
# Add labels at the end of the curves (stage = 1)
label_data <- plot_data_long[plot_data_long$stage == max(plot_data_long$stage), ]
label_data$label <- paste(label_data$species, label_data$gene)
p <- p + geom_text(data = label_data, aes(label = label), hjust = 0.8, vjust = -1, size = 4, fontface = "bold")
return(p)
}
# Usage example
species_colors <- c("human" = "#9467BD", "macaque" = "#A87B20", "mouse" = "#2091A8", "ferret" = "#2CA02C", "rat" = "#A83720")
plot_specified_genes(c("AXIN2"), datExpr, datTraits, species_colors, set_normalize = TRUE)
plot_specified_genes(c("WNT7B"), datExpr, datTraits, species_colors, set_normalize = T)
plot_specified_genes(c("WNT7B"), datExpr, datTraits, species_colors, set_normalize = T)
plot_specified_genes(c("WLS"), datExpr, datTraits, species_colors, set_normalize = T)
plot_specified_genes(c("LMO2"), datExpr, datTraits, species_colors, set_normalize = T)
plot_specified_genes(c("FRZB"), datExpr, datTraits, species_colors, set_normalize = T)
plot_specified_genes(c("AXIN2", "WNT3","WNT4","WNT5A","WNT5B","WNT7A","WNT7B", "WNT8B", "FRZB", "WLS", "LMO2"), datExpr, datTraits, species_colors)
plot_specified_genes(c("AXIN2"), datExpr, datTraits, species_colors)
plot_specified_genes(c("WLS"), datExpr, datTraits, species_colors)
plot_specified_genes(c("WNT7B"), datExpr, datTraits, species_colors)
plot_specified_genes(c("HMGA2"), datExpr, datTraits, species_colors)
plot_specified_genes(c("CCND1"), datExpr, datTraits, species_colors)

Five animals WGCNA.R at commit 5f906a6, no license · at the source

Overview

Authors: Yuki Y Yamauchi¹, Xuanhao D Sheu¹, Rafat Tarfder², Takuma Kumamoto³, Jun Hatakeyama⁴, Haruka Sato⁴, Pauline Rouillard², Merve Bilgic⁵, Shuto Deguchi^6,7, Tomonori Nakamura^6,7,8, Yusuke Kishi^5,9,10, Kazuo Emoto^2,11, Ikuo K Suzuki¹

ORCID iDs: Xuanhao D Sheu, Merve Bilgic, Shuto Deguchi, Yusuke Kishi, Kazuo Emoto, Ikuo K Suzuki

Laboratory of functional genomics for human evolution, Graduate School of Frontier Biosciences, The University of Osaka,Osaka, Japan
Department of Biological Sciences, Graduate School of Science, The University of Tokyo,Tokyo, Japan
Developmental Neuroscience Project, Department of Basic Medical Sciences, Tokyo Metropolitan Institute of Medical Science,Tokyo, Japan
Department of Brain Morphogenesis, Institute of Molecular Embryology and Genetics, Kumamoto University,Kumamoto, Japan
Laboratory of Molecular Neurobiology, Institute for Quantitative Biosciences, The University of Tokyo,Tokyo, Japan
Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University,Kyoto, Japan
Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University,Kyoto, Japan
The Hakubi Center for Advanced Research, Kyoto University,Kyoto, Japan
Laboratory of Molecular Biology, Graduate School of Pharmaceutical Sciences, The University of Tokyo,Tokyo, Japan
Division of Aging Biology, Research Institute for Science and Technology, Tokyo University of Science,Chiba, Japan
International Research Center for Neurointelligence, The University of Tokyo,Tokyo, Japan

Institutions: The University of Osaka (Japan); The University of Tokyo (Japan); Tokyo Metropolitan Institute of Medical Science (Japan); Kumamoto University (Japan); Kyoto University (Japan); Tokyo University of Science (Japan)

Journal: The EMBO journal, volume 45, issue 15, pages 5237-5266

Dates: received 31 January 2026; accepted 24 April 2026; published online 22 May 2026; in print August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s44318-026-00806-z · PMID 42174121 · PMCID PMC13434014 · OpenAlex W7162080797

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: rat (organism), developmental (subfield)

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials

Keywords: Development, Evolution & Ecology, Neuroscience

MeSH: Cerebral Cortex*, Neurogenesis*, Neurons*, Somatosensory Cortex*, Animals, Neural Stem Cells, Neurodevelopment, Rats, Species Specificity, Wnt Signaling Pathway (* major topic)

Topic: Neurogenesis and neuroplasticity mechanisms (Developmental Neuroscience, Neuroscience), according to OpenAlex

Funding: Japan Agency for Medical Research and Development (JP24ama121020, JP24tm0524007, JP20gm6310006, 25gm7010016h0001); Takeda Science Foundation; SECOM Science and 61 Technology Foundation; SPRING GX, The University of Tokyo (JPMJSP2108); WINGS-LST, The University of Tokyo; MEXT | JST | Fusion Oriented REsearch for disruptive Science and Technology (FOREST) (JPMJFR214T); Japan Society for the Promotion of Science (JP22H02628, JP20H04860, JP25K02280); MBSJ Tomizawa Jun-ichi & Keiko Fund of Molecular 51 Biology Society of Japan for Young Scientist

Citations: cited by 1 paper (Europe PMC); 100 references in the paper

Abstract

Mammals share a laminar cerebral cortex, with excitatory neuron subtypes organized in distinct layers. Although this framework is conserved, subtype balance varies markedly between species due to largely unknown mechanisms. Here, we show that species-specific neuronal composition arises from non-uniform scaling of the temporal dynamics of neurogenesis. Comparative histology of eight mammalian species reveals a significant, rat-specific expansion of the deep layer in the somatosensory cortex. This feature of the rat cortex results from a specific extension of the early neurogenetic phase of deep-layer neuron production before transitioning to the upper layer, as confirmed by neuronal birthdating and single-cell transcriptomics. The duration of deep-layer neuron production is regulated by a genetic program controlling neural progenitor cell aging, including canonical Wnt signaling. Comparative single-cell transcriptomics revealed that cortical progenitor cells in rats exhibit significantly elevated Wnt ligand expression. Therefore, while sequential cortical neurogenesis is conserved, its progression is non-uniformly scaled between species. Precise heterochronic fine-tuning allows evolutionary refinement of cellular configuration without drastic remodeling of the conserved corticogenesis program.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

XD-Sheu/Rodents-temporal-scaling_2026

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 5f906a62e7e096c127cee90388618d7b57e89e78, 17 March 2026

Languages: R (11)

Size: 12 files, 11 scripts

Software Heritage: not archived

Found in: “Data availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: ggplot2 (10 files), Seurat (10 files), tidyverse (10 files), ComplexHeatmap (9 files), ggpubr (7 files), Monocle 3 (7 files), patchwork (7 files), reshape2 (7 files), circlize (6 files), clusterProfiler (6 files), DESeq2 (6 files), cowplot (4 files), data.table (4 files), igraph (3 files), pheatmap (3 files), SingleCellExperiment (3 files), WGCNA (3 files), Plotly (2 files)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

12 files

five-mammalian-species-gene-expression-dynamics/Five animals WGCNA with unnormalized expression.R, R, 782 lines, 4 matches
five-mammalian-species-gene-expression-dynamics/Five animals WGCNA.R, R, 666 lines, 4 matches
five-mammalian-species-gene-expression-dynamics/Five animals double gene expression.R, R, 320 lines, 1 match
five-mammalian-species-gene-expression-dynamics/Five animals temporal scaling_cellalign.R, R, 242 lines, 1 match
five-mammalian-species-gene-expression-dynamics/Metacells and pseudotime model in five animals.R, R, 265 lines
mouse-and-rat-temporal-alignment/Metacells and pseudotime model.R, R, 389 lines
mouse-and-rat-temporal-alignment/Ordinal logistic regression for scoring murine.R, R, 545 lines, 1 match
mouse-and-rat-temporal-alignment/Preparation and filtering for murine datasets.R, R, 245 lines, 2 matches
mouse-and-rat-temporal-alignment/Rat cells integration and pseudotime calculation.R, R, 447 lines, 2 matches
mouse-and-rat-temporal-alignment/Rodent temporal scaling_cellalign.R, R, 137 lines, 1 match
mouse-and-rat-temporal-alignment/Rodent temporal scaling_distance.R, R, 159 lines
README.md, Text, 13 lines

At the source: github.com/XD-Sheu/Rodents-temporal-scaling_2026/

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

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:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
11 scripts, each with its path and the digest of its content;
16 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

biostudies:S-BIAD3170, at BioStudies; found in “Data availability”
geo:GSE287210, at NCBI GEO; found in “Data availability”

Data availability

All source data, including microscopic images and quantitative data necessary to reproduce the results, have been deposited in the BioImage Archive and are available at https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD3170. Analysis codes used in this study are available at the following GitHub repository (https://github.com/XD-Sheu/Rodents-temporal-scaling_2026/). Single-cell RNA sequencing data of rat cortical cells are available in the NCBI Gene Expression Omnibus (GEO: GSE287210 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE287210)). All animals and unique/stable reagents generated in this study are available from the lead contact with a completed Materials Transfer Agreement.

The source data of this paper are collected in the following database record: biostudies:S-SCDT-10_1038-S44318-026-00806-z (https://www.ebi.ac.uk/biostudies/sourcedata/studies/S-SCDT-10_1038-S44318-026-00806-z).

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 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 3 keywords, 10 MeSH terms, 8 funders, 95 references.

Cite

This paper

Yamauchi, Y. Y., Sheu, X. D., Tarfder, R., Kumamoto, T., Hatakeyama, J., Sato, H., Rouillard, P., Bilgic, M., Deguchi, S., Nakamura, T., Kishi, Y., Emoto, K., & Suzuki, I. K. (2026). Interspecific diversity in the neuronal composition of the mammalian cortex arises from heterochrony in neurogenesis. The EMBO journal, 45(15), 5237-5266. https://doi.org/10.1038/s44318-026-00806-z

BibTeX

@article{yamauchi2026interspecific, author = {Yamauchi, Yuki Y and Sheu, Xuanhao D and Tarfder, Rafat and Kumamoto, Takuma and Hatakeyama, Jun and Sato, Haruka and Rouillard, Pauline and Bilgic, Merve and Deguchi, Shuto and Nakamura, Tomonori and Kishi, Yusuke and Emoto, Kazuo and Suzuki, Ikuo K}, title = {{Interspecific diversity in the neuronal composition of the mammalian cortex arises from heterochrony in neurogenesis}}, journal = {The EMBO journal}, year = {2026}, month = may, volume = {45}, number = {15}, pages = {5237--5266}, publisher = {Nature Publishing Group}, issn = {0261-4189}, doi = {10.1038/s44318-026-00806-z}, url = {https://doi.org/10.1038/s44318-026-00806-z}, pmid = {42174121}, pmcid = {PMC13434014} }

RIS

TY - JOUR AU - Yamauchi, Yuki Y AU - Sheu, Xuanhao D AU - Tarfder, Rafat AU - Kumamoto, Takuma AU - Hatakeyama, Jun AU - Sato, Haruka AU - Rouillard, Pauline AU - Bilgic, Merve AU - Deguchi, Shuto AU - Nakamura, Tomonori AU - Kishi, Yusuke AU - Emoto, Kazuo AU - Suzuki, Ikuo K TI - Interspecific diversity in the neuronal composition of the mammalian cortex arises from heterochrony in neurogenesis T2 - The EMBO journal J2 - EMBO J PY - 2026 DA - 2026/05/22 VL - 45 IS - 15 SP - 5237 EP - 5266 SN - 0261-4189 PB - Nature Publishing Group DO - 10.1038/s44318-026-00806-z UR - https://doi.org/10.1038/s44318-026-00806-z LA - en ER -

CSL-JSON

{ "id": "10.1038/s44318-026-00806-z", "type": "article-journal", "title": "Interspecific diversity in the neuronal composition of the mammalian cortex arises from heterochrony in neurogenesis", "container-title": "The EMBO journal", "author": [ { "family": "Yamauchi", "given": "Yuki Y" }, { "family": "Sheu", "given": "Xuanhao D" }, { "family": "Tarfder", "given": "Rafat" }, { "family": "Kumamoto", "given": "Takuma" }, { "family": "Hatakeyama", "given": "Jun" }, { "family": "Sato", "given": "Haruka" }, { "family": "Rouillard", "given": "Pauline" }, { "family": "Bilgic", "given": "Merve" }, { "family": "Deguchi", "given": "Shuto" }, { "family": "Nakamura", "given": "Tomonori" }, { "family": "Kishi", "given": "Yusuke" }, { "family": "Emoto", "given": "Kazuo" }, { "family": "Suzuki", "given": "Ikuo K" } ], "container-title-short": "EMBO J", "volume": "45", "issue": "15", "page": "5237-5266", "DOI": "10.1038/s44318-026-00806-z", "PMID": "42174121", "PMCID": "PMC13434014", "ISSN": "0261-4189", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s44318-026-00806-z", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 22 ] ] } }

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