Sex- and age-specific development of ssRNA virus receptor expression in the human 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

[1] § Methods › Virus family enrichment across trajectory clusters ↔ byan2/Code/Figure 4.Rmd, lines 1604 to 1748 · score 0.86 · global family frequency, strong enrichment, global distribution, modest, permuting, virus family · Report a mismatch
[2] § Methods › Cell-type fidelity analysis ↔ byan2/Code/Figure 5.Rmd, lines 1000 to 1079 · score 0.86 · temporal cortex, Fidelity scores, cortical area, frontal, occipital, parietal · Report a mismatch
[3] § Methods › Cell-type fidelity analysis ↔ byan2/Code/Supplementary Figure 9.Rmd, lines 1002 to 1081 · score 0.86 · temporal cortex, Fidelity scores, cortical area, frontal, occipital, parietal · Report a mismatch
[4] § Methods › Analysis of virus receptor lifespan trajectories ↔ byan2/Code/Figure 2.Rmd, lines 194 to 284 · score 0.78 · ward.D2, Euclidean distance, cluster sum, hierarchically clustered, squares, optimal · Report a mismatch
[5] § Methods › Analysis of virus receptor lifespan trajectories ↔ byan2/Code/Supplementary Figure 7.Rmd, lines 197 to 289 · score 0.78 · ward.D2, Euclidean distance, cluster sum, hierarchically clustered, squares, optimal · Report a mismatch
[6] § Methods › Analyzing the high-dimensional pattern of virus receptor development among brain areas ↔ byan2/Code/Figure 2.Rmd, lines 342 to 382 · score 0.77 · dens_frac, dens_lambda, t-SNE, brain area sex, perplexity, seeds · Report a mismatch
[7] § Methods › Analyzing the high-dimensional pattern of virus receptor development among brain areas ↔ byan2/Code/Supplementary Figure 7.Rmd, lines 332 to 372 · score 0.77 · dens_frac, dens_lambda, t-SNE, brain area sex, perplexity, seeds · Report a mismatch
[8] § Methods › Human transcriptomic dataset ↔ byan2/Code/Figure 1.Rmd, lines 496 to 555 · score 0.72 · tissue sample, AMY, CBC, HIP, MD, hemispheres · Report a mismatch
[9] § Methods › Human transcriptomic dataset ↔ byan2/Code/Figure 2.Rmd, lines 385 to 432 · score 0.72 · subcortical areas, AMY, CBC, HIP, MD, DFC · Report a mismatch
[10] § Methods › Virus family enrichment across trajectory clusters ↔ byan2/Code/Figure 4.Rmd, lines 1604 to 1748 · score 0.71 · Jensen Shannon, virus family annotations, divergence, gene area, global, enrichment · Report a mismatch
[11] § Methods › Differential expression-sliding window analysis (DE-SWAN) ↔ byan2/Code/Supplementary Figure 8.rmd, lines 509 to 648 · score 0.68 · Benjamini Hochberg procedure, window center, DE SWAN, heatmap, LOESS, ages · Report a mismatch
[12] § Methods › Random Forest model ↔ byan2/Code/Figure 3.Rmd, lines 383 to 453 · score 0.66 · randomForest, accuracy, train, seeds, model, predict · Report a mismatch
[13] § Results › Prenatal cortical virus receptor expression is predominantly male-biased ↔ byan2/Code/Figure 6.Rmd, lines 1548 to 1628 · score 0.64 · female biased expression, cortical virus receptor, sex biases, Figure 6, prenatal, genes · Report a mismatch
[14] § Results › Sex differences in virus receptor development are most pronounced in the cortex ↔ byan2/Code/Supplementary Figure 7.Rmd, lines 375 to 427 · score 0.61 · subcortical areas, den, AMY, CBC, HIP, MD · Report a mismatch
[15] § Results › Sex differences in virus receptor development are most pronounced in the cortex ↔ byan2/Code/Figure 2.Rmd, lines 385 to 432 · score 0.60 · subcortical areas, AMY, CBC, HIP, MD, SNE · Report a mismatch
[16] § Methods › Random Forest model ↔ byan2/Code/Supplementary Figure 2.Rmd, lines 361 to 480 · score 0.59 · LOESS fitted, gene area combinations, LOESS curves, iteration, seeds, model · Report a mismatch
[17] § Methods › Human transcriptomic dataset ↔ byan2/Code/Kang Data Cleaning.Rmd, lines 57 to 74 · score 0.58 · tissue samples, Affymetrix, exon, Gene Expression, Probe, transcriptomic · Report a mismatch
[18] § Methods › Human transcriptomic dataset ↔ byan2/Code/Figure 1.Rmd, lines 1585 to 1640 · score 0.57 · Fr chet distances, area trajectory pairs, male gene, fitted, Figure 1, LOESS · Report a mismatch
[19] § Results › Selective cell-type associations characterize a subset of DE-SWAN clusters ↔ byan2/Code/Figure 5.Rmd, lines 1277 to 1348 · score 0.55 · DE SWAN clusters, unpaired median, confidence intervals, oligodendrocytes, Lollipop, neurons · Report a mismatch
[20] § Results › Cortical virus receptor expression predicts sex at the individual level ↔ byan2/Code/Figure 3.Rmd, lines 513 to 603 · score 0.55 · decision boundary, Random Forest, probability, predict, classification, smoothed · Report a mismatch
[21] § Results › Cortical virus receptor expression predicts sex at the individual level ↔ byan2/Code/Figure 3.Rmd, lines 513 to 603 · score 0.54 · decision boundary, RF, probabilities, SD, prediction, classification · Report a mismatch
[22] § Methods › Analysis of virus receptor lifespan trajectories ↔ byan2/Code/Supplementary Figure 6.Rmd, lines 780 to 811 · score 0.53 · Hodges Lehmann, cluster distance, analytic, median · Report a mismatch
[23] § Methods › Analysis of virus receptor lifespan trajectories ↔ byan2/Code/Supplementary Figure 2.Rmd, lines 361 to 480 · score 0.52 · fitting LOESS curves, nesting, Gaussian, log2, model, family · Report a mismatch
[24] § Results › Dynamic sex-by-age patterns of virus receptor expression in the cortex ↔ byan2/Code/Figure 4.Rmd, lines 1520 to 1602 · score 0.52 · DE SWAN cluster, virus families, Hierarchical clustering, Stacked, compositions, Figure 4 · Report a mismatch
[25] § Methods › Analysis of virus receptor lifespan trajectories ↔ byan2/Code/Supplementary Figure 2.Rmd, lines 517 to 561 · score 0.51 · Fr chet distance, male trajectories, iteration, simulated, female, gene · Report a mismatch
[26] § Results › Prenatal expression of Zika virus candidate entry receptors ↔ byan2/Code/Figure 6.Rmd, lines 1548 to 1628 · score 0.50 · female biased, confidence intervals, sex biased, Virus receptor, Figure 6, median · Report a mismatch

Paper

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The authors' code

R Markdown · 749 lines · 25 KB · no license · 4 matches

---
title: |
| Sex- and Age-Specific Development of ssRNA Virus Receptor Expression in the Human Brain
subtitle: |
Figure 2 A-D: High-dimensional Visulization of Developmental Trajectories
| *Markdown correspondence: [email hidden]
author: |
| Leanne Monteiro, Negeen Halabian, Jiawei Li, Kathryn M. Murphy*
date: "`r Sys.Date()`"
output:
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Load Packages
```{r, message=FALSE, warning=FALSE}
# Data Manipulation Libraries
library(tidyverse) # A collection of packages for data manipulation and visualization
library(data.table) # Provides an efficient way to handle and manipulate large data sets
library(magrittr) # For the pipe operator (%>%)
# Load libraries for graphics and visualization
library(ggplot2) # Base package for data visualization
library(ggforce) # For the facet_wrap_paginate() function
library(gridExtra) # Arranging ggplot objects on a grid, e.g., grid.arrange()
library(RColorBrewer) # For generating attractive color palettes
library(here) # For reading in data from directory
library(factoextra) # For creating and visualizing dendrograms
library(densvis) # For visualizing densities
# Additional Tools and Utilities
library(R.devices) # For saving plots with different graphical devices
library(elbow) # For finding the elbow point in a plot
library(pathviewr) # For visualizing the elbow point in a plot
library(patchwork) # The composer of plots
library(cowplot) # Streamlined Plot Theme and Plot Annotations for 'ggplot2'
```
```{r, echo=FALSE, include=FALSE}
# Define import and export file paths
import.path <- here::here("data")
export.path <- here::here("plots")
```
# Preparing the Data
```{r, message=FALSE, warning=FALSE}
# Load in the gene names that was used for the gene/brain area analysis
virus.gene.list <-
read_csv(here::here(import.path, "Human_2023_virus_gene_receptors.csv"))
# Load the raw Kang data
Kang.df <- fread(here::here(import.path, "Kang Data 17528 genes 1340 cases.csv"),
data.table = FALSE) %>%
dplyr::rename(Area = Region) # Rename Region as Brain Area
# Extract and deduplicate gene names
gene.list <- virus.gene.list %>%
select("Gene") %>%
unlist() %>%
as.vector() %>%
unique()
# Specify the areas
area<-c("VFC", "DFC" ,"MFC" ,"OFC","M1C" ,"S1C" ,"IPC" ,"STC" ,
"A1C" ,"ITC" ,"V1C" ,"MD" ,"CBC" ,"HIP" ,"AMY")
# Specify the cell type (used for naming output files)
cell.type <- "Genes"
# Decide whether to use the default span value (0.75)
# If FALSE, the mean span value will be calculated and used instead
include.prenatal <- FALSE
# Filter and retain only relevant genes from the 'gene.list'
gene.list <- intersect(gene.list, colnames(Kang.df)[21:ncol(Kang.df)])
num.genes <- length(gene.list)
```
\newpage
# Hierarchical Clustering on Brain Areas for Each Sex
## Data Preparation
```{r DataPreparation, message=FALSE, warning=FALSE}
# Define identifier columns from the original Kang dataset to retain
identifiers <- c("Sample","Sex", "Area", "Years")
# Specify the number of points/ages to extract for each curve
num.points <- 128
# Select relevant columns
gene.exp <- Kang.df %>%
select(all_of(identifiers), all_of(gene.list)) %>%
# Convert gene expression values to numeric
mutate_at(gene.list, as.numeric) %>%
# Retain only selected brain areas
filter(Area %in% area) # use %in% instead of == when you have multiple areas
# Adjust for prenatal and postnatal samples
if (!include.prenatal) {
# If include.prenatal is FALSE, keep only postnatal samples
gene.exp <- gene.exp %>%
filter(Years > 0)
} else {
# If include.prenatal is TRUE, add 40/52 to the 'Years' column to get rid of
# negative values
gene.exp <- gene.exp %>%
mutate(Years = Years + 40/52)
}
#### Quality Control Checks
# Check if there are duplicate gene labels
# if yes, throw error and return name of duplicates
if (identical(colnames(gene.exp), unique(colnames(gene.exp))) == FALSE) {
stop(paste("Data contains duplicate gene labels. The following gene has a duplicate:",
colnames(gene.exp)[duplicated(colnames(gene.exp))]))
}
# Check if there are any NA values
# if yes, throw error and return name of gene/column with NA
if (sum(is.na(gene.exp)) > 0) {
stop(paste("Data contains NA values in the following columns:",
colnames(gene.exp)[colSums(is.na(gene.exp)) > 0]))
}
#### Z-score normalization
gene.exp.zscore <- gene.exp %>%
mutate_at(gene.list, scale)
# Get long version of z-scored data
gene.exp.zscore.long <- gene.exp.zscore%>%
pivot_longer(cols = all_of(gene.list),
names_to = "Gene",
values_to = "Expression")
```
## Calculating the LOESS Fitted Values
```{r, message=FALSE, warning=FALSE}
# Group data by Gene, Sex, and Area and nest the data for each group
gene.exp.zscore.loess <- gene.exp.zscore.long %>%
group_by(Gene, Sex, Area) %>%
nest() %>%
# Fit a LOESS model for each group, smoothing expression as a function of log2(Years)
mutate(loess.model =
purrr::map(.x = data, ~loess(.x,
formula = Expression ~log2(Years),
span = 0.75,
degree = 2,
family = "gaussian",
control =
loess.control(surface = "interpolate")))) %>%
# Determine min and max age value for each LOESS curve
mutate(min = purrr::map(.x = data, .f = ~min(log2(0.5))),
max = purrr::map(.x = data, .f = ~max(log2(64)))) %>%
unnest(min) %>% unnest(max) %>%
# Create a vector of log2 age values for each LOESS curve
mutate(age.values =
map2(.x = min, .y = max, ~seq(.x, .y, (.y - .x)/(num.points-1)))) %>%
# Extract fitted expression values from the LOESS model along the specified age values
mutate(fitted.values = map2(.x = loess.model,
.y = age.values,
# Reverse log2 transformation to get actual Years
~data.frame("Years" = 2^(.y),
"Expression" = predict(.x, newdata = .y))))
# Create a data frame including only the fitted values of the LOESS curves of
# the z-scored data, including columns for Gene, Sex, and Race
gene.exp.zscore.loess.fitted <- gene.exp.zscore.loess %>%
select(Gene, Sex, Area, fitted.values) %>% # Select necessary columns
unnest(cols = c(fitted.values)) # Unnest the fitted values into separate rows
# Unite the Years and Gene columns,
# Area and Sex columns into a single column for following analysis
gene.exp.zscore.loess.fitted <- gene.exp.zscore.loess.fitted %>%
unite("Gene", c("Gene","Years"), sep = "_") %>%
unite("Area", c("Area", "Sex"), sep="_")
# Convert the data frame to wide format (one column per Gene_Sex_Area)
# and remove rows with any missing (N/A) values
wide.zscore.loess.fitted <- gene.exp.zscore.loess.fitted %>%
spread(key = Gene, value = Expression) %>%
drop_na()
```
\newpage
## Performing Hierarchical Clustering on Area-Sex
```{r, message=FALSE, warning=FALSE}
#===============#
# Set Parameters
#===============#
# Rename columns for easier processing
df.elbow <- wide.zscore.loess.fitted %>% column_to_rownames(var = "Area")
# Calculate the within-cluster sum of squares (WSS) for multiple number of clusters
nb_data <- fviz_nbclust(df.elbow, hcut,
method = c("wss"),
k.max = 15,
verbose = TRUE)
# Get the within-cluster sum of squares number and cluster size
wss_values<- nb_data$data$y
cluster_sizes <- 1:length(wss_values)
# Create a dataframe with cluster sizes and WSS values
df <-as.data.frame(cbind (cluster_sizes,wss_values))
#============#
# Elbow Plot
#============#
# We use 2 different elbow method pacakges to confirm the optimal number of clusters
# Plot elbow graph to identify optimal number of clusters
# Print the number of clusters
num.clusters <- find_curve_elbow(df, plot_curve = FALSE)
num.clusters
# Create a data frame with only loess values for the elbow plot
elbow.plot <- fviz_nbclust(df.elbow, hcut, method = "wss", diss = NULL,
k.max = 15)+
geom_vline(xintercept = num.clusters, linetype = 2) + #sets x intercept
theme(axis.text.x = element_text(size=10,angle = 90, vjust = 0.5))
# Print elbow plot
elbow.plot
#========================#
# Hierarchical Clustering
#========================#
# Create data frame for Hclust
all.loess.hclust <- wide.zscore.loess.fitted %>% column_to_rownames(var = "Area")
# Calculate Euclidean distance and perform hierarchical clustering
distance.row <- dist(all.loess.hclust,
method = "euclidean")
# Set seed
set.seed(123)
cluster.row <- hclust(distance.row,
method = "ward.D2")
# Cut the tree into k clusters and get the region clusters
region.cluster <- cutree(cluster.row, k = num.clusters)
# Assign the names to the clusters, store clusters in 'loess.cluster.members'
assign("loess.cluster.members", split(names(region.cluster), region.cluster))
#============#
# Dendrogram
#============#
# Create vector of colours to use for figures
colours <- c("#990099CC","#00DEA3", "#F7D823", "#F88800","#6693DE")
# Create dendrogram
dendrogram <- fviz_dend(cluster.row, k = num.clusters,
cex = 1, # Label size
horiz = FALSE,
k_colors = colours,
color_labels_by_k = TRUE, # Color labels by groups
rect=TRUE, rect_border="black", rect_lty = 1,
ggtheme = theme_classic(), # Change theme
labels_track_height = 25,
main = NULL) +
theme(axis.title.y = element_text(size=20),
axis.text.y = element_text(size=15),
axis.ticks = element_line(size=1),
axis.ticks.length.y = unit(0.25, "cm"),
plot.margin = margin(0, 10, 20, 10)
)
# Print Dendrogram
dendrogram
```
```{r, include=FALSE, echo=FALSE}
# Save plot
devEval(type = "tiff",
path = paste(export.path, "Fig 2", sep = "/"),
name = str_trim(paste("Dendrogram for", num.genes,"Genes-", num.clusters,
"Clusters", ifelse(include.prenatal, "(Prenatal)", ""))),
height = 9, width = 15, res=300, units ="in",
expr = {
print(dendrogram)
}
)
```
\newpage
# Dense tSNE visualization of the brain areas
```{r, message=FALSE, warning=FALSE}
# Define breaks for labeling age
mybreaks <- c(0.5, 1, 2, 4, 8, 16, 32, 64)
# Create custom color palette to match the area clusters
# Determined by hierarchical clustering
custom_palette <- c(
"CBC_F" = "#990099CC","CBC_M" = "#990099CC",
"STC_F" = "#F88800", "OFC_F" = "#F88800", "M1C_F" = "#F88800",
"V1C_F" = "#F88800", "IPC_F" = "#F88800", "ITC_F" = "#F88800", "DFC_F" = "#F88800",
"A1C_F" = "#F88800", "S1C_F" = "#F88800", "MFC_F" = "#F88800", "VFC_F" = "#F88800",
"MD_F" = "#F7D823", "MD_M" = "#F7D823",
"STC_M" = "#00DEA3", "OFC_M" = "#00DEA3", "M1C_M" = "#00DEA3", "V1C_M" = "#00DEA3",
"IPC_M" = "#00DEA3", "ITC_M" = "#00DEA3", "DFC_M" = "#00DEA3", "A1C_M" = "#00DEA3",
"S1C_M" = "#00DEA3", "MFC_M" = "#00DEA3", "VFC_M" = "#00DEA3", "AMY_M" = "#00DEA3",
"HIP_F" = "#6693DE", "HIP_M" = "#6693DE", "AMY_F" = "#6693DE"
)
# Create the custom color palette for Sex and Area
sex_area_palette <- c("F_Cortical" = "#ff68a1",
"M_Cortical" = "#4fae26",
"F_Subcortical" = "#FFC2D9",
"M_Subcortical" = "#B8DEA8")
# Separate the Gene column into two new columns: Gene and Years
# Convert to wide format
densne.df <- gene.exp.zscore.loess.fitted %>%
separate(Gene, into = c("Gene", "Years"), sep = "_") %>%
spread(key = Gene, value = Expression)
# Combine Area and Years to create a unique identifier for each row
densne.df$Years_Area <- paste(densne.df$Area, densne.df$Years, sep = "_")
densne.df.bind <- densne.df %>%
as.data.frame()
# Set the Years_Area as row names
row.names(densne.df) <- densne.df$Years_Area
# Remove the Area, Years, and Years_Area columns from the dataframe
densne.df <- densne.df[ , !(names(densne.df) %in% c("Area", "Years", "Years_Area"))]
# Set the parameters for the Dense t-SNE.
# Parameters were chosen by running the "Optimizing dense-tSNE code" at the end of
# this markdown.
seed <- 5555
perplexity <- 30
dens_frac <- 0.9
dens_lambda <- 0.1
# Set the seed
set.seed(seed)
# Run dense t-SNE using densne function
densne_result <-
densne(densne.df,
perplexity = perplexity,
dens_frac = dens_frac,
dens_lambda = dens_lambda,
max_iter = 1000,
verbose=FALSE) %>%
as.data.frame()
# Combine densne dataframe with the densne result and separate Area column into "Area" and "Sex"
densne_combined <- cbind(densne.df.bind, densne_result) %>%
mutate(Area_Sex = Area) %>%
separate(Area, into = c("Area", "Sex"), sep = "_")
# Define the order of brain areas combined with sex for plotting
Area_Sex_order <- c(
"CBC_F", "CBC_M", "AMY_M", "STC_M", "OFC_M", "M1C_M", "V1C_M", "IPC_M",
"ITC_M", "DFC_M", "A1C_M", "S1C_M", "MFC_M", "VFC_M", "MD_F", "MD_M",
"HIP_F", "HIP_M", "AMY_F", "STC_F", "OFC_F", "M1C_F", "V1C_F", "IPC_F",
"ITC_F", "DFC_F", "A1C_F", "S1C_F", "MFC_F", "VFC_F"
)
# Reorder the levels of Area_Sex
densne_combined$Area_Sex <-
factor(densne_combined$Area_Sex, levels = Area_Sex_order)
# Define the cortical and subcortical areas
cortical_areas <- c("STC", "OFC", "M1C",
"V1C", "IPC", "ITC", "DFC",
"A1C", "S1C", "MFC", "VFC")
subcortical_areas <- c("MD", "CBC", "AMY", "HIP")
# Add a new column "Area_Type" to classify regions as cortical, subcortical, or unknown
densne_combined <- densne_combined %>%
mutate(Area_Type = case_when(
Area %in% cortical_areas ~ "Cortical",
Area %in% subcortical_areas ~ "Subcortical",
TRUE ~ "Unknown" # Catch-all for any areas not specified above
))
# Create a new variable that combines Sex and Area_Type information
densne_combined$Sex_Area <- paste(densne_combined$Sex,
densne_combined$Area_Type, sep = "_")
# Convert the "Years" column to a numeric type for proper visualization
densne_combined$Years <- as.numeric(densne_combined$Years)
# Visualizing dense t-SNE results colored by 'Years'
densne_years_plot <- ggplot(densne_combined) +
aes(V1, V2) +
geom_point(size = 4, shape = 21, colour = "black") +
geom_point(size = 3.5, aes(colour = Years))+
scale_size(breaks = mybreaks) +
scale_colour_gradientn(name = "Age",
breaks = mybreaks,
colours = brewer.pal(9, "YlOrRd"),
trans = "log")+
labs(x = "Dense t-SNE component 1", y = "Dense t-SNE component 2") +
theme(
# plot.title = element_text(size = 15),
axis.title = element_text(size = 20),
axis.text = element_text(size = 20),
legend.text = element_text(size = 18),
legend.title = element_text(size = 19),
legend.key.size = unit(1, "cm"),
aspect.ratio = 5/5,
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black"))
# Visualizing dense t-SNE results colored by 'Area_Sex'
densne_area_sex_plot <- ggplot(densne_combined) +
aes(V1, V2, colour = Area_Sex) +
geom_point(size = 3.5) +
scale_colour_manual(values = custom_palette, name = "Area_Sex") +
# ggtitle(paste("Dense t-SNE,", "Seed =", seed, "Perplexity =", perplexity)) +
labs(x = "Dense t-SNE component 1", y = "Dense t-SNE component 2") +
theme(
# plot.title = element_text(size = 15),
axis.title = element_text(size = 20),
axis.text = element_text(size = 20),
legend.text = element_text(size = 18),
legend.title = element_text(size = 19),
legend.key.size = unit(0.8, "cm"),
aspect.ratio = 5/5,
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black"))
# Visualizing dense t-SNE results colored by combined 'Sex' information
densne_sex_plot <- ggplot(densne_combined) +
aes(x = V1, y = V2, colour = Sex_Area) +
geom_point(size = 3.5) +
scale_color_manual(values = sex_area_palette,
name = "Sex & Area",
labels = c("Female Cortex",
"Female Subcortex",
"Male Cortex",
"Male Subcortex")) +
# ggtitle(paste("Dense t-SNE,", "Seed =", seed, "Perplexity =", perplexity)) +
labs(x = "Dense t-SNE component 1", y = "Dense t-SNE component 2") +
theme(
# plot.title = element_text(size = 15),
axis.title = element_text(size = 20),
axis.text = element_text(size = 20),
legend.text = element_text(size = 18),
legend.title = element_text(size = 19),
legend.key.size = unit(0.8, "cm"),
aspect.ratio = 5/5,
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black"))
```
\newpage
Area Sex Labelled Dense t-SNE
```{r , fig.height=10, fig.width=10}
densne_area_sex_plot
```
\newpage
Years Labelled Dense t-SNE
```{r , fig.height=10, fig.width=10}
densne_years_plot
```
\newpage
Area Sex Labelled Dense t-SNE
```{r , fig.height=10, fig.width=10}
densne_sex_plot
```
\newpage
Combine all the plots
```{r Panel, fig.width=8}
layout <- "
AXB
CXD
"
densne_years_plot_with_space <- (plot_spacer() + densne_years_plot + plot_spacer()) +
plot_layout(widths = c(0.1, 1, 0.1))
combined.plot <-wrap_plots(
# assign each plot to corresponding letter
A = ggdraw() + draw_plot(dendrogram, height = 0.95),
B = densne_area_sex_plot,
C = densne_years_plot_with_space,
D = densne_sex_plot,
X = plot_spacer(),
# apply the layout
design = layout
) +
plot_layout(widths = c(1.5, 0.1, 1),
heights = c(1, 1)) +
# plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 25),
legend.text = element_text(size = 12),
legend.key.size = unit(0.5, "cm"),
legend.spacing.y = unit(0.10, "cm")
)
# add label manually
# x for "B" and "D" depends on the width and output file size
combined.plot <- ggdraw(combined.plot) +
draw_label("A", x = 0.01, y = 0.99, hjust = 0, vjust = 1, size = 48) +
draw_label("B", x = 0.51, y = 0.99, hjust = 0, vjust = 1, size = 48) +
draw_label("C", x = 0.01, y = 0.51, hjust = 0, vjust = 1, size = 48) +
draw_label("D", x = 0.51, y = 0.51, hjust = 0, vjust = 1, size = 48)
```
```{r echo=FALSE}
knitr::include_graphics(
here::here(paste(export.path, "Fig 2",
"Fig2. Densne panel_k5.pdf", sep="/")))
```
```{r, include=FALSE, echo=FALSE}
#Saving the plots
devEval(type = "pdf",
path = paste(export.path, "Fig 2", sep = "/"),
name = "Fig2. Densne panel_k5",
width = 25,
height = 20,
expr = {
print(combined.plot)
})
# Save the densne_area_sex_plot as a TIFF image
tiff(filename = paste(export.path, "Fig 2", "Fig2 Dense panel.tiff", sep = "/"),
width = 11.7,
height = 10,
units = "in",
res = 600,
compression = "lzw",
type = "cairo")
print(combined.plot)
dev.off()
```
```{r, include=FALSE, echo=FALSE}
#Saving the plots
devEval(type = "pdf",
path = paste(export.path, "Fig 2", sep = "/"),
name = "Fig2A. Sex_Labelled_DtSNE",
width = 10,
height = 10,
expr = {
print(densne_area_sex_plot)
})
devEval(type = "pdf",
path = paste(export.path, "Fig 2", sep = "/"),
name = "Fig2B. YearsLabelled_DtSNE",
width = 10,
height = 10,
expr = {
print(densne_years_plot)
})
devEval(type = "pdf",
path = paste(export.path, "Fig 2", sep = "/"),
name = "Fig2C. Area_SexLabelled_DtSNE",
width = 10,
height = 10,
expr = {
print(densne_area_sex_plot)
})
# Save the densne_area_sex_plot as a TIFF image
tiff(filename = paste(export.path, "Fig 2", "Fig2B Area_Sex_Labelled_DtSNE.tiff", sep = "/"),
width = 11.4,
height = 10,
units = "in",
res = 600,
compression = "lzw",
type = "cairo")
print(densne_area_sex_plot)
dev.off()
# Save the densne_years_plot as a TIFF image
tiff(filename = paste(export.path, "Fig 2", "Fig2C Years_Labelled_DtSNE.tiff", sep = "/"),
width = 10,
height = 10,
units = "in",
res = 600,
compression = "lzw",
type = "cairo")
print(densne_years_plot)
dev.off()
# Save the densne_area_sex_plot as a TIFF image
tiff(filename = paste(export.path, "Fig 2", "Fig2D Sex_Labelled_DtSNE.tiff", sep = "/"),
width = 11.7,
height = 10,
units = "in",
res = 600,
compression = "lzw",
type = "cairo")
print(densne_sex_plot)
dev.off()
```
\newpage
## Optimizing the parameters for running the dense-tSNE
```{r, eval=FALSE, include=TRUE}
# Define your seeds and perplexities
seeds <- c(5555, 611, 9037)
perplexities <- c(30, 60, 90, 120, 150)
dens_frac = 0.9
dens_lambda =0.1
# Initialize an empty list to store the plots
densne_sex_plot_list <- list()
densne_years_plot_list <- list()
densne_area_sex_plot_list <- list()
# Initialize a counter
plot_counter <- 1
# For each seed
for (seed in seeds) {
# Set the seed
set.seed(seed)
print(seed)
# For each perplexity
for (perplexity in perplexities) {
print(perplexity)
# Run dense t-SNE using densne function
densne_result <- densne(densne.df, perplexity = perplexity,
dens_frac = dens_frac, dens_lambda = dens_lambda,
max_iter = 1000, verbose=FALSE) %>% as.data.frame()
densne_combined <- cbind(densne.df.bind, densne_result)
densne_combined <- densne_combined %>%
mutate(Area_Sex = Area) %>%
separate(Area, into = c("Area", "Sex"), sep = "_")
densne_combined$Years <- as.numeric(densne_combined$Years)
# Color the plot by Sex
densne_sex_plot_list[[plot_counter]] <- ggplot(densne_combined) +
aes(V1,V2, colour = densne_combined$Sex) +
geom_point(size=4,shape=21,colour="black") +
geom_point(size=3.5,aes(colour = Sex))+
scale_colour_discrete(name = "Sex") +
labs(x = "Dense t-SNE component 1", y = "Dense t-SNE component 2") +
theme(plot.title =element_text(size=10),
aspect.ratio = 5/5,
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black"))+
ggtitle(paste("Seed =", seed, "Perplexity =", perplexity))
# Color the plot by Years
densne_years_plot_list[[plot_counter]] <- ggplot(densne_combined) +
aes(V1,V2) +
geom_point(size=4,shape=21,colour="black") +
geom_point(size=3.5,aes(colour = Years))+
scale_size(breaks = mybreaks) +
scale_colour_gradientn(name="Age",breaks = mybreaks,
colours =brewer.pal(9, "YlOrRd"),trans="log")+
labs(x = "Dense t-SNE component 1", y = "Dense t-SNE component 2") +
theme(plot.title =element_text(size=10),
aspect.ratio = 5/5,
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black"))+
ggtitle(paste("Seed =", seed, "Perplexity =", perplexity))
# Color the plot by Area_Sex
densne_area_sex_plot_list[[plot_counter]] <- ggplot(densne_combined) +
aes(V1, V2, colour = Area_Sex) +
geom_point(size=3.5) +
scale_colour_discrete(name = "Area_Sex") +
ggtitle(paste("Seed =", seed, "Perplexity =", perplexity)) +
labs(x = "Dense t-SNE component 1", y = "Dense t-SNE component 2") +
theme(plot.title = element_text(size=10),
aspect.ratio = 5/5,
panel.background = element_rect(fill = "white"),
axis.line = element_line(colour = "black"))
# Increment the counter
plot_counter <- plot_counter + 1
}
}
```
```{r include=FALSE, eval=FALSE}
devEval(type = "pdf",
path = paste(export.path, "Fig 2", "Perplexity Testing", sep = "/"),
name = "Area_Sex_Labelled_DtSNE_67Genes",
width = 60,
height = 20,
expr = {
print(do.call(grid.arrange, c(densne_area_sex_plot_list, ncol = 5, nrow = 3)))
})
devEval(type = "pdf",
path = paste(export.path, "Fig 2", "Perplexity Testing", sep = "/"),
name = "Sex_Labelled_DtSNE_67Genes",
width = 60,
height = 20,
expr = {
print(do.call(grid.arrange, c(densne_sex_plot_list, ncol = 5, nrow = 3)))
})
devEval(type = "pdf",
path = paste(export.path, "Fig 2", "Perplexity Testing", sep = "/"),
name = "Age_Labelled_DtSNE_67Genes",
width = 60,
height = 20,
expr = {
print(do.call(grid.arrange, c(densne_years_plot_list, ncol = 5, nrow = 3)))
})
```

Figure 2.Rmd, no license · at the source

Overview

Authors: Leanne Monteiro¹, Negeen Halabian¹, Jiawei Li², Maheshwar Panday¹, Nadine Barone¹, Peiyi Wang², Kathryn M Murphy^1,2

ORCID iDs: Nadine Barone, Peiyi Wang, Kathryn M Murphy

Institutions: McMaster University (Canada)

Journal: PloS one, volume 21, issue 10, article e0360526

Dates: received 17 April 2026; accepted 24 September 2026; published online 29 October 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1371/journal.pone.0360526 · PMID 42956020 · PMCID PMC13718946 · OpenAlex W7224377623

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: human (organism)

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

MeSH: Brain*, Receptors, Virus*, RNA Viruses*, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Gene Expression Regulation, Developmental, Humans, Infant, Male, Middle Aged, Sex Characteristics, Sex Factors, Young Adult (* major topic)

Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Citations: not cited yet (Europe PMC); 82 references in the paper

Abstract

Neurotropic single-stranded RNA (ssRNA) viruses can disrupt brain function, yet little is known about how host virus receptor expression develops across the human lifespan or whether these trajectories differ between females and males. We focused on postnatal development using postmortem transcriptomic data from 33 human donors (4 months–82 years; both sexes), comprising 52 cerebral hemispheres and 15 brain areas, to characterize developmental patterns of 67 host receptor genes that mediate entry of major ssRNA viral families. Using sex-specific LOESS trajectory modelling, hierarchical clustering, and sliding-window differential expression analyses, we identified multiple non-linear developmental programs governing virus receptor expression. About half of female–male gene–area trajectory pairs exhibited divergent developmental patterns. Sex differences were most pronounced in the cortex, where high-dimensional receptor expression profiles were sufficient to predict the sex of individual cases. Sex differential expression was most frequent during early childhood, coinciding with sensitive periods of cortical circuit refinement. A targeted prenatal analysis revealed that virus receptor expression was predominantly male-biased before birth, contrasting with the predominantly female-biased expression observed during the early postnatal period. Although receptors from most viral families were broadly distributed across developmental programs, a subset of sex-by-age expression clusters showed some enrichment for specific viral lineages and glial-associated cell-type signatures. Together, these findings reveal that virus receptor expression in the human brain is organized into structured, sex-biased trajectories that dynamically reorganize over development, providing the first developmental atlas of viral entry receptors in the human brain.

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 26 matches between paragraphs and lines of code.

OSF 4fgka

License: none: the authors keep all their rights

State: the link answers, verified on 31 October 2026

Evidence: files inventoried

Languages: R (16)

Size: 32 files, 16 scripts

Software Heritage: not checked

Found in: “Data Availability”

Holds: 16 notebooks

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

Tools: tidyverse (16 files), data.table (15 files), ggplot2 (15 files), cowplot (10 files), patchwork (10 files), reshape2 (10 files), randomForest (1 file)

Availability: 1 check, the latest on 31 October 2026: the link answers (HTTP 200)

31 October 2026: the link answers (HTTP 200)

16 files

byan2/Code/Figure 1.Rmd, R, 1,950 lines, 2 matches
byan2/Code/Figure 2.Rmd, R, 749 lines, 4 matches
byan2/Code/Figure 3.Rmd, R, 634 lines, 3 matches
byan2/Code/Figure 4.Rmd, R, 2,025 lines, 3 matches
byan2/Code/Figure 5.Rmd, R, 1,406 lines, 2 matches
byan2/Code/Figure 6.Rmd, R, 1,648 lines, 2 matches
byan2/Code/Kang Data Cleaning.Rmd, R, 355 lines, 1 match
byan2/Code/Supplementary Figure 1.Rmd, R, 432 lines
byan2/Code/Supplementary Figure 2.Rmd, R, 811 lines, 3 matches
byan2/Code/Supplementary Figure 5.Rmd, R, 1,035 lines
byan2/Code/Supplementary Figure 6.Rmd, R, 978 lines, 1 match
byan2/Code/Supplementary Figure 7.Rmd, R, 620 lines, 3 matches
byan2/Code/Supplementary Figure 8.rmd, R, 857 lines, 1 match
byan2/Code/Supplementary Figure 9.Rmd, R, 1,333 lines, 1 match
byan2/Code/Supplementary Figures 3-4.Rmd, R, 1,443 lines
byan2/Code/Supplementary-Figure-8.rmd, R, 857 lines

At the source: osf.io/4fgka/

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.

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

geo:GSE25219, at NCBI GEO; found in the text, “Human transcriptomic dataset”

Data Availability

Data and Code accessibility: The processed dataset analyzed in the current study, along with the R code and supplementary materials (figures and tables), are available on the Open Science Framework (Monteiro et al., 2025) https://osf.io/4fgka/. The transcriptomic dataset is from Kang et. al., (2011) and is available on the Gene Omnibus: (GSE25219).

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

Versions

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Version 1, 31 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 19 MeSH terms, 80 references.

Cite

This paper

Monteiro, L., Halabian, N., Li, J., Panday, M., Barone, N., Wang, P., & Murphy, K. M. (2026). Sex- and age-specific development of ssRNA virus receptor expression in the human brain. PloS one, 21(10), e0360526. https://doi.org/10.1371/journal.pone.0360526

BibTeX

@article{monteiro2026sex, author = {Monteiro, Leanne and Halabian, Negeen and Li, Jiawei and Panday, Maheshwar and Barone, Nadine and Wang, Peiyi and Murphy, Kathryn M}, title = {{Sex- and age-specific development of ssRNA virus receptor expression in the human brain}}, journal = {PloS one}, year = {2026}, month = oct, volume = {21}, number = {10}, pages = {e0360526}, publisher = {PLOS}, issn = {1932-6203}, doi = {10.1371/journal.pone.0360526}, url = {https://doi.org/10.1371/journal.pone.0360526}, pmid = {42956020}, pmcid = {PMC13718946} }

RIS

TY - JOUR AU - Monteiro, Leanne AU - Halabian, Negeen AU - Li, Jiawei AU - Panday, Maheshwar AU - Barone, Nadine AU - Wang, Peiyi AU - Murphy, Kathryn M TI - Sex- and age-specific development of ssRNA virus receptor expression in the human brain T2 - PloS one J2 - PLoS One PY - 2026 DA - 2026/10/29 VL - 21 IS - 10 SP - e0360526 SN - 1932-6203 PB - PLOS DO - 10.1371/journal.pone.0360526 UR - https://doi.org/10.1371/journal.pone.0360526 LA - en ER -

CSL-JSON

{ "id": "10.1371/journal.pone.0360526", "type": "article-journal", "title": "Sex- and age-specific development of ssRNA virus receptor expression in the human brain", "container-title": "PloS one", "author": [ { "family": "Monteiro", "given": "Leanne" }, { "family": "Halabian", "given": "Negeen" }, { "family": "Li", "given": "Jiawei" }, { "family": "Panday", "given": "Maheshwar" }, { "family": "Barone", "given": "Nadine" }, { "family": "Wang", "given": "Peiyi" }, { "family": "Murphy", "given": "Kathryn M" } ], "container-title-short": "PLoS One", "volume": "21", "issue": "10", "page": "e0360526", "DOI": "10.1371/journal.pone.0360526", "PMID": "42956020", "PMCID": "PMC13718946", "ISSN": "1932-6203", "publisher": "PLOS", "URL": "https://doi.org/10.1371/journal.pone.0360526", "language": "en", "issued": { "date-parts": [ [ 2026, 10, 29 ] ] } }

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