Gene network analysis
Maeva Techer
2025-06-27
Last updated: 2025-06-27
Checks: 5 2
Knit directory:
locust-comparative-genomics/
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1. Behavioral assay data
In the folder data/behavioral_data, the excel sheet
/Raw_data/All species_50specimens_update10Minutes contains
the measured from behavioral assay for 50 individuals per species per
density condition. As we do not have the exact behavioral data for each
specimen we sequenced, we estimated the phenotype by using the
median value of each group for the following
measures:
Moved distance (
MovedDistance) in cm.Stimulus zone time (
StimulusZoneTime) in seconds.Non-stimulus zone time (
NonStimulusZoneTime) in seconds.Time on stimulus wall (
TimeStimulusWall) in seconds.
We created an input file with data traits with a single line being the sample name (e.g., SAMER_G_Crd_SRR11815246) and the tissue used (Head = 0 and Thorax = 1) and rearing condition (Solitary = 0 and Crowded = 1).
One note for S. gregaria, Audelia generated over 500 trials in total and so to make species comparable, we will randomly subsample 50 trials of the crowded and solitary, each. We used the following python code to select the final list.
# To select 50 samples among the CROWDED trials
import random
# Original list of samples
samples = [
f"Trial {i}" for i in [
21, 24, 25, 27, 28, 29, 30, 31, 32, 33, 42, 43, 44, 45, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
62, 63, 65, 67, 68, 69, 70, 71, 72, 73, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
98, 99, 106, 107, 108, 109, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127,
128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147,
148, 149, 150, 151, 152, 153, 154, 199, 200, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212,
213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,
233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252,
253, 254, 255, 256, 257, 258, 297, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318,
319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338,
343, 344, 345, 346, 347, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 431, 432, 433, 434, 435,
436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455,
456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475,
476, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 509, 510, 511, 512, 513, 514, 515, 516, 517,
518, 519, 520, 521, 522, 523, 524, 525, 526, 527
]
]
# Randomly select 50 samples
random_samples = random.sample(samples, 50)
random_samples.sort() # Optional: sort if preferred for easier readability
import pandas as pd
import ace_tools as tools
df_random_samples = pd.DataFrame(random_samples, columns=["Randomized_Samples"])
tools.display_dataframe_to_user(name="Randomized 50 Samples", dataframe=df_random_samples)
# To select 50 samples among the ISOLATED trials
# New list of trials to sample from
new_samples = [
f"Trial {i}" for i in [
14, 15, 16, 17, 34, 35, 36, 37, 38, 39, 41, 46, 47, 59, 60, 61, 74, 91, 92, 93, 94, 95, 96, 97,
101, 102, 103, 104, 105, 110, 111, 112, 113, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,
184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 204, 259, 260, 261,
262, 263, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281,
282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 298, 299, 300, 301, 302,
303, 304, 339, 340, 341, 342, 348, 349, 350, 351, 352, 353, 354, 356, 358, 359, 360, 361, 362,
363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381,
382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400,
401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 413, 414, 415, 416, 417, 418, 419, 488,
489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507,
528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546,
547, 548, 549, 550, 551
]
]
# Randomly select 50 samples
random_new_samples = random.sample(new_samples, 50)
random_new_samples.sort() # Sort for readability
df_random_new_samples = pd.DataFrame(random_new_samples, columns=["Randomized_Samples"])
tools.display_dataframe_to_user(name="Randomized 50 Samples (Second List)", dataframe=df_random_new_samples)
Load the libraries
We start by loading all the required R packages.
#(install first from CRAN or Bioconductor)
library("knitr")
library("rmdformats")
library("tidyverse")
library("data.table")
library("DT") # for making interactive search table
library("plotly") # for interactive plots
library("ggthemes") # for theme_calc
library("reshape2")
library("RColorBrewer")
library("ggpubr")
library("ggplot2")
library("ggrepel")
library("kableExtra")
library("kableExtra")
library("WGCNA")
library("ggridges")
library("dplyr")
library("cowplot")
#library("multiWGCNA")
# Path for all species
workDir <- "/Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data"Plot the behavioral data
# Define the working directory and load the data
DATA_file <- file.path(workDir, "behavioral_data/50specimens_allspecies.csv")
DATA <- read.csv(DATA_file, header = TRUE, stringsAsFactors = FALSE)
#summary(DATA)
# Adjust the 'density' column for plotting
DATA %>% dplyr::select(species, density) %>%
mutate(density = recode(density, Crowded = "Crowded", Isolated = "Isolated")) %>%
table density
species Crowded Isolated
S.americana 50 50
S.cancellata 50 50
S.gregaria 50 50
S.nitens 50 50
S.piceifrons 50 50
S.serialis cubense 50 50# Define the names and limits for each variable
NAMES <- c("Distance moved (cm)", "Movement (s)", "Stimulus zone time (s)", "Non-stimulus zone time (s)",
"Neutral zone time (s)", "Climbing time (s)", "Rotation frequency (s)", "Stimulus wall time (s)",
"Distance to stimulus wall (cm)", "Velocity (cm/s)")
LIMS <- list(c(0, 1000), c(0, 750), c(0, 750), c(0, 750), c(0, 750), c(0, 750), c(0, 300), c(0, 750), c(0, 50), c(0, 8))
# Create the plots using ggplot and ggridges
PLT <- lapply(4:13, function(j) {
DATA %>%
ggplot(aes(x = DATA[, j], y = species, fill = density, point_shape = 21, point_fill = density)) +
geom_density_ridges(scale = 0.5, jittered_points = TRUE, position = "raincloud", alpha = 0.5,
point_size = 2, point_alpha = 1) +
scale_x_continuous(limits = LIMS[[j - 3]]) +
coord_flip() +
scale_fill_manual(values = c("#D95F02", "#1B9E77")) +
scale_discrete_manual(aesthetics = "point_fill", values = c("#D95F02", "#1B9E77")) +
theme(axis.text.x = element_text(face = "italic", angle = 45, hjust = 1),
axis.text = element_text(color = "black", size = 12),
axis.title = element_text(face = "bold", size = 13),
legend.position = "none",
panel.background = element_rect(fill = NA),
panel.border = element_rect(color = "black", fill = NA, size = 1)) +
labs(x = NAMES[j - 3], y = "Species") +
scale_y_discrete(limits = c("S.gregaria", "S.cancellata", "S.piceifrons", "S.americana", "S.serialis cubense", "S.nitens"),
labels = c("gregaria", "cancellata", "piceifrons", "americana", "cubense", "nitens"))
})
# Create the legend
LEG <- get_legend(DATA %>%
ggplot(aes(x = DATA[, 4], y = species, fill = density)) +
geom_density_ridges(scale = 0.5) +
scale_fill_manual(name = "Rearing Density", values = c("#D95F02", "#1B9E77"), labels = c("Crowded", "Isolated")) +
theme(legend.position = "bottom"))
# Combine the plots with the legend
plot_grid(plot_grid(plotlist = PLT, nrow = 2), LEG, rel_heights = c(1, 0.1), nrow = 2)
###################### For publication
# List of variable names (not column numbers anymore)
selected_vars <- c(
"Distance.moved..cm." = "Distance moved (cm)",
"Time.in.stimulus.zone..s." = "Stimulus zone time (s)",
"Time.in.isolated.zone..s." = "Non-stimulus zone time (s)",
"Time.on.stimulus.wall..s." = "Stimulus wall time (s)"
)
# Matching column names to limits
lims_custom <- list(
"Distance moved (cm)" = c(0, 1000),
"Stimulus zone time (s)" = c(0, 750),
"Non-stimulus zone time (s)" = c(0, 750),
"Stimulus wall time (s)" = c(0, 750)
)
selected_vars <- c(
"Time.on.stimulus.wall..s." = "Stimulus wall time (s)"
)
lims_custom <- list(
"Stimulus wall time (s)" = c(0, 750)
)
# Create the plots
# Plot function without point_shape and point_fill (just use fill)
plot_behavior <- function(varname, varlabel, lims) {
ggplot(DATA, aes(x = .data[[varname]], y = species, fill = density)) +
geom_density_ridges(
scale = 0.5,
jittered_points = TRUE,
position = "raincloud",
point_shape = 21,
point_size = 2,
alpha = 0.5,
point_alpha = 1,
aes(point_fill = density) # explicitly map this
) +
scale_x_continuous(limits = lims) +
# coord_flip() +
scale_fill_manual(values = c("Crowded" = "#D95F02", "Isolated" = "#1B9E77")) +
scale_discrete_manual(aesthetics = "point_fill", values = c("Crowded" = "#D95F02", "Isolated" = "#1B9E77")) +
theme(
text = element_text(family = "Helvetica"),
axis.text.y = element_text(face = "italic", angle = 0, hjust = 1, size = 16),
axis.text = element_text(color = "black", size = 12),
axis.title = element_text(face = "bold", size = 20),
legend.position = "none",
panel.background = element_rect(fill = NA),
# panel.border = element_rect(color = "black", fill = NA, size = 1)
panel.border = element_blank()
) +
labs(x = varlabel, y = "") +
scale_y_discrete(
# limits = c("S.gregaria", "S.cancellata", "S.piceifrons", "S.americana", "S.serialis cubense", "S.nitens"),
# labels = c("gregaria", "cancellata", "piceifrons", "americana", "cubense", "nitens")
limits = c("S.nitens", "S.serialis cubense", "S.americana", "S.piceifrons", "S.cancellata", "S.gregaria"),
labels = c("nitens", "cubense", "americana", "piceifrons", "cancellata", "gregaria")
)
}
# Create the 4 individual plots
custom_plots <- Map(plot_behavior,
names(selected_vars),
selected_vars,
lims_custom)
# Create a matching dummy plot to extract the full legend
legend_plot <- ggplot(DATA, aes(x = .data[[names(selected_vars)[1]]], y = species, fill = density)) +
geom_density_ridges(scale = 0.5, jittered_points = TRUE,
point_shape = 21, aes(point_fill = density)) +
scale_fill_manual(name = "Rearing Density", values = c("Crowded" = "#D95F02", "Isolated" = "#1B9E77")) +
scale_discrete_manual(aesthetics = "point_fill", values = c("Crowded" = "#D95F02", "Isolated" = "#1B9E77")) +
theme_minimal() +
theme(legend.position = "bottom")
# Extract legend
LEGEND <- cowplot::get_legend(legend_plot)
# Combine plots and legend
final_plot <- plot_grid(
plot_grid(plotlist = custom_plots, ncol = 1),
LEGEND,
rel_heights = c(1, 0.12),
ncol = 1
)
# Display final plot
print(final_plot)
We then took the median of each density and species to assign phenotypes to our transcriptomic data. We are aware that the behavioral data are only approximation as the individual behavior was not collected but we aim to use gene-network analysis to see some association with behavior.
# Load behavioral trait data
behavior_file <- file.path(workDir, "behavioral_data/Sample_phenotypes_WGCNA.csv")
behavior_df <- read.csv(behavior_file, header = TRUE, stringsAsFactors = FALSE)
# Display the behavioral data
datatable(behavior_df, options = list(
pageLength = 10, # Set initial page length
scrollX = TRUE, # Enable horizontal scrolling
autoWidth = TRUE, # Adjust column width automatically
searchHighlight = TRUE # Highlight search matches
), rownames = FALSE, escape = FALSE)Gene Co-Expression Network Analysis
Step 1: Load Normalized Data from DESeq2
We are taking as input the vst (varianceStabilizingTransformation)
from our DESeq2 results.
# Define species and tissue
species <- "gregaria"
tissue <- "Thorax"
# Load datExpr file
datExpr_file <- file.path(workDir, "WGCNA/input/Bulk_RNAseq", paste0("datExpr_", tissue, "_", species, ".csv"))
datExpr <- read.csv(datExpr_file, row.names = 1, check.names = FALSE) # Avoid unwanted renaming
# Ensure data is formatted correctly
datExpr <- as.data.frame(datExpr)
# Display dimensions
cat("✅ Loaded datExpr for", species, "-", tissue, "with dimensions:", dim(datExpr), "\n")✅ Loaded datExpr for gregaria - Thorax with dimensions: 10 12361 # Display first few row and column names for verification
cat("🔍 First few row names (should be genes or samples):\n")🔍 First few row names (should be genes or samples):print(head(rownames(datExpr)))[1] "SGRE-THOX-CRD-1" "SGRE-THOX-CRD-3" "SGRE-THOX-CRD-4" "SGRE-THOX-CRD-5"
[5] "SGRE-THOX-CRD-6" "SGRE-THOX-ISO-1"cat("🔍 First few column names (should be samples or genes):\n")🔍 First few column names (should be samples or genes):print(head(colnames(datExpr)))[1] "LOC126318536" "LOC126318656" "LOC126318743" "LOC126319294" "LOC126319460"
[6] "LOC126320026"# **Check if datExpr needs transposition**
if (grepl("Sample|^S[0-9]+$", rownames(datExpr)[1])) {
warning("⚠️ datExpr appears transposed. Transposing now...")
datExpr <- t(datExpr)
datExpr <- as.data.frame(datExpr) # Ensure it's still a dataframe after transposition
}
# **Ensure genes are rows and samples are columns**
if (length(setdiff(rownames(datExpr), colnames(datExpr))) == 0) {
stop("❌ datExpr row names match column names, indicating potential incorrect format. Check input file.")
}
# **Convert all values to numeric**
datExpr[] <- lapply(datExpr, as.numeric) # Faster and avoids redundant reassignment
# **Final structure verification**
cat("✅ Verified datExpr structure. Dimensions:", dim(datExpr), "\n")✅ Verified datExpr structure. Dimensions: 10 12361 cat("🔍 First few row names (should be genes):\n")🔍 First few row names (should be genes):print(head(rownames(datExpr)))[1] "SGRE-THOX-CRD-1" "SGRE-THOX-CRD-3" "SGRE-THOX-CRD-4" "SGRE-THOX-CRD-5"
[5] "SGRE-THOX-CRD-6" "SGRE-THOX-ISO-1"cat("🔍 First few column names (should be samples):\n")🔍 First few column names (should be samples):print(head(colnames(datExpr)))[1] "LOC126318536" "LOC126318656" "LOC126318743" "LOC126319294" "LOC126319460"
[6] "LOC126320026"Step 2: Align Behavioral Data
# Load behavioral trait data
behavior_file <- file.path(workDir, "behavioral_data/Sample_phenotypes_WGCNA.csv")
behavior_df <- read.csv(behavior_file, header = TRUE, stringsAsFactors = FALSE)
# Align datExpr with behavioral data
samples <- rownames(datExpr)
traitRows <- match(samples, behavior_df$SampleName)
datTraits <- behavior_df[traitRows, , drop = FALSE]
# Check for missing matches
if (any(is.na(traitRows))) {
warning("⚠ Some samples in datExpr do not match behavioral data. Check row names in behavior_df.")
}
# Ensure datTraits has correct row names
rownames(datTraits) <- datTraits$SampleName
datTraits <- datTraits[ , -which(names(datTraits) == "SampleName"), drop = FALSE] # Remove SampleName column if present
# Check dimensions
cat("✅ Aligned behavioral data with dimensions:", dim(datTraits), "\n")✅ Aligned behavioral data with dimensions: 10 8 Step 3: Check Data Integrity
# Check for good samples and genes
gsg <- goodSamplesGenes(datExpr, verbose = 3) Flagging genes and samples with too many missing values...
..step 1print(paste("All ok?", gsg$allOK))[1] "All ok? TRUE"# Remove problematic samples and genes, if any
if (!gsg$allOK) {
datExpr <- datExpr[gsg$goodSamples, gsg$goodGenes]
cat("🛠 Removed problematic samples and genes.\n")
}
# We need to check if there are outliers samples that we should remove
samplesTree <- hclust(d = dist(datExpr), method = "average")
plot(samplesTree, main = "Samples dendrogram", sub = "", xlab = "", cex.lab = 1, cex.axis = 1, cex.main = 1, cex = 0.5)
abline(h = 28, col = "red")
# Ensure datTraits aligns with datExpr
samples <- rownames(datExpr)
traitRows <- match(samples, rownames(datTraits))
datTraits <- datTraits[traitRows, , drop = FALSE]
# Warn if any mismatch remains
if (any(is.na(traitRows))) {
warning("⚠ Some samples in datExpr do not match datTraits. Verify sample names.")
}
cat("✅ Cleaned datExpr dimensions:", dim(datExpr), "\n")✅ Cleaned datExpr dimensions: 10 12361 cat("✅ Final aligned datTraits dimensions:", dim(datTraits), "\n")✅ Final aligned datTraits dimensions: 10 8 Step 4 : Choose a soft threshold
# Define power range to test
powers <- c(1:10, seq(12, 30, by = 2))
# Estimate the soft threshold
sft <- pickSoftThreshold(datExpr, powerVector = powers, verbose = 5)pickSoftThreshold: will use block size 3619.
pickSoftThreshold: calculating connectivity for given powers...
..working on genes 1 through 3619 of 12361 ..working on genes 3620 through 7238 of 12361
..working on genes 7239 through 10857 of 12361
..working on genes 10858 through 12361 of 12361
Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
1 1 0.262 0.822 0.373 4550.0 4490.00 6560
2 2 0.227 -0.317 0.236 2400.0 2180.00 4540
3 3 0.813 -0.690 0.790 1480.0 1210.00 3480
4 4 0.923 -0.859 0.907 1010.0 729.00 2810
5 5 0.947 -0.950 0.935 729.0 480.00 2350
6 6 0.953 -1.000 0.942 551.0 330.00 2010
7 7 0.954 -1.040 0.945 430.0 235.00 1750
8 8 0.949 -1.080 0.941 344.0 170.00 1550
9 9 0.943 -1.110 0.938 282.0 125.00 1390
10 10 0.935 -1.130 0.931 234.0 94.60 1250
11 12 0.935 -1.160 0.937 169.0 56.70 1040
12 14 0.925 -1.190 0.931 127.0 35.80 882
13 16 0.918 -1.210 0.928 98.0 23.60 761
14 18 0.915 -1.220 0.929 77.8 16.10 665
15 20 0.912 -1.240 0.930 63.0 11.40 587
16 22 0.914 -1.240 0.934 51.9 8.24 523
17 24 0.910 -1.250 0.934 43.4 6.09 469
18 26 0.911 -1.260 0.937 36.7 4.60 423
19 28 0.915 -1.260 0.942 31.3 3.54 384
20 30 0.918 -1.270 0.946 27.0 2.78 351# Check power selection
chosen_power <- sft$powerEstimate
if (is.na(chosen_power)) {
stop("❌ No suitable soft-threshold power found. Check your data.")
} else {
cat("✅ Chosen soft-threshold power:", chosen_power, "\n")
}✅ Chosen soft-threshold power: 4 # Create output directory for plots
output_dir <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species)
dir.create(output_dir, showWarnings = FALSE, recursive = TRUE)
# Plot results
plot_file <- file.path(output_dir, paste0("SoftThreshold_", tissue, "_", species, ".pdf"))
pdf(plot_file, width = 8, height = 6)
par(mfrow = c(1, 2))
corRaw = abs(cor(datExpr))
corSoft = abs(corRaw**sft$powerEstimate)
hist(corRaw, main="Raw correlations")
hist(corSoft, main="Soft-thresholded correlations")
# Scale-free topology plot
plot(
sft$fitIndices[, 1],
-sign(sft$fitIndices[, 3]) * sft$fitIndices[, 2],
type = "b", col = "blue", pch = 16,
xlab = "Soft Threshold (Power)",
ylab = expression("Scale-Free Topology Fit" ~ R^2),
main = "Scale-Free Topology Fit"
)
abline(v = seq(2, max(powers), by = 2), col = "lightgray", lty = "dashed") # Vertical gridlines
abline(h = 0.80, col = "black", lty = 2) # Threshold at R^2 = 0.8
abline(h = 0.90, col = "red", lty = 2) # Threshold at R^2 = 0.9
# Mean connectivity plot
plot(
sft$fitIndices[, 1],
sft$fitIndices[, 5],
type = "b", col = "darkgreen", pch = 16,
xlab = "Soft Threshold (Power)",
ylab = "Mean Connectivity",
main = "Mean Connectivity"
)
abline(v = seq(2, max(powers), by = 2), col = "lightgray", lty = "dashed")
dev.off()quartz_off_screen
2 # Output message
cat("✅ Soft threshold estimation plot saved to:", plot_file, "\n")✅ Soft threshold estimation plot saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/SoftThreshold_Thorax_gregaria.pdf Step 5: Network Construction and Module Detection
Control step
# Ensure datExpr is correctly formatted
if (!all(sapply(datExpr, is.numeric))) {
stop("❌ datExpr contains non-numeric values. Check your input data.")
}
# Ensure no missing values
if (anyNA(datExpr)) {
stop("❌ datExpr contains missing values. Please clean your data.")
}
# Check alignment between datExpr and datTraits
if (!all(rownames(datExpr) == rownames(datTraits))) {
stop("❌ Row names of datExpr and datTraits do not match. Verify alignment.")
}
# Convert non-numeric traits to numeric
non_numeric_cols <- which(sapply(datTraits, class) %in% c("character", "factor"))
if (length(non_numeric_cols) > 0) {
for (col in non_numeric_cols) {
datTraits[[col]] <- as.numeric(as.factor(datTraits[[col]]))
}
}
# Align `datTraits` with `datExpr`
samples <- rownames(datExpr)
traitRows <- match(samples, rownames(datTraits))
datTraits <- datTraits[traitRows, , drop = FALSE]
# Convert all non-numeric columns in `datTraits` to numeric
numeric_traits <- datTraits
if (length(non_numeric_cols) > 0) {
for (col in non_numeric_cols) {
numeric_traits[[col]] <- as.numeric(as.factor(datTraits[[col]]))
}
}
cat("✅ Numeric traits created with dimensions:", dim(numeric_traits), "\n")✅ Numeric traits created with dimensions: 10 8 cat("✅ datExpr dimensions:", dim(datExpr), "\n")✅ datExpr dimensions: 10 12361 cat("✅ Numeric traits dimensions:", dim(numeric_traits), "\n")✅ Numeric traits dimensions: 10 8 if (nrow(datExpr) != nrow(numeric_traits)) {
stop("❌ Number of samples in datExpr and numeric_traits do not match.")
}
# Find genes with zero MAD
zero_mad_genes <- rownames(datExpr)[apply(datExpr, 1, mad) == 0]
cat("⚠️ Genes with zero MAD:", length(zero_mad_genes), "\n")⚠️ Genes with zero MAD: 0 # Optionally, inspect some of them
if (length(zero_mad_genes) > 0) {
print(zero_mad_genes[1:min(10, length(zero_mad_genes))]) # Show up to 10 genes
}Constructing the network
picked_power <- 12
CutHeight <- 0.2
#similarityMat <- adjacency(datExpr, power = Power)
#head(similarityMat[, c(1:5)])
#TOM <- TOMsimilarity(similarityMat)
#colnames(TOM) <- colnames(similarityMat)
#rownames(TOM) <- rownames(similarityMat)
#head(TOM[, c(1:5)])
#dissTOM <- 1 - TOM
#head(dissTOM[, c(1:5)])
# Perform network construction and module detection
net <- blockwiseModules(
datExpr,
# == Adjacency Function ==
power = picked_power,
networkType = "unsigned",
# == Tree and Block Options ==
maxBlockSize = 20000, # Adjust for large datasets
minModuleSize = 30, # Minimum number of genes per module
reassignThreshold = 0, # No reassignment of weak genes
# == Module merging options ==
mergeCutHeight = CutHeight, # Threshold for merging similar modules
# == Output options ==
numericLabels = FALSE, # Use color labels instead of numeric
verbose = 5
) Calculating module eigengenes block-wise from all genes
Flagging genes and samples with too many missing values...
..step 1
..Working on block 1 .
TOM calculation: adjacency..
..will use 12 parallel threads.
Fraction of slow calculations: 0.000000
..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.
....clustering..
....detecting modules..
..done.
....calculating module eigengenes..
moduleEigengenes : Working on ME for module 1
moduleEigengenes : Working on ME for module 2
moduleEigengenes : Working on ME for module 3
moduleEigengenes : Working on ME for module 4
moduleEigengenes : Working on ME for module 5
moduleEigengenes : Working on ME for module 6
moduleEigengenes : Working on ME for module 7
moduleEigengenes : Working on ME for module 8
moduleEigengenes : Working on ME for module 9
moduleEigengenes : Working on ME for module 10
moduleEigengenes : Working on ME for module 11
moduleEigengenes : Working on ME for module 12
moduleEigengenes : Working on ME for module 13
moduleEigengenes : Working on ME for module 14
moduleEigengenes : Working on ME for module 15
moduleEigengenes : Working on ME for module 16
moduleEigengenes : Working on ME for module 17
moduleEigengenes : Working on ME for module 18
moduleEigengenes : Working on ME for module 19
moduleEigengenes : Working on ME for module 20
moduleEigengenes : Working on ME for module 21
moduleEigengenes : Working on ME for module 22
moduleEigengenes : Working on ME for module 23
moduleEigengenes : Working on ME for module 24
moduleEigengenes : Working on ME for module 25
moduleEigengenes : Working on ME for module 26
moduleEigengenes : Working on ME for module 27
moduleEigengenes : Working on ME for module 28
moduleEigengenes : Working on ME for module 29
moduleEigengenes : Working on ME for module 30
moduleEigengenes : Working on ME for module 31
moduleEigengenes : Working on ME for module 32
moduleEigengenes : Working on ME for module 33
moduleEigengenes : Working on ME for module 34
moduleEigengenes : Working on ME for module 35
moduleEigengenes : Working on ME for module 36
moduleEigengenes : Working on ME for module 37
moduleEigengenes : Working on ME for module 38
moduleEigengenes : Working on ME for module 39
moduleEigengenes : Working on ME for module 40
moduleEigengenes : Working on ME for module 41
moduleEigengenes : Working on ME for module 42
moduleEigengenes : Working on ME for module 43
moduleEigengenes : Working on ME for module 44
moduleEigengenes : Working on ME for module 45
moduleEigengenes : Working on ME for module 46
moduleEigengenes : Working on ME for module 47
moduleEigengenes : Working on ME for module 48
moduleEigengenes : Working on ME for module 49
....checking kME in modules..
..removing 1 genes from module 2 because their KME is too low.
..removing 1 genes from module 10 because their KME is too low.
..merging modules that are too close..
mergeCloseModules: Merging modules whose distance is less than 0.2
multiSetMEs: Calculating module MEs.
Working on set 1 ...
moduleEigengenes: Calculating 50 module eigengenes in given set.
multiSetMEs: Calculating module MEs.
Working on set 1 ...
moduleEigengenes: Calculating 33 module eigengenes in given set.
multiSetMEs: Calculating module MEs.
Working on set 1 ...
moduleEigengenes: Calculating 29 module eigengenes in given set.
Calculating new MEs...
multiSetMEs: Calculating module MEs.
Working on set 1 ...
moduleEigengenes: Calculating 29 module eigengenes in given set.# Save the network object for future reference
output_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("network_", tissue, "_", species, ".rds"))
saveRDS(net, file = output_file)
cat("✅ Network construction completed and saved to:", output_file, "\n")✅ Network construction completed and saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/network_Thorax_gregaria.rds Visualizing Module Dendrogram
# Plot module dendrogram
plot_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("ModuleDendrogram_", tissue, "_", species, ".pdf"))
pdf(plot_file, width = 12, height = 8)
plotDendroAndColors(
net$dendrograms[[1]],
net$colors[net$blockGenes[[1]]],
"Module Colors",
dendroLabels = FALSE,
hang = 0.03,
addGuide = TRUE,
guideHang = 0.05
)
dev.off()quartz_off_screen
2 cat("✅ Module dendrogram plot saved to:", plot_file, "\n")✅ Module dendrogram plot saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/ModuleDendrogram_Thorax_gregaria.pdf Visualizing Module Sizes
# Calculate module sizes
module_sizes <- table(net$colors)
# Print module sizes
print(module_sizes)
black blue brown cyan darkgreen
298 984 858 161 81
darkgrey darkorange darkred darkturquoise green
79 68 98 79 488
greenyellow grey grey60 lightcyan lightgreen
205 1586 117 135 108
lightyellow magenta midnightblue orange pink
101 252 140 73 295
purple red royalblue salmon skyblue
248 304 99 184 66
tan turquoise white yellow
202 4391 67 594 # Save module sizes
output_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("ModuleSizes_", tissue, "_", species, ".csv"))
write.csv(module_sizes, file = output_file, row.names = TRUE)
# Bar plot for module sizes
plot_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("ModuleSizes_", tissue, "_", species, ".pdf"))
pdf(plot_file, width = 10, height = 6)
barplot(
module_sizes,
main = "Module Sizes",
xlab = "Modules",
ylab = "Number of Genes",
col = names(module_sizes),
las = 2 # Rotate axis labels
)
dev.off()quartz_off_screen
2 cat("✅ Module sizes plot saved to:", plot_file, "\n")✅ Module sizes plot saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/ModuleSizes_Thorax_gregaria.pdf Step 6: Module-Trait Relationships
Check Data before correlation
# Ensure all traits are numeric
numeric_traits <- datTraits
non_numeric_cols <- which(sapply(datTraits, class) %in% c("character", "factor"))
if (length(non_numeric_cols) > 0) {
for (col in non_numeric_cols) {
numeric_traits[[col]] <- as.numeric(as.factor(datTraits[[col]]))
}
}
# Confirm alignment
cat("✅ Converted traits to numeric. Dimensions:", dim(numeric_traits), "\n")✅ Converted traits to numeric. Dimensions: 10 8 cat("✅ datExpr dimensions:", dim(datExpr), "\n")✅ datExpr dimensions: 10 12361 if (nrow(datExpr) != nrow(numeric_traits)) {
stop("❌ Number of samples in datExpr and numeric_traits do not match.")
}Calculate Module Eigengenes and correlation
# Calculate module eigengenes
module_eigengenes <- moduleEigengenes(datExpr, colors = net$colors)$eigengenes
module_eigengenes <- orderMEs(module_eigengenes)
# Correlate module eigengenes with traits
module_trait_correlation <- cor(module_eigengenes, numeric_traits, use = "p")
module_trait_pvalues <- corPvalueStudent(module_trait_correlation, nrow(datExpr))
cat("✅ Module-trait correlation calculated!\n")✅ Module-trait correlation calculated!# Save correlation and p-values
correlation_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("ModuleTraitCorrelation_", tissue, "_", species, ".csv"))
pvalues_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("ModuleTraitPValues_", tissue, "_", species, ".csv"))
write.csv(module_trait_correlation, correlation_file, row.names = TRUE)
write.csv(module_trait_pvalues, pvalues_file, row.names = TRUE)
cat("✅ Module-trait correlation data saved to:", correlation_file, "\n")✅ Module-trait correlation data saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/ModuleTraitCorrelation_Thorax_gregaria.csv cat("✅ Module-trait p-values saved to:", pvalues_file, "\n")✅ Module-trait p-values saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/ModuleTraitPValues_Thorax_gregaria.csv Heatmap visualization module trait relationships
# Extract module colors without "ME"
module_colors <- gsub("^ME", "", names(module_eigengenes))
# Create labels with color and corresponding gene count
module_labels <- paste0(module_colors, " (", module_sizes[module_colors], ")")
# Heatmap of module-trait relationships
plot_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("ModuleTraitRelationships_", tissue, "_", species, "_with_colors.pdf"))
pdf(plot_file, width = 12, height = 20)
labeledHeatmap(
Matrix = module_trait_correlation,
xLabels = colnames(numeric_traits),
yLabels = names(module_eigengenes),
colorLabels = FALSE,
colors = blueWhiteRed(50),
textMatrix = paste0(
signif(module_trait_correlation, 2), "\n(",
signif(module_trait_pvalues, 1), ")"
),
cex.text = 0.8,
zlim = c(-1, 1),
main = paste("Module-Trait Relationships -", tissue, species)
)
dev.off()quartz_off_screen
2 cat("✅ Module-trait relationships heatmap with real colors saved to:", plot_file, "\n")✅ Module-trait relationships heatmap with real colors saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/ModuleTraitRelationships_Thorax_gregaria_with_colors.pdf # Heatmap of module-trait relationships
plot_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("ModuleTraitRelationships_", tissue, "_", species, "_with_colors_name.pdf"))
pdf(plot_file, width = 12, height = 20)
labeledHeatmap(
Matrix = module_trait_correlation,
xLabels = colnames(numeric_traits),
yLabels = module_labels, # Add color names to labels
colorLabels = FALSE,
colors = blueWhiteRed(50),
textMatrix = paste0(
signif(module_trait_correlation, 2), "\n(",
signif(module_trait_pvalues, 1), ")"
),
cex.text = 0.8,
zlim = c(-1, 1),
main = paste("Module-Trait Relationships -", tissue, species)
)
dev.off()quartz_off_screen
2 cat("Module-trait relationships plot with colors saved to:", plot_file, "\n")Module-trait relationships plot with colors saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/ModuleTraitRelationships_Thorax_gregaria_with_colors_name.pdf Module–Module Co‐expression Network
library(WGCNA)
library(visNetwork)
library(scales)
# 1) Compute module eigengenes (reuse from Step 6)
MEs <- moduleEigengenes(datExpr, colors = net$colors)$eigengenes
MEs <- orderMEs(MEs)
# 2) Correlate eigengenes and threshold
modCor <- cor(MEs, use = "pairwise.complete.obs")
edge_thr <- 0.3
modCor[ abs(modCor) < edge_thr ] <- 0
# 3) Build edge list (only one direction)
modules <- colnames(modCor)
idx <- which(modCor != 0, arr.ind = TRUE)
edges_df <- data.frame(
from = modules[idx[,1]],
to = modules[idx[,2]],
weight = abs(modCor[idx]),
stringsAsFactors = FALSE
)
edges_df <- edges_df[idx[,1] < idx[,2], ]
# 4) Prepare nodes
modSizes <- table(net$colors)
nodes_df <- data.frame(
id = modules,
label = gsub("^ME", "", modules), # drop the "ME" prefix for the label
title = paste0(gsub("^ME","", modules),
"<br>Genes: ", modSizes[gsub("^ME","",modules)]),
value = as.numeric(modSizes[gsub("^ME","",modules)]),
stringsAsFactors = FALSE
)
# Assign each node its WGCNA color by stripping "ME" from the name
nodes_df$color.background <- gsub("^ME", "", modules)
nodes_df$color.border <- "black"
nodes_df$color.highlight.background <- gsub("^ME", "", modules)
nodes_df$color.highlight.border <- "black"
# 5) Interactive visNetwork
visNetwork(nodes_df, edges_df, height = "600px", width = "100%") %>%
visIgraphLayout(layout = "layout_with_fr", randomSeed = 42) %>%
visNodes(
shape = "dot",
shadow = TRUE
# We no longer need to set color here,
# visNetwork will pick up color.background & color.border from nodes_df
) %>%
visEdges(
smooth = FALSE,
color = list(color = "grey80")
) %>%
visOptions(
highlightNearest = TRUE,
nodesIdSelection = TRUE
) %>%
visLegend(useGroups = FALSE) # legend not needed since node colors are explicitStep 7: Identifying Hub Genes in Modules
Control check module membership calculation
# Calculate module membership (MM)
module_membership <- as.data.frame(cor(datExpr, module_eigengenes, use = "p"))
# Ensure proper column names without prefixes
colnames(module_membership) <- gsub("^ME", "", colnames(module_membership))
# Add the module colors to the data
module_membership$ModuleColor <- net$colors
# Calculate p-values for module membership
module_membership_pvalues <- as.data.frame(
corPvalueStudent(as.matrix(module_membership[, -ncol(module_membership)]), nrow(datExpr))
)
# Ensure proper column names for p-values
colnames(module_membership_pvalues) <- paste0("p.", colnames(module_membership)[-ncol(module_membership)])
# Save module membership and p-values
output_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("ModuleMembership_", tissue, "_", species, ".csv"))
write.csv(cbind(module_membership, module_membership_pvalues), file = output_file, row.names = TRUE)
cat("✅ Module membership data saved to:", output_file, "\n")✅ Module membership data saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/ModuleMembership_Thorax_gregaria.csv Identify Hub genes
# Define the target module color
target_module_color <- "green" # Change to desired module color
# Ensure the correct column exists
if (!target_module_color %in% colnames(module_membership)) {
stop(paste("❌ Column", target_module_color, "not found in module_membership. Check column names."))
}
# Filter for hub genes (Module Membership > 0.8)
hub_genes <- module_membership[
module_membership$ModuleColor == target_module_color &
abs(module_membership[[target_module_color]]) > 0.8,
]
# Save hub genes
hub_genes_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("HubGenes_", target_module_color, "_", tissue, "_", species, ".csv"))
write.csv(hub_genes, file = hub_genes_file, row.names = TRUE)
cat("✅ Hub genes for", target_module_color, "saved to:", hub_genes_file, "\n")✅ Hub genes for green saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/HubGenes_green_Thorax_gregaria.csv Visualing Hub Genes
# Check if we have any hub genes
if (nrow(hub_genes) > 0) {
# Plot top 20 hub genes by module membership
top_hub_genes <- hub_genes[order(-abs(hub_genes[[target_module_color]])), ][1:min(20, nrow(hub_genes)), ]
# Save bar plot
plot_file <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species, paste0("TopHubGenes_", target_module_color, "_", tissue, "_", species, ".pdf"))
pdf(plot_file, width = 10, height = 8)
barplot(
abs(top_hub_genes[[target_module_color]]),
names.arg = rownames(top_hub_genes),
las = 2, col = target_module_color,
main = paste("Top Hub Genes in", target_module_color, "Module"),
ylab = "Module Membership Score"
)
dev.off()
cat("✅ Top hub genes plot saved to:", plot_file, "\n")
} else {
cat("⚠️ No hub genes found in", target_module_color, "\n")
}✅ Top hub genes plot saved to: /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/TopHubGenes_green_Thorax_gregaria.pdf Extracting the adjacency matrix for the target module
# Ensure target module color is set
target_module_color <- "green" # Change this as needed
# Extract genes belonging to the target module
target_genes <- names(net$colors)[net$colors == target_module_color]
# Check if module has enough genes
if (length(target_genes) < 2) {
stop("❌ The selected module has too few genes for network extraction.")
}
# Make sure all module genes really are column names
if (! all(target_genes %in% colnames(datExpr)) ) {
stop("Some turquoise genes not found in datExpr columns!")
}
# Subset columns = genes
datExpr_module <- datExpr[, target_genes, drop = FALSE]
# Quick sanity check
cat("✔️ datExpr_module dims (samples × genes):", dim(datExpr_module), "\n")✔️ datExpr_module dims (samples × genes): 10 488 # e.g. 40 samples × 1500 module genes
# Now compute adjacency
adjacency_matrix <- adjacency(
datExpr_module,
power = picked_power,
type = "unsigned"
)
# And confirm
cat("✔️ adjacency_matrix dims:", dim(adjacency_matrix), "\n")✔️ adjacency_matrix dims: 488 488 # should be 1500 × 1500 (genes × genes)
# Compute TOM similarity **only for module genes**
TOM_module <- TOMsimilarity(adjacency_matrix)..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.# Verify dimensions
if (!all(dim(TOM_module) == length(target_genes))) {
stop("❌ TOM matrix dimensions do not match target genes.")
}
# Assign proper row and column names
rownames(TOM_module) <- colnames(TOM_module) <- target_genes
# Save adjacency and TOM matrices
output_dir <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species)
dir.create(output_dir, showWarnings = FALSE, recursive = TRUE)
adj_matrix_file <- file.path(
output_dir,
paste0("AdjacencyMatrix_", target_module_color, "_", tissue, "_", species, ".csv")
)
TOM_file <- file.path(
output_dir,
paste0("TOMMatrix_", target_module_color, "_", tissue, "_", species, ".csv")
)
write.csv(adjacency_matrix, file = adj_matrix_file, row.names = TRUE)
write.csv(TOM_module, file = TOM_file, row.names = TRUE)
cat("✅ Adjacency and TOM matrices saved:\n",
" • Adjacency →", adj_matrix_file, "\n",
" • TOM →", TOM_file, "\n")✅ Adjacency and TOM matrices saved:
• Adjacency → /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/AdjacencyMatrix_green_Thorax_gregaria.csv
• TOM → /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/TOMMatrix_green_Thorax_gregaria.csv library(WGCNA)
# 1) Define module & genes
target_module_color <- "green"
modGenes <- names(net$colors)[net$colors == target_module_color]
# 2) Define where to write your Cytoscape files
output_dir <- file.path(workDir, "WGCNA/output/Bulk_RNAseq", species)
edgeFile <- file.path(output_dir, paste0("Cytoscape_", target_module_color, "_edges.txt"))
nodeFile <- file.path(output_dir, paste0("Cytoscape_", target_module_color, "_nodes.txt"))
# 3) Export using WGCNA helper
exportNetworkToCytoscape(
TOM_module,
edgeFile = edgeFile,
nodeFile = nodeFile,
weighted = TRUE,
threshold = 0.005, # adjust to keep a reasonable number of edges
nodeNames = modGenes,
nodeAttr = module_membership[modGenes, c(target_module_color, "ModuleColor")]
)$edgeData
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LOC126328317 LOC126328317 NA -0.8747045 green# 4) Quick sanity check
if (!file.exists(edgeFile) || !file.exists(nodeFile)) {
stop("❌ Cytoscape export failed: edge or node file not found.")
}
cat("✅ Cytoscape files ready:\n",
" • Edges →", edgeFile, "\n",
" • Nodes →", nodeFile, "\n")✅ Cytoscape files ready:
• Edges → /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/Cytoscape_green_edges.txt
• Nodes → /Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data/WGCNA/output/Bulk_RNAseq/gregaria/Cytoscape_green_nodes.txt Network within target module
library(WGCNA)
library(visNetwork)
library(scales)
# 0) Module to plot
target_module_color <- "green"
# 1) Genes in that module
modGenes <- names(net$colors)[ net$colors == target_module_color ]
# 2) Adjacency for those genes
datExpr_mod <- datExpr[, modGenes, drop = FALSE]
adj_full <- adjacency(datExpr_mod, power = picked_power, type = "unsigned")
# 3) Threshold to top 1% edges
thr_edge <- quantile(adj_full[upper.tri(adj_full)], 0.99)
adj_thresh <- adj_full
adj_thresh[ adj_full < thr_edge ] <- 0
# 4) Build edge dataframe
gene_names <- colnames(adj_thresh)
idx <- which(adj_thresh > 0, arr.ind = TRUE)
edges_df <- data.frame(
from = gene_names[ idx[,1] ],
to = gene_names[ idx[,2] ],
weight = adj_thresh[ idx ],
stringsAsFactors = FALSE
)
# 5) Precompute degrees for each gene
degree_mod <- rowSums(adj_thresh > 0)
# 6) Build node dataframe (fixed)
nodes_df <- data.frame(
id = modGenes,
label = modGenes,
group = net$colors[modGenes], # module color
value = degree_mod[modGenes], # node size
title = paste0(modGenes, "<br>Degree: ", degree_mod[modGenes]),
stringsAsFactors = FALSE
)
# 7) Interactive network
visNetwork(nodes_df, edges_df, height = "700px", width = "100%") %>%
visIgraphLayout(layout = "layout_with_fr") %>%
visNodes(shape = "dot", shadow = TRUE) %>%
visEdges(color = list(color = "grey80"), smooth = FALSE) %>%
visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE) %>%
visLegend(useGroups = TRUE, position = "right", main = "Module")Cross-tissues Gene Network Inference
library(WGCNA)
allowWGCNAThreads()Allowing multi-threading with up to 12 threads.options(stringsAsFactors = FALSE)
# ────────────────────────────────────────────────────────────────────
# 0) USER PARAMETER: change this to rerun for any other species
species <- "gregaria"
# ────────────────────────────────────────────────────────────────────
tissues <- c("Head", "Thorax")
softPowerOpts <- list() # to store chosen β for each tissue
exprs <- list() # expression data (samples × genes)
moduleColors <- list() # module assignments for each tissue
# ────────────────────────────────────────────────────────────────────
# A) Load, orient, and numeric-ify each tissue’s datExpr
# ────────────────────────────────────────────────────────────────────
for(tiss in tissues) {
path <- file.path(workDir, "WGCNA/input/Bulk_RNAseq",
paste0("datExpr_", tiss, "_", species, ".csv"))
raw <- read.csv(path, row.names = 1, check.names = FALSE, stringsAsFactors = FALSE)
# Detect orientation: if rownames look like gene IDs (e.g. LOC…), then transpose
if (grepl("^LOC", rownames(raw)[1]) || grepl("^ENS", rownames(raw)[1])) {
dat <- t(raw)
} else {
dat <- raw
}
dat <- as.data.frame(dat)
dat[] <- lapply(dat, as.numeric)
exprs[[tiss]] <- dat
# Quick check
cat(sprintf("✔ Loaded %s: %d samples × %d genes\n",
tiss, nrow(dat), ncol(dat)))
}✔ Loaded Head: 12 samples × 12775 genes
✔ Loaded Thorax: 10 samples × 12361 genes# ────────────────────────────────────────────────────────────────────
# B) For each tissue: pick a soft threshold, then detect modules
# ────────────────────────────────────────────────────────────────────
for(tiss in tissues) {
dat <- exprs[[tiss]]
# 1) Soft‐thresholding analysis
powers <- c(1:10, seq(12,30,2))
sft <- pickSoftThreshold(dat, powerVector = powers, verbose = 0)
beta <- sft$powerEstimate
if (is.na(beta)) {
warning("No powerEstimate for ", tiss, "; defaulting to 6")
beta <- 6
}
softPowerOpts[[tiss]] <- beta
cat(sprintf(" ➔ %s: chosen soft‐power β = %d\n", tiss, beta))
# 2) Module detection
net <- blockwiseModules(
dat,
power = beta,
networkType = "unsigned",
minModuleSize = 30,
mergeCutHeight = 0.20,
numericLabels = FALSE,
verbose = 0
)
moduleColors[[tiss]] <- net$colors
tbl <- table(net$colors)
cat(sprintf(" ➔ %s: detected %d modules (incl. grey)\n\n",
tiss, length(tbl)))
} Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
1 1 0.0283 0.251 0.574 4360.00 4340.000 6560
2 2 0.4170 -0.640 0.766 2130.00 2000.000 4280
3 3 0.6960 -0.964 0.860 1220.00 1060.000 3080
4 4 0.7850 -1.110 0.904 773.00 615.000 2340
5 5 0.8060 -1.210 0.909 522.00 378.000 1850
6 6 0.8290 -1.270 0.926 370.00 244.000 1500
7 7 0.8310 -1.330 0.929 272.00 163.000 1240
8 8 0.8430 -1.360 0.941 205.00 112.000 1040
9 9 0.8470 -1.390 0.947 159.00 79.300 891
10 10 0.8590 -1.410 0.957 125.00 57.600 767
11 12 0.8590 -1.460 0.960 81.80 31.800 585
12 14 0.8600 -1.490 0.964 56.00 18.700 458
13 16 0.8610 -1.510 0.968 39.90 11.500 367
14 18 0.8670 -1.520 0.974 29.30 7.420 299
15 20 0.8770 -1.530 0.981 22.10 4.940 247
16 22 0.8810 -1.540 0.985 17.00 3.370 206
17 24 0.8890 -1.540 0.988 13.30 2.330 174
18 26 0.8950 -1.540 0.991 10.60 1.670 149
19 28 0.8980 -1.540 0.992 8.54 1.220 128
20 30 0.9010 -1.540 0.994 6.97 0.916 111
➔ Head: chosen soft‐power β = 10
➔ Head: detected 64 modules (incl. grey)
Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
1 1 0.262 0.822 0.373 4550.0 4490.00 6560
2 2 0.227 -0.317 0.236 2400.0 2180.00 4540
3 3 0.813 -0.690 0.790 1480.0 1210.00 3480
4 4 0.923 -0.859 0.907 1010.0 729.00 2810
5 5 0.947 -0.950 0.935 729.0 480.00 2350
6 6 0.953 -1.000 0.942 551.0 330.00 2010
7 7 0.954 -1.040 0.945 430.0 235.00 1750
8 8 0.949 -1.080 0.941 344.0 170.00 1550
9 9 0.943 -1.110 0.938 282.0 125.00 1390
10 10 0.935 -1.130 0.931 234.0 94.60 1250
11 12 0.935 -1.160 0.937 169.0 56.70 1040
12 14 0.925 -1.190 0.931 127.0 35.80 882
13 16 0.918 -1.210 0.928 98.0 23.60 761
14 18 0.915 -1.220 0.929 77.8 16.10 665
15 20 0.912 -1.240 0.930 63.0 11.40 587
16 22 0.914 -1.240 0.934 51.9 8.24 523
17 24 0.910 -1.250 0.934 43.4 6.09 469
18 26 0.911 -1.260 0.937 36.7 4.60 423
19 28 0.915 -1.260 0.942 31.3 3.54 384
20 30 0.918 -1.270 0.946 27.0 2.78 351
➔ Thorax: chosen soft‐power β = 4
➔ Thorax: detected 79 modules (incl. grey)# Show a quick summary
print(softPowerOpts)$Head
[1] 10
$Thorax
[1] 4print(lapply(moduleColors, table))$Head
antiquewhite4 bisque4 black blue brown
34 53 486 1391 977
brown4 coral1 coral2 cyan darkgreen
54 35 34 132 92
darkgrey darkmagenta darkolivegreen darkorange darkorange2
86 68 70 81 54
darkred darkseagreen4 darkslateblue darkturquoise floralwhite
96 35 52 90 55
green greenyellow grey grey60 honeydew1
729 156 1103 103 35
ivory lavenderblush3 lightcyan lightcyan1 lightgreen
56 36 111 58 103
lightpink4 lightsteelblue1 lightyellow magenta maroon
37 60 103 183 41
mediumorchid mediumpurple3 midnightblue navajowhite2 orange
31 63 123 43 84
orangered4 paleturquoise palevioletred3 pink plum1
64 72 44 214 64
plum2 purple red royalblue saddlebrown
49 159 692 96 77
salmon salmon4 sienna3 skyblue skyblue3
141 47 68 77 65
steelblue tan thistle1 thistle2 turquoise
76 149 48 48 2149
violet white yellow yellowgreen
72 80 924 67
$Thorax
antiquewhite4 bisque4 black blue blue2
43 53 244 1189 36
brown brown2 brown4 coral1 coral2
991 36 56 44 43
cyan darkgreen darkgrey darkmagenta darkolivegreen
133 99 89 66 67
darkolivegreen4 darkorange darkorange2 darkred darkseagreen4
36 85 59 102 44
darkslateblue darkturquoise darkviolet firebrick4 floralwhite
52 91 32 37 59
green greenyellow grey grey60 honeydew1
332 144 26 118 44
indianred4 ivory lavenderblush3 lightcoral lightcyan
38 59 45 40 125
lightcyan1 lightgreen lightpink4 lightsteelblue lightsteelblue1
59 118 45 40 61
lightyellow magenta maroon mediumorchid mediumpurple2
111 187 45 43 40
mediumpurple3 midnightblue navajowhite2 orange orangered3
61 131 46 85 40
orangered4 paleturquoise palevioletred3 pink plum
62 78 46 208 41
plum1 plum2 plum3 purple red
63 51 31 171 331
royalblue saddlebrown salmon salmon4 sienna3
102 80 134 47 64
skyblue skyblue1 skyblue2 skyblue3 steelblue
82 41 43 63 78
tan thistle1 thistle2 turquoise violet
140 49 50 3759 69
white yellow yellow4 yellowgreen
82 422 41 64 # ────────────────────────────────────────────────────────────────────
# C) Module preservation: Head (ref) → Thorax (test)
# ────────────────────────────────────────────────────────────────────
multiExpr <- list(
Head = list(data = exprs[["Head"]]),
Thorax = list(data = exprs[["Thorax"]])
)
multiColor <- list(
Head = moduleColors[["Head"]],
Thorax = moduleColors[["Thorax"]]
)
mp <- modulePreservation(
multiExpr, multiColor,
referenceNetworks = 1,
nPermutations = 200,
networkType = "unsigned",
verbose = 0
)
# After running mp <- modulePreservation(...)
# 1) Find the indices of Head (reference) and Thorax (test) in the list
refIndex <- 1 # Head is first in multiExpr
testIndex <- 2 # Thorax is second
# 2) Extract the Z‐stat matrix and the observed‐stat matrix
Zmat <- mp$preservation$Z[[ refIndex ]][[ testIndex ]]
Omat <- mp$preservation$observed[[ refIndex ]][[ testIndex ]]
# 3) Pull out the columns you want
Zsummary <- Zmat[ , "Zsummary.pres" ] # Z‐summary preservation
medianRank <- Omat[ , "medianRank.pres" ] # median‐rank preservation
# 4) Get module names
mods <- rownames(Zmat)
# 5) Plot them
par(mfrow = c(1,2), mar = c(6,4,3,1))
# 1) Z‐summary plot
x <- seq_along(Zsummary)
plot(
x, Zsummary,
pch = 21,
bg = mods,
cex = 2, # bigger dots
xaxt = "n",
xlab = "",
ylab = "Z-summary preservation",
main = paste(species, "Head → Thorax (Zsummary)")
)
axis(1, at = x, labels = mods, las = 2, cex.axis = 0.8)
abline(h = 2, col = "blue", lty = 2) # moderate
abline(h = 10, col = "red", lty = 2) # strong
# label modules with Zsummary > 10
highZ <- which(Zsummary > 10)
text(
x[highZ], Zsummary[highZ],
labels = mods[highZ],
pos = 3, # above
cex = 0.8,
font = 2 # bold
)
# 2) Median‐rank plot
x2 <- seq_along(medianRank)
plot(
x2, medianRank,
pch = 21,
bg = mods,
cex = 2, # bigger dots
xaxt = "n",
xlab = "",
ylab = "Median rank preservation",
main = paste(species, "Head → Thorax (Median rank)")
)
axis(1, at = x2, labels = mods, las = 2, cex.axis = 0.8)
abline(
h = mean(medianRank, na.rm = TRUE),
col = "darkgreen", lty = 2
)
# label modules with medianRank < overall mean (best preserved)
lowMr <- which(medianRank < mean(medianRank, na.rm = TRUE))
text(
x2[lowMr], medianRank[lowMr],
labels = mods[lowMr],
pos = 3,
cex = 0.8,
font = 2
)
par(mfrow = c(1,1))What it shows: for each module, the Z-summary statistic quantifies how well the connectivity patterns (topology) of that module in Head are preserved in Thorax.
Z-summary plot:
- Z = 10 (red): modules above this are strongly preserved.
- Z = 2 (blue): modules between 2 and 10 are moderately preserved.
- Below 2: little to no evidence of preservation.
Median-rank plot:
The plot shows the median rank of preservation statistics for each module, for which lower value is is better. The dashed line indicates the average median-rank across all modules. Modules with rank below this line are better preserved.
Network comparison across Schistocerca
Data loading and preprocessing
We will be using the BioNERO R package and will follow
the tutorial
to: - Infer gene co-expression network across tissues within a species.
- Infer regulatory network across tissues within a species. - Compare
networks across species
library(BioNERO)
library(SummarizedExperiment)
library(S4Vectors)
set.seed(456) # for reproducibility
# A) Your list of species
species_list <- c(
"gregaria",
"americana",
"cancellata",
"piceifrons",
"cubense",
"nitens"
)
# B) Loop
for (species in species_list) {
message("↪ Processing species: ", species)
# 1) Paths
head_file <- file.path(
workDir, "WGCNA/input/Bulk_RNAseq",
paste0("datExpr_Head_", species, ".csv")
)
thorax_file <- file.path(
workDir, "WGCNA/input/Bulk_RNAseq",
paste0("datExpr_Thorax_", species, ".csv")
)
# 2) Read (rows=samples, cols=genes)
head_df <- read.csv(head_file, row.names = 1, check.names = FALSE)
thorax_df <- read.csv(thorax_file, row.names = 1, check.names = FALSE)
# 3) Transpose → (genes×samples)
head_mat <- t(as.matrix(head_df))
thorax_mat <- t(as.matrix(thorax_df))
# 4) Build per‐tissue SEs
for (tiss in c("Head","Thorax")) {
mat <- if (tiss=="Head") head_mat else thorax_mat
samples <- colnames(mat)
coldata <- DataFrame(
Sample = samples,
Tissue = factor(tiss, levels=c("Head","Thorax")),
Species = factor(species, levels=species_list),
row.names = samples
)
se <- SummarizedExperiment(
assays = SimpleList(vst = mat),
colData = coldata,
rowData = DataFrame(GeneID = rownames(mat))
)
out_file <- file.path(
workDir, "WGCNA/input/Bulk_RNAseq",
paste0("SE_", tiss, "_", gsub(" ", "_", species), ".rds")
)
saveRDS(se, file = out_file)
message(" • Saved ", basename(out_file))
}
# 5) Combined Head+Thorax
common_genes <- intersect(rownames(head_mat), rownames(thorax_mat))
head_mat_c <- head_mat[common_genes, , drop=FALSE]
thorax_mat_c <- thorax_mat[common_genes, , drop=FALSE]
comb_mat <- cbind(head_mat_c, thorax_mat_c)
samples <- colnames(comb_mat)
nHead <- ncol(head_mat_c)
nThorax <- ncol(thorax_mat_c)
coldata <- DataFrame(
Sample = samples,
Tissue = factor(
c(rep("Head", nHead),
rep("Thorax", nThorax)),
levels=c("Head","Thorax")
),
Species = factor(rep(species, length(samples)), levels=species_list),
row.names = samples
)
se_comb <- SummarizedExperiment(
assays = SimpleList(vst = comb_mat),
colData = coldata,
rowData = DataFrame(GeneID = common_genes)
)
out_file <- file.path(
workDir, "WGCNA/input/Bulk_RNAseq",
paste0("SE_HeadThorax_", gsub(" ", "_", species), ".rds")
)
saveRDS(se_comb, file = out_file)
message(" • Saved ", basename(out_file), "\n")
}We then will remove the missing values if any, non-expressed genes:
# ───────────────────────────────────────────────────────────
# 1) Load your SummarizedExperiment (Head / Thorax combined)
# ───────────────────────────────────────────────────────────
se_file <- file.path(
workDir, "WGCNA/input/Bulk_RNAseq",
"SE_Head_gregaria.rds"
)
final_exp <- readRDS(se_file)
# ───────────────────────────────────────────────────────────
# 2) (Optional) Preprocess for WGCNA
# ───────────────────────────────────────────────────────────
# final_exp <- exp_preprocess(
# final_exp,
# min_exp = 5,
# variance_filter = TRUE,
# n = 10000
# )
print(final_exp)class: SummarizedExperiment
dim: 12775 12
metadata(0):
assays(1): vst
rownames(12775): LOC126318536 LOC126318656 ... LOC126328317
LOC126330722
rowData names(1): GeneID
colnames(12): SGRE-HEAD-CRD-1 SGRE-HEAD-CRD-2 ... SGRE-HEAD-ISO-5
SGRE-HEAD-ISO-6
colData names(3): Sample Tissue Species# ───────────────────────────────────────────────────────────
# 3) Import behavioral traits into colData
# ───────────────────────────────────────────────────────────
behavior_file <- file.path(
workDir, "behavioral_data", "Sample_phenotypes_WGCNA.csv"
)
behavior_df <- read.csv(behavior_file, stringsAsFactors = FALSE)
# set rownames to SampleName for easy subsetting
rownames(behavior_df) <- behavior_df$SampleName
# define which columns are your traits
trait_cols <- c(
"RearingCondition",
"MovedDistance",
"StimulusZoneTime",
"NonStimulusZoneTime",
"TimeStimulusWall"
)
# subset & reorder to match final_exp samples
meta_sub <- behavior_df[colnames(final_exp), trait_cols, drop = FALSE]
# re‐initialize colData to only the core columns
cd0 <- colData(final_exp)[, c("Sample", "Tissue", "Species")]
# add each trait without duplication
for (col in trait_cols) {
if (col == "RearingCondition") {
# categorical
cd0[[col]] <- factor(
meta_sub[[col]],
levels = c("Isolated", "Crowded")
)
} else {
# continuous
cd0[[col]] <- as.numeric(meta_sub[[col]])
}
}
# overwrite colData with this clean version
colData(final_exp) <- DataFrame(cd0)
# sanity check
head(colData(final_exp))DataFrame with 6 rows and 8 columns
Sample Tissue Species RearingCondition
<character> <factor> <factor> <factor>
SGRE-HEAD-CRD-1 SGRE-HEAD-CRD-1 Head gregaria Crowded
SGRE-HEAD-CRD-2 SGRE-HEAD-CRD-2 Head gregaria Crowded
SGRE-HEAD-CRD-3 SGRE-HEAD-CRD-3 Head gregaria Crowded
SGRE-HEAD-CRD-4 SGRE-HEAD-CRD-4 Head gregaria Crowded
SGRE-HEAD-CRD-5 SGRE-HEAD-CRD-5 Head gregaria Crowded
SGRE-HEAD-CRD-6 SGRE-HEAD-CRD-6 Head gregaria Crowded
MovedDistance StimulusZoneTime NonStimulusZoneTime
<numeric> <numeric> <numeric>
SGRE-HEAD-CRD-1 132.673 220.447 109.832
SGRE-HEAD-CRD-2 132.673 220.447 109.832
SGRE-HEAD-CRD-3 132.673 220.447 109.832
SGRE-HEAD-CRD-4 132.673 220.447 109.832
SGRE-HEAD-CRD-5 132.673 220.447 109.832
SGRE-HEAD-CRD-6 132.673 220.447 109.832
TimeStimulusWall
<numeric>
SGRE-HEAD-CRD-1 119.282
SGRE-HEAD-CRD-2 119.282
SGRE-HEAD-CRD-3 119.282
SGRE-HEAD-CRD-4 119.282
SGRE-HEAD-CRD-5 119.282
SGRE-HEAD-CRD-6 119.282Gene coexpression network inference
Assessing module stability
# ───────────────────────────────────────────────────────────
# 2) Infer the network as before…
# ───────────────────────────────────────────────────────────
sft <- SFT_fit(final_exp, net_type = "unsigned", cor_method = "pearson") Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
1 3 0.696 -0.964 0.860 1220.0 1060.00 3080
2 4 0.785 -1.110 0.904 773.0 615.00 2340
3 5 0.806 -1.210 0.909 522.0 378.00 1850
4 6 0.829 -1.270 0.926 370.0 244.00 1500
5 7 0.831 -1.330 0.929 272.0 163.00 1240
6 8 0.843 -1.360 0.941 205.0 112.00 1040
7 9 0.847 -1.390 0.947 159.0 79.30 891
8 10 0.859 -1.410 0.957 125.0 57.60 767
9 11 0.863 -1.430 0.962 101.0 42.40 667
10 12 0.859 -1.460 0.960 81.8 31.80 585
11 13 0.858 -1.470 0.962 67.3 24.30 516
12 14 0.860 -1.490 0.964 56.0 18.70 458
13 15 0.860 -1.510 0.967 47.1 14.60 409
14 16 0.861 -1.510 0.968 39.9 11.50 367
15 17 0.865 -1.520 0.971 34.1 9.20 330
16 18 0.867 -1.520 0.974 29.3 7.42 299
17 19 0.872 -1.530 0.978 25.4 6.03 271
18 20 0.877 -1.530 0.981 22.1 4.94 247sft$plot
power <- sft$power
net <- exp2gcn(
final_exp,
net_type = "unsigned",
SFTpower = 6,
cor_method = "pearson"
)..connectivity..
..matrix multiplication (system BLAS)..
..normalization..
..done.names(net)[1] "adjacency_matrix" "MEs" "genes_and_modules"
[4] "kIN" "correlation_matrix" "params"
[7] "dendro_plot_objects"plot_dendro_and_colors(net)
# Eigengene networks
plot_eigengene_network(net)
plot_ngenes_per_module(net)
module_stability(final_exp, net, nRuns = 10) ...working on run 1 ..
...working on run 2 ..
...working on run 3 ..
...working on run 4 ..
...working on run 5 ..
...working on run 6 ..
...working on run 7 ..
...working on run 8 ..
...working on run 9 ..
...working on run 10 ..
...working on run 11 ..
Module-trait associations
# ───────────────────────────────────────────────────────────
# 6) Module–Trait associations
# ───────────────────────────────────────────────────────────
MEtrait <- module_trait_cor(
exp = final_exp,
MEs = net$MEs,
metadata_cols = trait_cols
)
head(MEtrait) ME trait cor pvalue group
1 MEaliceblue Crowded -0.97339612 1.003740e-07 RearingCondition
2 MEaliceblue Isolated 0.97339612 1.003740e-07 RearingCondition
3 MEantiquewhite Crowded -0.76772497 3.550596e-03 RearingCondition
4 MEantiquewhite Isolated 0.76772497 3.550596e-03 RearingCondition
5 MEantiquewhite1 Crowded -0.02770547 9.318883e-01 RearingCondition
6 MEantiquewhite1 Isolated 0.02770547 9.318883e-01 RearingConditionplot_module_trait_cor(MEtrait)
plot_expression_profile(
exp = final_exp,
metadata_cols = "RearingCondition",
net = net,
plot_module = TRUE,
modulename = "aliceblue"
)
### Hub gene identification
hubs <- get_hubs_gcn(final_exp, net)
head(hubs) Gene Module kWithin
1 LOC126355959 aliceblue 519.3610
2 LOC126299042 aliceblue 514.1214
3 LOC126334624 aliceblue 512.9888
4 LOC126283985 aliceblue 511.1058
5 LOC126297619 aliceblue 510.8234
6 LOC126272084 aliceblue 507.1924edges <- get_edge_list(net, module="aliceblue")
head(edges) Gene1 Gene2 Weight
2221 LOC126319460 LOC126320326 -0.6046428
4441 LOC126319460 LOC126320407 0.7492392
4442 LOC126320326 LOC126320407 -0.5617575
6661 LOC126319460 LOC126320662 0.5943654
6662 LOC126320326 LOC126320662 -0.4490141
6663 LOC126320407 LOC126320662 0.6385784# Remove edges based on optimal scale-free topology fit
edges_filtered <- get_edge_list(net, module = "aliceblue", filter = TRUE)
dim(edges_filtered)[1] 21574 3edges_filtered <- get_edge_list(
net, module = "aliceblue",
filter = TRUE, method = "pvalue",
nSamples = ncol(final_exp)
)
dim(edges_filtered)[1] 1177405 3edges_filtered <- get_edge_list(
net, module = "aliceblue",
filter = TRUE, method = "min_cor", rcutoff = 0.7
)
dim(edges_filtered)[1] 329122 3plot_gcn(
edgelist_gcn = edges_filtered,
net = net,
color_by = "module",
hubs = hubs
)
library(networkD3)
#plot_gcn(
# edgelist_gcn = edges_filtered,
# net = net,
# color_by = "module",
# hubs = hubs,
# interactive = TRUE,
# dim_interactive = c(500, 500)
#)Gene regulatory network inference
Consensus GRN inference
sessionInfo()R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.5
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Asia/Tokyo
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] networkD3_0.4.1 SummarizedExperiment_1.36.0
[3] Biobase_2.66.0 GenomicRanges_1.58.0
[5] GenomeInfoDb_1.42.3 IRanges_2.40.1
[7] S4Vectors_0.44.0 BiocGenerics_0.52.0
[9] MatrixGenerics_1.18.1 matrixStats_1.5.0
[11] BioNERO_1.14.0 scales_1.4.0
[13] visNetwork_2.1.3 cowplot_1.1.3
[15] ggridges_0.5.6 WGCNA_1.73
[17] fastcluster_1.3.0 dynamicTreeCut_1.63-1
[19] kableExtra_1.4.0 ggrepel_0.9.6
[21] ggpubr_0.6.0 RColorBrewer_1.1-3
[23] reshape2_1.4.4 ggthemes_5.1.0
[25] plotly_4.11.0 DT_0.33
[27] data.table_1.17.6 lubridate_1.9.4
[29] forcats_1.0.0 stringr_1.5.1
[31] dplyr_1.1.4 purrr_1.0.4
[33] readr_2.1.5 tidyr_1.3.1
[35] tibble_3.3.0 ggplot2_3.5.2
[37] tidyverse_2.0.0 rmdformats_1.0.4
[39] knitr_1.50
loaded via a namespace (and not attached):
[1] splines_4.4.2 later_1.4.2 GENIE3_1.28.0
[4] ggnetwork_0.5.13 preprocessCore_1.68.0 XML_3.99-0.18
[7] rpart_4.1.24 lifecycle_1.0.4 minet_3.64.0
[10] rstatix_0.7.2 edgeR_4.4.2 doParallel_1.0.17
[13] rprojroot_2.0.4 MASS_7.3-65 lattice_0.22-7
[16] crosstalk_1.2.1 backports_1.5.0 magrittr_2.0.3
[19] limma_3.62.2 Hmisc_5.2-3 sass_0.4.10
[22] rmarkdown_2.29 jquerylib_0.1.4 yaml_2.3.10
[25] httpuv_1.6.16 DBI_1.2.3 abind_1.4-8
[28] zlibbioc_1.52.0 nnet_7.3-20 sva_3.54.0
[31] git2r_0.36.2 circlize_0.4.16 GenomeInfoDbData_1.2.13
[34] data.tree_1.1.0 genefilter_1.88.0 annotate_1.84.0
[37] svglite_2.2.1 DelayedArray_0.32.0 codetools_0.2-20
[40] xml2_1.3.8 tidyselect_1.2.1 shape_1.4.6.1
[43] UCSC.utils_1.2.0 farver_2.1.2 base64enc_0.1-3
[46] jsonlite_2.0.0 GetoptLong_1.0.5 Formula_1.2-5
[49] survival_3.8-3 iterators_1.0.14 systemfonts_1.2.3
[52] foreach_1.5.2 tools_4.4.2 Rcpp_1.0.14
[55] glue_1.8.0 SparseArray_1.6.2 gridExtra_2.3
[58] mgcv_1.9-3 xfun_0.52 withr_3.0.2
[61] fastmap_1.2.0 digest_0.6.37 timechange_0.3.0
[64] R6_2.6.1 textshaping_1.0.1 colorspace_2.1-1
[67] Cairo_1.6-2 GO.db_3.20.0 dichromat_2.0-0.1
[70] RSQLite_2.4.1 RhpcBLASctl_0.23-42 generics_0.1.4
[73] httr_1.4.7 htmlwidgets_1.6.4 S4Arrays_1.6.0
[76] whisker_0.4.1 pkgconfig_2.0.3 gtable_0.3.6
[79] blob_1.2.4 workflowr_1.7.1 ComplexHeatmap_2.22.0
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[85] carData_3.0-5 bookdown_0.43 clue_0.3-66
[88] png_0.1-8 ggdendro_0.2.0 NetRep_1.2.7
[91] rstudioapi_0.17.1 tzdb_0.5.0 rjson_0.2.23
[94] nlme_3.1-168 coda_0.19-4.1 statnet.common_4.12.0
[97] checkmate_2.3.2 cachem_1.1.0 GlobalOptions_0.1.2
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[103] pillar_1.10.2 grid_4.4.2 vctrs_0.6.5
[106] promises_1.3.3 car_3.1-3 xtable_1.8-4
[109] cluster_2.1.8.1 htmlTable_2.4.3 evaluate_1.0.4
[112] magick_2.8.7 locfit_1.5-9.12 cli_3.6.5
[115] compiler_4.4.2 rlang_1.1.6 crayon_1.5.3
[118] ggsignif_0.6.4 intergraph_2.0-4 labeling_0.4.3
[121] plyr_1.8.9 fs_1.6.6 stringi_1.8.7
[124] viridisLite_0.4.2 network_1.19.0 BiocParallel_1.40.2
[127] Biostrings_2.74.1 lazyeval_0.2.2 Matrix_1.7-3
[130] patchwork_1.3.1 hms_1.1.3 bit64_4.6.0-1
[133] KEGGREST_1.46.0 statmod_1.5.0 igraph_2.1.4
[136] broom_1.0.8 memoise_2.0.1 bslib_0.9.0
[139] bit_4.6.0