Behavior and transcriptomics following RNAi
Devon Boland and Maeva Techer
2025-02-12
Last updated: 2025-02-12
Checks: 5 2
Knit directory:
locust-comparative-genomics/
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Following the overlap analysis of bulk tissue RNA-seq data from the whole head and thorax across all species, we selected a subset of differentially expressed genes between isolated and crowded individuals. The selection criteria were as follows:
- Genes must be shared by at least two or three locust species.
- Genes were ranked based on log fold change, prioritizing those with the highest absolute values (whether upregulated or downregulated in gregarious nymphs), and only genes with a significant corrected p-value were considered.
- Genes with functional descriptions suggesting a role in phenotypic plasticity in other arthropods were prioritized.
A total of X genes were included in this list and used for functional validation to assess their impact on collective behavior and the transcriptome landscape of gregarious nymphs in the Desert Locust S. gregaria. Following RNAi probes engineering, only genes with a knockdown efficacy exceeding X% in both males and females were kept for further analysis.
Hypothesis: Genes that are highly differentiated between phases are part of the downstream molecular machinery responding to density changes. If these genes do not directly drive rapid behavioral changes, they may instead contribute to the maintenance of phase-specific traits. Disrupting their function could interfere with gene-gene interactions essential for stabilizing either the solitarious or gregarious phase, triggering compensatory maintenance mechanims.
1. RNAi probe engineering
For Seema to add her part
2. Behavioral assays
For Seema and Audelia to add their parts
3. DEGs and enrichment of RNAi samples
The following results were obtained using the same RNA-seq workflow
as the non-RNAi bulk tissue transcriptomics. This includes RNA
extraction using Maxwell Promega simplyRNA tisse kit, RNA library
preparation with the Illumina Total Stranded RNA kit with RiboDepletion,
and short-read sequencing on an Illumina NovaSeq PE150 platform.
Differentially expressed genes between GFP-injected controls and
RNAi-injected last nymphal instar females of the gregarious phase were
analyzed using DESeq2.
We start by loading all the required R packages with in particular
DESeq2 for DEG analysis, biomaRt for pathway
annotations and clusterProfiler for GO enrichment and
visualization.
library("DESeq2")
library("ggplot2")
library("ggrepel")
library("ggConvexHull")
library("AnnotationHub")
library("ensembldb")
library("ComplexHeatmap")
library("RColorBrewer")
library("circlize")
library("EnhancedVolcano")
library("clusterProfiler")
library("sva")
library("cowplot")
library("ashr")
library("dplyr")
library("purrr")
library("httr2")
library("biomaRt")
library("rafalib")
workDir <- "/Users/maevatecher/Documents/GitHub/locust-comparative-genomics/data"
setwd(workDir)Head tissue
Minor changes here are made compared to the DESeq2 results regarding the importation of samples to transform into a matrix. Sample names are structured as follow: {Sg}{gene}{#} {Sg} = Schistocerca gregaria {gene} = gene abbreviation gfp, hex1, hex2, jhmt, miox and unch {#} = biological replicate
saveDir <- paste0(workDir,"/DEG_results/RNAi/Head")
dir.create(saveDir)
### Prepare Sample CSV file #####
samples <- read.delim(file.path(workDir, "list/RNAi/Head_RNAisample_list.csv"), sep = ",", row.names = 1, header = TRUE)
files <- file.path(workDir, "readcounts/RNAi/", samples$Tissue, samples$Filename)
names(files) <- row.names(samples)
all(file.exists(files))[1] TRUE### Create count sample matrix
cts <- map_dfc(files, function(sample) {
data_count <- read.delim(sample, sep = "\t", header = FALSE)
col_name <- gsub("_counts.txt", "", basename(sample))
setNames(data.frame(data_count[, 2]), col_name)
})
row_get <- read.delim(files[1], sep = "\t", row.names = 1, header = F) # Get proper row names
rownames(cts) <- rownames(row_get)
rm(row_get) # remove unused object from memoryWhile for bulk RNAseq on head and thorax for all species, the DEGs model was made between isolated and crowded individuals (with isolated as the reference state), here, the DEG analysis will be carried between GFP knock-down nymphs (as reference state) vs Hexamerins / Juvenile Hormones / Uncharacterized proteins
### Build DESeq2 Object
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = samples,
design = ~ Gene)
dds$Gene <- relevel(dds$Gene, ref = "GFP")
smallestGroupSize <- 5
keep <- rowSums(counts(dds) >= 10) >= smallestGroupSize
dds <- dds[keep,]
dds <- DESeq(dds)Following the generation of the DEseq2 object, we
annotate the genes with the GeneID using biomaRt.
### Fetch Annotation Gene IDs using biomaRt
ensembl <- useMart("metazoa_mart", host = "https://metazoa.ensembl.org")
metazoa_list <- listDatasets(ensembl)
dataset <- useMart("metazoa_mart", dataset = "sggca023897955v2rs_eg_gene",
host = "https://metazoa.ensembl.org")
#listAttributes(dataset)
test_raw_counts <- as.data.frame(counts(dds))
rownames(test_raw_counts) <- as.character(rownames(test_raw_counts))
test_raw_counts$ensembl_gene_id <- row.names(test_raw_counts)
annotations <- getBM(attributes = c("ensembl_gene_id", "geneid"),
filters = "ensembl_gene_id",
values = rownames(test_raw_counts),
mart = dataset)
# Merge dataframes to retain geneid information from biomaRt
test_raw_counts_annotated <- merge(test_raw_counts, annotations,
by = "ensembl_gene_id",
all.x = T)
write.csv(test_raw_counts_annotated, file=paste0(saveDir,"/Head_raw_counts.csv"))
#####Normalization and PCA
# Plot PCA and investigate quality metrics
vsd <- vst(dds, blind=T)
p1 <- plotPCA(vsd, intgroup=c("Gene")) +
geom_text_repel(aes(label = rownames(samples)), size = 4, max.overlaps = 20) +
geom_point(size=3) +
theme_bw() +
theme(legend.title = element_blank()) +
theme(legend.text = element_text(face="bold", size=16)) +
theme(axis.text = element_text(size=12)) +
theme(axis.title = element_text(size=12))
p1
ggsave(paste0(saveDir,"/PCA_labelled_plots.png"), width =10 , height = 10,
dpi = 600, device = "png", plot = p1)
p2 <- plotPCA(vsd, intgroup=c("Gene")) +
geom_point(size=3) +
theme_bw() +
theme(legend.title = element_blank()) +
theme(legend.text = element_text(face="bold", size=16)) +
theme(axis.text = element_text(size=12)) +
theme(axis.title = element_text(size=12)) +
geom_convexhull(aes(fill = Gene), alpha = 0.5) +
ggtitle("PCA on S. gregaria Head tissues", subtitle = "vst transformation")
p2
ggsave(paste0(saveDir,"/PCA_plot.png"), width =10 , height = 10,
dpi = 600, device = "png", plot = p2)The PCA plot shows clear distinction between tissue types, while gene silencing has a large variation within each tissue, and presents no distinct clear groupings for a single gene.
Surrogate Variable Analysis
### SVA analysis to control for technical variation
dat <- counts(dds, normalized = TRUE)
idx <- rowMeans(dat) > 1
dat <- dat[idx, ]
mod <- model.matrix(~ Gene, colData(dds))
mod0 <- model.matrix(~ 1, colData(dds))
svseq <- svaseq(dat, mod, mod0)Number of significant surrogate variables is: 3
Iteration (out of 5 ):1 2 3 4 5 # Set up layout: 3 plots in a 3x1 grid
par(mfrow = c(3, 1), mar = c(4,5,3,1))
# Create color mapping for each unique combination of Tissue + Gene
tissue_gene_groups <- interaction(dds$Tissue, dds$Gene, drop = TRUE) # Create grouping
unique_groups <- unique(tissue_gene_groups) # Get unique combinations
group_colors <- setNames(colorRampPalette(brewer.pal(min(length(unique_groups), 8), "Set1"))(length(unique_groups)), unique_groups)
# Loop through first 3 surrogate variables
for (i in 1:3) {
# Extract SV values
sv_values <- svseq$sv[, i]
# Assign colors per group
assigned_colors <- group_colors[as.factor(tissue_gene_groups)]
stripchart(sv_values ~ tissue_gene_groups,
vertical = TRUE,
pch = 15, # Use solid circles
cex = 1.5, # Increase point size
col = group_colors, # Correctly map colors per group
main = paste0("Surrogate Variable ", i, " (SV", i, ") - Technical Variation"),
ylab = "SV Value",
cex.axis = 1, cex.lab = 1.5, cex.main = 2)
abline(h = 0, lty = 2, col = "gray") # Dashed reference line
}
# Add a legend with correctly mapped colors
#legend("topright", legend = unique_groups, col = group_colors, pch = 16, cex = 1.2, bty = "n", title = "Tissue + Gene")
# Save the plot **AFTER** displaying it for knitr
dev.copy(png, filename = paste0(saveDir, "/sva_analysis.png"), width = 15000, height = 5000, res = 600)quartz_off_screen
3 dev.off()quartz_off_screen
2 bigpar() # Optimize plotting parameters
# Set up layout: 3 rows, 1 column
par(mfrow = c(2, 2), mar = c(5, 5, 3, 2)) # Adjust margins for readability
# Generate color mapping for each unique Gene
gene_labels <- as.character(dds$Gene)
unique_genes <- unique(gene_labels)
gene_colors <- setNames(colorRampPalette(brewer.pal(min(length(unique_genes), 8), "Set1"))(length(unique_genes)), unique_genes)
# === Plot 1: SV1 vs SV2 ===
plot(svseq$sv[,1], svseq$sv[,2],
col = gene_colors[gene_labels],
pch = 16, cex = 1.5,
xlab = "SV1", ylab = "SV2",
main = "SVA Analysis: SV1 vs SV2")
# === Plot 2: SV1 vs SV3 ===
plot(svseq$sv[,1], svseq$sv[,3],
col = gene_colors[gene_labels],
pch = 16, cex = 1.5,
xlab = "SV1", ylab = "SV3",
main = "SVA Analysis: SV1 vs SV3")
# === Plot 3: SV2 vs SV3 ===
plot(svseq$sv[,2], svseq$sv[,3],
col = gene_colors[gene_labels],
pch = 16, cex = 1.5,
xlab = "SV2", ylab = "SV3",
main = "SVA Analysis: SV2 vs SV3")
# === Fourth Plot (Empty) with Legend ===
plot.new()
legend("topright", legend = unique_genes, pch = 16, col = gene_colors, cex = 1, title = "Gene", ncol = 3)
dev.copy(png, filename = paste0(saveDir, "/sva_analysis_interact.png"), width = 15000, height = 5000, res = 600)quartz_off_screen
3 dev.off()quartz_off_screen
2 We rerun the DESeq2 model but this time including the
surrogate variable as a covariate, as we know that the modeled variation
is more likely explained by tissue and gene variation rather than batch
effects.
ddssva <- dds
ddssva$SV1 <- svseq$sv[,1]
ddssva$SV2 <- svseq$sv[,2]
ddssva$SV3 <- svseq$sv[,3]
design(ddssva) <- ~ SV1 + SV2 + SV3 + Gene
ddssva$Gene <- relevel(ddssva$Gene, ref = "GFP")
smallestGroupSize <- 5
keep <- rowSums(counts(ddssva) >= 10) >= smallestGroupSize
ddssva <- ddssva[keep,]
ddssva <- DESeq(ddssva)
ddssva <- ddssva[which(mcols(ddssva)$betaConv),] # remove non converging rows
### Extract results
resultsNames(ddssva)[1] "Intercept" "SV1" "SV2" "SV3"
[5] "Gene_HEX1_vs_GFP" "Gene_HEX2_vs_GFP" "Gene_JHMT_vs_GFP" "Gene_MIOX_vs_GFP"
[9] "Gene_UNCH_vs_GFP"hex1 <- results(ddssva, name = "Gene_HEX1_vs_GFP", alpha = 0.05)
hex2 <- results(ddssva, name = "Gene_HEX2_vs_GFP", alpha = 0.05)
jhmt <- results(ddssva, name = "Gene_JHMT_vs_GFP", alpha = 0.05)
miox <- results(ddssva, name = "Gene_MIOX_vs_GFP", alpha = 0.05)
unch <- results(ddssva, name = "Gene_UNCH_vs_GFP", alpha = 0.05)Volcano plots and Heatmaps
First we create function to generate the plots we are interested to obtain and then run the whole pipeline for each gene.
create_output_dirs <- function(label) {
dir.create(file.path(saveDir, label), showWarnings = FALSE)
return()
}
# Retrieve various accession IDs
get_sig_genes <- function(res) {
sig_genes <- res[which(res$padj < 0.05 & abs(res$log2FoldChange)>=1.0), ]
sig_genes <- sig_genes[order(sig_genes, decreasing = T), ]
return(sig_genes)
}
create_volcano <- function(res, label) {
mypalette <- brewer.pal(9, "Set1")
volcano <-EnhancedVolcano(res,
lab=rownames(res),
x='log2FoldChange',
y='padj',
title=paste("Volcano Plot:", label),
col=c(mypalette[9], mypalette[3], mypalette[2],
mypalette[1]),
labSize = 4,
pCutoff = 0.05,
FCcutoff = 1,
pointSize = 3,
drawConnectors = T,
widthConnectors = 0.5,
colConnectors = "black",
max.overlaps = 25,
gridlines.major = F,
gridlines.minor = F)
ggsave(paste0(saveDir, "/", label,"/volcano_plot_",label,".tiff"), device = "tiff",
plot = volcano, width = 10, height = 10)
return()
}
create_heatmap <- function(res, label, contrast_) {
mat <- counts(dds, normalized=T)
mat.z <- t(apply(mat, 1, scale))
colnames(mat.z) <- colnames(mat)
mat.z <- mat.z[rownames(res), contrast_, drop=F]
rownames(mat.z) <- rownames(res)
tiff(paste0(saveDir, "/", label,"/heatmap_plot_",label,".tiff"),
units="in", res= 300, width =5, height=10 )
draw(Heatmap(mat.z,
cluster_rows = T,
cluster_columns = F,
column_labels = contrast_,
name = "Z-Transformed Counts",
row_labels = rownames(mat.z),
row_names_gp = gpar(fontsize=8)))
dev.off()
return()
}
# Define contrast_sets
hex1_samples <- c("SggfpH1","SggfpH2","SggfpH3","SggfpH4","SggfpH5",
"Sghex1H1","Sghex1H2","Sghex1H3","Sghex1H4","Sghex1H5")
hex2_samples <- c("SggfpH1","SggfpH2","SggfpH3","SggfpH4","SggfpH5",
"Sghex2H1","Sghex2H2","Sghex2H3","Sghex2H4","Sghex2H5")
jhmt_samples <- c("SggfpH1","SggfpH2","SggfpH3","SggfpH4","SggfpH5",
"SgjhmtH1","SgjhmtH2","SgjhmtH3","SgjhmtH4","SgjhmtH5")
miox_samples <- c("SggfpH1","SggfpH2","SggfpH3","SggfpH4","SggfpH5",
"SgmioxH1","SgmioxH2","SgmioxH3","SgmioxH4","SgmioxH5")
unch_samples <- c("SggfpH1","SggfpH2","SggfpH3","SggfpH4","SggfpH5",
"SgunchH1","SgunchH2","SgunchH3","SgunchH4","SgunchH5")
visualize_data <- function(res, label, contrast_) {
sig_genes <- get_sig_genes(res)
create_output_dirs(label)
write.csv(as.data.frame(sig_genes), paste0(saveDir, "/",label, "/DGE_results_", label,
".csv"))
create_volcano(res, label)
create_heatmap(sig_genes, label, contrast_)
return()
}
# Run full analysis
visualize_data(hex1,
"Hex1_vs_GFP",
hex1_samples)NULLvisualize_data(hex2,
"Hex2_vs_GFP",
hex2_samples)NULLvisualize_data(jhmt,
"JHMT_vs_GFP",
jhmt_samples)NULLvisualize_data(miox,
"MIOX_vs_GFP",
miox_samples)NULLvisualize_data(unch,
"UNCH_vs_GFP",
unch_samples)NULLGO and KEGG enrichment
get_ids <- function(res) {
rownames(res) <- as.character(rownames(res))
res$ensembl_gene_id <- row.names(res)
annotations <- getBM(attributes = c("ensembl_gene_id", "geneid"),
filters = "ensembl_gene_id",
values = rownames(res),
mart = dataset)
return(annotations$geneid)
}
GOMFEnrichment <- function(res, label) {
# Check if there are valid gene IDs
if (!is.null(res)) {
# Perform GO enrichment analysis
ego <- enrichGO(
gene = rownames(res),
OrgDb = org.Sgregaria.eg.db,
keyType = "GID",
ont = "MF", # Cellular Component
pAdjustMethod = "BH", # Benjamini-Hochberg adjustment
pvalueCutoff = 0.1
)
# Check if the result has any significant enrichment terms
if (nrow(as.data.frame(ego)) > 0) {
# Create the barplot
go_barplot <- barplot(ego, showCategory = 20) + # Show top 20 categories
ggtitle(paste("GO MF Enrichment:", label))
# Print the plot
ggsave(paste0(save_dir, "/", label,"/gp_MF_barplot_",label,".tiff"), device = "tiff",
plot=go_barplot, width=10, height = 10)
change_vec <- res$log2FoldChange
names(change_vec) <- rownames(res)
RYD = brewer.pal(n = 8, name = "RdBu")
go_network <- cnetplot(ego, foldChange=change_vec) +
scale_color_gradientn(colours = RYD, limits=c(-2,2))
ggsave(paste0(save_dir, "/", label,"/gp_MF_cnetplot_",label,".tiff"), device = "tiff",
plot=go_network, width=30, height = 30, bg = "white")
write.csv(as.data.frame(ego), paste0(save_dir, "/", label,
"/GO_MF_Enrichment_Results_", label,".csv"))
} else {
message("No significant MF GO terms found.")
}
} else {
message("No valid gene IDs found.")
}
return()
}
GOCCEnrichment <- function(res, label) {
# Check if there are valid gene IDs
if (!is.null(res)) {
# Perform GO enrichment analysis
ego <- enrichGO(
gene = rownames(res),
OrgDb = org.Sgregaria.eg.db,
keyType = "GID",
ont = "CC", # Cellular Component
pAdjustMethod = "BH", # Benjamini-Hochberg adjustment
pvalueCutoff = 0.1
)
# Check if the result has any significant enrichment terms
if (nrow(as.data.frame(ego)) > 0) {
# Create the barplot
go_barplot <- barplot(ego, showCategory = 20) + # Show top 20 categories
ggtitle(paste("GO CC Enrichment:", label))
# Print the plot
ggsave(paste0(save_dir, "/", label,"/gp_CC_barplot_",label,".tiff"), device = "tiff",
plot=go_barplot, width=10, height = 10)
change_vec <- res$log2FoldChange
names(change_vec) <- rownames(res)
RYD = brewer.pal(n = 8, name = "RdBu")
go_network <- cnetplot(ego, foldChange=change_vec) +
scale_color_gradientn(colours = RYD, limits=c(-2,2))
ggsave(paste0(save_dir, "/", label,"/gp_CC_cnetplot_",label,".tiff"), device = "tiff",
plot=go_network, width=30, height = 30, bg = "white")
write.csv(as.data.frame(ego), paste0(save_dir, "/", label,
"/GO_CC_Enrichment_Results_", label,".csv"))
} else {
message("No significant CC GO terms found.")
}
} else {
message("No valid gene IDs found.")
}
return()
}
GOBPEnrichment <- function(res, label) {
# Check if there are valid gene IDs
if (!is.null(res)) {
# Perform GO enrichment analysis
ego <- enrichGO(
gene = rownames(res),
OrgDb = org.Sgregaria.eg.db,
keyType = "GID",
ont = "BP", # Cellular Component
pAdjustMethod = "BH", # Benjamini-Hochberg adjustment
pvalueCutoff = 0.1
)
# Check if the result has any significant enrichment terms
if (nrow(as.data.frame(ego)) > 0) {
# Create the barplot
go_barplot <- barplot(ego, showCategory = 20) + # Show top 20 categories
ggtitle(paste("GO BP Enrichment:", label))
# Print the plot
ggsave(paste0(save_dir, "/", label,"/gp_BP_barplot_",label,".tiff"), device = "tiff",
plot=go_barplot, width=10, height = 10)
change_vec <- res$log2FoldChange
names(change_vec) <- rownames(res)
RYD = brewer.pal(n = 8, name = "RdBu")
go_network <- cnetplot(ego, foldChange=change_vec) +
scale_color_gradientn(colours = RYD, limits=c(-2,2))
ggsave(paste0(save_dir, "/", label,"/gp_BP_cnetplot_",label,".tiff"), device = "tiff",
plot=go_network, width=30, height = 30, bg="white")
write.csv(as.data.frame(ego), paste0(save_dir, "/", label,
"/GO_BP_Enrichment_Results_", label,".csv"))
} else {
message("No significant BP GO terms found.")
}
} else {
message("No valid gene IDs found.")
}
return()
}
KEGGEnrichment <- function(res, label) {
# Check if there are valid gene IDs
if (!is.null(res)) {
kk <- enrichKEGG(gene = rownames(res),
organism = "sgre",
pvalueCutoff = 0.1)
# Check if the result has any significant enrichment terms
if (nrow(as.data.frame(kk)) > 0) {
kk_barplot <- barplot(kk) + ggtitle(paste("KEGG Enrichment:", label))
ggsave(paste0(save_dir, "/", label,"/kk_barplot_",label,".tiff"), device = "tiff",
plot=kk_barplot, width=10, height = 10)
change_vec <- res$log2FoldChange
names(change_vec) <- rownames(res)
RYD = brewer.pal(n = 8, name = "RdBu")
kk_network <- cnetplot(kk, foldChange=change_vec) +
scale_color_gradientn(colours = RYD, limits=c(-2,2))
ggsave(paste0(save_dir, "/", label,"/kk_cnetplot_",label,".tiff"), device = "tiff",
plot=kk_network, width=30, height = 30, bg = "white")
write.csv(as.data.frame(kk), paste0(save_dir, "/",label,
"/KEGG_Enrichment_Results_",
label,".csv"))
} else {
message("No significant KEGG terms found.")
}
} else {
message("No valid gene IDs found.")
}
return()
}
enrich_data <- function(res, label, contrast_) {
sig_genes <- get_sig_genes(res)
create_output_dirs(label)
GOMFEnrichment(sig_genes, label)
GOBPEnrichment(sig_genes, label)
GOCCEnrichment(sig_genes, label)
KEGGEnrichment(sig_genes, label)
return()
}
#enrich_data(hex1, "Hex1_vs_GFP", hex1_samples)
#enrich_data(hex2,"Hex2_vs_GFP", hex2_samples)
#enrich_data(jhmt,"JHMT_vs_GFP",jhmt_samples)
#enrich_data(miox,"MIOX_vs_GFP",miox_samples)
#enrich_data(unch,"UNCH_vs_GFP",unch_samples)Thorax tissue
saveDir <- paste0(workDir,"/DEG_results/RNAi/Thorax")
dir.create(saveDir)
### Prepare Sample CSV file #####
samples <- read.delim(file.path(workDir, "list/RNAi/Thorax_RNAisample_list.csv"), sep = ",", row.names = 1, header = TRUE)
files <- file.path(workDir, "readcounts/RNAi/", samples$Tissue, samples$Filename)
names(files) <- row.names(samples)
all(file.exists(files))[1] TRUE### Create count sample matrix
cts <- map_dfc(files, function(sample) {
data_count <- read.delim(sample, sep = "\t", header = FALSE)
col_name <- gsub("_counts.txt", "", basename(sample))
setNames(data.frame(data_count[, 2]), col_name)
})
row_get <- read.delim(files[1], sep = "\t", row.names = 1, header = F) # Get proper row names
rownames(cts) <- rownames(row_get)
rm(row_get) # remove unused object from memoryWhile for bulk RNAseq on head and thorax for all species, the DEGs model was made between isolated and crowded individuals (with isolated as the reference state), here, the DEG analysis will be carried between GFP knock-down nymphs (as reference state) vs Hexamerins / Juvenile Hormones / Uncharacterized proteins
### Build DESeq2 Object
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = samples,
design = ~ Gene)
dds$Gene <- relevel(dds$Gene, ref = "GFP")
smallestGroupSize <- 5
keep <- rowSums(counts(dds) >= 10) >= smallestGroupSize
dds <- dds[keep,]
dds <- DESeq(dds)Following the generation of the DEseq2 object, we
annotate the genes with the GeneID using biomaRt.
### Fetch Annotation Gene IDs using biomaRt
ensembl <- useMart("metazoa_mart", host = "https://metazoa.ensembl.org")
metazoa_list <- listDatasets(ensembl)
dataset <- useMart("metazoa_mart", dataset = "sggca023897955v2rs_eg_gene",
host = "https://metazoa.ensembl.org")
#listAttributes(dataset)
test_raw_counts <- as.data.frame(counts(dds))
rownames(test_raw_counts) <- as.character(rownames(test_raw_counts))
test_raw_counts$ensembl_gene_id <- row.names(test_raw_counts)
annotations <- getBM(attributes = c("ensembl_gene_id", "geneid"),
filters = "ensembl_gene_id",
values = rownames(test_raw_counts),
mart = dataset)
# Merge dataframes to retain geneid information from biomaRt
test_raw_counts_annotated <- merge(test_raw_counts, annotations,
by = "ensembl_gene_id",
all.x = T)
write.csv(test_raw_counts_annotated, file=paste0(saveDir,"/Thorax_raw_counts.csv"))
#####Normalization and PCA
# Plot PCA and investigate quality metrics
vsd <- vst(dds, blind=T)
p1 <- plotPCA(vsd, intgroup=c("Gene")) +
geom_text_repel(aes(label = rownames(samples)), size = 4, max.overlaps = 20) +
geom_point(size=3) +
theme_bw() +
theme(legend.title = element_blank()) +
theme(legend.text = element_text(face="bold", size=16)) +
theme(axis.text = element_text(size=12)) +
theme(axis.title = element_text(size=12))
p1
ggsave(paste0(saveDir,"/PCA_labelled_plots.png"), width =10 , height = 10,
dpi = 600, device = "png", plot = p1)
p2 <- plotPCA(vsd, intgroup=c("Gene")) +
geom_point(size=3) +
theme_bw() +
theme(legend.title = element_blank()) +
theme(legend.text = element_text(face="bold", size=16)) +
theme(axis.text = element_text(size=12)) +
theme(axis.title = element_text(size=12)) +
geom_convexhull(aes(fill = Gene), alpha = 0.5) +
ggtitle("PCA on S. gregaria Thorax tissues", subtitle = "vst transformation")
p2
ggsave(paste0(saveDir,"/PCA_plot.png"), width =10 , height = 10,
dpi = 600, device = "png", plot = p2)The PCA plot shows clear distinction between tissue types, while gene silencing has a large variation within each tissue, and presents no distinct clear groupings for a single gene.
Surrogate Variable Analysis
### SVA analysis to control for technical variation
dat <- counts(dds, normalized = TRUE)
idx <- rowMeans(dat) > 1
dat <- dat[idx, ]
mod <- model.matrix(~ Gene, colData(dds))
mod0 <- model.matrix(~ 1, colData(dds))
svseq <- svaseq(dat, mod, mod0)Number of significant surrogate variables is: 4
Iteration (out of 5 ):1 2 3 4 5 # Set up layout: 3 plots in a 3x1 grid
par(mfrow = c(3, 1), mar = c(4,5,3,1))
# Create color mapping for each unique combination of Tissue + Gene
tissue_gene_groups <- interaction(dds$Tissue, dds$Gene, drop = TRUE) # Create grouping
unique_groups <- unique(tissue_gene_groups) # Get unique combinations
group_colors <- setNames(colorRampPalette(brewer.pal(min(length(unique_groups), 8), "Set1"))(length(unique_groups)), unique_groups)
# Loop through first 3 surrogate variables
for (i in 1:3) {
# Extract SV values
sv_values <- svseq$sv[, i]
# Assign colors per group
assigned_colors <- group_colors[as.factor(tissue_gene_groups)]
stripchart(sv_values ~ tissue_gene_groups,
vertical = TRUE,
pch = 15, # Use solid circles
cex = 1.5, # Increase point size
col = group_colors, # Correctly map colors per group
main = paste0("Surrogate Variable ", i, " (SV", i, ") - Technical Variation"),
ylab = "SV Value",
cex.axis = 1, cex.lab = 1.5, cex.main = 2)
abline(h = 0, lty = 2, col = "gray") # Dashed reference line
}
# Add a legend with correctly mapped colors
#legend("topright", legend = unique_groups, col = group_colors, pch = 16, cex = 1.2, bty = "n", title = "Tissue + Gene")
# Save the plot **AFTER** displaying it for knitr
dev.copy(png, filename = paste0(saveDir, "/sva_analysis.png"), width = 15000, height = 5000, res = 600)quartz_off_screen
3 dev.off()quartz_off_screen
2 bigpar() # Optimize plotting parameters
# Set up layout: 3 rows, 1 column
par(mfrow = c(2, 2), mar = c(5, 5, 3, 2)) # Adjust margins for readability
# Generate color mapping for each unique Gene
gene_labels <- as.character(dds$Gene)
unique_genes <- unique(gene_labels)
gene_colors <- setNames(colorRampPalette(brewer.pal(min(length(unique_genes), 8), "Set1"))(length(unique_genes)), unique_genes)
# === Plot 1: SV1 vs SV2 ===
plot(svseq$sv[,1], svseq$sv[,2],
col = gene_colors[gene_labels],
pch = 16, cex = 1.5,
xlab = "SV1", ylab = "SV2",
main = "SVA Analysis: SV1 vs SV2")
# === Plot 2: SV1 vs SV3 ===
plot(svseq$sv[,1], svseq$sv[,3],
col = gene_colors[gene_labels],
pch = 16, cex = 1.5,
xlab = "SV1", ylab = "SV3",
main = "SVA Analysis: SV1 vs SV3")
# === Plot 3: SV2 vs SV3 ===
plot(svseq$sv[,2], svseq$sv[,3],
col = gene_colors[gene_labels],
pch = 16, cex = 1.5,
xlab = "SV2", ylab = "SV3",
main = "SVA Analysis: SV2 vs SV3")
# === Fourth Plot (Empty) with Legend ===
plot.new()
legend("topright", legend = unique_genes, pch = 16, col = gene_colors, cex = 1, title = "Gene", ncol = 3)
dev.copy(png, filename = paste0(saveDir, "/sva_analysis_interact.png"), width = 15000, height = 5000, res = 600)quartz_off_screen
3 dev.off()quartz_off_screen
2 We rerun the DESeq2 model but this time including the
surrogate variable as a covariate, as we know that the modeled variation
is more likely explained by tissue and gene variation rather than batch
effects.
ddssva <- dds
ddssva$SV1 <- svseq$sv[,1]
ddssva$SV2 <- svseq$sv[,2]
ddssva$SV3 <- svseq$sv[,3]
design(ddssva) <- ~ SV1 + SV2 + SV3 + Gene
ddssva$Gene <- relevel(ddssva$Gene, ref = "GFP")
smallestGroupSize <- 5
keep <- rowSums(counts(ddssva) >= 10) >= smallestGroupSize
ddssva <- ddssva[keep,]
ddssva <- DESeq(ddssva)
ddssva <- ddssva[which(mcols(ddssva)$betaConv),] # remove non converging rows
### Extract results
resultsNames(ddssva)[1] "Intercept" "SV1" "SV2" "SV3"
[5] "Gene_HEX1_vs_GFP" "Gene_HEX2_vs_GFP" "Gene_JHMT_vs_GFP" "Gene_MIOX_vs_GFP"
[9] "Gene_UNCH_vs_GFP"hex1 <- results(ddssva, name = "Gene_HEX1_vs_GFP", alpha = 0.05)
hex2 <- results(ddssva, name = "Gene_HEX2_vs_GFP", alpha = 0.05)
jhmt <- results(ddssva, name = "Gene_JHMT_vs_GFP", alpha = 0.05)
miox <- results(ddssva, name = "Gene_MIOX_vs_GFP", alpha = 0.05)
unch <- results(ddssva, name = "Gene_UNCH_vs_GFP", alpha = 0.05)Volcano plots and Heatmaps
First we create function to generate the plots we are interested to obtain and then run the whole pipeline for each gene.
create_output_dirs <- function(label) {
dir.create(file.path(saveDir, label), showWarnings = FALSE)
return()
}
# Retrieve various accession IDs
get_sig_genes <- function(res) {
sig_genes <- res[which(res$padj < 0.05 & abs(res$log2FoldChange)>=1.0), ]
sig_genes <- sig_genes[order(sig_genes, decreasing = T), ]
return(sig_genes)
}
create_volcano <- function(res, label) {
mypalette <- brewer.pal(9, "Set1")
volcano <-EnhancedVolcano(res,
lab=rownames(res),
x='log2FoldChange',
y='padj',
title=paste("Volcano Plot:", label),
col=c(mypalette[9], mypalette[3], mypalette[2],
mypalette[1]),
labSize = 4,
pCutoff = 0.05,
FCcutoff = 1,
pointSize = 3,
drawConnectors = T,
widthConnectors = 0.5,
colConnectors = "black",
max.overlaps = 25,
gridlines.major = F,
gridlines.minor = F)
ggsave(paste0(saveDir, "/", label,"/volcano_plot_",label,".tiff"), device = "tiff",
plot = volcano, width = 10, height = 10)
return()
}
create_heatmap <- function(res, label, contrast_) {
mat <- counts(dds, normalized=T)
mat.z <- t(apply(mat, 1, scale))
colnames(mat.z) <- colnames(mat)
mat.z <- mat.z[rownames(res), contrast_, drop=F]
rownames(mat.z) <- rownames(res)
tiff(paste0(saveDir, "/", label,"/heatmap_plot_",label,".tiff"),
units="in", res= 300, width =5, height=10 )
draw(Heatmap(mat.z,
cluster_rows = T,
cluster_columns = F,
column_labels = contrast_,
name = "Z-Transformed Counts",
row_labels = rownames(mat.z),
row_names_gp = gpar(fontsize=8)))
dev.off()
return()
}
# Define contrast_sets
hex1_samples <- c("SggfpT1","SggfpT2","SggfpT3","SggfpT4","SggfpT5",
"Sghex1T1","Sghex1T2","Sghex1T3","Sghex1T4","Sghex1T5")
hex2_samples <- c("SggfpT1","SggfpT2","SggfpT3","SggfpT4","SggfpT5",
"Sghex2T1","Sghex2T2","Sghex2T3","Sghex2T4","Sghex2T5")
jhmt_samples <- c("SggfpT1","SggfpT2","SggfpT3","SggfpT4","SggfpT5",
"SgjhmtT1","SgjhmtT2","SgjhmtT3","SgjhmtT4","SgjhmtT5")
miox_samples <- c("SggfpT1","SggfpT2","SggfpT3","SggfpT4","SggfpT5",
"SgmioxT1","SgmioxT2","SgmioxT3","SgmioxT4","SgmioxT5")
unch_samples <- c("SggfpT1","SggfpT2","SggfpT3","SggfpT4","SggfpT5",
"SgunchT1","SgunchT2","SgunchT3","SgunchT4","SgunchT5")
visualize_data <- function(res, label, contrast_) {
sig_genes <- get_sig_genes(res)
create_output_dirs(label)
write.csv(as.data.frame(sig_genes), paste0(saveDir, "/",label, "/DGE_results_", label,".csv"))
create_volcano(res, label)
create_heatmap(sig_genes, label, contrast_)
return()
}
# Run full analysis
visualize_data(hex1,
"Hex1_vs_GFP",
hex1_samples)NULLvisualize_data(hex2,
"Hex2_vs_GFP",
hex2_samples)NULLvisualize_data(jhmt,
"JHMT_vs_GFP",
jhmt_samples)NULLvisualize_data(miox,
"MIOX_vs_GFP",
miox_samples)NULLvisualize_data(unch,
"UNCH_vs_GFP",
unch_samples)NULLGO and KEGG enrichment
get_ids <- function(res) {
rownames(res) <- as.character(rownames(res))
res$ensembl_gene_id <- row.names(res)
annotations <- getBM(attributes = c("ensembl_gene_id", "geneid"),
filters = "ensembl_gene_id",
values = rownames(res),
mart = dataset)
return(annotations$geneid)
}
GOMFEnrichment <- function(res, label) {
# Check if there are valid gene IDs
if (!is.null(res)) {
# Perform GO enrichment analysis
ego <- enrichGO(
gene = rownames(res),
OrgDb = org.Sgregaria.eg.db,
keyType = "GID",
ont = "MF", # Cellular Component
pAdjustMethod = "BH", # Benjamini-Hochberg adjustment
pvalueCutoff = 0.1
)
# Check if the result has any significant enrichment terms
if (nrow(as.data.frame(ego)) > 0) {
# Create the barplot
go_barplot <- barplot(ego, showCategory = 20) + # Show top 20 categories
ggtitle(paste("GO MF Enrichment:", label))
# Print the plot
ggsave(paste0(save_dir, "/", label,"/gp_MF_barplot_",label,".tiff"), device = "tiff",
plot=go_barplot, width=10, height = 10)
change_vec <- res$log2FoldChange
names(change_vec) <- rownames(res)
RYD = brewer.pal(n = 8, name = "RdBu")
go_network <- cnetplot(ego, foldChange=change_vec) +
scale_color_gradientn(colours = RYD, limits=c(-2,2))
ggsave(paste0(save_dir, "/", label,"/gp_MF_cnetplot_",label,".tiff"), device = "tiff",
plot=go_network, width=30, height = 30, bg = "white")
write.csv(as.data.frame(ego), paste0(save_dir, "/", label,
"/GO_MF_Enrichment_Results_", label,".csv"))
} else {
message("No significant MF GO terms found.")
}
} else {
message("No valid gene IDs found.")
}
return()
}
GOCCEnrichment <- function(res, label) {
# Check if there are valid gene IDs
if (!is.null(res)) {
# Perform GO enrichment analysis
ego <- enrichGO(
gene = rownames(res),
OrgDb = org.Sgregaria.eg.db,
keyType = "GID",
ont = "CC", # Cellular Component
pAdjustMethod = "BH", # Benjamini-Hochberg adjustment
pvalueCutoff = 0.1
)
# Check if the result has any significant enrichment terms
if (nrow(as.data.frame(ego)) > 0) {
# Create the barplot
go_barplot <- barplot(ego, showCategory = 20) + # Show top 20 categories
ggtitle(paste("GO CC Enrichment:", label))
# Print the plot
ggsave(paste0(save_dir, "/", label,"/gp_CC_barplot_",label,".tiff"), device = "tiff",
plot=go_barplot, width=10, height = 10)
change_vec <- res$log2FoldChange
names(change_vec) <- rownames(res)
RYD = brewer.pal(n = 8, name = "RdBu")
go_network <- cnetplot(ego, foldChange=change_vec) +
scale_color_gradientn(colours = RYD, limits=c(-2,2))
ggsave(paste0(save_dir, "/", label,"/gp_CC_cnetplot_",label,".tiff"), device = "tiff",
plot=go_network, width=30, height = 30, bg = "white")
write.csv(as.data.frame(ego), paste0(save_dir, "/", label,
"/GO_CC_Enrichment_Results_", label,".csv"))
} else {
message("No significant CC GO terms found.")
}
} else {
message("No valid gene IDs found.")
}
return()
}
GOBPEnrichment <- function(res, label) {
# Check if there are valid gene IDs
if (!is.null(res)) {
# Perform GO enrichment analysis
ego <- enrichGO(
gene = rownames(res),
OrgDb = org.Sgregaria.eg.db,
keyType = "GID",
ont = "BP", # Cellular Component
pAdjustMethod = "BH", # Benjamini-Hochberg adjustment
pvalueCutoff = 0.1
)
# Check if the result has any significant enrichment terms
if (nrow(as.data.frame(ego)) > 0) {
# Create the barplot
go_barplot <- barplot(ego, showCategory = 20) + # Show top 20 categories
ggtitle(paste("GO BP Enrichment:", label))
# Print the plot
ggsave(paste0(save_dir, "/", label,"/gp_BP_barplot_",label,".tiff"), device = "tiff",
plot=go_barplot, width=10, height = 10)
change_vec <- res$log2FoldChange
names(change_vec) <- rownames(res)
RYD = brewer.pal(n = 8, name = "RdBu")
go_network <- cnetplot(ego, foldChange=change_vec) +
scale_color_gradientn(colours = RYD, limits=c(-2,2))
ggsave(paste0(save_dir, "/", label,"/gp_BP_cnetplot_",label,".tiff"), device = "tiff",
plot=go_network, width=30, height = 30, bg="white")
write.csv(as.data.frame(ego), paste0(save_dir, "/", label,
"/GO_BP_Enrichment_Results_", label,".csv"))
} else {
message("No significant BP GO terms found.")
}
} else {
message("No valid gene IDs found.")
}
return()
}
KEGGEnrichment <- function(res, label) {
# Check if there are valid gene IDs
if (!is.null(res)) {
kk <- enrichKEGG(gene = rownames(res),
organism = "sgre",
pvalueCutoff = 0.1)
# Check if the result has any significant enrichment terms
if (nrow(as.data.frame(kk)) > 0) {
kk_barplot <- barplot(kk) + ggtitle(paste("KEGG Enrichment:", label))
ggsave(paste0(save_dir, "/", label,"/kk_barplot_",label,".tiff"), device = "tiff",
plot=kk_barplot, width=10, height = 10)
change_vec <- res$log2FoldChange
names(change_vec) <- rownames(res)
RYD = brewer.pal(n = 8, name = "RdBu")
kk_network <- cnetplot(kk, foldChange=change_vec) +
scale_color_gradientn(colours = RYD, limits=c(-2,2))
ggsave(paste0(save_dir, "/", label,"/kk_cnetplot_",label,".tiff"), device = "tiff",
plot=kk_network, width=30, height = 30, bg = "white")
write.csv(as.data.frame(kk), paste0(save_dir, "/",label,
"/KEGG_Enrichment_Results_",
label,".csv"))
} else {
message("No significant KEGG terms found.")
}
} else {
message("No valid gene IDs found.")
}
return()
}
enrich_data <- function(res, label, contrast_) {
sig_genes <- get_sig_genes(res)
create_output_dirs(label)
GOMFEnrichment(sig_genes, label)
GOBPEnrichment(sig_genes, label)
GOCCEnrichment(sig_genes, label)
KEGGEnrichment(sig_genes, label)
return()
}
#enrich_data(hex1, "Hex1_vs_GFP", hex1_samples)
#enrich_data(hex2,"Hex2_vs_GFP", hex2_samples)
#enrich_data(jhmt,"JHMT_vs_GFP",jhmt_samples)
#enrich_data(miox,"MIOX_vs_GFP",miox_samples)
#enrich_data(unch,"UNCH_vs_GFP",unch_samples)
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS 15.3
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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] rafalib_1.0.0 biomaRt_2.60.1
[3] httr2_1.1.0 purrr_1.0.4
[5] dplyr_1.1.4 ashr_2.2-63
[7] cowplot_1.1.3 sva_3.52.0
[9] BiocParallel_1.38.0 genefilter_1.86.0
[11] mgcv_1.9-1 nlme_3.1-167
[13] clusterProfiler_4.12.6 EnhancedVolcano_1.22.0
[15] circlize_0.4.16 RColorBrewer_1.1-3
[17] ComplexHeatmap_2.20.0 ensembldb_2.28.1
[19] AnnotationFilter_1.28.0 GenomicFeatures_1.56.0
[21] AnnotationDbi_1.66.0 AnnotationHub_3.12.0
[23] BiocFileCache_2.12.0 dbplyr_2.5.0
[25] ggConvexHull_0.1.0 ggrepel_0.9.6
[27] ggplot2_3.5.1 DESeq2_1.44.0
[29] SummarizedExperiment_1.34.0 Biobase_2.64.0
[31] MatrixGenerics_1.16.0 matrixStats_1.5.0
[33] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[35] IRanges_2.38.1 S4Vectors_0.42.1
[37] BiocGenerics_0.50.0
loaded via a namespace (and not attached):
[1] splines_4.4.1 later_1.4.1 BiocIO_1.14.0
[4] bitops_1.0-9 ggplotify_0.1.2 filelock_1.0.3
[7] tibble_3.2.1 R.oo_1.27.0 polyclip_1.10-7
[10] XML_3.99-0.18 lifecycle_1.0.4 mixsqp_0.3-54
[13] edgeR_4.2.2 doParallel_1.0.17 rprojroot_2.0.4
[16] lattice_0.22-6 MASS_7.3-64 magrittr_2.0.3
[19] limma_3.60.6 sass_0.4.9 rmarkdown_2.29
[22] jquerylib_0.1.4 yaml_2.3.10 httpuv_1.6.15
[25] DBI_1.2.3 abind_1.4-8 zlibbioc_1.50.0
[28] R.utils_2.12.3 ggraph_2.2.1 RCurl_1.98-1.16
[31] yulab.utils_0.2.0 tweenr_2.0.3 rappdirs_0.3.3
[34] git2r_0.35.0 GenomeInfoDbData_1.2.12 enrichplot_1.24.4
[37] irlba_2.3.5.1 tidytree_0.4.6 annotate_1.82.0
[40] codetools_0.2-20 DelayedArray_0.30.1 xml2_1.3.6
[43] DOSE_3.30.5 ggforce_0.4.2 tidyselect_1.2.1
[46] shape_1.4.6.1 aplot_0.2.4 UCSC.utils_1.0.0
[49] farver_2.1.2 viridis_0.6.5 GenomicAlignments_1.40.0
[52] jsonlite_1.8.9 GetoptLong_1.0.5 tidygraph_1.3.1
[55] survival_3.8-3 iterators_1.0.14 systemfonts_1.2.1
[58] foreach_1.5.2 progress_1.2.3 tools_4.4.1
[61] ragg_1.3.3 treeio_1.28.0 Rcpp_1.0.14
[64] glue_1.8.0 gridExtra_2.3 SparseArray_1.4.8
[67] xfun_0.50 qvalue_2.36.0 withr_3.0.2
[70] BiocManager_1.30.25 fastmap_1.2.0 truncnorm_1.0-9
[73] digest_0.6.37 R6_2.5.1 gridGraphics_0.5-1
[76] textshaping_1.0.0 colorspace_2.1-1 GO.db_3.19.1
[79] RSQLite_2.3.9 R.methodsS3_1.8.2 tidyr_1.3.1
[82] generics_0.1.3 data.table_1.16.4 rtracklayer_1.64.0
[85] prettyunits_1.2.0 graphlayouts_1.2.2 httr_1.4.7
[88] S4Arrays_1.4.1 scatterpie_0.2.4 pkgconfig_2.0.3
[91] gtable_0.3.6 blob_1.2.4 workflowr_1.7.1
[94] XVector_0.44.0 shadowtext_0.1.4 htmltools_0.5.8.1
[97] fgsea_1.30.0 ProtGenerics_1.36.0 clue_0.3-66
[100] scales_1.3.0 png_0.1-8 ggfun_0.1.8
[103] knitr_1.49 rstudioapi_0.17.1 reshape2_1.4.4
[106] rjson_0.2.23 curl_6.2.0 cachem_1.1.0
[109] GlobalOptions_0.1.2 stringr_1.5.1 BiocVersion_3.19.1
[112] parallel_4.4.1 restfulr_0.0.15 pillar_1.10.1
[115] vctrs_0.6.5 promises_1.3.2 xtable_1.8-4
[118] cluster_2.1.8 evaluate_1.0.3 invgamma_1.1
[121] cli_3.6.3 locfit_1.5-9.11 compiler_4.4.1
[124] Rsamtools_2.20.0 rlang_1.1.5 crayon_1.5.3
[127] SQUAREM_2021.1 labeling_0.4.3 plyr_1.8.9
[130] fs_1.6.5 stringi_1.8.4 viridisLite_0.4.2
[133] munsell_0.5.1 Biostrings_2.72.1 lazyeval_0.2.2
[136] GOSemSim_2.30.2 Matrix_1.7-2 hms_1.1.3
[139] patchwork_1.3.0 bit64_4.6.0-1 statmod_1.5.0
[142] KEGGREST_1.44.1 igraph_2.1.4 memoise_2.0.1
[145] bslib_0.9.0 ggtree_3.12.0 fastmatch_1.1-6
[148] bit_4.5.0.1 gson_0.1.0 ape_5.8-1