Figure 2 - DE
Estef Vazquez
2025-05-05
Last updated: 2025-05-05
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Knit directory: Ulceration_paper_github/
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Differential expression analysis
This document incorporates differential expression analysis and PCA, in relation to ulceration status in acral melanoma samples. It generates PCA and volcano plots.
# Load required libraries
library(tidyverse)
library(DESeq2)
library(factoextra)
library(RColorBrewer)
library(ggrepel)
library(plotly)
library(here)# Data Preparation - subset metadata
exp_design <- metadata %>% dplyr::select(sample_id, batch_number, sex, age, ulceration)
# Load raw count data
cts <- readRDS("data/rawcounts_am.rds")
# Scale age
exp_design$age_scaled <- scale(exp_design$age)
# Evaluate ulceration effect while accounting for batch, sex, and age
dds <- DESeqDataSetFromMatrix(
countData = cts,
colData = exp_design,
design = ~ batch_number + age_scaled + sex + ulceration,
tidy = FALSE
)
# Estimate size factors
dds <- estimateSizeFactors(dds)
# Pre-filtering
keep <- rowSums( counts(dds, normalized = TRUE) >= 10 ) >=20
dds <- dds[keep,]
# Set reference level for ulceration
dds$ulceration <- relevel(dds$ulceration, ref = "0")
# Extract normalized counts
normalized_counts_deseq <- counts(dds, normalized=TRUE)
# Run DE
dds_ulc <- DESeq(dds)
dim(dds_ulc)[1] 19319 59# Apply apeglm shrinkage
resLFC <- lfcShrink(dds_ulc, coef="ulceration_1_vs_0", type="apeglm")
# Extract results with ulceration 1 vs 0 contrast
res <- results(dds_ulc, contrast = c("ulceration", "1", "0"), alpha = 0.05)
# Variance stabilizing transformation for visualization
vsd <- vst(dds_ulc, blind=FALSE)
# Processing results - order by LFC
resOrdered <- res[order(res$log2FoldChange),]
ranked_GSEA <- as.data.frame(resOrdered)
#saveRDS(ranked_GSEA, "DE_results_ulceration_ranked.rds")
# Extract significant genes
sig_genes <- subset(res, padj < 0.05)
# Order significant genes by fold change
LFC_ordered <-( sig_genes[ order( sig_genes$log2FoldChange ), ] )
LFC_ordered_df <- as.data.frame(LFC_ordered)
# Prepare results with gene IDs
res_ids <- as.data.frame(res) %>%
rownames_to_column(var = "ENSEMBL_GENE_ID")
# Load gene annotation mapping
gene_ann <- readRDS("data/annotation.rds")
# Join results
res_final <- inner_join(res_ids, gene_ann, by="ENSEMBL_GENE_ID") %>%
relocate(external_gene_name, .after = ENSEMBL_GENE_ID)
# Make unique rownames
names <- make.unique(res_final$external_gene_name)
rownames(res_final) <- names
# Order complete results by LFC
res_final_ordered_LFC <- ( res_final[ order( res_final$log2FoldChange ), ] )
# Process significant genes with gene symbols
significant_genes_ids <- LFC_ordered_df %>%
rownames_to_column(var = "ENSEMBL_GENE_ID")
# Join with gene symbols
significant_genes_final <- inner_join(significant_genes_ids, gene_ann, by="ENSEMBL_GENE_ID") %>%
relocate(external_gene_name, .after = ENSEMBL_GENE_ID)
# Make row names
names <- make.unique(significant_genes_final$external_gene_name)
rownames(significant_genes_final) <- names
# write_csv(significant_genes_final, "DE_results_significant.csv")2A - Principal Component Analysis
# PCA with top 1000 highly variable genes
pcaData <- plotPCA(vsd, intgroup=c("ulceration"),
ntop=1000,
returnData=TRUE)
# Extract variance percentages
percentVar <- round(100 * attr(pcaData, "percentVar"))
# Create plot
ggplot(pcaData, aes(x = PC1, y = PC2, color = group)) +
geom_point(size = 3) +
theme_bw() +
scale_color_manual(
values = c("#730769", "#E8CC03"),
name = "Ulceration Status",
labels = c("No", "Yes")
) +
theme(
legend.position = "right",
panel.grid.minor = element_blank(),
axis.title = element_text(size = 12, face = "bold"),
legend.title = element_text(size = 11, face = "bold"),
legend.text = element_text(size = 10)
) +
ggtitle("PCA Plot of Gene Expression by Ulceration Status") +
xlab(paste0("PC1 (", percentVar[1], "% variance)")) +
ylab(paste0("PC2 (", percentVar[2], "% variance)"))
| Version | Author | Date |
|---|---|---|
| 3148fdc | Estef Vazquez | 2025-04-04 |
2B - Volcano Plot
# Theme
theme_set(theme_classic(base_size = 20) +
theme(
axis.title.y = element_text(face = "bold", margin = margin(0, 20, 0, 0), size = rel(1.1), color = 'black'),
axis.title.x = element_text(hjust = 0.5, face = "bold", margin = margin(20, 0, 0, 0), size = rel(1.1), color = 'black'),
plot.title = element_text(hjust = 0.5)
))
# Create and save plot
create_volcano_plot <- function(data,
fc_threshold = 1.5,
padj_threshold = 0.05,
ylim = c(0, 10),
xlim = c(-6, 6),
top_genes = 100,
output_file = NULL,
italic_labels = TRUE) {
# Classify genes by expression
data$diffexpressed <- "NO"
data$diffexpressed[data$log2FoldChange > fc_threshold & data$padj < padj_threshold] <- "UP"
data$diffexpressed[data$log2FoldChange < -fc_threshold & data$padj < padj_threshold] <- "DOWN"
# Label top differentially expressed genes
data$delabel <- ifelse(
data$external_gene_name %in% head(data[order(data$padj), "external_gene_name"], top_genes),
data$external_gene_name,
NA
)
plot <- ggplot(data = data,
aes(x = log2FoldChange,
y = -log10(padj),
col = diffexpressed,
label = delabel)) +
geom_vline(xintercept = c(-fc_threshold, fc_threshold), col = "gray", linetype = 'dashed') +
geom_hline(yintercept = -log10(padj_threshold), col = "gray", linetype = 'dashed') +
geom_point(size = 2.5) +
scale_color_manual(
values = c("#00AFBB", "grey", "#bb0c00"),
labels = c("Downregulated", "Not significant", "Upregulated")
) +
coord_cartesian(ylim = ylim, xlim = xlim) +
labs(
color = 'DE Genes',
x = expression("log"[2]*"FC"),
y = expression("-log"[10]*"padj")
) +
scale_x_continuous(breaks = seq(-10, 10, 2)) +
ggtitle('Ulcerated vs Non-ulcerated Acral Melanoma') +
geom_text_repel(
max.overlaps = Inf,
fontface = if(italic_labels) "italic" else "plain"
)
if (!is.null(output_file)) {
pdf(file = output_file, width = 13, height = 12)
print(plot)
dev.off()
}
return(plot)
}
# Generate final plot
volcanoplot <- create_volcano_plot(
data = res_final_ordered_LFC,
fc_threshold = 1.5,
padj_threshold = 0.05,
ylim = c(0, 10),
xlim = c(-6, 6),
top_genes = 100,
italic_labels = TRUE,
output_file = "volcanoplot.pdf"
)
volcanoplot
Interactive Volcano
dat <- tibble(genename = (res_final$external_gene_name),
x = res_final$log2FoldChange,
y = -log10(res_final$padj),
col = ifelse(res_final$padj < 0.05 & res_final$log2FoldChange > 1.5, "Upregulated",
ifelse(res_final$padj < 0.05 & res_final$log2FoldChange < -1.5, "Downregulated", "Non-significant")))
fig <- plot_ly(dat, x = ~x, y = ~y,
color = ~col,
colors = c("Downregulated" = "#00AFBB", "Non-significant" = "grey", "Upregulated" = "#bb0c00"),
text = ~genename,
hoverinfo = "text",
type = "scatter",
mode = "markers")
fig <- fig %>%
layout(plot_bgcolor = 'white',
paper_bgcolor = 'white',
xaxis = list(title = 'Log2 fold change',
range = c(-6, 6),
zeroline = FALSE),
yaxis = list(title = '-Log10 Padj',
range = c(0, 10)),
legend = list(title = list(text = '<b> Genes </b>'),
traceorder = "reversed"))
fig <- fig %>%
layout(
legend = list(
title = list(text = '<b> Genes </b>'),
traceorder = "normal"
)
)
figsessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=es_MX.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=es_MX.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=es_MX.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=es_MX.UTF-8 LC_IDENTIFICATION=C
time zone: America/Mexico_City
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] here_1.0.1 plotly_4.10.4
[3] ggrepel_0.9.6 RColorBrewer_1.1-3
[5] factoextra_1.0.7 DESeq2_1.44.0
[7] SummarizedExperiment_1.34.0 Biobase_2.64.0
[9] MatrixGenerics_1.16.0 matrixStats_1.4.1
[11] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[13] IRanges_2.38.1 S4Vectors_0.42.1
[15] BiocGenerics_0.50.0 lubridate_1.9.4
[17] forcats_1.0.0 stringr_1.5.1
[19] dplyr_1.1.4 purrr_1.0.2
[21] readr_2.1.5 tidyr_1.3.1
[23] tibble_3.2.1 ggplot2_3.5.1
[25] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] rlang_1.1.4 magrittr_2.0.3 git2r_0.33.0
[4] compiler_4.4.0 getPass_0.2-4 callr_3.7.6
[7] vctrs_0.6.5 pkgconfig_2.0.3 crayon_1.5.3
[10] fastmap_1.2.0 XVector_0.44.0 labeling_0.4.3
[13] promises_1.3.2 rmarkdown_2.29 tzdb_0.4.0
[16] UCSC.utils_1.0.0 ps_1.8.1 xfun_0.49
[19] zlibbioc_1.50.0 cachem_1.1.0 jsonlite_1.8.9
[22] later_1.4.1 DelayedArray_0.30.1 BiocParallel_1.38.0
[25] parallel_4.4.0 R6_2.5.1 bslib_0.8.0
[28] stringi_1.8.4 jquerylib_0.1.4 numDeriv_2016.8-1.1
[31] Rcpp_1.0.13-1 knitr_1.49 httpuv_1.6.15
[34] Matrix_1.6-5 timechange_0.3.0 tidyselect_1.2.1
[37] rstudioapi_0.17.1 abind_1.4-5 yaml_2.3.10
[40] codetools_0.2-19 processx_3.8.4 lattice_0.22-5
[43] plyr_1.8.9 withr_3.0.2 coda_0.19-4.1
[46] evaluate_1.0.1 pillar_1.10.0 whisker_0.4.1
[49] generics_0.1.3 rprojroot_2.0.4 emdbook_1.3.13
[52] hms_1.1.3 munsell_0.5.1 scales_1.3.0
[55] glue_1.8.0 lazyeval_0.2.2 tools_4.4.0
[58] apeglm_1.26.1 data.table_1.16.4 locfit_1.5-9.10
[61] fs_1.6.5 mvtnorm_1.3-2 grid_4.4.0
[64] bbmle_1.0.25.1 crosstalk_1.2.1 bdsmatrix_1.3-7
[67] colorspace_2.1-1 GenomeInfoDbData_1.2.12 cli_3.6.3
[70] S4Arrays_1.4.1 viridisLite_0.4.2 gtable_0.3.6
[73] sass_0.4.9 digest_0.6.37 SparseArray_1.4.8
[76] farver_2.1.2 htmlwidgets_1.6.4 htmltools_0.5.8.1
[79] lifecycle_1.0.4 httr_1.4.7 MASS_7.3-60
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=es_MX.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=es_MX.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=es_MX.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=es_MX.UTF-8 LC_IDENTIFICATION=C
time zone: America/Mexico_City
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] here_1.0.1 plotly_4.10.4
[3] ggrepel_0.9.6 RColorBrewer_1.1-3
[5] factoextra_1.0.7 DESeq2_1.44.0
[7] SummarizedExperiment_1.34.0 Biobase_2.64.0
[9] MatrixGenerics_1.16.0 matrixStats_1.4.1
[11] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[13] IRanges_2.38.1 S4Vectors_0.42.1
[15] BiocGenerics_0.50.0 lubridate_1.9.4
[17] forcats_1.0.0 stringr_1.5.1
[19] dplyr_1.1.4 purrr_1.0.2
[21] readr_2.1.5 tidyr_1.3.1
[23] tibble_3.2.1 ggplot2_3.5.1
[25] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] rlang_1.1.4 magrittr_2.0.3 git2r_0.33.0
[4] compiler_4.4.0 getPass_0.2-4 callr_3.7.6
[7] vctrs_0.6.5 pkgconfig_2.0.3 crayon_1.5.3
[10] fastmap_1.2.0 XVector_0.44.0 labeling_0.4.3
[13] promises_1.3.2 rmarkdown_2.29 tzdb_0.4.0
[16] UCSC.utils_1.0.0 ps_1.8.1 xfun_0.49
[19] zlibbioc_1.50.0 cachem_1.1.0 jsonlite_1.8.9
[22] later_1.4.1 DelayedArray_0.30.1 BiocParallel_1.38.0
[25] parallel_4.4.0 R6_2.5.1 bslib_0.8.0
[28] stringi_1.8.4 jquerylib_0.1.4 numDeriv_2016.8-1.1
[31] Rcpp_1.0.13-1 knitr_1.49 httpuv_1.6.15
[34] Matrix_1.6-5 timechange_0.3.0 tidyselect_1.2.1
[37] rstudioapi_0.17.1 abind_1.4-5 yaml_2.3.10
[40] codetools_0.2-19 processx_3.8.4 lattice_0.22-5
[43] plyr_1.8.9 withr_3.0.2 coda_0.19-4.1
[46] evaluate_1.0.1 pillar_1.10.0 whisker_0.4.1
[49] generics_0.1.3 rprojroot_2.0.4 emdbook_1.3.13
[52] hms_1.1.3 munsell_0.5.1 scales_1.3.0
[55] glue_1.8.0 lazyeval_0.2.2 tools_4.4.0
[58] apeglm_1.26.1 data.table_1.16.4 locfit_1.5-9.10
[61] fs_1.6.5 mvtnorm_1.3-2 grid_4.4.0
[64] bbmle_1.0.25.1 crosstalk_1.2.1 bdsmatrix_1.3-7
[67] colorspace_2.1-1 GenomeInfoDbData_1.2.12 cli_3.6.3
[70] S4Arrays_1.4.1 viridisLite_0.4.2 gtable_0.3.6
[73] sass_0.4.9 digest_0.6.37 SparseArray_1.4.8
[76] farver_2.1.2 htmlwidgets_1.6.4 htmltools_0.5.8.1
[79] lifecycle_1.0.4 httr_1.4.7 MASS_7.3-60