Figure 2 - GSEA
Estef Vazquez
2025-05-05
Last updated: 2025-05-05
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Knit directory: Ulceration_paper_github/
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| html | 07d0307 | Estef Vazquez | 2025-04-04 | Build site. |
| Rmd | 90f2ad0 | Estef Vazquez | 2025-04-04 | Add data download system and update gitignore |
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| html | dd1d8cb | Estef Vazquez | 2025-04-04 | wflow_rename("analysis/test_render_GSEA_TF.Rmd", "analysis/figure2_GSEA.Rmd") |
We perform Gene Set Enrichment Analysis (GSEA) using GO terms and PROGENy to identify pathway activity in ulcerated vs non-ulcerated acral melanoma samples.
# Load required libraries
library(tidyverse)
library(clusterProfiler)
library(enrichplot)
library(DOSE)
library(biomaRt)
library(org.Hs.eg.db)
library(progeny)
library(decoupleR)
library(here)2C - 2D - GSEA
# Data loading
# Load differential expression results pre-ranked for GSEA
ranked_GSEA <- readRDS("data/DE_results_ranked.rds")
ranked_GSEA <- rownames_to_column(ranked_GSEA, var = "ENSEMBL_GENE_ID")
# Load gene annotation
gene_ann <- readRDS("data/annotation.rds")
#gene_ann <- rownames_to_column(gene_ann, var = "ENSEMBL_GENE_ID")
# Add gene symbols
ranked_idmatch <- inner_join(ranked_GSEA, gene_ann, by="ENSEMBL_GENE_ID")
# Make rownames unique
names <- make.unique(ranked_idmatch$external_gene_name)
rownames(ranked_idmatch) <- names
# Extract LFC and create a named vector for GSEA
geneList_FC <- ranked_idmatch[,3]
names(geneList_FC) <- as.character(ranked_idmatch[,1]) # Use ENSEMBL IDs as names
# Sort gene list in decreasing order by LFC
geneList_FC_ordered <- sort(geneList_FC, decreasing = TRUE)
# ---GSEA ---
# Perform Gene Set Enrichment Analysis using GO Biological Process
gsea_BP <- gseGO(geneList = geneList_FC_ordered,
ont ="BP",
keyType = "ENSEMBL",
minGSSize = 3,
maxGSSize = 800,
pvalueCutoff = 0.05,
verbose = TRUE,
OrgDb = org.Hs.eg.db,
pAdjustMethod = "BH",
eps = 0)
go_BP_df <- (as.data.frame(gsea_BP))
# saveRDS(gsea_BP, "results/GSEA_BP_results.rds")
# write_csv(go_BP_df, "results/GSEA_analysis_BP.csv")# --- Visualization ---
# Dotplot showing activated and repressed pathways
dotplot_gsea <- dotplot(
gsea_BP,
showCategory = 10,
split = ".sign",
font.size = 6
) +
facet_grid(.~.sign) +
ggtitle("GSEA analysis") +
theme_classic()
print(dotplot_gsea)
| Version | Author | Date |
|---|---|---|
| 3148fdc | Estef Vazquez | 2025-04-04 |
# NES plot for top enriched terms
top_results <- go_BP_df[1:11, ]
library(stringr)
top_results$Description <- str_wrap(top_results$Description, width = 40)
nes_plot <- ggplot(top_results, aes(x = NES, y = reorder(Description, NES))) +
geom_point(aes(color = NES, size = setSize)) +
scale_color_gradient2(
low = ulcer_colors[2],
mid = "white",
high = ulcer_colors[1],
midpoint = 0,
name = "NES Score"
) +
labs(
x = "Normalized Enrichment Score",
y = NULL,
title = "Gene Set Enrichment Analysis (GSEA)",
subtitle = "Top enriched GO terms",
size = "Gene Set Size"
) +
theme_classic() +
theme(
axis.text.y = element_text(size = 10),
axis.text.x = element_text(size = 11),
axis.title.x = element_text(size = 14),
plot.title = element_text(size = 16, hjust = 0.5)
)
print(nes_plot)
| Version | Author | Date |
|---|---|---|
| 3148fdc | Estef Vazquez | 2025-04-04 |
# GSEA enrichment plot - most up and down-regulated gene sets
top_indices <- c(which.max(gsea_BP@result$NES), which.min(gsea_BP@result$NES))
enrichment_plots <- gseaplot2(
gsea_BP,
geneSetID = top_indices,
color = ulcer_colors)
print(enrichment_plots)
| Version | Author | Date |
|---|---|---|
| 3148fdc | Estef Vazquez | 2025-04-04 |
Pathway Analysis
# Data Preparation
# Extract t-statistic
gene_stats <- ranked_idmatch[, 5]
names(gene_stats) <- ranked_idmatch$external_gene_name
# Get the PROGENy weight matrix
progeny_matrix <- getModel(organism = "Human", top = 100)
# Find common genes between data and the model
common_genes <- intersect(names(gene_stats), rownames(progeny_matrix))
# Subset genes
progeny_matrix_subset <- progeny_matrix[common_genes, , drop = FALSE]
gene_stats_subset <- gene_stats[common_genes]
# Calculate pathway scores using mt multiplication
pathway_scores <- t(progeny_matrix_subset) %*% gene_stats_subset
# Convert to tidy df
progeny_df <- data.frame(
pathway = rownames(pathway_scores),
score = as.numeric(pathway_scores),
stringsAsFactors = FALSE
)
# Normalize (Z-score)
progeny_df$normalized_score <- scale(progeny_df$score)[,1]
# Sort by absolute score to keep most important pathways at top
progeny_df <- progeny_df %>%
arrange(desc(abs(normalized_score)))
# --- Visualization ---
progeny_plot <- ggplot(progeny_df, aes(x = reorder(pathway, normalized_score),
y = normalized_score,
fill = normalized_score > 0)) +
geom_bar(stat = "identity", color = "black", width = 0.7) +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray50") +
scale_fill_manual(values = c("#00AFBB", "#bb0c00"),
labels = c("Inhibited", "Activated"),
name = "Pathway Status") +
coord_flip() +
labs(x = "Signaling Pathway",
y = "Normalized Activity Score",
title = "PROGENy Pathway Analysis - Ulcerated vs Non-ulcerated tumors") +
theme_classic() +
theme(
axis.title = element_text(size = 14, face = "bold"),
axis.text = element_text(size = 12),
axis.text.y = element_text(face = "bold", color = "black"),
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 14, hjust = 0.5, margin = margin(b = 20)),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 11),
legend.position = "bottom",
plot.margin = margin(t = 20, r = 20, b = 20, l = 20)
)
progeny_plot
| Version | Author | Date |
|---|---|---|
| 3148fdc | Estef Vazquez | 2025-04-04 |
#ggsave("figures/PROGENy_pathway_activity.pdf",
# progeny_plot,
# width = 12,
# height = 10,
# dpi = 300)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 decoupleR_2.10.0 progeny_1.26.0
[4] org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0 IRanges_2.38.1
[7] S4Vectors_0.42.1 Biobase_2.64.0 BiocGenerics_0.50.0
[10] biomaRt_2.60.1 DOSE_3.30.5 enrichplot_1.24.4
[13] clusterProfiler_4.12.6 lubridate_1.9.4 forcats_1.0.0
[16] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[19] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[22] ggplot2_3.5.1 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.8.9
[4] magrittr_2.0.3 farver_2.1.2 rmarkdown_2.29
[7] fs_1.6.5 zlibbioc_1.50.0 vctrs_0.6.5
[10] memoise_2.0.1 ggtree_3.12.0 progress_1.2.3
[13] htmltools_0.5.8.1 curl_6.0.1 gridGraphics_0.5-1
[16] parallelly_1.41.0 sass_0.4.9 bslib_0.8.0
[19] plyr_1.8.9 httr2_1.0.7 cachem_1.1.0
[22] whisker_0.4.1 igraph_2.1.2 lifecycle_1.0.4
[25] pkgconfig_2.0.3 gson_0.1.0 Matrix_1.6-5
[28] R6_2.5.1 fastmap_1.2.0 GenomeInfoDbData_1.2.12
[31] digest_0.6.37 aplot_0.2.4 colorspace_2.1-1
[34] patchwork_1.3.0 ps_1.8.1 rprojroot_2.0.4
[37] RSQLite_2.3.9 labeling_0.4.3 filelock_1.0.3
[40] timechange_0.3.0 httr_1.4.7 polyclip_1.10-7
[43] compiler_4.4.0 bit64_4.5.2 withr_3.0.2
[46] BiocParallel_1.38.0 viridis_0.6.5 DBI_1.2.3
[49] ggforce_0.4.2 R.utils_2.12.3 MASS_7.3-60
[52] rappdirs_0.3.3 tools_4.4.0 scatterpie_0.2.4
[55] ape_5.8-1 httpuv_1.6.15 R.oo_1.27.0
[58] glue_1.8.0 callr_3.7.6 nlme_3.1-165
[61] GOSemSim_2.30.2 promises_1.3.2 shadowtext_0.1.4
[64] grid_4.4.0 getPass_0.2-4 reshape2_1.4.4
[67] fgsea_1.30.0 generics_0.1.3 gtable_0.3.6
[70] tzdb_0.4.0 R.methodsS3_1.8.2 data.table_1.16.4
[73] hms_1.1.3 xml2_1.3.6 tidygraph_1.3.1
[76] XVector_0.44.0 ggrepel_0.9.6 pillar_1.10.0
[79] yulab.utils_0.1.8 later_1.4.1 splines_4.4.0
[82] tweenr_2.0.3 BiocFileCache_2.12.0 treeio_1.28.0
[85] lattice_0.22-5 bit_4.5.0.1 tidyselect_1.2.1
[88] GO.db_3.19.1 Biostrings_2.72.1 knitr_1.49
[91] git2r_0.33.0 gridExtra_2.3 xfun_0.49
[94] graphlayouts_1.2.1 stringi_1.8.4 UCSC.utils_1.0.0
[97] lazyeval_0.2.2 ggfun_0.1.8 yaml_2.3.10
[100] evaluate_1.0.1 codetools_0.2-19 ggraph_2.2.1
[103] qvalue_2.36.0 ggplotify_0.1.2 cli_3.6.3
[106] munsell_0.5.1 processx_3.8.4 jquerylib_0.1.4
[109] Rcpp_1.0.13-1 GenomeInfoDb_1.40.1 dbplyr_2.5.0
[112] png_0.1-8 parallel_4.4.0 blob_1.2.4
[115] prettyunits_1.2.0 viridisLite_0.4.2 tidytree_0.4.6
[118] scales_1.3.0 crayon_1.5.3 rlang_1.1.4
[121] cowplot_1.1.3 fastmatch_1.1-4 KEGGREST_1.44.1
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 decoupleR_2.10.0 progeny_1.26.0
[4] org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0 IRanges_2.38.1
[7] S4Vectors_0.42.1 Biobase_2.64.0 BiocGenerics_0.50.0
[10] biomaRt_2.60.1 DOSE_3.30.5 enrichplot_1.24.4
[13] clusterProfiler_4.12.6 lubridate_1.9.4 forcats_1.0.0
[16] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
[19] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[22] ggplot2_3.5.1 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.8.9
[4] magrittr_2.0.3 farver_2.1.2 rmarkdown_2.29
[7] fs_1.6.5 zlibbioc_1.50.0 vctrs_0.6.5
[10] memoise_2.0.1 ggtree_3.12.0 progress_1.2.3
[13] htmltools_0.5.8.1 curl_6.0.1 gridGraphics_0.5-1
[16] parallelly_1.41.0 sass_0.4.9 bslib_0.8.0
[19] plyr_1.8.9 httr2_1.0.7 cachem_1.1.0
[22] whisker_0.4.1 igraph_2.1.2 lifecycle_1.0.4
[25] pkgconfig_2.0.3 gson_0.1.0 Matrix_1.6-5
[28] R6_2.5.1 fastmap_1.2.0 GenomeInfoDbData_1.2.12
[31] digest_0.6.37 aplot_0.2.4 colorspace_2.1-1
[34] patchwork_1.3.0 ps_1.8.1 rprojroot_2.0.4
[37] RSQLite_2.3.9 labeling_0.4.3 filelock_1.0.3
[40] timechange_0.3.0 httr_1.4.7 polyclip_1.10-7
[43] compiler_4.4.0 bit64_4.5.2 withr_3.0.2
[46] BiocParallel_1.38.0 viridis_0.6.5 DBI_1.2.3
[49] ggforce_0.4.2 R.utils_2.12.3 MASS_7.3-60
[52] rappdirs_0.3.3 tools_4.4.0 scatterpie_0.2.4
[55] ape_5.8-1 httpuv_1.6.15 R.oo_1.27.0
[58] glue_1.8.0 callr_3.7.6 nlme_3.1-165
[61] GOSemSim_2.30.2 promises_1.3.2 shadowtext_0.1.4
[64] grid_4.4.0 getPass_0.2-4 reshape2_1.4.4
[67] fgsea_1.30.0 generics_0.1.3 gtable_0.3.6
[70] tzdb_0.4.0 R.methodsS3_1.8.2 data.table_1.16.4
[73] hms_1.1.3 xml2_1.3.6 tidygraph_1.3.1
[76] XVector_0.44.0 ggrepel_0.9.6 pillar_1.10.0
[79] yulab.utils_0.1.8 later_1.4.1 splines_4.4.0
[82] tweenr_2.0.3 BiocFileCache_2.12.0 treeio_1.28.0
[85] lattice_0.22-5 bit_4.5.0.1 tidyselect_1.2.1
[88] GO.db_3.19.1 Biostrings_2.72.1 knitr_1.49
[91] git2r_0.33.0 gridExtra_2.3 xfun_0.49
[94] graphlayouts_1.2.1 stringi_1.8.4 UCSC.utils_1.0.0
[97] lazyeval_0.2.2 ggfun_0.1.8 yaml_2.3.10
[100] evaluate_1.0.1 codetools_0.2-19 ggraph_2.2.1
[103] qvalue_2.36.0 ggplotify_0.1.2 cli_3.6.3
[106] munsell_0.5.1 processx_3.8.4 jquerylib_0.1.4
[109] Rcpp_1.0.13-1 GenomeInfoDb_1.40.1 dbplyr_2.5.0
[112] png_0.1-8 parallel_4.4.0 blob_1.2.4
[115] prettyunits_1.2.0 viridisLite_0.4.2 tidytree_0.4.6
[118] scales_1.3.0 crayon_1.5.3 rlang_1.1.4
[121] cowplot_1.1.3 fastmatch_1.1-4 KEGGREST_1.44.1