Kinase Activity (Kinase Library + MEA)
DPA
FGCZ
17 February, 2026
Source:vignettes/Analysis_MEA.Rmd
Analysis_MEA.RmdOverview
Motif Enrichment Analysis (MEA) results for DPA analysis using the kinase-library package.
MEA identifies kinases whose known substrate motifs are enriched at the extremes of ranked phosphoproteomics data, indicating altered kinase activity.
Load MEA Results
if (pipeline_mode) {
# Pipeline mode: load from kinaselib directory
mea_files <- list.files(params$kinaselib_dir, pattern = "^mea_.*\\.csv$", full.names = TRUE)
if (length(mea_files) == 0) {
stop("No MEA result files found in: ", params$kinaselib_dir)
}
# Load all MEA results
mea_results <- mea_files |>
set_names(gsub("^mea_|\\.csv$", "", basename(mea_files))) |>
map_dfr(~ read.csv(.x, stringsAsFactors = FALSE), .id = "contrast")
output_dir <- params$kinaselib_dir
} else {
# Vignette mode: use bundled example data
bundled_zip <- system.file("extdata", "mea_results.zip", package = "prophosqua")
if (!file.exists(bundled_zip)) {
stop("Bundled MEA example data not found. Run in pipeline mode with actual data.")
}
temp_dir <- tempdir()
unzip(bundled_zip, exdir = temp_dir)
mea_files <- list.files(temp_dir, pattern = "^mea_.*\\.csv$", full.names = TRUE)
mea_results <- mea_files |>
set_names(gsub("^mea_|\\.csv$", "", basename(mea_files))) |>
map_dfr(~ read.csv(.x, stringsAsFactors = FALSE), .id = "contrast")
output_dir <- tempdir()
message("Using bundled MEA example data from prophosqua package")
}
# Standardize column names (MEA output uses Kinase, we need kinase)
if ("Kinase" %in% names(mea_results)) {
mea_results <- mea_results |> rename(kinase = Kinase)
}
if ("p.value" %in% names(mea_results)) {
mea_results <- mea_results |> rename(pvalue = `p.value`)
} else if ("p-value" %in% names(mea_results)) {
mea_results <- mea_results |> rename(pvalue = `p-value`)
}
# Rename size column if present
if ("Subs.fraction" %in% names(mea_results)) {
mea_results <- mea_results |> mutate(size = as.numeric(sub("/.*", "", `Subs.fraction`)))
} else if ("Subs fraction" %in% names(mea_results)) {
mea_results <- mea_results |> mutate(size = as.numeric(sub("/.*", "", `Subs fraction`)))
}
cat("Loaded", nrow(mea_results), "results from", length(mea_files), "contrasts\n")## Loaded 622 results from 2 contrasts## Contrasts: KO_vs_WT_at_Early, KO_vs_WT
# Clean and prepare results using shared helper
mea_clean <- prepare_enrichment_data(mea_results, "FDR", 0.1)
# Summary
summary_df <- mea_clean |>
group_by(contrast) |>
summarize(
total_kinases = n(),
sig_up = sum(FDR < 0.1 & NES > 0, na.rm = TRUE),
sig_down = sum(FDR < 0.1 & NES < 0, na.rm = TRUE),
.groups = "drop"
)
knitr::kable(summary_df, caption = paste("MEA Summary -", params$analysis_type))| contrast | total_kinases | sig_up | sig_down |
|---|---|---|---|
| KO_vs_WT | 311 | 79 | 58 |
| KO_vs_WT_at_Early | 311 | 55 | 118 |
Results by Contrast
for (ctr in unique(mea_clean$contrast)) {
cat("\n\n## ", ctr, "\n\n")
ctr_data <- mea_clean |> filter(contrast == ctr)
n_sig <- sum(ctr_data$FDR < 0.1, na.rm = TRUE)
cat("**Significant kinases (FDR < 0.1):** ", n_sig, "\n\n")
# Top kinases dotplot using shared function
p <- plot_enrichment_dotplot(
ctr_data,
item_col = "kinase",
fdr_col = "FDR",
title = paste0(params$analysis_type, " - ", ctr),
subtitle = "Top 30 kinases by FDR"
)
print(p)
cat("\n\n")
# Significant kinases table
cat("### Significant Kinases\n\n")
sig_table <- ctr_data |>
filter(FDR < 0.1) |>
select(kinase, NES, pvalue, FDR, n_substrates = size) |>
arrange(FDR) |>
mutate(across(where(is.numeric), ~round(.x, 4)))
print(htmltools::tagList(
DT::datatable(sig_table,
extensions = 'Buttons',
options = list(pageLength = 15, scrollX = TRUE,
dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel')))
))
cat("\n\n")
}
Summary Heatmap
# Using shared heatmap function
plot_enrichment_heatmap(
mea_clean,
item_col = "kinase",
fdr_col = "FDR",
n_top = 30,
title = paste0("MEA Summary Heatmap - ", params$analysis_type)
)
Volcano Plot
# Using shared volcano function
plot_enrichment_volcano(
mea_clean,
item_col = "kinase",
fdr_col = "FDR",
title = paste0("MEA Volcano Plots - ", params$analysis_type)
)
Diagnostics
pval_diag <- mea_clean |>
group_by(contrast) |>
summarise(
`Min p-value` = signif(min(pvalue, na.rm = TRUE), 3),
`p < 0.05` = sum(pvalue < 0.05, na.rm = TRUE),
`p < 0.01` = sum(pvalue < 0.01, na.rm = TRUE),
Total = n(),
.groups = "drop"
) |>
rename(Contrast = contrast)
knitr::kable(pval_diag, caption = paste("Raw p-value Distribution -", params$analysis_type))| Contrast | Min p-value | p < 0.05 | p < 0.01 | Total |
|---|---|---|---|---|
| KO_vs_WT | 0.001 | 145 | 102 | 311 |
| KO_vs_WT_at_Early | 0.001 | 174 | 123 | 311 |
All Results
# All kinases across all contrasts
all_results_dt <- mea_clean |>
select(contrast, kinase, NES, pvalue, FDR, size) |>
arrange(contrast, FDR) |>
mutate(across(where(is.numeric), ~round(.x, 4)))
DT::datatable(all_results_dt,
filter = "top",
extensions = 'Buttons',
options = list(pageLength = 15, scrollX = TRUE,
dom = 'Bfrtip', buttons = c('copy', 'csv', 'excel')),
caption = "All kinases across all contrasts")Export Results
# Export to Excel
export_list <- list(
all_results = mea_clean |>
select(contrast, kinase, NES, pvalue, FDR, size) |>
arrange(contrast, FDR),
significant = mea_clean |>
filter(FDR < 0.1) |>
select(contrast, kinase, NES, pvalue, FDR, size) |>
arrange(contrast, FDR),
summary = summary_df
)
xlsx_file <- file.path(output_dir, paste0("MEA_", params$analysis_type, "_results.xlsx"))
writexl::write_xlsx(export_list, xlsx_file)
rds_file <- file.path(output_dir, paste0("MEA_", params$analysis_type, "_results.rds"))
saveRDS(mea_clean, rds_file)
cat("Exported:\n -", xlsx_file, "\n -", rds_file, "\n")
message("Vignette mode: File export skipped.")Session Info
## R version 4.5.2 (2025-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Tahoe 26.3
##
## Matrix products: default
## BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## 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: Europe/Zurich
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] prophosqua_0.3.0 writexl_1.5.4 forcats_1.0.1 purrr_1.2.1
## [5] ggplot2_4.0.2 tidyr_1.3.2 DT_0.34.0 dplyr_1.2.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 jsonlite_2.0.0 compiler_4.5.2 tidyselect_1.2.1
## [5] jquerylib_0.1.4 systemfonts_1.3.1 scales_1.4.0 textshaping_1.0.4
## [9] yaml_2.3.12 fastmap_1.2.0 R6_2.6.1 labeling_0.4.3
## [13] patchwork_1.3.2 generics_0.1.4 knitr_1.51 htmlwidgets_1.6.4
## [17] tibble_3.3.1 bookdown_0.46 desc_1.4.3 RColorBrewer_1.1-3
## [21] bslib_0.9.0 pillar_1.11.1 rlang_1.1.7 cachem_1.1.0
## [25] xfun_0.55 S7_0.2.1 fs_1.6.6 sass_0.4.10
## [29] otel_0.2.0 cli_3.6.5 withr_3.0.2 pkgdown_2.2.0
## [33] magrittr_2.0.4 crosstalk_1.2.2 digest_0.6.39 grid_4.5.2
## [37] lifecycle_1.0.5 vctrs_0.7.1 evaluate_1.0.5 glue_1.8.0
## [41] farver_2.1.2 ggseqlogo_0.2.2 ragg_1.5.0 rmarkdown_2.30
## [45] tools_4.5.2 pkgconfig_2.0.3 htmltools_0.5.9