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scripts: Add normal GSEA
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2 changed files with 201 additions and 45 deletions
181
scripts/gsea.R
181
scripts/gsea.R
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@ -1,59 +1,156 @@
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# This script performs a gene set enrichment analysis (GSEA) across the whole
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# ranking of ubiquituos genes using g:Profiler.
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# Size of each gene bucket. The GSEA is done once for each bucket within the
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# ranking.
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bucket_size <- 500
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library(data.table)
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library(here)
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i_am("scripts/gsea.R")
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file_path <- here("scripts", "output", "ubigen_gsea.Rds")
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image_path <- here("scripts", "output", "ubigen_gsea.svg")
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# The result will be saved in `file_path` to avoid unnecessary API calls.
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result <- if (file.exists(file_path)) {
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readRDS(file_path)
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} else {
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data <- copy(ubigen::genes)
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data[, bucket := ceiling(rank / bucket_size)]
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ranking_gtex <- ubigen::rank_genes(ubigen::gtex_all)
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ranking_cmap <- ubigen::rank_genes(ubigen::cmap)
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result <- data[, .(analysis = list(gprofiler2::gost(gene))), by = bucket]
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saveRDS(result, file = file_path)
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data <- merge(
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ranking_gtex[, .(gene, score, percentile)],
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ranking_cmap[, .(gene, score, percentile)],
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by = "gene",
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suffixes = c(x = "_gtex", y = "_cmap")
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)
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result
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data[, score := score_gtex * score_cmap]
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setorder(data, -score)
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data[, percentile := (.N - .I) / .N]
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gsea_1_0 <- gprofiler2::gost(
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data[percentile_gtex >= 0.95 & percentile_cmap < 0.95, gene],
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domain_scope = "custom_annotated",
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custom_bg = data[, gene]
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)
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gsea_1_1 <- gprofiler2::gost(
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data[percentile_gtex >= 0.95 & percentile_cmap >= 0.95, gene],
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domain_scope = "custom_annotated",
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custom_bg = data[, gene]
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)
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# This code is based on gostplot.R from the gprofiler2 package.
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gsea_sources <- c(
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"GO:MF",
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"GO:BP",
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"GO:CC",
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"KEGG",
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"REAC",
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"WP",
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"TF",
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"MIRNA",
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"HPA",
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"CORUM",
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"HP"
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)
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gsea_source_colors <- data.table(
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source = gsea_sources,
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color = c(
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"#dc3912",
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"#ff9900",
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"#109618",
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"#dd4477",
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"#3366cc",
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"#0099c6",
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"#5574a6",
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"#22aa99",
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"#6633cc",
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"#66aa00",
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"#990099"
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)
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)
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lerp <- function(x) {
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(x - min(x)) / (max(x) - min(x))
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}
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result[, result := lapply(analysis, function(a) a$result)]
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result <- result[, rbindlist(result), by = bucket]
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gsea_plot <- function(
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gsea_result,
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sources = c("GO:MF", "GO:BP", "GO:CC", "KEGG", "REAC", "WP", "TF", "HP")) {
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source_data <- gsea_source_colors[source %chin% sources]
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data <- result[, .(count = .N), by = c("bucket", "source")]
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data[, total := sum(count), by = bucket]
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source_data[,
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width := gsea_result$meta$result_metadata[[source]]$number_of_terms,
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by = source
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]
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smooth_model <- loess(total ~ bucket, data, span = 0.3)
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bucket_smoothed <- seq(1, nrow(data), 0.1)
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total_smoothed <- predict(smooth_model, bucket_smoothed)
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source_data[seq_len(.N - 1), width := width + 2000]
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source_data[, source_x := cumsum(width) - width]
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source_data[, source_center := source_x + width / 2]
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fig <- plotly::plot_ly(data) |>
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plotly::add_bars(
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x = ~bucket,
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y = ~count,
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color = ~source
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) |>
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plotly::add_lines(
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x = bucket_smoothed,
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y = total_smoothed,
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name = "All (smoothed)"
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data <- gsea_result$result |> as.data.table()
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data <- merge(data, source_data, by = "source")
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data[, x := source_x + source_order]
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data[, y := -log10(p_value)]
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data[y > 16, y := 17]
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plotly::plot_ly() |>
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plotly::add_markers(
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data = data,
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x = ~x,
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y = ~y,
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text = ~term_name,
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marker = list(
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size = ~ 4 + 6 * lerp(term_size),
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color = ~color,
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line = list(width = 0)
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),
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cliponaxis = FALSE
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) |>
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plotly::layout(
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xaxis = list(title = glue::glue("Bucket of genes (n = {bucket_size})")),
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yaxis = list(title = "Number of associated terms"),
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barmode = "stack",
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legend = list(title = list(text = "<b>Source of term</b>"))
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xaxis = list(
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title = "",
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range = c(0, source_data[.N, source_x + width]),
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tickmode = "array",
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tickvals = source_data[, source_center],
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ticktext = source_data[, source],
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showgrid = FALSE,
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zeroline = FALSE
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),
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yaxis = list(
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title = "−log₁₀(p)",
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range = c(0, 18),
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tickmode = "array",
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tickvals = c(2, 4, 6, 8, 10, 12, 14, 16),
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ticktext = c("2", "4", "6", "8", "10", "12", "14", "≥ 16")
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),
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font = list(size = 8),
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margin = list(
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pad = 2,
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l = 0,
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r = 0,
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t = 0,
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b = 0
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)
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)
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}
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plotly::save_image(fig, image_path, width = 1200, height = 800)
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fig_gsea_1_0 <- gsea_plot(gsea_1_0)
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fig_gsea_1_1 <- gsea_plot(gsea_1_1)
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gsea_plot_ranking <- fig
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usethis::use_data(gsea_plot_ranking, internal = TRUE, overwrite = TRUE)
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# Plotly specifies all sizes in pixels, including font size. Because of
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# that, we can actually think of these pixels as points. One point is defined as
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# 1/72 inch and SVG uses 96 DPI as the standard resolution.
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#
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# 1 plotly pixel = 1 point = 1/72 inch = 1 1/3 actual pixels
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#
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# So, we specify width and height in points (= plotly pixels) and scale up the
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# image by 96/72 to convert everything from points to pixels at 96 DPI.
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plotly::save_image(
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fig_gsea_1_0,
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file = here("scripts/output/gsea_1_0.svg"),
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width = 6.27 * 72,
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height = 3.135 * 72,
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scale = 96 / 72
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)
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plotly::save_image(
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fig_gsea_1_1,
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file = here("scripts/output/gsea_1_1.svg"),
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width = 6.27 * 72,
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height = 3.135 * 72,
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scale = 96 / 72
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)
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59
scripts/sliding_gsea.R
Normal file
59
scripts/sliding_gsea.R
Normal file
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@ -0,0 +1,59 @@
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# This script performs a gene set enrichment analysis (GSEA) across the whole
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# ranking of ubiquituos genes using g:Profiler.
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# Size of each gene bucket. The GSEA is done once for each bucket within the
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# ranking.
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bucket_size <- 500
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library(data.table)
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library(here)
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i_am("scripts/gsea.R")
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file_path <- here("scripts", "output", "ubigen_gsea.Rds")
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image_path <- here("scripts", "output", "ubigen_gsea.svg")
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# The result will be saved in `file_path` to avoid unnecessary API calls.
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result <- if (file.exists(file_path)) {
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readRDS(file_path)
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} else {
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data <- copy(ubigen::genes)
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data[, bucket := ceiling(rank / bucket_size)]
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result <- data[, .(analysis = list(gprofiler2::gost(gene))), by = bucket]
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saveRDS(result, file = file_path)
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result
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}
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result[, result := lapply(analysis, function(a) a$result)]
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result <- result[, rbindlist(result), by = bucket]
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data <- result[, .(count = .N), by = c("bucket", "source")]
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data[, total := sum(count), by = bucket]
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smooth_model <- loess(total ~ bucket, data, span = 0.3)
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bucket_smoothed <- seq(1, nrow(data), 0.1)
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total_smoothed <- predict(smooth_model, bucket_smoothed)
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fig <- plotly::plot_ly(data) |>
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plotly::add_bars(
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x = ~bucket,
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y = ~count,
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color = ~source
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) |>
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plotly::add_lines(
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x = bucket_smoothed,
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y = total_smoothed,
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name = "All (smoothed)"
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) |>
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plotly::layout(
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xaxis = list(title = glue::glue("Bucket of genes (n = {bucket_size})")),
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yaxis = list(title = "Number of associated terms"),
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barmode = "stack",
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legend = list(title = list(text = "<b>Source of term</b>"))
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)
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plotly::save_image(fig, image_path, width = 1200, height = 800)
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gsea_plot_ranking <- fig
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usethis::use_data(gsea_plot_ranking, internal = TRUE, overwrite = TRUE)
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