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Add drug plots
This commit is contained in:
parent
cf8e9e79d5
commit
785b748ba4
8 changed files with 365 additions and 185 deletions
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@ -237,5 +237,6 @@ server <- function(custom_dataset = NULL) {
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})
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output$gsea_plot_ranking <- plotly::renderPlotly(gsea_plot_ranking)
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output$fig_drug_scores <- plotly::renderPlotly(fig_drug_scores)
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}
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}
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BIN
R/sysdata.rda
BIN
R/sysdata.rda
Binary file not shown.
16
R/ui.R
16
R/ui.R
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@ -272,7 +272,21 @@ ui <- function(custom_dataset = NULL) {
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"Note: Click on the legend items to toggle single sources. A ",
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"double-click will isolate a single source of interest."
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))),
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plotly::plotlyOutput("gsea_plot_ranking", height = "600px")
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plotly::plotlyOutput("gsea_plot_ranking", height = "600px"),
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h2("Drug effects"),
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p(HTML(paste0(
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"Scores for drugs based on the genes that are significantly ",
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"influenced by them. To compute a score for each drug, the scores ",
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"of all influenced genes based on “GTEx (all)” (X-axis) and ",
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"“CMap” (Y-axis) are averaged with weights based on the fold ",
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"change of the interactions. The position of each drug in this ",
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"plot is therefore a result of how ubiquitous the genes that it ",
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"influences are."
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))),
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p(HTML(paste0(
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"Note: Hover over the markers to see drug names."
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))),
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plotly::plotlyOutput("fig_drug_scores", height = "1200px")
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)
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),
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tabPanel(
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@ -1,136 +0,0 @@
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library(data.table)
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library(here)
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i_am("scripts/cmap_drugs_analysis.R")
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data <- fread(here("scripts/output/cmap_drugs.csv"))
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data[, c("drug", "concentration", "cell_line") :=
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tstrsplit(drug, "_", fixed = TRUE)]
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data[, concentration := as.double(concentration)]
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data <- data[,
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.(abs_mean_change = mean(abs(mean_change))),
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by = .(drug, group)
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]
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# Source: PubChem ID list upload based on identifiers converted from CMap
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# drug names using the PubChem ID exchange.
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pubchem_data <- fread(here("scripts/input/pubchem_data.csv"))
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pubchem_data <- pubchem_data[, .(cid, cmpdname, annotation)]
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pubchem_data <- unique(pubchem_data, by = "cid")
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pubchem_data <- pubchem_data[,
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.(
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cmpdname,
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annotation = strsplit(annotation, "|", fixed = TRUE) |> unlist()
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),
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by = cid
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]
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# Filter for WHO ATC annotations
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pubchem_data <- pubchem_data[stringr::str_detect(annotation, "^[A-Z] - ")]
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# Extract ATC levels
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pubchem_data[, atc_1 := stringr::str_match(
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annotation,
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"^[A-Z] - ([^>]*)"
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)[, 2] |> stringr::str_trim()]
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pubchem_data[, atc_2 := stringr::str_match(
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annotation,
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"> [A-Z][0-9][0-9] - ([^>]*)"
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)[, 2] |> stringr::str_trim()]
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pubchem_data[, atc_3 := stringr::str_match(
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annotation,
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"> [A-Z][0-9][0-9][A-Z] - ([^>]*)"
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)[, 2] |> stringr::str_trim()]
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# Source: PubChem ID exchange
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drugs_pubchem_mapping <- fread(here("scripts/input/drugs_pubchem.tsv")) |>
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na.omit()
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data <- merge(data, drugs_pubchem_mapping, by = "drug", allow.cartesian = TRUE)
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data <- merge(data, pubchem_data, by = "cid", allow.cartesian = TRUE)
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data[, drug_category := atc_1]
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# Select top drug categories
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results_drug_categories <- data[,
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.(score = mean(abs_mean_change)),
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by = .(group, drug_category)
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]
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results_drug_categories <- results_drug_categories[,
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.(mean_score = mean(score)),
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by = drug_category
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]
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setorder(results_drug_categories, -mean_score)
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top_drug_categories <- results_drug_categories[1:7, drug_category]
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drug_categories <- c(top_drug_categories, "Other")
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# Merge other drug categories
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data[!(drug_category %chin% top_drug_categories), drug_category := "Other"]
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# Recompute results with new categories
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results <- data[,
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.(score = mean(abs_mean_change)),
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by = .(group, drug_category)
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]
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group_plots <- list()
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for (group_value in results[, unique(group)]) {
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group_plot <- plotly::plot_ly() |>
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plotly::add_bars(
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data = results[group == group_value],
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x = ~drug_category,
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y = ~score,
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color = ~drug_category
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) |>
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plotly::layout(
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xaxis = list(
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categoryarray = drug_categories,
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title = "",
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showticklabels = FALSE
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),
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yaxis = list(
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range = c(0.0, 0.03),
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nticks = 4,
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title = ""
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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 = 48,
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r = 0,
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t = 0,
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b = 36
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)
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)
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plotly::save_image(
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group_plot |> plotly::hide_legend(),
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file = here(glue::glue("scripts/output/drug_categories_{group_value}.svg")),
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width = 3 * 72,
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height = 4 * 72,
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scale = 96 / 72
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)
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group_plots <- c(group_plots, list(group_plot))
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}
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plotly::save_image(
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group_plot,
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file = here(glue::glue("scripts/output/drug_categories_legend.svg")),
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width = 6.27 * 72,
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height = 6.27 * 72,
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scale = 96 / 72
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)
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@ -1,47 +0,0 @@
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library(data.table)
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library(gprofiler2)
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library(here)
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i_am("scripts/cmap_drugs_input.R")
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# Source: custom
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load(here("scripts", "input", "CMap_20180808.RData"))
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data <- CMap$"HT_HG-U133A"
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rm(CMap)
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transcripts <- dimnames(data)$transcripts
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genes <- gconvert(
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transcripts,
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numeric_ns = "ENTREZGENE_ACC",
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mthreshold = 1,
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filter_na = FALSE
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)$target
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dimnames(data)[[1]] <- genes
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data_drugs <- as.data.table(data)
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data_drugs <- na.omit(data_drugs)
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data_drugs <- data_drugs[data == "logFoldChange", .(transcripts, drugs, value)]
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setnames(
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data_drugs,
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c("transcripts", "drugs", "value"),
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c("gene", "drug", "change")
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)
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genes_0_0 <- scan(here("scripts/output/genes_0_0.txt"), what = character())
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genes_0_1 <- scan(here("scripts/output/genes_0_1.txt"), what = character())
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genes_1_0 <- scan(here("scripts/output/genes_1_0.txt"), what = character())
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genes_1_1 <- scan(here("scripts/output/genes_1_1.txt"), what = character())
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data_drugs[gene %chin% genes_0_0, group := "genes_0_0"]
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data_drugs[gene %chin% genes_0_1, group := "genes_0_1"]
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data_drugs[gene %chin% genes_1_0, group := "genes_1_0"]
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data_drugs[gene %chin% genes_1_1, group := "genes_1_1"]
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data_drugs <- na.omit(data_drugs)
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results <- data_drugs[, .(mean_change = mean(change)), by = .(drug, group)]
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fwrite(results, file = here("scripts/output/cmap_drugs.csv"))
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write(data_drugs[, unique(drug)], file = here("scripts/output/drugs.txt"))
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237
scripts/drugs_analysis.R
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237
scripts/drugs_analysis.R
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@ -0,0 +1,237 @@
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library(data.table)
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library(here)
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i_am("scripts/drugs_analysis.R")
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drugs_cmap <- fread(here("scripts/output/drugs_cmap.csv"))
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# Only keep significant changes
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drugs_cmap <- drugs_cmap[p_value <= 0.05]
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# Keep one row per gene and drug, with the most significant change.
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setkey(drugs_cmap, gene, drug, p_value)
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drugs_cmap <- drugs_cmap[
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rowid(gene, drug) == 1,
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.(gene, drug, log_fold_change, p_value)
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]
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drugs_cmap[, negative_log_10_p := -log10(p_value)]
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ranking_data <- fread(here("scripts/output/gsea_vs_cmap_groups.csv"))
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n_ubiquitous <- ranking_data[percentile_gtex >= 0.95, .N]
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n_non_ubiquitous <- ranking_data[percentile_gtex < 0.95, .N]
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data <- merge(drugs_cmap, ranking_data, by = "gene")
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drugs <- fread(here("scripts/output/drugs.csv"), na.strings = "")
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data <- merge(data, drugs, by = "drug", all.x = TRUE, allow.cartesian = TRUE)
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# Use CMap names as fallback (for drugs not present in drugs.csv above)
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data[is.na(name), name := stringr::str_to_sentence(drug)]
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# Figures for single drugs
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results_drugs <- unique(data, by = c("drug", "gene"))
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results_drugs[,
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`:=`(
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proportion_ubiquitous =
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.SD[percentile_gtex >= 0.95, .N / n_ubiquitous],
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proportion_non_ubiquitous =
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.SD[percentile_gtex < 0.95, .N / n_non_ubiquitous],
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drug_score_gtex = weighted.mean(score_gtex, abs(log_fold_change)),
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drug_score_cmap = weighted.mean(score_cmap, abs(log_fold_change))
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),
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by = drug
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]
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results_drugs[, bias := proportion_ubiquitous / proportion_non_ubiquitous]
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setorder(results_drugs, -bias)
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results_drugs_unique <- unique(results_drugs, by = "drug")
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# Exclude some exotic drugs
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results_drugs_unique <- results_drugs_unique[!is.na(indication)]
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n_drugs <- nrow(results_drugs_unique)
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selected_drugs <- c(
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results_drugs_unique[1:10, drug],
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results_drugs_unique[(n_drugs - 9):n_drugs, drug]
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)
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fig_drug_scores_new <- plotly::plot_ly(results_drugs_unique) |>
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plotly::add_markers(
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x = ~drug_score_gtex,
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y = ~drug_score_cmap,
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text = ~name,
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marker = list(size = 4)
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) |>
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plotly::layout(
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xaxis = list(
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title = "Score based on GTEx (all)"
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),
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yaxis = list(
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title = "Score based on CMap"
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)
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)
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# To not overwrite other data:
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load(here("R/sysdata.rda"))
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fig_drug_scores <- fig_drug_scores_new
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usethis::use_data(
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fig_drug_scores,
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gsea_plot_ranking, # From R/sysdata.rda
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internal = TRUE,
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overwrite = TRUE
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)
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results_drugs_unique <- results_drugs_unique[drug %chin% selected_drugs]
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fig_drugs <- plotly::plot_ly(results_drugs_unique) |>
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plotly::add_bars(
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x = ~proportion_ubiquitous,
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y = ~name
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) |>
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plotly::add_bars(
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x = ~ -proportion_non_ubiquitous,
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y = ~name
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) |>
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plotly::layout(
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xaxis = list(
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range = c(-0.8, 0.8),
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title = "Proportion of genes that are influenced significantly",
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tickformat = ".0%"
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),
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yaxis = list(
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categoryarray = rev(results_drugs_unique[, name]),
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title = ""
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),
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barmode = "relative",
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showlegend = FALSE,
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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 = 36
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)
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)
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# Figure for mechanisms of action
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results_moa <- unique(
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data[!is.na(mechanism_of_action) & mechanism_of_action != "Unknown"],
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by = c("drug", "gene", "mechanism_of_action")
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)
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results_moa <- results_moa[,
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.(
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percentile_gtex = percentile_gtex[1],
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log_fold_change = mean(log_fold_change),
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score_gtex = mean(score_gtex)
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),
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by = c("mechanism_of_action", "gene")
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]
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results_moa[,
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`:=`(
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proportion_ubiquitous = .SD[percentile_gtex >= 0.95, .N / n_ubiquitous],
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proportion_non_ubiquitous =
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.SD[percentile_gtex < 0.95, .N / n_non_ubiquitous],
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moa_score = weighted.mean(score_gtex, abs(log_fold_change))
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),
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by = mechanism_of_action
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]
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results_moa[, bias := proportion_ubiquitous / proportion_non_ubiquitous]
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setorder(results_moa, -bias)
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results_moa_unique <- unique(results_moa, by = "mechanism_of_action")
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n_moa <- nrow(results_moa_unique)
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selected_moas <- c(
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results_moa_unique[1:10, mechanism_of_action],
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results_moa_unique[(n_moa - 9):n_moa, mechanism_of_action]
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)
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results_moa_unique <-
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results_moa_unique[mechanism_of_action %chin% selected_moas]
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fig_moas <- plotly::plot_ly(results_moa_unique) |>
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plotly::add_bars(
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x = ~proportion_ubiquitous,
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y = ~mechanism_of_action
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) |>
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plotly::add_bars(
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x = ~ -proportion_non_ubiquitous,
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y = ~mechanism_of_action
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) |>
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plotly::layout(
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xaxis = list(
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range = c(-0.8, 0.8),
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title = "Proportion of genes that are influenced significantly",
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tickformat = ".0%"
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),
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yaxis = list(
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categoryarray = rev(results_moa_unique[, mechanism_of_action]),
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title = ""
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),
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barmode = "relative",
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showlegend = FALSE,
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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 = 36
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)
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)
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plotly::save_image(
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fig_drug_scores |> plotly::layout(
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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 = 36,
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r = 0,
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t = 0,
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b = 36
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)
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),
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file = here("scripts/output/drug_scores.svg"),
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width = 6.27 * 72,
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height = 6.27 * 72,
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scale = 96 / 72
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)
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plotly::save_image(
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fig_drugs,
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file = here("scripts/output/drugs_labels.svg"),
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width = 3.135 * 72,
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height = 6.27 * 72,
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scale = 96 / 72
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)
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plotly::save_image(
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fig_drugs |> plotly::layout(yaxis = list(showticklabels = FALSE)),
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file = here("scripts/output/drugs.svg"),
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width = 3.135 * 72,
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height = 6.27 * 72,
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scale = 96 / 72
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)
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plotly::save_image(
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fig_moas,
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file = here("scripts/output/moas_labels.svg"),
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width = 3.135 * 72,
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height = 6.27 * 72,
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scale = 96 / 72
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)
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plotly::save_image(
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fig_moas |> plotly::layout(yaxis = list(showticklabels = FALSE)),
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file = here("scripts/output/moas.svg"),
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width = 3.135 * 72,
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height = 6.27 * 72,
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scale = 96 / 72
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)
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103
scripts/drugs_input.R
Normal file
103
scripts/drugs_input.R
Normal file
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@ -0,0 +1,103 @@
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library(data.table)
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library(here)
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i_am("scripts/drugs_input.R")
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# Source: PubChem ID exchange based on CMap drug identifiers.
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drugs_cmap_pubchem <- fread(here("scripts/input/drugs_cmap_pubchem.tsv"))
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drugs_cmap_pubchem <- na.omit(drugs_cmap_pubchem)
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# Source: UniChem ID mapping
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drugs_chembl_pubchem <- fread(here("scripts/input/drugs_chembl_pubchem.tsv"))
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# Source: ChEMBL SQLite database
|
||||
# SELECT DISTINCT
|
||||
# chembl_id,
|
||||
# synonyms AS name,
|
||||
# mesh_heading AS indication,
|
||||
# mechanism_of_action
|
||||
# FROM molecule_dictionary
|
||||
# LEFT JOIN drug_indication
|
||||
# ON molecule_dictionary.molregno = drug_indication.molregno
|
||||
# LEFT JOIN drug_mechanism
|
||||
# ON molecule_dictionary.molregno = drug_mechanism.molregno
|
||||
# LEFT JOIN (
|
||||
# SELECT molregno, synonyms FROM molecule_synonyms WHERE syn_type == 'INN'
|
||||
# ) AS molecule_synonyms
|
||||
# ON molecule_dictionary.molregno = molecule_synonyms.molregno
|
||||
# WHERE name IS NOT NULL
|
||||
# OR indication IS NOT NULL
|
||||
# OR mechanism_of_action IS NOT NULL;
|
||||
drugs_chembl <- fread(here("scripts/input/drugs_chembl.csv"))
|
||||
|
||||
# Source: PubChem ID list upload based on identifiers converted from CMap
|
||||
# drug names using the PubChem ID exchange.
|
||||
drugs_pubchem <- fread(here("scripts/input/drugs_pubchem.csv"))
|
||||
|
||||
drugs_pubchem <- drugs_pubchem[, .(cid, cmpdname, annotation)]
|
||||
drugs_pubchem <- unique(drugs_pubchem, by = "cid")
|
||||
drugs_pubchem <- drugs_pubchem[,
|
||||
.(
|
||||
cmpdname,
|
||||
annotation = strsplit(annotation, "|", fixed = TRUE) |> unlist()
|
||||
),
|
||||
by = cid
|
||||
]
|
||||
|
||||
# Filter for WHO ATC annotations
|
||||
drugs_pubchem <- drugs_pubchem[stringr::str_detect(annotation, "^[A-Z] - ")]
|
||||
|
||||
# Extract ATC levels
|
||||
|
||||
drugs_pubchem[, atc_1 := stringr::str_match(
|
||||
annotation,
|
||||
"^[A-Z] - ([^>]*)"
|
||||
)[, 2] |> stringr::str_trim()]
|
||||
|
||||
drugs_pubchem[, atc_2 := stringr::str_match(
|
||||
annotation,
|
||||
"> [A-Z][0-9][0-9] - ([^>]*)"
|
||||
)[, 2] |> stringr::str_trim()]
|
||||
|
||||
drugs_pubchem[, atc_3 := stringr::str_match(
|
||||
annotation,
|
||||
"> [A-Z][0-9][0-9][A-Z] - ([^>]*)"
|
||||
)[, 2] |> stringr::str_trim()]
|
||||
|
||||
drugs_pubchem <- drugs_pubchem[, .(cid, cmpdname, atc_1, atc_2, atc_3)]
|
||||
setnames(drugs_pubchem, c("cid", "cmpdname"), c("pubchem_cid", "pubchem_name"))
|
||||
|
||||
drugs <- merge(
|
||||
drugs_cmap_pubchem,
|
||||
drugs_chembl_pubchem,
|
||||
by = "pubchem_cid",
|
||||
all.x = TRUE
|
||||
)
|
||||
|
||||
drugs <- merge(
|
||||
drugs,
|
||||
drugs_chembl,
|
||||
by = "chembl_id",
|
||||
all.x = TRUE
|
||||
)
|
||||
|
||||
drugs <- merge(
|
||||
drugs,
|
||||
drugs_pubchem,
|
||||
by = "pubchem_cid",
|
||||
all.x = TRUE,
|
||||
allow.cartesian = TRUE
|
||||
)
|
||||
|
||||
# Prefer INN name, then PubChem, then CMap:
|
||||
drugs[name == "", name := NA]
|
||||
drugs[is.na(name), name := pubchem_name]
|
||||
drugs[name == "", name := NA]
|
||||
drugs[is.na(name), name := stringr::str_to_sentence(drug)]
|
||||
drugs[, pubchem_name := NULL]
|
||||
|
||||
# Clean up empty values:
|
||||
drugs[indication == "", indication := NA]
|
||||
drugs[mechanism_of_action == "", mechanism_of_action := NA]
|
||||
|
||||
fwrite(drugs, file = here("scripts/output/drugs.csv"))
|
||||
|
|
@ -55,5 +55,13 @@ fig <- plotly::plot_ly(data) |>
|
|||
|
||||
plotly::save_image(fig, image_path, width = 1200, height = 800)
|
||||
|
||||
# To not overwrite other data:
|
||||
load(here("R/sysdata.rda"))
|
||||
gsea_plot_ranking <- fig
|
||||
usethis::use_data(gsea_plot_ranking, internal = TRUE, overwrite = TRUE)
|
||||
|
||||
usethis::use_data(
|
||||
gsea_plot_ranking,
|
||||
fig_drug_scores, # From R/sysdata.rda
|
||||
internal = TRUE,
|
||||
overwrite = TRUE
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue