From d71e97ebfca2ded9a9b364580954527255d18ed0 Mon Sep 17 00:00:00 2001
From: LMBradford <97983129+LMBradford@users.noreply.github.com>
Date: Thu, 1 Aug 2024 11:57:52 -0400
Subject: [PATCH] TGx-HDACi reporting module (#252)
* add tgx-hdaci report module
* make tgx-hdaci report work in pipeline
* fix gene names for hwt2.0
* fix typo
* use ensembl IDs not gene symbols
* uncomment summarize_across_facet lines
* improve gene mismatch messages
* tgx-hdaci for human datasets only
* move bibtex files to their own folder
---
R.ODAF.utils/R/write_reports.R | 25 +
Rmd/DESeq2_report_new.Rmd | 3 +-
Rmd/Sample_QC.Rmd | 2 +-
Rmd/data_explorer_report.Rmd | 1 +
Rmd/go_pathway_report.Rmd | 1 +
Rmd/stats_report.Rmd | 1 +
Rmd/tgx-hdaci.Rmd | 579 ++++++++++++++++++
Rmd/tgx_ddi.Rmd | 9 +-
inputs/config/config.default.yaml | 1 +
{Rmd => references}/references.bib | 0
{Rmd => references}/tgx-ddi-references.bib | 0
references/tgx-hdaci-references.bib | 17 +
.../HDACi_classifier_TK6_wID_wlog2FC.txt | 82 +++
scripts/render_DESeq2_report.parallel.R | 25 +-
14 files changed, 737 insertions(+), 9 deletions(-)
create mode 100644 Rmd/tgx-hdaci.Rmd
rename {Rmd => references}/references.bib (100%)
rename {Rmd => references}/tgx-ddi-references.bib (100%)
create mode 100644 references/tgx-hdaci-references.bib
create mode 100644 resources/HDACi_classifier_TK6_wID_wlog2FC.txt
diff --git a/R.ODAF.utils/R/write_reports.R b/R.ODAF.utils/R/write_reports.R
index e0bb7caa..b02961c0 100644
--- a/R.ODAF.utils/R/write_reports.R
+++ b/R.ODAF.utils/R/write_reports.R
@@ -127,3 +127,28 @@ make_tgxddi_reports <- function(pars, paths, facet) {
render_report(tgxddi_report, tgxddi_file, pars)
}
}
+
+#' Generate TGx-HDACi Report
+#'
+#' Renders the TGx-HDACi report HTML file for a given facet using the specified parameters.
+#' The function updates the `reports_filter` in `pars` based on the facet provided.
+#'
+#' @param pars A list of parameters used for report generation.
+#' @param facet A character string specifying the facet for which to generate the report.
+#' @param paths A list of paths used throughout the analysis.
+#' @return Invisible NULL. The function is called for its side effect of rendering an HTML report.
+#' @export
+make_hdaci_reports <- function(pars, paths, facet) {
+ if (is.na(pars$deseq_facet) && is.na(pars$reports_facet)) {
+ pars$reports_filter <- NULL
+ } else {
+ pars$reports_filter <- facet
+ }
+ prefix <-get_prefix(prefix_pars = pars, prefix_facet = facet)
+ if (pars$generate_tgxhdaci_report) {
+ message("Generating TGX-HDACi report")
+ tgxhdaci_report <- file.path(paths$projectdir, "Rmd", "tgx-hdaci.Rmd")
+ tgxhdaci_file <- file.path(paths$reports_dir, paste0("tgxhdaci_", prefix, ".html"))
+ render_report(tgxhdaci_report, tgxhdaci_file, pars)
+ }
+}
\ No newline at end of file
diff --git a/Rmd/DESeq2_report_new.Rmd b/Rmd/DESeq2_report_new.Rmd
index dd569940..ce2f563d 100644
--- a/Rmd/DESeq2_report_new.Rmd
+++ b/Rmd/DESeq2_report_new.Rmd
@@ -57,6 +57,7 @@ params:
generate_data_explorer_report: TRUE
generate_go_pathway_report: TRUE
generate_tgxddi_report: TRUE
+ generate_tgxhdaci_report: TRUE
output_digits: 5
parallel: FALSE
species_data: null
@@ -74,7 +75,7 @@ output:
code_folding: hide
theme: spacelab # flatly spacelab sandstone cerulean
code_download: true
-bibliography: "`r file.path(params$projectdir, 'Rmd/references.bib')`"
+bibliography: "`r file.path(params$projectdir, 'references/references.bib')`"
---
diff --git a/Rmd/Sample_QC.Rmd b/Rmd/Sample_QC.Rmd
index a7607225..248cf23b 100644
--- a/Rmd/Sample_QC.Rmd
+++ b/Rmd/Sample_QC.Rmd
@@ -43,7 +43,7 @@ output:
code_folding: hide
theme: spacelab # flatly spacelab sandstone cerulean
code_download: true
-bibliography: "`r file.path(params$projectdir, 'Rmd/references.bib')`"
+bibliography: "`r file.path(params$projectdir, 'references/references.bib')`"
---
```{r setup, include=FALSE}
diff --git a/Rmd/data_explorer_report.Rmd b/Rmd/data_explorer_report.Rmd
index 8cbc36bc..89abf1ee 100644
--- a/Rmd/data_explorer_report.Rmd
+++ b/Rmd/data_explorer_report.Rmd
@@ -57,6 +57,7 @@ params:
generate_data_explorer_report: TRUE
generate_go_pathway_report: TRUE
generate_tgxddi_report: TRUE
+ generate_tgxhdaci_report: TRUE
output_digits: 5
parallel: FALSE
species_data: null
diff --git a/Rmd/go_pathway_report.Rmd b/Rmd/go_pathway_report.Rmd
index 251293fd..e2b13555 100644
--- a/Rmd/go_pathway_report.Rmd
+++ b/Rmd/go_pathway_report.Rmd
@@ -57,6 +57,7 @@ params:
generate_data_explorer_report: TRUE
generate_go_pathway_report: TRUE
generate_tgxddi_report: TRUE
+ generate_tgxhdaci_report: TRUE
output_digits: 5
parallel: FALSE
species_data: null
diff --git a/Rmd/stats_report.Rmd b/Rmd/stats_report.Rmd
index d052f8f2..71ba51d9 100644
--- a/Rmd/stats_report.Rmd
+++ b/Rmd/stats_report.Rmd
@@ -57,6 +57,7 @@ params:
generate_data_explorer_report: TRUE
generate_go_pathway_report: TRUE
generate_tgxddi_report: TRUE
+ generate_tgxhdaci_report: TRUE
output_digits: 5
parallel: FALSE
species_data: null
diff --git a/Rmd/tgx-hdaci.Rmd b/Rmd/tgx-hdaci.Rmd
new file mode 100644
index 00000000..be0cbdb9
--- /dev/null
+++ b/Rmd/tgx-hdaci.Rmd
@@ -0,0 +1,579 @@
+---
+params:
+ projectdir: null # Use when loading. If null, here::here() will be used. Can also hard code if for some reason you need that
+ project_title: "Test" # Change depending on your project name
+ researcher_name: "Name" # Name of the researcher leading the project
+ bioinformatician_name: "Name" # Name of the person running the R-ODAF analyses
+ metadata_file: "metadata.txt" # Name of the metadata file. Must be in inputs/metadata/
+ contrasts_file: "contrasts.txt" # Name of the contrasts file. Must be in inputs/contrasts/
+ project_description: null # description of project for report, optional
+ analysis_name: "default" # short string identifying analysis settings. This will appear in the analysis directory and file names
+ batch_var: "batch" # For example, multiple plates
+ dose: "dose" # If there is a dose in the experiment; otherwise use NULL
+ platform: "TempO-Seq" # TempO-Seq Or RNA-Seq
+ nmr_threshold: 100000 # 10% of 1M reads for TempOSeq = 100,000; 10% of 10M reads for RNA-Seq = 1,000,000.
+ write_additional_output: FALSE # export BMD, biomarker, and biosets output? facet variable should contain chemical and timepoint information
+ celltype: "MCF7 cells" # only required for biosets output. Cell type or species used
+ units: "uM" # only required for biosets output. Units of dose
+ species: "human" # one of human, mouse, rat, hamster
+ design: "group" # single experimental group of interest; entries in this column must match the contrast names.
+ intgroup: ["group"] # experimental group of interest plus covariates; can be more than one
+ intgroup_to_plot: ["group"] # for PCA plots, add tabs for one or more groups to color
+ formula_override: null # e.g., "~batch + condition", to enable users to specify the DESeq2 formula to use
+ deseq_facet: "chemical" # If you have many different experimental groups, you may subset the report by specifying a column in the metadata to filter groups, and then setting the group of interest in deseq_filter
+ deseq_filter: null # Which group will this report be done on?
+ reports_facet: null #
+ reports_filter: null #
+ sortcol: "dose" # Optionally, a column by which to sort the contrasts
+ solvent_control: "solvent_control"
+ lenient_contrasts: FALSE # Use either column (exp, cont) of contrasts file to limit what is included in the report (instead of just exp column)
+ strict_contrasts: FALSE # Use BOTH columns (exp, cont) of contrasts file to limit what is included in the report
+ exclude_samples: null # Optionally, a vector of sample names to exclude from the analysis
+ exclude_groups: null # Optionally, a vector of groups to exclude from the analysis. By default this is assumed to be in the column specified by params$design.
+ include_only_column: null # Restrict analysis to group(s) in the column listed here based on params$include_only_group.
+ include_only_group: null # Restrict analysis to this/these group(s) within the column listed in params$include_only_column
+ cpus: 41 # Set to a lower number (e.g., 2 to 4) if you aren't working in a server environment
+ run_pathway_analysis: TRUE # Optionally disable pathway analysis if not available for your organism
+ wikipathways_directory: "~/shared/dbs/wikipathways"
+ linear_fc_filter_DEGs: 1.5 # Default 1.5
+ linear_fc_filter_biosets: 1.2 # Default 1.2. Used for biosets output when write_additional_output=True
+ biospyder_dbs: "~/shared/dbs/biospyder/"
+ biospyder_manifest_file: "181019_Human_S1500_Surrogate_1.2_Manifest.txt"
+ # "191113_Human_S1500_Surrogate_2.0_Manifest.csv"
+ # "181019_Human_S1500_Surrogate_1.2_Manifest.txt"
+ # "191004_Human_Whole_Transcriptome_2.0_Manifest.txt"
+ wikipathways_filename: "wikipathways-20210810-gmt-Homo_sapiens.gmt"
+ KEGGpathways_filename: "c2.cp.kegg_medicus.v2023.2.Hs.symbols.gmt"
+ # "wikipathways-20210810-gmt-Mus_musculus.gmt"
+ # "wikipathways-20210810-gmt-Rattus_norvegicus.gmt"
+ nBestFeatures: 20 # The number of best features to make plots of their counts
+ nBest: 100 # Number of features to include in table and limiting PCA/clustering analysis
+ nHeatmap: 50 # Number of most variable genes for heatmap
+ nHeatmapDEGs: 50 # Number of DEGs for heatmap
+ cooks: FALSE # the DESeq Cook's distance cutoff, or FALSE to disable it
+ filter_gene_counts: FALSE # Filter genes to those with at least 1 count in 1 sample. Disabled by default for biomarker file output. Can enable to increase speed of analysis
+ generate_main_report: TRUE
+ generate_stats_report: TRUE
+ generate_data_explorer_report: TRUE
+ generate_go_pathway_report: TRUE
+ generate_tgxddi_report: TRUE
+ generate_tgxhdaci_report: TRUE
+ output_digits: 5
+ parallel: FALSE
+ species_data: null
+ count_data_file: null
+ sampledata_sep: null
+ MinCount: null
+ alpha: 0.05
+ feature_id: null
+ biospyder: null
+output:
+ html_document:
+ toc: true
+ toc_float: true
+ number_sections: true
+ code_folding: hide
+ theme: spacelab # flatly spacelab sandstone cerulean
+ code_download: true
+bibliography: "`r file.path(params$projectdir, 'references/tgx-hdaci-references.bib')`"
+
+---
+
+
+```{r docSetup, warning = FALSE, message = FALSE, include = F}
+#### Record start time
+startTime <- Sys.time()
+
+library(tidyverse)
+library(cluster)
+library(Cairo)
+library(heatmap3)
+library(dendextend)
+library(ggplot2)
+library(pheatmap)
+library(viridis)
+library(knitr)
+library(kableExtra)
+library(plotly)
+library(gtable)
+library(grid)
+```
+
+
+---
+title: "`r params$platform` TGx-HDACi biomarker report"
+---
+
+# `r params$project_title` {-}
+
+Date report generated: `r format(Sys.time(), '%d %B, %Y')`
+
+Report prepared by: `r params$bioinformatician_name`
+
+Report prepared for: `r params$researcher_name`
+
+***
+
+
+WARNING: The TGx-HDACi biomarker was developed using the TK6 cell line after 4 hours of exposure [@cho_hdaci_2019].
+The results of this analysis may not be applicable to other cell lines, tissues, or experimental designs.
+
+
+Cell line used in this study: `r params$celltype`
+
+This report shows the results of the TGx-HDACi biomarker analysis for the `r params$platform` platform.
+
+TGX-HDACi was developed as a toxicogenomics signature to identify chemicals that cause histone deacetylase inhibition (HDACi).
+It uses an 81-gene classification tool generated from a training set of 20 chemicals: 10 HDACi and 10 non-HDACi chemicals. TK6 cells were exposed to specific doses of these chemicals for 4 hours, then the gene expression changes were measured using a whole-transcriptome TempO-Seq platform.
+
+The probability that a tested chemical is HDACi is calculated based on the similarity of its gene expression profile to the agents in the classifier.
+
+Three analyses are performed on test chemicals using the TGx-HDACi biomarker:
+
+1. Principal component analysis
+
+2. 2-dimensional hierarchical clustering
+
+3. Nearest Shrunken Centroids (NSC) probability analysis, visualized by heatmaps
+
+The test chemical is considered histone deacetylase inhibiting if it is positive in at least one of these analyses.
+Results of all three analyses are summarized in a table at the end of this report.
+
+
+```{r load_facet_data, include=FALSE}
+# Load facet data. Gets allBiomarkers and contrasts_subset for current facet
+load_facet_data(paths, params)
+
+# Have to deal with weird data setup in case2
+if (is.na(params$deseq_facet) && !is.na(params$reports_facet)) {
+ allBiomarkers <- allBiomarkers[["all"]]
+}
+# in case1 and case3, allBiomarkers is fine as is
+```
+
+```{r import_control_info}
+# Get the control from the contrasts (first contrast)
+# This will only work if there's just one control for all the groups in the report
+control <- contrasts_subset %>%
+ dplyr::slice(1) %>%
+ pull(V2)
+```
+
+```{r get_IDs}
+# Temposeq rows are named by gene symbol
+# So get the ensembl IDs from the biospyder manifest
+
+if (params$platform == "TempO-Seq") {
+ biospyder_manifest_file <- paste0(params$biospyder_dbs, params$biospyder_manifest_file)
+ biospyder_manifest_file <- read.delim(biospyder_manifest_file, sep = ",")
+
+ biospyder_IDs <- biospyder_manifest_file %>%
+ dplyr::select(Probe_Name, Ensembl_Gene_ID)
+
+ # Can't just use the Gene_symbol column because sometimes that's an alias and won't match the sample data
+
+ # Take the Probe_Name column, remove everything after the underscore, and group probes for same gene
+ biospyder_IDs$Probe_Name <- sub("_.*", "", biospyder_IDs$Probe_Name)
+ biospyder_IDs <- biospyder_IDs %>%
+ dplyr::distinct() %>%
+ dplyr::rename(SampleID = Probe_Name)
+
+ df <- as.data.frame(allBiomarkers) %>%
+ merge(biospyder_IDs, by = "SampleID") %>%
+ dplyr::rename(ID = Ensembl_Gene_ID) %>%
+ dplyr::select(-SampleID) %>%
+ dplyr::select(ID, everything()) %>%
+ dplyr::distinct()
+} else {
+ df <- as.data.frame(allBiomarkers) %>%
+ dplyr::rename(ID = SampleID)
+}
+```
+
+```{r process_data}
+# Convert all columns except ID to numeric, collapse replicate IDs
+df <- df %>%
+ mutate(across(-ID, as.numeric)) %>%
+ group_by(ID) %>%
+ summarise_all(mean)
+
+## Average the dose replicates
+# Replace sample names with info from the metadata column used for deseq2 design
+designcol <- params$design
+df_no_id <- dplyr::select(df, -ID)
+name_mapping <- setNames(exp_metadata[[designcol]], exp_metadata$original_names)
+colnames(df_no_id) <- name_mapping[colnames(df_no_id)]
+
+# Calculate the row-wise mean for each unique base name, add ID column back on
+df_means <- df_no_id %>%
+ split.default(names(df_no_id)) %>%
+ map_df(rowMeans, na.rm = TRUE)
+df_means <- bind_cols(df["ID"], df_means)
+
+# Subset dataframe to just the reports group in case2
+if (is.na(params$deseq_facet) && !is.na(params$reports_facet)) {
+ # Just want columns for the report facet plus the control
+ filter_columns <- contrasts_subset[[1]]
+ filter_columns <- c("ID", filter_columns, control)
+ #Replace "." with "-" in filter_columns
+ filter_columns <- gsub("\\.", "-", filter_columns)
+
+
+ # Subset the columns from the formatted df
+ df_means <- df_means %>%
+ select(all_of(filter_columns))
+}
+
+# Log2 ratios vs control (from contrasts)
+log2ratios <- df_means
+log2ratios[, -1] <- sweep(log2ratios[, -1], 1, log2ratios[[control]], `-`)
+# Remove the control column
+log2ratios <- dplyr::select(log2ratios, -one_of(control))
+```
+
+```{r load_classifier}
+# Classifier for TK6 cells
+classifier_path <- file.path(params$projectdir, "resources", "HDACi_classifier_TK6_wID_wlog2FC.txt")
+classifier <- read.delim(classifier_path, sep = "\t")
+
+# Save the classifier IDs for later
+classifier_IDs <- classifier %>%
+ dplyr::select(ID, Entrez_ID, Ensembl_Gene_ID) %>%
+ dplyr::rename(Gene_Symbol = ID)
+
+classifier <- classifier %>%
+ dplyr::select(-ID) %>%
+ dplyr::rename(ID = Ensembl_Gene_ID) %>%
+ dplyr::select(ID, everything()) %>%
+ distinct() %>%
+ dplyr::rename("5-FU" = X5.FU,
+ "2-DG" = X2.DG)
+
+HDACi_chems <- c("Api", "Mo", "Oxa", "Pan", "SBHA", "Scr", "Sodbuty", "Tac", "TSA", "Vor")
+
+nHDACi_chems <- c("2-DG", "5-FU", "Ant", "Ble", "CdCl2", "Cisp", "CPT", "HU", "Tun", "Vin")
+```
+
+```{r merge}
+merged <- merge(classifier, log2ratios, by = "ID")
+
+nmatched <- nrow(merged)
+
+missed <- setdiff(classifier$ID, merged$ID)
+```
+
+### Mismatches
+There are **81** genes in the biomarker and your data matched **`r nmatched`** of them.
+
+The following genes were not found in your data: **`r missed`**.
+
+Mismatches can happen because of differences in gene annotation between temposeq platforms.
+
+# Principal Component Analysis
+
+The PCA plot is interactive. Hover over the points on the PCA plot to see chemical names.
+
+```{r PCA, warning=FALSE}
+# Done on log2 ratios
+
+# Save the ID column
+id_column <- merged$ID
+
+# Save just the log2FC columns
+merged_log2 <- merged %>%
+ dplyr::select(-c(1:9))
+
+# Calculate PCA using the classifier's mean and standard deviation
+pc_calc <- (merged_log2 - merged$pc.mean) / merged$pc.sd
+rownames(pc_calc) <- id_column
+
+pc1 <- as.vector(merged$pcl.1) %*% as.matrix(pc_calc)
+pc2 <- as.vector(merged$pcl.2) %*% as.matrix(pc_calc)
+
+df_pca <- data.frame(PC1 = t(pc1), PC2 = t(pc2))
+
+# Add group column
+df_pca <- df_pca %>% mutate(Group = case_when(
+ rownames(df_pca) %in% HDACi_chems ~ "HDACi",
+ rownames(df_pca) %in% nHDACi_chems ~ "Non-HDACi",
+ TRUE ~ "Test"
+))
+
+# Add a new column with the row names
+df_pca$Label <- rownames(df_pca)
+
+testchem_pc1 <- df_pca[21:nrow(df_pca), ] %>%
+ dplyr::select(PC1, Label) %>%
+ dplyr::rename(ID = Label)
+
+all_pc1 <- df_pca %>%
+ dplyr::select(PC1, Label) %>%
+ dplyr::rename(ID = Label)
+
+# Plot
+p <- ggplot(df_pca, aes(PC1, PC2)) +
+ geom_point(aes(color = Group, shape = Group, size = 1.2, text = Label)) +
+ scale_shape_manual(values = c("HDACi" = 19, "Non-HDACi" = 19, "Test" = 17)) +
+ scale_color_manual(values = c("HDACi" = "red", "Non-HDACi" = "blue", "Test" = "darkorange"), name = "Group") +
+ geom_text(data = subset(df_pca, Group == "Test"), aes(label = Label), nudge_y = 0.1, nudge_x = 0) +
+ geom_vline(xintercept = 0, linetype = "dashed", color = "darkgrey") +
+ theme_bw()
+
+# Convert to plotly object
+p <- ggplotly(p, tooltip = "text") # Hover text shows chemical names
+
+# Remove the legend title
+p <- p %>% layout(legend = list(title = list(text = "")))
+
+# Adjust point size, test chemical points are a bit bigger
+p$x$data[[1]]$marker$size <- p$x$data[[1]]$marker$size / 2
+p$x$data[[2]]$marker$size <- p$x$data[[2]]$marker$size / 2
+p$x$data[[3]]$marker$size <- p$x$data[[3]]$marker$size / 1.5
+
+p
+
+# Add the ID column back to merged_log2
+merged_log2 <- merged_log2 %>%
+ mutate(ID = id_column) %>%
+ select(ID, everything())
+```
+
+
+# Hierarchical Clustering {.tabset .tabset-pills}
+```{r Dendrograms, results='asis'}
+# Done on centred and scaled log2 ratios
+
+# Initialize a data frame to store the HC grouping results
+dendro_results_testchems <- data.frame(ID = character(), HC = character(), stringsAsFactors = FALSE)
+dendro_biomarker <- data.frame(ID = names(pc_calc[, 1:20]),
+ HC = ifelse(names(pc_calc[, 1:20]) %in% HDACi_chems, "HDACi", "Non-HDACi"),
+ stringsAsFactors = FALSE)
+
+# Dendrogram for each dose
+for (j in 21:ncol(pc_calc)){
+ cat(paste0("## ", colnames(pc_calc)[j], "\n"))
+ dendro <- dist(t(pc_calc[, c(1:20, j)]))
+ dendro <- hclust(dendro, method = "average")
+ dendro <- as.dendrogram(dendro)
+ labels_colors(dendro) <- c(rep("red", 10),
+ rep("blue", 10),
+ "darkorange")[order.dendrogram(dendro)]
+
+ # Increase the bottom margin to make room for x-axis labels
+ par(mar = c(10, 4, 4, 2)) # Default is c(5, 4, 4, 2) + 0.1
+
+ plot(dendro, horiz = FALSE, main = paste("Euclidean, Average Linkage (log2),", colnames(pc_calc)[j]))
+ cat("\n\n")
+
+ # Reset to default margins
+ par(mar = c(5, 4, 4, 2) + 0.1)
+
+ # Cut the dendrogram into 2 groups
+ dendro_groups <- cutree(dendro, k = 2)
+ test_sample_group <- dendro_groups[length(dendro_groups)]
+
+ # Count the number of HDACi chemicals that are in the same group as the test sample
+ same_group_count <- sum(dendro_groups[names(dendro_groups) %in% HDACi_chems] == test_sample_group)
+
+ # Find if test sample is in the same group as all HDACi chemicals
+ if (same_group_count == length(HDACi_chems)) {
+ result <- "HDACi"
+ } else if (same_group_count > 0) {
+ result <- "UNCERTAIN"
+ } else {
+ result <- "Non-HDACi"
+ }
+
+ # Save results for summary table
+ dendro_results_testchems <- rbind(dendro_results_testchems, data.frame(ID = colnames(pc_calc)[j], HC = result, stringsAsFactors = FALSE))
+}
+
+# Save results for all chemicals
+dendro_results_all <- rbind(dendro_biomarker, dendro_results_testchems)
+```
+
+
+# Nearest Shrunken Centroid Prediction and Heatmap
+```{r NSC}
+# Calculate probabilities of HDACi vs non-HDACi
+my_pred <- data.frame(samples = names(merged)[-c(1:9)], probH = 0, probNH = 0)
+for (k in 1:(ncol(merged) - 9)){
+ #k <- 1
+ H <- sum(((merged[, k + 9] - merged$HDACi.score) / merged$std.dev)^2) - 2 * log(0.5)
+ NH <- sum(((merged[, k + 9] - merged$NonHDACi.score) / merged$std.dev)^2) - 2 * log(0.5)
+ probH <- 1 / (1 + exp(H / 2 - NH / 2))
+ probNH <- 1 - probH
+
+ my_pred[k, 2] <- floor(100 * probH) / 100
+ my_pred[k, 3] <- floor(100 * probNH) / 100
+}
+
+# Save prediction info for summary table
+pred <- my_pred[21:nrow(my_pred), ] %>%
+ dplyr::select(samples, probH) %>%
+ dplyr::rename(ID = samples)
+
+pred_all <- my_pred %>%
+ dplyr::select(samples, probH) %>%
+ dplyr::rename(ID = samples)
+```
+
+```{r HM_annotations}
+# Make summary table for all chemicals, used for annotation bars on heatmap
+t1 <- all_pc1 %>%
+ merge(dendro_results_all, by = "ID") %>%
+ merge(pred_all, by = "ID") %>%
+ dplyr::rename(PCA = PC1, NSC = probH)
+
+t2 <- t1
+
+t2$PCA <- ifelse(sign(t1$PCA) == -1, "HDACi", "Non-HDACi")
+t2$NSC <- ifelse(t1$NSC > 0.5, "HDACi", "Non-HDACi")
+t2$Overall <- apply(t2[, c("PCA", "HC", "NSC")], 1, function(x) ifelse("HDACi" %in% x, "HDACi", "Non-HDACi"))
+
+rownames(t2) <- t2$ID
+t2 <- t2[, c("PCA", "HC", "NSC", "Overall")]
+
+# Convert all columns to factor
+t2[] <- lapply(t2, factor)
+```
+
+```{r setup_heatmap, fig.show='hide'}
+
+winsorize_sd <- function(x) {
+ mn <- mean(x)
+ sd <- sd(x)
+ x <- (x - mn) / sd
+ x[x > 3] <- 3
+ x[x < -3] <- -3
+ return(x)
+}
+
+# Standardize against PC.mn and PC.std from classifier, transform to log10
+standardized_log2 <- as.matrix(merged_log2[, -1])
+standardized_log2 <- (standardized_log2 - merged$pc.mean) / merged$pc.sd
+
+# Add the ID column back, column name "ID"
+standardized_log2 <- cbind(ID = merged_log2$ID, standardized_log2)
+
+# Change the IDs to gene symbol
+standardized_log2 <- standardized_log2 %>%
+ merge(classifier_IDs, by.x = "ID", by.y = "Ensembl_Gene_ID") %>%
+ select(-ID, -Entrez_ID) %>%
+ select(Gene_Symbol, everything())
+
+rownames(standardized_log2) <- standardized_log2$Gene_Symbol
+standardized_log2 <- standardized_log2[, -1]
+
+# Don't know why everything is chr now, but make it numeric
+standardized_log2[] <- lapply(standardized_log2, as.numeric)
+
+# Winsorize because temposeq data can be very bright on heatmap
+# What about RNA-seq data? Should I do a conditional? Should talk to Andrew
+std_win_log2 <- apply(standardized_log2, 2, winsorize_sd)
+
+# Calculate the color scale using the full data set
+breaks <- seq(min(std_win_log2, na.rm = TRUE), max(std_win_log2, na.rm = TRUE), length.out = 17)
+color_scale <- colorRampPalette(c("blue", "white", "gold1"))(17)
+#color_scale <- viridis(17, option = "magma") #will play with colour scale
+
+# Define the color mapping for each annotation bar
+annotation_colors <- list(
+ PCA = c("Non-HDACi" = "blue", "HDACi" = "red"),
+ HC = c("Non-HDACi" = "blue", "UNCERTAIN" = "yellow", "HDACi" = "red"),
+ NSC = c("Non-HDACi" = "blue", "HDACi" = "red"),
+ Overall = c("Non-HDACi" = "blue", "HDACi" = "red")
+)
+
+# Reorder the rows in the annotation to match the matrix
+t2 <- t2[match(colnames(std_win_log2), rownames(t2)), ]
+
+# Create the heatmap
+p <- pheatmap(std_win_log2,
+ cluster_cols = FALSE,
+ # cluster_rows = FALSE,
+ gaps_col = 20,
+ color = color_scale,
+ breaks = breaks,
+ clustering_method = "average",
+ clustering_distance_rows = "correlation",
+ clustering_distance_cols = "correlation",
+ annotation_col = t2,
+ annotation_colors = annotation_colors)
+```
+
+```{r display_heatmap, fig.width=8, fig.height=12}
+# This is necessary to make a heatmap with a reasonable legend
+# Remove the legends for PCA, HC, and NSC
+gt <- p$gtable
+
+# Change the legend title
+annlegend_grob <- gt$grobs[[7]]
+annlegend_grob$children[[1]]$label <- "Prediction"
+
+# Create the new gTree
+new_annlegend_grob <- gTree(children = gList(annlegend_grob$children[[1]], annlegend_grob$children[[2]], annlegend_grob$children[[3]]), cl = "gTree")
+new_annlegend_grob$widths <- annlegend_grob$widths
+new_annlegend_grob$heights <- annlegend_grob$heights
+new_annlegend_grob$vp <- annlegend_grob$vp
+
+# Replace the original legend with the modified one
+gt$grobs[[7]] <- new_annlegend_grob
+
+# Display the modified heatmap
+plot.new()
+grid::grid.draw(gt)
+```
+
+# Summary Table
+
+```{r summary}
+# Create a summary table of test chemicals
+summary_table1 <- testchem_pc1 %>%
+ merge(dendro_results_testchems, by = "ID") %>%
+ merge(pred, by = "ID") %>%
+ dplyr::rename(PCA = PC1, NSC = probH)
+
+# Initialize
+summary_table2 <- summary_table1
+
+# Assign "HDACi" or "Non-HDACi" based on results in the three tests
+summary_table2$PCA <- ifelse(sign(summary_table1$PCA) == -1, "HDACi", "Non-HDACi")
+summary_table2$HC <- summary_table1$HC
+summary_table2$NSC <- ifelse(summary_table1$NSC == 1, "HDACi", "Non-HDACi")
+summary_table2$Overall <- apply(summary_table2[, c("PCA", "HC", "NSC")], 1, function(x) ifelse("HDACi" %in% x, "HDACi", "Non-HDACi"))
+
+# Color table cells
+color_cells <- function(x) {
+ ifelse(x == "HDACi", cell_spec(x, "html", color = "red"), cell_spec(x, "html", color = "blue"))
+}
+summary_table2$Overall <- sapply(summary_table2$Overall, color_cells)
+
+# Print the formatted table
+kable(summary_table2, caption = "Summary Table", escape = FALSE) %>%
+ kable_styling("striped", full_width = FALSE)
+
+
+# Output the summary table to a file
+# Remove HTML tags from the 'Overall' column
+summary_table2$Overall <- gsub("<.*?>", "", summary_table2$Overall)
+
+write.csv(summary_table2, file = file.path(paths$reports_dir, paste0(params$reports_filter, "_tgx-HDACi_results.csv")), row.names = FALSE)
+```
+
+
+# References
+
+---
+nocite: '@*'
+---
+
+
+
+
+
+```{r, child=file.path(paths$projectdir,'Rmd','session_info.Rmd')}
+```
+
+
+
\ No newline at end of file
diff --git a/Rmd/tgx_ddi.Rmd b/Rmd/tgx_ddi.Rmd
index af7c9e28..601165e1 100644
--- a/Rmd/tgx_ddi.Rmd
+++ b/Rmd/tgx_ddi.Rmd
@@ -57,6 +57,7 @@ params:
generate_data_explorer_report: TRUE
generate_go_pathway_report: TRUE
generate_tgxddi_report: TRUE
+ generate_tgxhdaci_report: TRUE
output_digits: 5
parallel: FALSE
species_data: null
@@ -74,7 +75,7 @@ output:
code_folding: hide
theme: spacelab # flatly spacelab sandstone cerulean
code_download: true
-bibliography: "`r file.path(params$projectdir, 'Rmd/tgx-ddi-references.bib')`"
+bibliography: "`r file.path(params$projectdir, 'references/tgx-ddi-references.bib')`"
---
@@ -297,8 +298,10 @@ missed <- setdiff(classifier$ID, merged_log10$ID)
# classifier_IDs %>% dplyr::filter(Ensembl_Gene_ID %in% missed)
```
-There are 64 genes in the biomarker and your data matched `r nmatched` of them.
-The following genes were not found in your data: `r missed`.
+### Mismatches
+There are **64** genes in the biomarker and your data matched **`r nmatched`** of them.
+
+The following genes were not found in your data: **`r missed`.**
Mismatches can happen because of differences in gene annotation between temposeq platforms.
diff --git a/inputs/config/config.default.yaml b/inputs/config/config.default.yaml
index e58406d3..00e704d8 100644
--- a/inputs/config/config.default.yaml
+++ b/inputs/config/config.default.yaml
@@ -90,5 +90,6 @@ DESeq2:
generate_data_explorer_report: TRUE
generate_go_pathway_report: FALSE
generate_tgxddi_report: FALSE
+ generate_tgxhdaci_report: FALSE
output_digits: 5 # control rounding of DEG output. Primarily used to suppress RNG variability so our tests don't fail :)
parallel: FALSE
diff --git a/Rmd/references.bib b/references/references.bib
similarity index 100%
rename from Rmd/references.bib
rename to references/references.bib
diff --git a/Rmd/tgx-ddi-references.bib b/references/tgx-ddi-references.bib
similarity index 100%
rename from Rmd/tgx-ddi-references.bib
rename to references/tgx-ddi-references.bib
diff --git a/references/tgx-hdaci-references.bib b/references/tgx-hdaci-references.bib
new file mode 100644
index 00000000..d75424db
--- /dev/null
+++ b/references/tgx-hdaci-references.bib
@@ -0,0 +1,17 @@
+@article{cho_hdaci_2019,
+ title = {Assessment of the performance of the {TGx}‐{DDI} biomarker to detect {DNA} damage‐inducing agents using quantitative {RT}‐{PCR} in {TK6} cells},
+ volume = {60},
+ issn = {0893-6692},
+ url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6588084/},
+ doi = {10.1002/em.22257},
+ number = {2},
+ urldate = {2024-06-24},
+ journal = {Environmental and Molecular Mutagenesis},
+ author = {Cho, Eunnara and Buick, Julie K. and Williams, Andrew and Chen, Renxiang and Li, Heng‐Hong and Corton, J. Christopher and Fornace, Albert J. and Aubrecht, Jiri and Yauk, Carole L.},
+ month = mar,
+ year = {2019},
+ pmid = {30488505},
+ pmcid = {PMC6588084},
+ keywords = {tgx-ddi, HC, tgx},
+ pages = {122--133},
+}
diff --git a/resources/HDACi_classifier_TK6_wID_wlog2FC.txt b/resources/HDACi_classifier_TK6_wID_wlog2FC.txt
new file mode 100644
index 00000000..86baa0cd
--- /dev/null
+++ b/resources/HDACi_classifier_TK6_wID_wlog2FC.txt
@@ -0,0 +1,82 @@
+ID Ensembl_Gene_ID Entrez_ID pcl.1 pcl.2 pc.mean pc.sd NonHDACi.score HDACi.score std.dev Api Mo Oxa Pan SBHA Scr Sodbuty Tac TSA Vor X2.DG X5.FU Ant Ble CdCl2 Cisp CPT HU Tun Vin
+AKAP8 ENSG00000105127 10270 0.115859770739922 -0.138421527463321 -0.92165828112778 0.947987624394474 -0.085666477 -1.757650085 0.930348038 -1.80990180021116 -0.902281794815164 -1.78282862275653 -2.17182875889112 -2.04941500589546 -2.28634847796368 -1.7246166868828 -0.796392801046356 -1.94451204575672 -2.10837486056958 -0.267875026261747 0.170296162939418 0.12973289763948 -0.372494789404157 -0.201635143916079 0.0258788855112313 0.438617432008389 -0.421825902594567 -0.384331524262062 0.0269722405730661
+AP5S1 ENSG00000125843 55317 0.11595360370849 -0.121343545727294 -0.876345955892737 1.10258022412672 0.048784837 -1.801476749 1.091951269 -1.90523446982151 -0.319943553906973 -2.16879030962848 -2.41439126808957 -1.97426238190666 -2.5214798264613 -1.68454609671011 -0.505775843695639 -2.2291083222361 -2.29123542005034 0.352453140181404 -0.30152249612238 0.16687563635385 0.360262643990298 -0.084883812214965 -0.289294539867159 0.0495775481110412 0.108184114439983 0.326582467263338 -0.200386327483467
+ATP1B1 ENSG00000143153 481 -0.107855588904442 -0.102955577825268 0.311601979887496 1.04967146676658 -0.645081646 1.268285605 0.897743325 1.89241542928173 0.953699742538177 1.58511259405767 0.809696061753205 0.993817113487322 1.11800315355616 1.2560883332636 1.88711543008855 1.25902867389169 0.927879521575813 -0.665259592081634 -0.400290496817061 -0.952747360561048 -0.875158826811167 -0.676947355882386 0.00646054149037623 -0.285980521821657 -0.862392008306592 -1.27126447277617 -0.46723636217666
+BMF ENSG00000104081 90427 -0.116715104178636 -0.0560085432699146 1.84970450630023 1.87095899407299 0.135304768 3.564104244 1.170680954 3.34700479235924 2.51531016171849 3.24868361488142 4.47466492483518 4.03183896045518 4.41529144559086 2.73202262836304 3.34252983497908 3.70656895281459 3.82712712528591 -0.227004862169451 -0.309619202382523 -0.542015882288924 -0.10089553197272 -0.990820241253614 0.518076809700435 0.90010804850286 0.534401656261773 0.666936562103603 0.903880328220142
+BNIP1 ENSG00000113734 662 0.111960126503611 -0.0468629611944358 -0.217157882673267 0.769277521845119 0.464348467 -0.898664232 0.845091166 -0.665315702108007 -0.482470558178822 -0.80214237360902 -1.0059635790914 -1.00131606367685 -1.3784825303205 -0.640012001919188 -0.688848725206258 -1.04777826760432 -1.27431252031384 -0.102123793976483 0.824128110885472 0.700399255036531 0.0306953775591014 0.848991097212763 0.319898907927956 0.942258534620934 0.306033135598763 0.209194271889612 0.564009771808209
+C9orf69 ENSG00000238227 NA 0.113553192224616 0.0665640465918842 -0.548760126972285 0.947484179098044 0.244559458 -1.342079712 1.013845903 -1.47646637884909 -0.63954571613176 -1.37681306515381 -1.6819128483941 -1.33273447272076 -1.86811333647517 -1.42301732979568 -0.729072302584902 -1.42860571922445 -1.46451595173134 0.807936611597533 -0.344433134528286 -0.366568178595455 1.03973846717839 0.160985580545463 0.134127848355251 0.723596649232702 0.976576984638689 -0.258665177388054 -0.427701069420874
+CCDC144NL ENSG00000205212 339184 -0.110308239006416 -0.0441253151553521 0.55147753139759 1.64072301447962 -0.852005126 1.954960189 1.323555371 3.0896945346627 0.61313351089874 2.32797099722984 1.84035092415902 1.8918533100394 2.09922227309357 2.04684446053128 1.39534301730433 2.20785873133414 2.03733012644617 -0.809017812842854 -0.794853673540899 0.922089925303996 -0.671595358793107 -2.85370065005405 -0.627634640954242 -0.421391122445308 -1.20957571379148 -1.51595439383324 -0.538417816796211
+CDK5R1 ENSG00000176749 8851 0.113678732420909 -0.151019607299035 -0.749518953639277 0.917929702285931 0.042625827 -1.541663735 0.953929064 -1.64414242498978 -0.642664489121737 -1.90212780376715 -1.62818193003971 -1.78864251158722 -1.99538855245458 -1.59220295435527 -0.507062969096927 -1.83100172833452 -1.88522198211609 0.340716897459824 0.199019558757816 0.187242560661834 0.116932869213782 -0.598415494682729 -0.414387416985718 0.220053499430467 -0.0899978673496196 0.433782452863804 0.0313112137079775
+CEP68 ENSG00000011523 23177 -0.101535902637366 -0.113773473358221 0.575491030676457 1.31137140475822 -0.508016145 1.658998206 1.230237815 0.974176492049851 1.51606477285506 0.87684270171603 1.87365251244482 2.06892134155989 2.46818124970864 0.928803378807022 2.05332769788202 1.80351798177071 2.02649393056122 -0.947318855004843 -0.661280467604359 0.574816533405787 -1.17936662735214 -1.36532760310219 0.247374745054832 0.867541899631119 -0.628835403809208 -1.64793061927696 -0.339835047768165
+COIL ENSG00000121058 8161 0.117177284546795 -0.0843816474268829 -0.801683104216662 1.15403451608762 0.190939196 -1.794305405 1.073213766 -1.78809362878888 -0.691599441447359 -1.73445913803988 -2.15386274786357 -2.18624663390705 -2.75654474782672 -1.49843982170043 -0.397861924340187 -2.27605715048434 -2.45988881350392 0.563151154798879 0.203175082936679 0.366973118930345 0.426014291296234 0.18757799249182 0.0390864055441029 0.118063087521228 0.227358029068776 -0.0107810065881659 -0.211226192430808
+CPEB4 ENSG00000113742 80315 -0.110784088926371 -0.0256962854943266 0.654911901904206 1.23006529765735 -0.370405666 1.680229469 1.170540829 1.25102668503607 0.748363153847769 1.08094309875945 2.00401399806854 1.79606187025515 2.37967976243238 2.23429209394501 0.881537908855967 2.31118461811643 2.11519150456646 -1.11251209468679 -0.0513547449716091 0.747453960447968 -0.573262658509253 -0.857566624980449 -1.28880208332358 -0.13768057273277 -0.732347498329465 0.608315326525757 -0.306299665238924
+DMXL1 ENSG00000172869 1657 -0.117229867573605 0.026095309613864 0.509544601600794 1.17038400325092 -0.496039683 1.515128886 1.083286109 1.58367097130746 -0.0187525093624211 1.59566938025278 1.92411604802826 1.78064467864 2.12202351375272 2.11208692899333 0.75797049087447 1.42122497386684 1.87263438708376 -0.551609794267578 -0.390561006536631 0.0270191010862703 -0.531481433860874 -1.09562892481257 -0.459350071420792 -1.27057089233653 -0.671250203463307 -0.0788416556851499 0.0618780498758459
+DTWD1 ENSG00000104047 56986 0.105481550554621 -0.0152698817965754 -0.426753080588313 0.748006722863828 0.227369844 -1.080876005 0.854914264 -0.481255495242793 -0.939320560384244 -0.549337106501515 -1.60600284789455 -0.998008666667176 -1.06845117815398 -1.49036350375909 -1.17154176096493 -1.22258878764324 -1.28189014439951 -0.10949591676742 0.426583937096676 0.555610408967419 0.129655191870007 -0.368316583757954 0.260369120144386 0.682097068004162 0.312398286078424 0.349093952605826 0.0357029756032339
+DYRK3 ENSG00000143479 8444 -0.117438768630885 0.0542101011050083 0.869165805765749 0.98218436764526 -0.030766832 1.769098443 0.85964732 1.86422754813974 0.844048379594601 1.84416202259817 2.04985798097167 2.00637254836798 1.96402056252602 1.92688649241753 1.26739295617695 1.89594246068775 2.02807348308815 0.141642959043604 -0.100257956055151 -0.487067364013293 -0.0585314981728196 -0.371595083204415 0.186051308936427 0.486180822224416 0.153141841673756 -0.153732337885591 -0.103501011800512
+E2F8 ENSG00000129173 79733 0.113082507575848 0.013995283986726 -0.635244297290653 0.994983852999771 0.226441342 -1.496929937 0.984564218 -1.98878454735252 -1.10518824584196 -1.83623614818628 -1.69978247406669 -1.46267005587183 -1.68518974578224 -1.74479931623855 -0.549771240471098 -1.47682547777816 -1.42005211497778 0.344218898424835 0.0078247092479995 -0.166975417187894 0.422842780153401 0.0232572534679472 0.221733713751551 0.962363419823129 1.14077279045129 -0.543080819298219 -0.148543908079988
+EAF2 ENSG00000145088 55840 -0.117415525372472 0.0821715791448977 0.588025705654047 0.736420565941719 -0.05837635 1.234427761 0.844443644 1.34551143960939 0.162106530311275 1.42276661056908 1.34054178599933 1.53895241402856 1.63955814615922 1.23809051895568 0.839092869841899 1.34099010442872 1.47666719116361 -0.258340759855735 0.0264060545986993 -0.000409263138653119 -0.085685337106199 -0.198113381156636 -0.125928612176105 -0.238851992808822 0.259541259320317 0.149578137679166 -0.111959603341848
+ERRFI1 ENSG00000116285 54206 -0.107949141231499 -0.203471717903297 0.662231817963452 0.957984148759186 -0.133882619 1.458346254 1.02982749 1.904049571921 1.02937669988561 1.43278290595251 1.59565414259718 1.14586051069086 1.9334577339747 1.33210347963422 1.59865768304063 1.42868280370606 1.18283701297588 -0.794415084741465 -0.0229639179276393 -0.358614733023249 -0.789774620308456 -0.673126622938245 0.355640304675895 0.434352943063346 -0.911572204614213 0.477863449481017 0.9437843012234
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+ZNF383 ENSG00000188283 163087 0.1054993201924 -0.0956893578778185 -0.499682429493632 0.962250935171608 0.300588452 -1.299953311 1.031066406 -1.32364791195613 -0.922389300822329 -1.31410255458144 -1.51531072276698 -1.46537208483228 -1.47040184009961 -1.10830602537613 -0.682599774539738 -1.57837470665135 -1.61902818965651 -0.386593808790548 0.275993952279023 1.0731394834613 0.0702894602140562 -0.38303435907183 0.176493332208175 1.78476797753015 0.302853216447656 0.083232178496063 0.00874308863581004
diff --git a/scripts/render_DESeq2_report.parallel.R b/scripts/render_DESeq2_report.parallel.R
index 63ce3387..51bc4a2f 100644
--- a/scripts/render_DESeq2_report.parallel.R
+++ b/scripts/render_DESeq2_report.parallel.R
@@ -117,11 +117,28 @@ if (params$generate_tgxddi_report) {
}
}
+if (params$generate_tgxhdaci_report) {
+ if (params$species == "human") {
+ base::mapply(FUN = make_hdaci_reports, facet = facets, MoreArgs = list(pars = params, paths = paths))
+ # Concatenate all the TGxHDACi output csv files into one
+ tgxhdaci_files <- list.files(paths$reports_dir, pattern = "_tgx-HDACi_results.csv", full.names = TRUE)
+ tgxhdaci_df <- readr::read_csv(tgxhdaci_files[1], show_col_types = FALSE)
+ for (i in 2:length(tgxhdaci_files)) {
+ tgxhdaci_df <- dplyr::bind_rows(tgxhdaci_df, readr::read_csv(tgxhdaci_files[i], show_col_types = FALSE))
+ }
+ # Write out the concatenated file
+ readr::write_csv(tgxhdaci_df, file.path(paths$reports_dir, paste0("tgx-hdaci_results_summary.csv")))
+ # Delete the individual files
+ file.remove(tgxhdaci_files)
+ }
+ else {
+ message("TGx-HDACi report generation is currently only human datasets. Your parameters indicate that the data is from", params$species, ". Skipping TGx-HDACi analysis.")
+ }
+}
-# Add back after troubleshooting above code...
-# if (!is.na(params$reports_facet)) {
-# summarize_across_facets(overallResListAll, overallResListDEGs, filtered_table, facets, params)
-# }
+if (!is.na(params$reports_facet)) {
+ summarize_across_facets(overallResListAll, overallResListDEGs, filtered_table, facets, params)
+}
# NOTE Manually clean up temporary files
# This is required because of the clean_tmpfiles_mod() workaround!