Fix tidyr::separate_rows issue with plots

.Rbuildignore

@@ -1,7 +1,10 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^doc$
+^docker$
 ^Meta$
 ^validation$
 ^CLAUDE\.md$
 ^CLAUDE_CHANGELOG\.md$
+^\.positai$
+^\.claude$

.gitignore

@@ -28,3 +28,4 @@
 CoSIA.Rproj
 /doc/
 /Meta/
+.positai

R/CoSIA-plots-method.R

@@ -74,7 +74,7 @@ setMethod(
         filter_gex <- filter_gex[
             filter_gex$Anatomical_entity_name %in% single_tissue,
         ]
-        filter_gex <- tidyr::separate_rows(filter_gex, VST)
+        filter_gex <- tidyr::unnest(filter_gex, VST)
         filter_gex$VST <- as.numeric(filter_gex$VST)
         filter_gex <- as.data.frame(filter_gex)
         brewer.pal.info <- RColorBrewer::brewer.pal.info
@@ -102,6 +102,7 @@ setMethod(
         fig <- fig %>%
             plotly::add_trace(
                 type = "violin",
+                mode = NULL,
                 spanmode = "hard",
                 showlegend = FALSE
             )
@@ -212,7 +213,7 @@ setMethod(
         ids <- as.character(ids)
         filter_gex <- gex_dataframe[gex_dataframe$Ensembl_ID %in% ids, ]
         filter_gex <- filter_gex[filter_gex$Species %in% single_species, ]
-        filter_gex <- tidyr::separate_rows(filter_gex, VST)
+        filter_gex <- tidyr::unnest(filter_gex, VST)
         filter_gex$VST <- as.numeric(filter_gex$VST)
 
         brewer.pal.info <- RColorBrewer::brewer.pal.info
@@ -242,6 +243,7 @@ setMethod(
         fig <- fig %>%
             plotly::add_trace(
                 type = "violin",
+                mode = NULL,
                 spanmode = "hard",
                 showlegend = FALSE
             )
@@ -403,7 +405,7 @@ setMethod("plotDSGEx", signature(object = "CoSIAn"), function(object) {
 #'         "h_sapiens", "r_norvegicus"
 #'     ), output_ids = c("Ensembl_id", "Symbol"),
 #'     mapping_tool = "annotationDBI", ortholog_database = "HomoloGene",
-#'     map_tissues = c("adult mammalian kidney","heart"), 
+#'     map_tissues = c("adult mammalian kidney","heart"),
 #'     map_species = c("h_sapiens", "r_norvegicus"),
 #'     metric_type = "CV_Tissue"
 #' )
@@ -429,15 +431,15 @@ setGeneric("plotCVGEx", function(object) standardGeneric("plotCVGEx"))
 #'         "h_sapiens", "r_norvegicus"
 #'     ), output_ids = c("Ensembl_id", "Symbol"),
 #'     mapping_tool = "annotationDBI", ortholog_database = "HomoloGene",
-#'     map_tissues = c("adult mammalian kidney","heart"), 
+#'     map_tissues = c("adult mammalian kidney","heart"),
 #'     map_species = c("h_sapiens", "r_norvegicus"),
 #'     metric_type = "CV_Tissue"
 #' )
 #' Kidney_gene_conversion <- CoSIA::getConversion(Kidney_Genes)
 #' Kidney_gene_metric <- getGExMetrics(Kidney_gene_conversion)
 #' plot <- plotCVGEx(Kidney_gene_metric)
-#' @references Dot plot in R with the dotchart function [with examples]. 
-#' R CODER. (2020, November 20). Retrieved from https://r-coder.com/dot-plot-r/ 
+#' @references Dot plot in R with the dotchart function [with examples].
+#' R CODER. (2020, November 20). Retrieved from https://r-coder.com/dot-plot-r/
 setMethod("plotCVGEx", signature(object = "CoSIAn"), function(object) {
     # set object slots into variables
     metric_type <- object@metric_type

docker/Dockerfile_1.10.1

@@ -0,0 +1,83 @@
+FROM bioconductor/bioconductor:devel-r-4.6.0
+# includes: BIOCONDUCTOR_DOCKER_VERSION=3.23.22
+
+##----------------------------------------------------------------------------##
+## SYSTEM PACKAGES
+##----------------------------------------------------------------------------##
+
+RUN apt-get update --yes && \
+  apt-get upgrade --yes
+
+RUN apt-get install --yes \
+  python3-pip \
+  python3-dev \
+  python3-pip \
+  python3-tk
+
+
+RUN apt-get install --yes \
+  libcurl4-openssl-dev \
+  libssl-dev \
+  libcairo2-dev libxt-dev \
+  libxml2-dev \
+  libudunits2-dev \
+  libhdf5-dev \
+  libv8-dev \
+  libgdal-dev \
+  xorg libx11-dev libglu1-mesa-dev \
+  samtools libboost-all-dev \
+  libgsl-dev
+
+RUN apt-get install -y \
+	wget \
+	unzip \
+	git
+
+
+##----------------------------------------------------------------------------##
+##  R PACKAGES
+##----------------------------------------------------------------------------##
+
+#### Leave the top line the same for each new project. Uncomment lines to add ####
+RUN R -e 'install.packages(c("tidyverse", "remotes",  "BiocManager", "devtools", "knitr", "lintr", "styler", "here", "markdown", "rmarkdown", "reticulate"))' \
+#  && R -e 'install.packages(c("< insert CRAN repos here >"))' \
+#  && R -e 'devtools::install_github("< insert GitHub repos here >")' \
+#  && R -e 'remotes::install_github("< insert GitHub repos here >")' \
+  && R -e 'BiocManager::install(c("BiocFileCache", "BiocGenerics", "AnnotationHub", "ExperimentHub", "CoSIA", "CoSIAdata"))'
+
+
+##----------------------------------------------------------------------------##
+## PYTHON PACKAGES
+##----------------------------------------------------------------------------##
+
+
+RUN pip3 install kaleido plotly --break-system-packages
+RUN wget -q -O - https://dl.google.com/linux/linux_signing_key.pub | apt-key add - \
+  && echo "deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main" > /etc/apt/sources.list.d/google-chrome.list \
+  && apt-get update --yes \
+  && apt-get install --yes google-chrome-stable
+
+##----------------------------------------------------------------------------##
+## 3RD PARTY PROGRAMS
+##----------------------------------------------------------------------------##
+
+#### Uncomment these lines if you need a conda env in your docker ####
+
+# Install miniconda
+# ENV CONDA_DIR /opt/conda
+# RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && \
+#     /bin/bash ~/miniconda.sh -b -p /opt/conda
+# ENV PATH=$CONDA_DIR/bin:$PATH
+
+
+##----------------------------------------------------------------------------##
+## CONDA ENV
+##----------------------------------------------------------------------------##
+
+#### Uncomment these lines if you need a conda env in your docker ####
+
+#RUN conda config --set channel_priority false && \
+#    conda env create -f opt/BLAZE-1.1.0/conda_env/environment.yml
+
+#SHELL ["conda", "run", "-n", "blaze", "/bin/bash", "-c"]
+

tests/test.sh

@@ -1,11 +1,10 @@
 #!/usr/bin/env bash
 set -euo pipefail
 
-PROJECT_PATH="/data/user/$USER/CoSIA"
-DEV_PATH="/data/user/$USER/CoSIA"
+PROJECT_PATH="$(pwd)"
 CONTAINER_PATH="$PROJECT_PATH/bin/container"
-SIF="${CONTAINER_PATH}/bioc_cosia_1.10.0.sif"
-SIF_URI="${COSIA_SIF_URI:-docker://lizzyr/bioc_cosia:1.10.0}"
+SIF="${CONTAINER_PATH}/bioc_cosia_1.10.1.sif"
+SIF_URI="${COSIA_SIF_URI:-docker://lasseignelab/bioc_cosia:1.10.1}"
 
 ensure_sif() {
     mkdir -p "$CONTAINER_PATH"
@@ -33,7 +32,6 @@ mkdir -p \
 singularity exec --cleanenv \
   --containall \
   --home "${CACHE_BASE}/home:/home/${USER}" \
-  -B "${DEV_PATH}" \
   -B "${PROJECT_PATH}" \
   -B "${CACHE_BASE}:${CACHE_BASE}" \
   -B "${TMP_BASE}:/tmp" \

tests/testthat/test-plots.R

@@ -0,0 +1,97 @@
+# plotSpeciesGEx and plotTissueGEx tests
+# These run offline — VST data is injected as a list column to match the real
+# ExperimentHub format and catch the separate_rows vs unnest regression.
+
+make_plot_cosian <- function() {
+    obj <- make_cosian(map_species = "m_musculus", map_tissues = "heart")
+
+    mock_gex <- data.frame(
+        Anatomical_entity_name = c("heart", "heart"),
+        Ensembl_ID             = c("ENSG00000008710", "ENSMUSG00000032855"),
+        Sample_size            = c(3L, 3L),
+        Experiment_ID          = c("EXP1", "EXP2"),
+        Anatomical_entity_ID   = c("UBERON:0000948", "UBERON:0000948"),
+        Species                = c("Homo_sapiens", "Mus_musculus"),
+        Scaled_Median_VST      = c(1.0, 1.0),
+        mad                    = c(0.1, 0.1),
+        stringsAsFactors       = FALSE
+    )
+    mock_gex$VST <- list(c(10.5, 11.0, 12.0), c(11.0, 11.5, 11.8))
+
+    obj@gex <- mock_gex
+    obj@converted_id <- data.frame(
+        h_sapiens_ensembl_id  = "ENSG00000008710",
+        h_sapiens_symbol      = "PKD1",
+        m_musculus_ensembl_id = "ENSMUSG00000032855",
+        m_musculus_symbol     = "Pkd1",
+        stringsAsFactors      = FALSE
+    )
+    obj
+}
+
+# --- plotSpeciesGEx ---
+
+test_that("plotSpeciesGEx returns a plotly object", {
+    obj <- make_plot_cosian()
+    fig <- plotSpeciesGEx(obj, "heart", "ENSG00000008710")
+    expect_true(inherits(fig, "plotly"))
+})
+
+test_that("plotSpeciesGEx VST values are non-NA (list-column unnest)", {
+    obj <- make_plot_cosian()
+    fig <- plotly::plotly_build(plotSpeciesGEx(obj, "heart", "ENSG00000008710"))
+
+    all_y <- unlist(lapply(fig$x$data, function(tr) tr$y))
+    expect_false(
+        all(is.na(all_y)),
+        label = "all VST values are NA — separate_rows regression likely"
+    )
+    expect_true(all(all_y > 0, na.rm = TRUE))
+})
+
+
+# --- plotTissueGEx ---
+
+make_tissue_cosian <- function() {
+    obj <- make_cosian(map_species = "h_sapiens", map_tissues = c("heart", "brain"))
+
+    mock_gex <- data.frame(
+        Anatomical_entity_name = c("heart", "brain"),
+        Ensembl_ID             = c("ENSG00000008710", "ENSG00000008710"),
+        Sample_size            = c(3L, 4L),
+        Experiment_ID          = c("EXP1", "EXP2"),
+        Anatomical_entity_ID   = c("UBERON:0000948", "UBERON:0000955"),
+        Species                = c("Homo_sapiens", "Homo_sapiens"),
+        Scaled_Median_VST      = c(1.0, 1.0),
+        mad                    = c(0.1, 0.1),
+        stringsAsFactors       = FALSE
+    )
+    mock_gex$VST <- list(c(10.5, 11.0, 12.0), c(9.5, 10.0, 10.5, 11.0))
+
+    obj@gex <- mock_gex
+    obj@converted_id <- data.frame(
+        h_sapiens_ensembl_id = "ENSG00000008710",
+        h_sapiens_symbol     = "PKD1",
+        stringsAsFactors     = FALSE
+    )
+    obj
+}
+
+test_that("plotTissueGEx returns a plotly object", {
+    obj <- make_tissue_cosian()
+    fig <- plotTissueGEx(obj, "h_sapiens", "ENSG00000008710")
+    expect_true(inherits(fig, "plotly"))
+})
+
+test_that("plotTissueGEx VST values are non-NA (list-column unnest)", {
+    obj <- make_tissue_cosian()
+    fig <- plotly::plotly_build(plotTissueGEx(obj, "h_sapiens", "ENSG00000008710"))
+
+    all_y <- unlist(lapply(fig$x$data, function(tr) tr$y))
+    expect_false(
+        all(is.na(all_y)),
+        label = "all VST values are NA — separate_rows regression likely"
+    )
+    expect_true(all(all_y > 0, na.rm = TRUE))
+})
+

validation/gene_expression.R

@@ -0,0 +1,69 @@
+# validation/gene_expression.R
+#
+# Creates the gene expression plot that was not working after updating CoSIA.
+#
+# Usage (from repo root):
+#   Run the script in RStudio.
+#
+# Output: Gene expression plot in the RStudio Viewer window
+
+devtools::load_all(".")
+
+loaded_version <- as.character(packageVersion("CoSIA"))
+loaded_path    <- find.package("CoSIA")
+
+cat("CoSIA version  :", loaded_version, "\n")
+cat("CoSIA path     :", loaded_path, "\n\n")
+
+GENE_SET   <- c("ENSG00000008710", "ENSG00000118762")
+I_SPECIES  <- "h_sapiens"
+INPUT_ID   <- "Ensembl_id"
+OUTPUT_IDS <- c("Ensembl_id")
+TOOL       <- "annotationDBI"
+ORTHO_DB   <- "HomoloGene"
+TISSUE     <- "brain"
+MULTI_SPECIES <- c("h_sapiens", "m_musculus")
+
+make_obj <- function(map_species, map_tissues, metric_type,
+  o_species = map_species) {
+  obj <- CoSIAn(
+    gene_set          = GENE_SET,
+    i_species         = I_SPECIES,
+    input_id          = INPUT_ID,
+    o_species         = o_species,
+    output_ids        = OUTPUT_IDS,
+    mapping_tool      = TOOL,
+    ortholog_database = ORTHO_DB,
+    map_tissues       = map_tissues,
+    map_species       = map_species,
+    metric_type       = metric_type
+  )
+  getConversion(obj)
+}
+
+run_metric <- function(label, ...) {
+  cat("---", label, "---\n")
+  tryCatch({
+    obj    <- make_obj(...)
+    obj    <- getGEx(obj)
+    cat("  SUCCESS \n")
+    obj
+  }, error = function(e) {
+    cat("  ERROR:", conditionMessage(e), "\n\n")
+    NULL
+  })
+}
+
+gex_species_multi = run_metric(
+  "DS_Gene multi-species",
+  map_species  = MULTI_SPECIES,
+  map_tissues  = TISSUE,
+  metric_type  = "DS_Gene",
+  o_species    = MULTI_SPECIES
+)
+
+# print(CoSIA::plotSpeciesGEx(gex_species_multi, "brain", "ENSG00000008710"))
+
+dir.create("validation/output", showWarnings = FALSE, recursive = TRUE)
+p <- CoSIA::plotSpeciesGEx(gex_species_multi, "brain", "ENSG00000008710")
+plotly::save_image(p, "validation/output/gex_plot.png", width = 800, height = 600, scale = 2)