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Merge branch 'devel' into layer_stat_cor_plot_original_colors
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‎DESCRIPTION‎

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Package: spatialLIBD
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Title: spatialLIBD: an R/Bioconductor package to visualize spatially-resolved
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transcriptomics data
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Version: 1.19.5
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Date: 2024-12-16
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Version: 1.19.7
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Date: 2025-02-28
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Authors@R:
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c(
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person("Leonardo", "Collado-Torres", role = c("aut", "cre"),

‎NEWS.md‎

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# spatialLIBD 1.19.7
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BUG FIXES
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* Removed code that made the app very slow to load using `HDF5Array` objects.
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See
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<https://github.com/LieberInstitute/spatialLIBD/commit/b80d92c3271a6ad92859f79a3bc343f77bad9bf2>
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for details.
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* Fixed a bug on the Z-score calculation in `multi_gene_z_score()`. See
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<https://github.com/LieberInstitute/spatialLIBD/commit/2d17ea2c3d1b73b38fbd50503765052c8487b9b1>
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for details. Implemented by @Nick-Eagles.
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* Fix a bug in the documentation of `run_app()` for the example stitched data.
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* No longer point to Twitter, instead point to Bluesky. This is for the package
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main README file as well as the default app documentation files.
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# spatialLIBD 1.19.5
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BUG FIXES

‎R/add10xVisiumAnalysis.R‎

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#'
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#' ## Note that ?SpatialExperiment::read10xVisium doesn't include all the files
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#' ## we need to illustrate read10xVisiumWrapper().
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add10xVisiumAnalysis <- function(spe,
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visium_analysis) {
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add10xVisiumAnalysis <- function(
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spe,
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visium_analysis) {
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col_info <- colData(spe)
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barcode_present <- "barcode" %in% colnames(col_info)
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if (!barcode_present) {

‎R/add_images.R‎

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#' ))
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#' }
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add_images <-
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function(spe,
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image_dir,
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image_pattern,
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image_id_current = "lowres",
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image_id = image_pattern,
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image_paths = locate_images(spe, image_dir, image_pattern)) {
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function(
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spe,
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image_dir,
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image_pattern,
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image_id_current = "lowres",
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image_id = image_pattern,
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image_paths = locate_images(spe, image_dir, image_pattern)) {
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stopifnot(length(names(image_paths)) > 0)
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stopifnot(all(names(image_paths) %in% unique(spe$sample_id)))
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stopifnot(!any(duplicated(names(image_paths))))

‎R/annotate_registered_clusters.R‎

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#' ## More relaxed merging threshold
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#' annotate_registered_clusters(cor_stats_layer, cutoff_merge_ratio = 1)
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annotate_registered_clusters <-
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function(cor_stats_layer,
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confidence_threshold = 0.25,
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cutoff_merge_ratio = 0.25) {
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function(
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cor_stats_layer,
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confidence_threshold = 0.25,
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cutoff_merge_ratio = 0.25) {
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annotated <-
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apply(cor_stats_layer,
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1,
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}
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annotate_registered_cluster <-
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function(remaining,
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label = "",
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current = NULL,
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cutoff_merge_ratio = 0.25) {
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function(
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remaining,
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label = "",
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current = NULL,
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cutoff_merge_ratio = 0.25) {
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## Filter negative correlations
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remaining <- remaining[remaining > 0]
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‎R/app_server.R‎

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}
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})
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# Set the max based on the assay
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observeEvent(input$assayname, {
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updateNumericInput(
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session,
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inputId = "minCount",
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value = 0,
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min = -1,
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max = max(assays(spe)[[input$assayname]]),
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step = 1
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)
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})
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## Static plotting functions
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static_histology <- reactive({
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if (input$cluster == "ManualAnnotation") {

‎R/app_ui.R‎

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label = "Minimum count value",
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value = 0,
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min = -1,
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max = max(assays(spe)[[length(assayNames(spe))]]),
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step = 1
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),
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helpText(
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numericInput(
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"grid_nrow",
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label = "N rows",
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value = 1,
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min = 1,
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max = 3
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value = round(length(unique(spe$sample_id)) / 3 + 0.5),
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min = 1
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),
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numericInput(
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"grid_ncol",
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label = "N columns",
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value = 3,
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min = 1,
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max = 4
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min = 1
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),
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actionButton("grid_update", label = "Update grid plot"),
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downloadButton("downloadPlotHistologyGrid", "Download PDF"),
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numericInput(
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"gene_grid_nrow",
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label = "N rows",
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value = 1,
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min = 1,
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max = 3
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value = round(length(unique(spe$sample_id)) / 3 + 0.5),
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min = 1
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),
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numericInput(
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"gene_grid_ncol",
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label = "N columns",
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value = 3,
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min = 1,
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max = 4
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min = 1
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),
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actionButton("gene_grid_update", label = "Update grid plot"),
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downloadButton("downloadPlotGeneGrid", "Download PDF"),

‎R/check_sce.R‎

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#' ## Check the object
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#' check_sce(sce_example)
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#' }
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check_sce <- function(sce,
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variables = c(
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"GraphBased",
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"ManualAnnotation",
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"Maynard",
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"Martinowich",
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paste0("SNN_k50_k", 4:28),
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"spatialLIBD",
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"cell_count",
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"sum_umi",
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"sum_gene",
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"expr_chrM",
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"expr_chrM_ratio",
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"SpatialDE_PCA",
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"SpatialDE_pool_PCA",
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"HVG_PCA",
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"pseudobulk_PCA",
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"markers_PCA",
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"SpatialDE_UMAP",
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"SpatialDE_pool_UMAP",
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"HVG_UMAP",
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"pseudobulk_UMAP",
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"markers_UMAP",
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"SpatialDE_PCA_spatial",
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"SpatialDE_pool_PCA_spatial",
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"HVG_PCA_spatial",
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"pseudobulk_PCA_spatial",
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"markers_PCA_spatial",
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"SpatialDE_UMAP_spatial",
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"SpatialDE_pool_UMAP_spatial",
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"HVG_UMAP_spatial",
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"pseudobulk_UMAP_spatial",
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"markers_UMAP_spatial"
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)) {
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check_sce <- function(
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sce,
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variables = c(
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"GraphBased",
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"ManualAnnotation",
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"Maynard",
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"Martinowich",
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paste0("SNN_k50_k", 4:28),
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"spatialLIBD",
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"cell_count",
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"sum_umi",
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"sum_gene",
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"expr_chrM",
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"expr_chrM_ratio",
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"SpatialDE_PCA",
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"SpatialDE_pool_PCA",
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"HVG_PCA",
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"pseudobulk_PCA",
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"markers_PCA",
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"SpatialDE_UMAP",
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"SpatialDE_pool_UMAP",
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"HVG_UMAP",
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"pseudobulk_UMAP",
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"markers_UMAP",
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"SpatialDE_PCA_spatial",
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"SpatialDE_pool_PCA_spatial",
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"HVG_PCA_spatial",
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"pseudobulk_PCA_spatial",
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"markers_PCA_spatial",
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"SpatialDE_UMAP_spatial",
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"SpatialDE_pool_UMAP_spatial",
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"HVG_UMAP_spatial",
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"pseudobulk_UMAP_spatial",
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"markers_UMAP_spatial"
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)) {
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## Should be a SingleCellExperiment object
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stopifnot(is(sce, "SingleCellExperiment"))
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‎R/check_spe.R‎

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#' ## Check the object
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#' check_spe(spe)
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#' }
28-
check_spe <- function(spe,
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variables = c(
30-
"sum_umi",
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"sum_gene",
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"expr_chrM",
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"expr_chrM_ratio"
34-
)) {
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check_spe <- function(
29+
spe,
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variables = c(
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"sum_umi",
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"sum_gene",
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"expr_chrM",
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"expr_chrM_ratio"
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)) {
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## Should be a SpatialExperiment object
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stopifnot(is(spe, "SpatialExperiment"))
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‎R/fetch_data.R‎

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#' #> 172.28 MB
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#' }
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fetch_data <-
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function(
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type = c(
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"sce",
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"sce_layer",
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"modeling_results",
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"sce_example",
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"spe",
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"spatialDLPFC_Visium",
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"spatialDLPFC_Visium_example_subset",
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"spatialDLPFC_Visium_pseudobulk",
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"spatialDLPFC_Visium_modeling_results",
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"spatialDLPFC_Visium_SPG",
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"spatialDLPFC_snRNAseq",
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"Visium_SPG_AD_Visium_wholegenome_spe",
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"Visium_SPG_AD_Visium_targeted_spe",
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"Visium_SPG_AD_Visium_wholegenome_pseudobulk_spe",
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"Visium_SPG_AD_Visium_wholegenome_modeling_results",
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"visiumStitched_brain_spe",
105-
"visiumStitched_brain_spaceranger",
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"visiumStitched_brain_Fiji_out"
107-
),
108-
destdir = tempdir(),
109-
eh = ExperimentHub::ExperimentHub(),
110-
bfc = BiocFileCache::BiocFileCache()) {
87+
function(type = c(
88+
"sce",
89+
"sce_layer",
90+
"modeling_results",
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"sce_example",
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"spe",
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"spatialDLPFC_Visium",
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"spatialDLPFC_Visium_example_subset",
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"spatialDLPFC_Visium_pseudobulk",
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"spatialDLPFC_Visium_modeling_results",
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"spatialDLPFC_Visium_SPG",
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"spatialDLPFC_snRNAseq",
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"Visium_SPG_AD_Visium_wholegenome_spe",
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"Visium_SPG_AD_Visium_targeted_spe",
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"Visium_SPG_AD_Visium_wholegenome_pseudobulk_spe",
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"Visium_SPG_AD_Visium_wholegenome_modeling_results",
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"visiumStitched_brain_spe",
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"visiumStitched_brain_spaceranger",
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"visiumStitched_brain_Fiji_out"
106+
),
107+
destdir = tempdir(),
108+
eh = ExperimentHub::ExperimentHub(),
109+
bfc = BiocFileCache::BiocFileCache()) {
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## Some variables
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sce <-
113112
sce_layer <- modeling_results <- sce_sub <- spe <- NULL

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