Merge pull request #82 from LieberInstitute/stitched_plotting

Stitched plotting

NAMESPACE

@@ -48,6 +48,7 @@ export(vis_grid_gene)
 import(ExperimentHub)
 import(MatrixGenerics)
 import(SingleCellExperiment)
+import(SpatialExperiment)
 import(ggplot2)
 import(grid)
 import(paletteer)

R/prep_stitched_data.R

@@ -0,0 +1,75 @@
+#' Prepare stitched data for plotting
+#'
+#' Given a \code{SpatialExperiment} built with \code{visiumStitched::build_spe()}
+#' <http://research.libd.org/visiumStitched/reference/build_spe.html>, drop
+#' exluded spots (specified by \code{spe$exclude_overlapping}) and compute an
+#' appropriate spot size for plotting with \code{vis_gene()} or
+#' \code{vis_clus()}, assuming the plot will be written to a PDF of default
+#' dimensions (i.e. \code{width = 7} and \code{height = 7}).
+#'
+#' @param spe A \code{SpatialExperiment} built with
+#' \code{visiumStitched::build_spe()}, containing a logical
+#' \code{spe$exclude_overlapping} column specifying which spots to display in
+#' plots
+#' @inheritParams vis_clus
+#'
+#' @return A list with names \code{spe} and \code{point_size} containing a
+#' filtered, ready-to-plot \code{SpatialExperiment} and an appopriate spot size
+#' (passed to \code{vis_gene()} or \code{vis_clus()}), respectively
+#'
+#' @import SpatialExperiment
+#' @author Nicholas J. Eagles
+#' @keywords internal
+prep_stitched_data = function(spe, point_size, image_id) {
+    #   State assumptions about columns expected to be in the colData
+    expected_cols <- c("array_row", "array_col", "exclude_overlapping")
+    if (!all(expected_cols %in% colnames(colData(spe)))) {
+        stop(
+            sprintf(
+                'Missing at least one of the following colData columns: "%s"',
+                paste(expected_cols, collapse = '", "')
+            ),
+            call. = FALSE
+        )
+    }
+
+    if(any(is.na(spe$exclude_overlapping))) {
+        stop("spe$exclude_overlapping must not have NAs", call. = FALSE)
+    }
+
+    #   Drop excluded spots; verify some spots are not excluded
+    subset_cols = !spe$exclude_overlapping
+    if (length(which(subset_cols)) == 0) {
+        stop(
+            "spe$exclude_overlapping must include some FALSE values to plot",
+            call. = FALSE
+        )
+    }
+    spe <- spe[, subset_cols]
+
+    #   Compute an appropriate spot size for this sample
+
+    #   Determine some pixel values for the horizontal bounds of the spots
+    MIN_COL <- min(spatialCoords(spe)[, "pxl_row_in_fullres"])
+    MAX_COL <- max(spatialCoords(spe)[, "pxl_row_in_fullres"])
+
+    #   The distance between spots (in pixels) is double the average distance
+    #   between array columns
+    INTER_SPOT_DIST_PX <- 2 * (MAX_COL - MIN_COL) /
+        (max(spe$array_col) - min(spe$array_col))
+
+    #   Find the appropriate spot size for this donor. This can vary because
+    #   ggplot downscales a plot to fit desired output dimensions (in this
+    #   case presumably a square region on a PDF), and stitched images can vary
+    #   in aspect ratio. Also, lowres images always have a larger image
+    #   dimension of 1200, no matter how many spots fit in either dimension.
+    small_image_data <- imgData(spe)[
+        imgData(spe)$image_id == image_id,
+    ]
+
+    #   The coefficient of 100 was determined empirically
+    point_size <- point_size * 100 * INTER_SPOT_DIST_PX *
+        small_image_data$scaleFactor / max(dim(small_image_data$data[[1]]))
+
+    return(list(spe = spe, point_size = point_size))
+}

R/vis_clus.R

@@ -29,6 +29,12 @@
 #' alpha blending already, which will make non-NA values pop up more and the NA
 #' values will show with a lighter color. This behavior is lost when `alpha` is
 #' set to a non-`NA` value.
+#' @param is_stitched A \code{logical(1)} vector: If true, expects a
+#' \code{SpatialExperiment} built with \code{visiumStitched::build_spe()}
+#' <http://research.libd.org/visiumStitched/reference/build_spe.html>; in
+#' particular, expects a logical colData column \code{exclude_overlapping}
+#' specifying which spots to exclude from the plot. Sets \code{auto_crop = FALSE}
+#' when TRUE.
 #' @param ... Passed to [paste0()][base::paste] for making the title of the
 #' plot following the `sampleid`.
 #'
@@ -116,6 +122,7 @@ vis_clus <- function(spe,
     point_size = 2,
     auto_crop = TRUE,
     na_color = "#CCCCCC40",
+    is_stitched = FALSE,
     ...) {
     #   Verify existence and legitimacy of 'sampleid'
     if (
@@ -130,7 +137,26 @@ vis_clus <- function(spe,
         )
     }
 
+    #   Check validity of spatial coordinates
+    if (!setequal(c("pxl_col_in_fullres", "pxl_row_in_fullres"), colnames(spatialCoords(spe)))) {
+        stop(
+            "Abnormal spatial coordinates: should have 'pxl_row_in_fullres' and 'pxl_col_in_fullres' columns.",
+            call. = FALSE
+        )
+    }
+
     spe_sub <- spe[, spe$sample_id == sampleid]
+
+    if (is_stitched) {
+        #   Drop excluded spots and calculate an appropriate point size
+        temp = prep_stitched_data(spe_sub, point_size, image_id)
+        spe_sub = temp$spe
+        point_size = temp$point_size
+
+        #   Frame limits are poorly defined for stitched data
+        auto_crop = FALSE
+    }
+
     d <- as.data.frame(cbind(colData(spe_sub), SpatialExperiment::spatialCoords(spe_sub)), optional = TRUE)
 
     vis_clus_p(

R/vis_gene.R

@@ -172,6 +172,7 @@ vis_gene <-
     auto_crop = TRUE,
     na_color = "#CCCCCC40",
     multi_gene_method = c("z_score", "pca", "sparsity"),
+    is_stitched = FALSE,
     ...) {
         multi_gene_method <- rlang::arg_match(multi_gene_method)
         #   Verify existence and legitimacy of 'sampleid'
@@ -192,7 +193,26 @@ vis_gene <-
             stop(sprintf("'%s' is not an assay in 'spe'", assayname), call. = FALSE)
         }
 
+        #   Check validity of spatial coordinates
+        if (!setequal(c("pxl_col_in_fullres", "pxl_row_in_fullres"), colnames(spatialCoords(spe)))) {
+            stop(
+                "Abnormal spatial coordinates: should have 'pxl_row_in_fullres' and 'pxl_col_in_fullres' columns.",
+                call. = FALSE
+            )
+        }
+
         spe_sub <- spe[, spe$sample_id == sampleid]
+
+        if (is_stitched) {
+            #   Drop excluded spots and calculate an appropriate point size
+            temp = prep_stitched_data(spe_sub, point_size, image_id)
+            spe_sub = temp$spe
+            point_size = temp$point_size
+
+            #   Frame limits are poorly defined for stitched data
+            auto_crop = FALSE
+        }
+
         d <- as.data.frame(cbind(colData(spe_sub), SpatialExperiment::spatialCoords(spe_sub)), optional = TRUE)
 
         #   Verify legitimacy of names in geneid

inst/extdata/metadata_Visium_LS.csv

@@ -0,0 +1,4 @@
+Title,Description,BiocVersion,Genome,SourceType,SourceUrl,SourceVersion,Species,TaxonomyId,Coordinate_1_based,DataProvider,Maintainer,RDataClass,DispatchClass,RDataPath,Tags
+Visium_LS_spe,SpatialExperiment object at the spot-level for the spatially stitched lateral septum human brain (LS) spatial transcriptomics data (n = 3) from the Visium platform from 10x Genomics generated by the Lieber Institute for Brain Development (LIBD) and available through the spatialLIBD Bioconductor package.,3.19,GRCh38,GTF,https://bioconductor.org/packages/spatialLIBD,June 11 2024,Homo sapiens,9606,TRUE,LIBD,Leonardo Collado-Torres <[email protected]>,SpatialExperiment,Rds,spatialLIBD/spatialLIBD_files/Visium_LS_spe.rds,VisiumLS_Visium_stitched_spatialLIBD
+Visium_LS_spaceranger,Spaceranger outputs for the spatially stitched lateral septum human brain (LS) spatial transcriptomics data (n = 3) from the Visium platform from 10x Genomics generated by the Lieber Institute for Brain Development (LIBD) and available through the spatialLIBD Bioconductor package. Can be used with visiumStitched::build_spe() to construct a SpatialExperiment.,3.19,GRCh38,Zip,https://bioconductor.org/packages/spatialLIBD,June 11 2024,Homo sapiens,9606,TRUE,LIBD,Leonardo Collado-Torres <[email protected]>,list,Zip,spatialLIBD/spatialLIBD_files/Visium_LS_spaceranger.zip,VisiumLS_Visium_stitched_spatialLIBD
+Visium_LS_ImageJ_out,Stitched PNG image and XML file from aligning lateral septum human brain (LS) spatial transcriptomics data (n = 3) in ImageJ. Data to align was from the Visium platform from 10x Genomics generated by the Lieber Institute for Brain Development (LIBD) and is available through the spatialLIBD Bioconductor package.,3.19,GRCh38,Zip,https://bioconductor.org/packages/spatialLIBD,June 11 2024,Homo sapiens,9606,TRUE,LIBD,Leonardo Collado-Torres <[email protected]>,list,Zip,spatialLIBD/spatialLIBD_files/Visium_LS_imagej_out.zip,VisiumLS_Visium_stitched_spatialLIBD