Work on docs, trying to debug pkgdown error

R/add_key.R

@@ -21,13 +21,20 @@
 #'     head(spe$key)
 #'
 #'     ## We can clean it
+#'     spe$key_original <- spe$key
 #'     spe$key <- NULL
 #'
 #'     ## and then add it back
-#'     head(add_key(spe)$key)
+#'     spe <- add_key(spe)
+#'     head(spe$key)
 #'
 #'     ## Note that the original 'key' order was 'sample_id'_'barcode' and we'
 #'     ## have since changed it to 'barcode'_'sample_id'.
+#'
+#'     ## Below we restore the original 'key'
+#'     spe$key <- spe$key_original
+#'     spe$key_original <- NULL
+#'     head(spe$key)
 #' }
 add_key <- function(spe, overwrite = TRUE) {
     if ("key" %in% colnames(colData(spe))) {

R/add_qc_metrics.R

@@ -90,7 +90,7 @@
 #' vis_clus(spe_qc, sample_id = "Br6432_ant", clustervar = "our_low_lib_edge")
 #'
 #' ## Clean up
-#' rm(spe_qc, spec_pre_qc)
+#' rm(spe_qc, spe_pre_qc, vars)
 #'
 add_qc_metrics <- function(spe, overwrite = FALSE) {
     stopifnot("in_tissue" %in% colnames(colData(spe)))

R/check_sce_layer.R

@@ -14,10 +14,11 @@
 #'
 #' @examples
 #'
-#' ## Obtain the necessary data
+#' ## Obtain example data from the HumanPilot project
+#' ## (Maynard, Collado-Torres, et al, 2021)
 #' if (!exists("sce_layer")) sce_layer <- fetch_data("sce_layer")
 #'
-#' ## Check the object
+#' ## Check the pseudo-bulked data
 #' check_sce_layer(sce_layer)
 check_sce_layer <- function(sce_layer, variables = "spatialLIBD") {
     ## Should be a SingleCellExperiment object

R/vis_clus.R

@@ -4,7 +4,11 @@
 #' using (by default) the histology information on the background. To visualize
 #' gene-level (or any continuous variable) use [vis_gene()].
 #'
-#' @inheritParams run_app
+#' @param spe A
+#' [SpatialExperiment-class][SpatialExperiment::SpatialExperiment-class]
+#' object. See [fetch_data()] for how to download some example objects or
+#' [read10xVisiumWrapper()] to read in `spaceranger --count` output files and
+#' build your own `spe` object.
 #' @param sampleid A `character(1)` specifying which sample to plot from
 #' `colData(spe)$sample_id` (formerly `colData(spe)$sample_name`).
 #' @param clustervar A `character(1)` with the name of the `colData(spe)`

R/vis_clus_p.R

@@ -6,8 +6,8 @@
 #' gene-level (or any continuous variable) use [vis_gene_p()].
 #'
 #' @inheritParams vis_clus
-#' @param d A data.frame with the sample-level information. This is typically
-#' obtained using `cbind(colData(spe), spatialCoords(spe))`.
+#' @param d A `data.frame()` with the sample-level information. This is
+#' typically obtained using `cbind(colData(spe), spatialCoords(spe))`.
 #' @param title The title for the plot.
 #'
 #' @return A [ggplot2][ggplot2::ggplot] object.

R/vis_gene.R

@@ -7,11 +7,11 @@
 #'
 #' @inheritParams vis_clus
 #' @param geneid A `character()` specifying the gene ID(s) stored in
-#' `rowData(spe)$gene_search` or a continuous variable(s) stored in `colData(spe)`
-#' to visualize. For each ID, if `rowData(spe)$gene_search` is missing, then
-#' `rownames(spe)` is used to search for the gene ID. When a vector of length > 1
-#' is supplied, the continuous variables are combined according to \code{multi_gene_method},
-#' producing a single value for each spot.
+#' `rowData(spe)$gene_search` or a continuous variable(s) stored in
+#' `colData(spe)` to visualize. For each ID, if `rowData(spe)$gene_search` is
+#' missing, then `rownames(spe)` is used to search for the gene ID. When a
+#' vector of length > 1 is supplied, the continuous variables are combined
+#' according to `multi_gene_method`, producing a single value for each spot.
 #' @param assayname The name of the `assays(spe)` to use for extracting the
 #' gene expression data. Defaults to `logcounts`.
 #' @param minCount A `numeric(1)` specifying the minimum gene expression (or
@@ -25,9 +25,9 @@
 #' dependent on cell density.
 #' @param cont_colors A `character()` vector of colors that supersedes the
 #' `viridis` argument.
-#' @param multi_gene_method A \code{character(1)}: either "pca", "sparsity", or
-#' "z_score". This parameter controls how multiple continuous variables are
-#' combined for visualization, and only applies when \code{geneid} has length
+#' @param multi_gene_method A `character(1)`: either `"pca"`, `"sparsity"`, or
+#' `"z_score"`. This parameter controls how multiple continuous variables are
+#' combined for visualization, and only applies when `geneid` has length
 #' great than 1. `z_score`: to summarize multiple continuous variables, each is
 #' normalized to represent a Z-score. The multiple scores are then averaged.
 #' `pca`: PCA dimension reduction is conducted on the matrix formed by the

R/vis_gene_p.R

@@ -3,12 +3,12 @@
 #' This function visualizes the gene expression stored in `assays(spe)` or any
 #' continuous variable stored in `colData(spe)` for one given sample at the
 #' spot-level using (by default) the histology information on the background.
-#' This is the function that does all the plotting behind [vis_gene()].
+#' This is the function that does all the plotting behind [vis_gene()]
 #' To visualize clusters (or any discrete variable) use [vis_clus_p()].
 #'
-#' @param d A data.frame with the sample-level information. This is typically
-#' obtained using `cbind(colData(spe), spatialCoords(spe))`.
-#' The data.frame has to contain
+#' @param d A `data.frame()` with the sample-level information. This is
+#' typically obtained using `cbind(colData(spe), spatialCoords(spe))`.
+#' The `data.frame` has to contain
 #' a column with the continuous variable data to plot stored under `d$COUNT`.
 #' @param legend_title A `character(1)` specifying the legend title.
 #' @inheritParams vis_clus_p