This function runs the shiny application that allows users to interact with the Visium spatial transcriptomics data from LIBD (by default) or any other data that you have shaped according to our object structure.
run_app(
spe = fetch_data(type = "spe"),
sce_layer = fetch_data(type = "sce_layer"),
modeling_results = fetch_data(type = "modeling_results"),
sig_genes = sig_genes_extract_all(n = nrow(sce_layer), modeling_results =
modeling_results, sce_layer = sce_layer),
docs_path = system.file("app", "www", package = "spatialLIBD"),
title = "spatialLIBD",
spe_discrete_vars = c("spatialLIBD", "GraphBased", "ManualAnnotation", "Maynard",
"Martinowich", paste0("SNN_k50_k", 4:28), "SpatialDE_PCA", "SpatialDE_pool_PCA",
"HVG_PCA", "pseudobulk_PCA", "markers_PCA", "SpatialDE_UMAP", "SpatialDE_pool_UMAP",
"HVG_UMAP", "pseudobulk_UMAP", "markers_UMAP", "SpatialDE_PCA_spatial",
"SpatialDE_pool_PCA_spatial", "HVG_PCA_spatial", "pseudobulk_PCA_spatial",
"markers_PCA_spatial", "SpatialDE_UMAP_spatial", "SpatialDE_pool_UMAP_spatial",
"HVG_UMAP_spatial", "pseudobulk_UMAP_spatial",
"markers_UMAP_spatial"),
spe_continuous_vars = c("cell_count", "sum_umi", "sum_gene", "expr_chrM",
"expr_chrM_ratio"),
default_cluster = "spatialLIBD",
auto_crop_default = TRUE,
is_stitched = FALSE,
...
)Defaults to the output of
fetch_data(type = 'spe'). This is a
SpatialExperiment-class
object with the spot-level Visium data and information required for
visualizing the histology. See fetch_data() for more details.
Defaults to the output of
fetch_data(type = 'sce_layer'). This is a
SingleCellExperiment
object with the spot-level Visium data compressed via pseudo-bulking to the
layer-level (group-level) resolution. See fetch_data() for more details.
Defaults to the output of
fetch_data(type = 'modeling_results'). This is a list of tables with the
columns f_stat_* or t_stat_* as well as p_value_* and fdr_* plus
ensembl. The column name is used to extract the statistic results, the
p-values, and the FDR adjusted p-values. Then the ensembl column is used
for matching in some cases. See fetch_data() for more details. Typically
this is the set of reference statistics used in layer_stat_cor().
The output of sig_genes_extract_all() which is a table
in long format with the modeling results. You can subset this if the object
requires too much memory.
A character(1) specifying the path to the directory
containing the website documentation files. The directory has to contain
the files: documentation_sce_layer.md, documentation_spe.md,
favicon.ico, footer.html and README.md.
A character(1) specifying the title for the app.
A character() vector of discrete variables that
will be available to visualize in the app. Basically, the set of variables
with spot-level groups. They will have to be present in colData(spe).
A character() vector of continuous variables
that will be available to visualize in the app using the same scale
as genes. They will have to be present in colData(sce).
A character(1) with the name of the main cluster
(discrete) variable to use. It will have to be present in both colData(spe)
and colData(sce_layer).
A logical(1) specifying the default value for
automatically cropping the images. Set this to FALSE if your images do not
follow the Visium grid size expectations, which are key for enabling
auto-cropping.
A logical(1) vector: If TRUE, expects a
SpatialExperiment-class built
with visiumStitched::build_spe().
http://research.libd.org/visiumStitched/reference/build_spe.html; in
particular, expects a logical colData column exclude_overlapping
specifying which spots to exclude from the plot. Sets auto_crop = FALSE.
Other arguments passed to the list of golem options for running the application.
A shiny.appobj that contains the input data.
If you don't have the pseudo-bulked analysis results like we computed them
in our project https://doi.org/10.1038/s41593-020-00787-0 you can
set sce_layer, modeling_results and sig_genes to NULL. Doing so
will disable the pseudo-bulked portion of the web application. See the
examples for one such case as well as the vignette that describes how
you can use spatialLIBD with public data sets provided by 10x Genomics.
That vignette is available at
http://research.libd.org/spatialLIBD/articles/TenX_data_download.html.
if (FALSE) { # \dontrun{
## The default arguments will download the data from the web
## using fetch_data(). If this is the first time you have run this,
## the files will need to be cached by ExperimentHub. Otherwise it
## will re-use the files you have previously downloaded.
if (enough_ram(4e9)) {
## Obtain the necessary data
if (!exists("spe")) spe <- fetch_data("spe")
## Create the interactive website
run_app(spe)
## You can also run a custom version without the pseudo-bulked
## layer information. This is useful if you are only interested
## in the spatial transcriptomics features.
run_app(spe,
sce_layer = NULL, modeling_results = NULL, sig_genes = NULL,
title = "spatialLIBD without layer info"
)
## When using shinyapps.io aim for less than 3 GB of RAM with your
## objects. Check each input object with:
## lobstr::obj_size(x)
## Do not create the large input objects on the app.R script before
## subsetting them. Do this outside app.R since the app.R script is
## run at shinyapps.io, so subsetting on that script to reduce the
## memory load is pointless. You have to do it outside of app.R.
}
## How to run locally the spatialDLPFC Sp09 spatialLIBD app. That is,
## from http://research.libd.org/spatialDLPFC/#interactive-websites
## how to run https://libd.shinyapps.io/spatialDLPFC_Visium_Sp09 locally.
if (enough_ram(9e9)) {
## Download the 3 main objects needed
spe <- fetch_data("spatialDLPFC_Visium")
sce_pseudo <- fetch_data("spatialDLPFC_Visium_pseudobulk")
modeling_results <- fetch_data("spatialDLPFC_Visium_modeling_results")
## These are optional commands to further reduce the memory required.
#
## Keep only the "lowres" images. Reduces the object from 6.97 GB to 4.59 GB
# imgData(spe) <- imgData(spe)[imgData(spe)$image_id == "lowres", ]
## Drop the regular counts (keep only the logcounts). Reduces the object
## from 4.59 GB to 2.45 GB.
# counts(spe) <- NULL
## For sig_genes_extract_all() to work
sce_pseudo$spatialLIBD <- sce_pseudo$BayesSpace
## Compute the significant genes
sig_genes <- sig_genes_extract_all(
n = nrow(sce_pseudo),
modeling_results = modeling_results,
sce_layer = sce_pseudo
)
## Reduce the memory from 423.73 MB to 78.88 MB
lobstr::obj_size(sig_genes)
sig_genes$in_rows <- NULL
sig_genes$in_rows_top20 <- NULL
lobstr::obj_size(sig_genes)
## Specify the default variable
spe$BayesSpace <- spe$BayesSpace_harmony_09
## Get all variables
vars <- colnames(colData(spe))
## Set default cluster colors
colors_BayesSpace <- Polychrome::palette36.colors(28)
names(colors_BayesSpace) <- c(1:28)
m <- match(as.character(spe$BayesSpace_harmony_09), names(colors_BayesSpace))
stopifnot(all(!is.na(m)))
spe$BayesSpace_colors <- spe$BayesSpace_harmony_09_colors <- colors_BayesSpace[m]
## Download documentation files we use
temp_www <- file.path(tempdir(), "www")
dir.create(temp_www)
download.file(
"https://raw.githubusercontent.com/LieberInstitute/spatialDLPFC/main/README.md",
file.path(temp_www, "README.md")
)
download.file(
"https://raw.githubusercontent.com/LieberInstitute/spatialDLPFC/main/code/deploy_app_k09/www/documentation_sce_layer.md",
file.path(temp_www, "documentation_sce_layer.md")
)
download.file(
"https://raw.githubusercontent.com/LieberInstitute/spatialDLPFC/main/code/deploy_app_k09/www/documentation_spe.md",
file.path(temp_www, "documentation_spe.md")
)
download.file(
"https://raw.githubusercontent.com/LieberInstitute/spatialDLPFC/main/img/favicon.ico",
file.path(temp_www, "favicon.ico")
)
download.file(
"https://raw.githubusercontent.com/LieberInstitute/spatialDLPFC/main/code/deploy_app_k09/www/footer.html",
file.path(temp_www, "footer.html")
)
list.files(temp_www)
## Run the app locally
run_app(
spe,
sce_layer = sce_pseudo,
modeling_results = modeling_results,
sig_genes = sig_genes,
title = "spatialDLPFC, Visium, Sp09",
spe_discrete_vars = c( # this is the variables for the spe object not the sce_pseudo object
"BayesSpace",
"ManualAnnotation",
vars[grep("^SpaceRanger_|^scran_", vars)],
vars[grep("^BayesSpace_harmony", vars)],
vars[grep("^BayesSpace_pca", vars)],
"graph_based_PCA_within",
"PCA_SNN_k10_k7",
"Harmony_SNN_k10_k7",
"manual_layer_label",
"wrinkle_type",
"BayesSpace_colors"
),
spe_continuous_vars = c(
"sum_umi",
"sum_gene",
"expr_chrM",
"expr_chrM_ratio",
vars[grep("^VistoSeg_", vars)],
vars[grep("^layer_", vars)],
vars[grep("^broad_", vars)]
),
default_cluster = "BayesSpace",
docs_path = temp_www
)
}
## See also:
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/deploy_app_k09
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/deploy_app_k09_position
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/deploy_app_k09_position_noWM
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/deploy_app_k16
## * https://github.com/LieberInstitute/spatialDLPFC/tree/main/code/analysis_IF/03_spatialLIBD_app
## Example for an object with multiple capture areas stitched together with
## <http://research.libd.org/visiumStitched/>.
spe_stitched <- fetch_data("Visium_LS_spe")
## Inspect this object
spe_stitched
## Notice the use of "exclude_overlapping"
table(spe_stitched$exclude_overlapping, useNA = "ifany")
## Run the app with this stitched data
run_app(
spe = spe_stitched,
sce_layer = NULL, modeling_results = NULL, sig_genes = NULL,
title = "visiumStitched example data",
spe_discrete_vars = c("capture_area", "scran_quick_cluster", "ManualAnnotation"),
spe_continuous_vars = c("sum_umi", "sum_gene", "expr_chrM", "expr_chrM_ratio"),
default_cluster = "scran_quick_cluster",
is_stitched = TRUE
)
} # }