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Run SCExplorer

Usage

RunSCExplorer(
  base_dir = "SCExplorer",
  data_file = "data.hdf5",
  meta_file = "meta.hdf5",
  title = "SCExplorer",
  initial_dataset = NULL,
  initial_reduction = NULL,
  initial_group = NULL,
  initial_feature = NULL,
  initial_assay = NULL,
  initial_slot = NULL,
  initial_label = FALSE,
  initial_cell_palette = "Paired",
  initial_feature_palette = "Spectral",
  initial_theme = "theme_scop",
  initial_size = 4,
  initial_ncol = 3,
  initial_arrange = NULL,
  initial_raster = NULL,
  create_script = TRUE,
  style_script = requireNamespace("styler", quietly = TRUE),
  overwrite = TRUE,
  return_app = TRUE
)

Arguments

base_dir

The base directory of the SCExplorer app. Default is "SCExplorer".

data_file

The name of the HDF5 file that stores data matrices for each dataset. Default is "data.hdf5".

meta_file

The name of the HDF5 file that stores metadata for each dataset. Default is "meta.hdf5".

title

The title of the SCExplorer app. Default is "SCExplorer".

initial_dataset

The initial dataset to be loaded into the app. Default is NULL.

initial_reduction

The initial dimensional reduction method to be loaded into the app. Default is NULL.

initial_group

The initial variable to group cells in the app. Default is NULL.

initial_feature

The initial feature to be loaded into the app. Default is NULL.

initial_assay

The initial assay to be loaded into the app. Default is NULL.

initial_slot

The initial layer to be loaded into the app. Default is NULL.

initial_label

Whether to add labels in the initial plot. Default is FALSE.

initial_cell_palette

The initial color palette for cells. Default is "Paired".

initial_feature_palette

The initial color palette for features. Default is "Spectral".

initial_theme

The initial theme for plots. Default is "theme_scop".

initial_size

The initial size of plots. Default is 4.

initial_ncol

The initial number of columns for arranging plots. Default is 3.

initial_arrange

Whether to use "Row" as the initial arrangement. Default is TRUE.

initial_raster

Whether to perform rasterization in the initial plot. By default, it is set to automatic, meaning it will be TRUE if the number of cells in the initial datasets exceeds 100,000. Default is NULL.

create_script

Whether to create the SCExplorer app script. Default is TRUE.

style_script

Whether to style the SCExplorer app script. Default is TRUE.

overwrite

Whether to overwrite existing files. Default is TRUE.

return_app

Whether to return the SCExplorer app. Default is TRUE.

Examples

if (FALSE) { # \dontrun{
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
data(panc8_sub)
panc8_sub <- integration_scop(
  panc8_sub,
  batch = "tech",
  integration_method = "Seurat"
)

PrepareSCExplorer(
  list(
    mouse_pancreas = pancreas_sub,
    human_pancreas = panc8_sub
  ),
  base_dir = "./SCExplorer"
)

# Create the app.R script
app <- RunSCExplorer(
  base_dir = "./SCExplorer",
  initial_dataset = "mouse_pancreas",
  initial_group = "CellType",
  initial_feature = "Ncoa2"
)
# Check files
list.files("./SCExplorer")

# Run shiny app
if (interactive()) {
  shiny::runApp(app)
}
# Note: If scop installed in the isolated environment using renv,
# add `renv::activate(project = "path/to/scop_env")` to the app.R script.


# You can deploy the app on the self-hosted shiny server
# (https://www.rstudio.com/products/shiny/shiny-server/).
# Or deploy the app on the website
# (https://www.shinyapps.io) for free:

# step1: install "rsconnect" package and authorize your account
# install.packages("rsconnect")
# library(rsconnect)
# setAccountInfo(
#   name = "<NAME>",
#   token = "<TOKEN>",
#   secret = "<SECRET>"
# )

### step2: deploy the app
# deployApp("./SCExplorer")
} # }