Run SCExplorer

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 = "Chinese",
  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 = TRUE,
  overwrite = TRUE,
  return_app = TRUE,
  verbose = TRUE
)

Arguments

base_dir: The base directory of the SCExplorer app.
data_file: HDF5 file that stores data matrices for each dataset.
meta_file: HDF5 file that stores metadata for each dataset.
title: The title of the SCExplorer app.
initial_dataset: The initial dataset to be loaded into the app.
initial_reduction: The initial dimensional reduction method to be loaded into the app.
initial_group: The initial variable to group cells in the app.
initial_feature: The initial feature to be loaded into the app.
initial_assay: The initial assay to be loaded into the app.
initial_slot: The initial layer to be loaded into the app.
initial_label: Whether to add labels in the initial plot.
initial_cell_palette: The initial color palette for cells.
initial_feature_palette: The initial color palette for features.
initial_theme: The initial theme for plots.
initial_size: The initial size of plots.
initial_ncol: The initial number of columns for arranging plots.
initial_arrange: Whether to use "Row" as the initial arrangement.
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.
create_script: Whether to create the SCExplorer app script.
style_script: Whether to style the SCExplorer app script.
overwrite: Whether to overwrite existing data in the data file.
return_app: Whether to return the SCExplorer app.
verbose: Whether to print the message. Default is TRUE.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-10-11 23:45:40] Start standard processing workflow...
#> ℹ [2026-10-11 23:45:40] Checking a list of <Seurat>...
#> ! [2026-10-11 23:45:40] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:45:40] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:45:40] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:45:40] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:45:40] Number of available HVF: 2000
#> ℹ [2026-10-11 23:45:40] Finished check
#> ℹ [2026-10-11 23:45:40] Perform `ScaleData()`
#> ℹ [2026-10-11 23:45:40] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:45:41] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 23:45:41] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:45:41] Reorder clusters...
#> ℹ [2026-10-11 23:45:41] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:45:41] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 23:45:49] Standard processing workflow completed
data(panc8_sub)
panc8_sub <- RunIntegration(
  panc8_sub,
  batch = "tech",
  integration_methods = "Harmony"
)
#> ◌ [2026-10-11 23:45:49] Run integration workflow...
#> ℹ [2026-10-11 23:45:50] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-10-11 23:45:50] Checking a list of <Seurat>...
#> ! [2026-10-11 23:45:51] Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:45:51] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/5 of `srt_list`...
#> ℹ [2026-10-11 23:45:51] Perform `FindVariableFeatures()` on 1/5 of `srt_list`...
#> ! [2026-10-11 23:45:51] Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:45:51] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 2/5 of `srt_list`...
#> ℹ [2026-10-11 23:45:51] Perform `FindVariableFeatures()` on 2/5 of `srt_list`...
#> ! [2026-10-11 23:45:51] Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:45:51] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 3/5 of `srt_list`...
#> ℹ [2026-10-11 23:45:51] Perform `FindVariableFeatures()` on 3/5 of `srt_list`...
#> ! [2026-10-11 23:45:51] Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:45:51] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 4/5 of `srt_list`...
#> ℹ [2026-10-11 23:45:51] Perform `FindVariableFeatures()` on 4/5 of `srt_list`...
#> ! [2026-10-11 23:45:51] Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:45:51] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 5/5 of `srt_list`...
#> ℹ [2026-10-11 23:45:51] Perform `FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-10-11 23:45:51] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:45:52] Number of available HVF: 2000
#> ℹ [2026-10-11 23:45:52] Finished check
#> ℹ [2026-10-11 23:45:54] Perform `Seurat::ScaleData()`
#> ℹ [2026-10-11 23:45:54] Perform linear dimension reduction("pca")
#> ℹ [2026-10-11 23:45:55] Perform Harmony integration
#> ℹ [2026-10-11 23:45:55] Using "Harmonypca" (1:20) as input
#> ℹ [2026-10-11 23:45:55] Adjust neighbor k from 20 to 20 for small-sample clustering
#> ℹ [2026-10-11 23:45:56] Perform `Seurat::FindClusters()` with "louvain"
#> ℹ [2026-10-11 23:45:56] Reorder clusters...
#> ℹ [2026-10-11 23:45:56] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:45:56] Perform umap nonlinear dimension reduction using Harmony (1:20)
#> ℹ [2026-10-11 23:46:02] Perform umap nonlinear dimension reduction using Harmony (1:20)
#> ℹ [2026-10-11 23:46:09] Perform umap nonlinear dimension reduction using Harmonypca (1:20)
#> ✔ [2026-10-11 23:46:16] Harmony integration completed
panc8_sub <- RunStandardWorkflow(panc8_sub)
#> ℹ [2026-10-11 23:46:16] Start standard processing workflow...
#> ℹ [2026-10-11 23:46:16] Checking a list of <Seurat>...
#> ℹ [2026-10-11 23:46:16] Data 1/1 of the `srt_list` has been log-normalized
#> ℹ [2026-10-11 23:46:16] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:46:17] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:46:17] Number of available HVF: 2000
#> ℹ [2026-10-11 23:46:17] Finished check
#> ℹ [2026-10-11 23:46:17] Perform `ScaleData()`
#> ℹ [2026-10-11 23:46:17] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:46:17] Use stored estimated dimensions 1:26 for Standardpca
#> ℹ [2026-10-11 23:46:18] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:46:18] Reorder clusters...
#> ℹ [2026-10-11 23:46:18] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:46:18] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 23:46:27] Standard processing workflow completed

PrepareSCExplorer(
  list(
    mouse_pancreas = pancreas_sub,
    human_pancreas = panc8_sub
  ),
  base_dir = paste0(tempdir(), "/SCExplorer")
)
#> ℹ [2026-10-11 23:46:27] Create SCExplorer base directory: /tmp/RtmpvxnYGg/SCExplorer
#> ℹ [2026-10-11 23:46:27] Prepare data for object: "mouse_pancreas"
#> ℹ [2026-10-11 23:46:27] Write the expression matrix to: /tmp/RtmpvxnYGg/SCExplorer/data.hdf5
#> ℹ [2026-10-11 23:46:29] Write the meta information to: /tmp/RtmpvxnYGg/SCExplorer/meta.hdf5
#> ℹ [2026-10-11 23:46:29] Prepare data for object: "human_pancreas"
#> ℹ [2026-10-11 23:46:29] Write the expression matrix to: /tmp/RtmpvxnYGg/SCExplorer/data.hdf5
#> ℹ [2026-10-11 23:46:33] Write the meta information to: /tmp/RtmpvxnYGg/SCExplorer/meta.hdf5

# Create the app.R script
app <- RunSCExplorer(
  base_dir = paste0(tempdir(), "/SCExplorer"),
  initial_dataset = "mouse_pancreas",
  initial_group = "CellType",
  initial_feature = "Ncoa2"
)
#> ℹ [2026-10-11 23:46:35] Create the SCExplorer app script: /tmp/RtmpvxnYGg/SCExplorer/app.R
#> ℹ [2026-10-11 23:46:35] Styling the script...
#> Loading required package: shiny
#> ✔ [2026-10-11 23:46:44] rhdf5, HDF5Array, shiny, ggplot2, ragg, htmlwidgets, plotly, bslib, promises, and thisplot installed successfully
#> 
#> Attaching package: ‘bslib’
#> The following object is masked from ‘package:utils’:
#> 
#>     page
#> 
#> Attaching package: ‘rlang’
#> The following object is masked from ‘package:Biobase’:
#> 
#>     exprs
#>           ⬢          .        ⬡             ⬢     .
#>              __  __    _              __       __
#>             / /_/ /_  (_)_____ ____  / /____  / /_
#>            / __/ __ ./ // ___// __ ./ // __ ./ __/
#>           / /_/ / / / /(__  )/ /_/ / // /_/ / /_
#>           .__/_/ /_/_//____// .___/_/ .____/.__/
#>                            /_/
#>       ⬡               ⬢      .        ⬡          ⬢
#> ------------------------------------------------------------
#> Version: 0.4.6 (2026-09-26 update)
#> Website: https://mengxu98.github.io/thisplot/
#> 
#> This message can be suppressed by:
#>   suppressPackageStartupMessages(library(thisplot))
#>   or options(log_message.verbose = FALSE)
#> ------------------------------------------------------------
# Check files
list.files(paste0(tempdir(), "/SCExplorer"))
#> [1] "app.R"     "data.hdf5" "meta.hdf5"

# Run shiny app
# 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")

Usage

Arguments

See also

Examples