Getting started

The fastest way to evaluate Asc-Seurat is to use the built-in Demo tab, which auto-loads a small PBMC dataset and lets you walk through the whole single-sample workflow without supplying any data of your own.

Launching the app

If you installed the R package:

ascseurat::run_app()

If you are using the Docker image:

docker pull pereiralabbio/asc-seurat:3 && \\
docker run --rm -d --name asc-seurat -p 3838:3838 pereiralabbio/asc-seurat:3 && open http://localhost:3838

then open http://localhost:3838 in a browser.

For your own data, launch Docker from the folder that contains the data and mount that folder into the app:

cd "/path/to/folder/that/contains/your/data"
docker run --rm -p 3838:3838 \
  -v "$PWD:/home/ascseurat/data:ro" \
  pereiralabbio/asc-seurat:3

Then use paths such as data/sample/filtered_feature_bc_matrix in the app. See Installation for additional mount examples.

Asc-Seurat home screen. — The home screen. Click **Try it with Demo Data** or use the top navigation to jump straight to a workflow.

The Demo tab

The Demo tab auto-loads a 2,000-cell subset of the PBMC 3k reference dataset that ships with the package. It is the recommended starting point for first-time users: the dataset is small enough to finish every step in a few minutes while still exercising QC, normalization, clustering, differential expression, and visualization.

Suggested first run:

Open the app.
Click Try it with Demo Data on the home page (or the Demo tab in the navigation bar). The PBMC object is loaded automatically.
Step through QC, Normalize & Cluster, and DE / Visualization with the default settings.
From the DE results table, send a gene set directly into the visualization module to inspect markers without re-importing a CSV.

Demo tab with example dataset loaded. — The Demo tab. The example dataset is auto-loaded and every step (Load, QC, Normalize & Cluster, Explore) is available in a single scroll.

Top-level navigation

The navigation bar groups the workflows into:

Home — landing page with the Demo button and a quick-start guide.
Single Sample — load, QC, cluster, and explore one sample.
Integration — declare multiple samples inline, set per-sample QC, and integrate with RPCA or Harmony.
Trajectory Inference — Slingshot, PAGA, and Monocle 3 in one tab.
Tools (dropdown) — Cell-type Annotation (SingleR) and Advanced Plots (stacked violin / multi-gene dot plot).
Demo — the auto-loaded PBMC walkthrough described above.

Working with your own data

Once you are comfortable with the demo, load your own dataset:

For a single sample, see Loading the data of an individual sample.
For multiple samples (integration), see Loading the data and integration of multiple samples.
For trajectory inference, see Trajectory inference.
For cell-type annotation, see Cell-type annotation.