Overview

STANDS offers a variety of functionalities, including but not limited to: region anomaly detection on spatial transcriptome slices, multi-sample batch correction, detection of anomalous subdomains, and combinations of these functionalities. Here, we will provide a brief overview of these main features to help you quickly understand STANDS.

Preparations before tutorials

Before starting the tutorial, we need to make some preparations, including: installing STANDS and its required Python packages, downloading the datasets required for the tutorial, and so on. The preparations is available at STANDS Preparations. Additionally, when dealing with multimodal data structures involving both images and gene expression matrices, we strongly recommend using a GPU and pretraining STANDS on large-scale public spatial transcriptomics datasets. This ensures faster execution of STANDS and improved performance in modules related to image feature extraction and feature fusion.

Outline of tutorials

• Tutorial 0: Pretrain STANDS basic extractor
• Tutorial 1: Identify cancerous domains in single ST dataset
• Tutorial 2: Identify cancerous domains across multiple ST datasets concurrently
• Tutorial 3: Align multiple ST datasets sharing identical domain types
• Tutorial 4: Align multiple ST datasets with non-overlapping domain types
• Tutorial 5: Discern biologically distinct anomalous tissue subdomains in single ST datasets
• Tutorial 6: Discern biologically distinct anomalous tissue subdomains across multiple ST datasets