Welcome to ARIEL’s documentation!

We develop a fast, omics-driven sample integration tool called ARIEL, which enables effortless landmark detection, spatial alignment and information transfer for multi-sample spatial transcriptomic data. ARIEL can be applied in diverse situation, including cross-individual, cross-platform, cross-resolution, cross-omics, and cross-disease scenarios. We provide four demos to demonstrate the application of ARIEL, including DLPFC, Hippocampus, Brain, and Hepatic Lobule. You can download the demo datasets and code here:

• Dataset (figshare): ARIEL-data

• Code (GitHub): shilab-ecnu/ARIEL

The main program of ARIEL can be summarized in the figure:

Contents:

• Installation and Usage
  • Installation
  • Usage
  • DLPFC(Alignment)
  • Hippocampus
  • Brain
  • Hepatic lobule
• DEMO: DLPFC
  • Spatial alignment result under different landmark number
  • Change coordinate system and use ARIEL to align the samples
• DEMO: Hippocampus
  • ARIEL
  • Results and downstream works
• DEMO: Brain
  • ARIEL
  • Downstream works(Protein Prediction)
• DEMO: Hepatic lobule
  • COSMX
  • Two new Visium liver (different health states)

End