The goal of this breakout session is to introduce the concept of spot deconvolution. It is divided into three sections: Experimental Design, Data Analysis, and Data Exploration. We are using Visium FFPE data from a mouse model of Alzheimer’s disease. For more detailed information on this dataset, see our application note Single cell and spatial multiomics identifies Alzheimer’s disease markers.

A link to the presentation slides is provided in the Additional Resources section below.

The first section of this session covers the goal of the study, mouse model, scientific question, and hypothesis.

The second section of the session introduces the data analysis workflow. Topics covered in this section include:

• Spot Resolution and Cell Type Deconvolution
• Community-developed Tools
• General Analysis Steps

We will use a community-developed tool called STdeconvolve for cell type spot deconvolution. Data used in this section are provided at the links below, and an interactive Google Colab notebook is available for running an example analysis.

• A Google Colab notebook with the example analysis is here.

• All input files used for the example analysis, including the gene expression data in HDF5 format, and the astrocyte marker list, are located here.

• The raw .R script for the analysis is located here.

The third section of this session covers exploring STdeconvolve’s results and a brief discussion of next steps.

Topics covered in this section include:

• Quality Control

  • Topic annotation example files are available here: topic_12_genes.csv.
  • Topic files for the participant challenge are available here: topic_10_genes.csv, topic_15_genes.csv.
  • A link to the enrichment analysis tool Enrichr is here.
  • Astrocyte specific topic files are here: topic_6_genes.csv, topic_13_genes.csv.

• Single cell RNA-seq

• Additional mechanistic studies

• Xenium In Situ

We have additional resources on the following topics at the provided links.

Presentation

• The breakout session presentation slide deck is provided at this link.

Spot deconvolution

• Publication Highlight: Benchmarking Methods to Integrate Spatial and Single-cell Transcriptomics Data
• Integrating 10x Visium and Chromium data with R
• Integrating Single Cell and Visium Spatial Gene Expression Data

Cell type/topic annotation

• Web resources for cell type annotation

Getting started with 10x data analysis

• 10 Tips for Biologists Learning Bioinformatics
• Continuing Your Journey After Running Space Ranger
• Hands-on Tour of the Single Cell Analysis Journey, From Cloud to Loupe to Community-Developed Tools