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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.
The third section of this session covers exploring STdeconvolve’s results and a brief discussion of next steps.
Topics covered in this section include:
- 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.
- The breakout session presentation slide deck is provided at this link.
- 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
Getting started with 10x data analysis