Developing practical 1

[rasools]

In the preliminary version of the practical 1, I try to cover following topics:

• [ ] Introduction to Imaging-Based Spatial Transcriptomics (Xenium Platform)

• Overview of Xenium technology and its application in spatial transcriptomics.

• Differences between segmentation-based and segmentation-free analysis in imaging data.

• [ ] Data Preprocessing and QC

• Loading Xenium spatial transcriptomics data (mouse brain, cancer).

• Key preprocessing steps: background subtraction, normalization.

• Quality control metrics for imaging data (e.g., gene detection rates, signal-to-noise ratio).

• [ ] Spatial Data Structures

• Understanding the structure of spatial transcriptomics datasets: gene expression matrices and spatial coordinates.

• Mapping gene expression to spatial coordinates (2D or 3D visualization of mouse brain tissue sections).

• [ ] Exploratory Data Analysis

• Visualizing spatial gene expression patterns using Python libraries (e.g., Scanpy, Squidpy, napari).

• Identifying tissue-specific or cancer-related markers from the dataset.

• [ ] Segmentation-Free Analysis

• Explanation of segmentation-free methods and how they differ from traditional segmentation.

• Application of segmentation-free spatial clustering methods to identify regions of interest in the brain (e.g., tumor regions vs. healthy brain tissue).

• [ ] Functional Annotation of Spatial Domains

• Linking spatial clusters to biological functions or cell types using reference atlases or databases.

• Performing gene set enrichment analysis for spatially defined regions.

• [ ] Interpreting Results and Biological Insights

• How to interpret spatial patterns in the context of cancer biology and mouse brain structure.

• Potential downstream analyses: differential expression between cancerous and non-cancerous regions, spatial heterogeneity analysis.

[Rafael-Silva-Oliveira]

Any possibility on adding some Visium HD examples with Bin2Cell? Great work :)