Defining substructures within tissue samples is essential for spatial analysis. The Lasso function (see Mouse Tools) offers an intuitive way to delineate regions of interest (ROIs) manually—whether they are scattered or continuous—and automatically groups regions that share the same labeled name.
Define and Classify Tissue Substructures
In the workspace, use the selection tools (lasso, brush, and eraser) to outline your ROI. Click the Save button to finish drawing and assign a region name and group name; the ROI's statistics will appear in the panel. Once you click Confirm, the bins or cells within that ROI are added to your Group list under the specified label, and the selection shape is saved for future use.
If you exit Lasso mode after saving the bins but later decide to include additional regions, lasso again and simply use the same label name to extend your selection.
Export ROI Data and Matrices for Downstream Analysis
After labeling your regions, you can save the coordinate data and generate a spatial feature expression matrix for the selected area. To do this, click 
next to the group name and choose your desired format (coordinate data as CSV or export spatial matrices in GEM/GEF) to export your data.
When working with a multi-omics dataset, the export dialog allows you to select the specific omics data you want to export.
For generating the spatial expession matrices for large chips or large-scale datasets, we recommended exporting the data as a CSV file and submitting it to the SAW reanalyze lasso pipeline using the --lasso-csvargument for efficient processing. Ensure that the CSV file is accessible to both SAW and the computing environment running the pipeline.
For generating new matrices with fixed-sized square bins, input a .gef using the --gef argument and specify bin sizes with --bin-size.
For generating new matrices in cell bins, input a .cellbin.gef file using the --cellbin-gef argument.
The newly generated matrices can be used for further analysis.
Generating Matrices with Cell Bin
Sometimes, you may want to explore how different bin sizes affect your analysis while keeping the same ROI. Simply activate cell layer, reload your saved lasso shape into the workspace, and save the cells to a new group. This lets you easily compare outcomes across various resolutions.
The same approach applies to the cell layer. Activate the cell layer, reload the lasso shape, and save the selected cells into a new cell bin group. This flexible process enables you to quickly experiment with different bin sizes for both bin and cell data.
