Working with spatial data, users frequently encounter experimental designs that require cross-slice integration, such as case-control cohorts, longitudinal studies, multi-region tissue profiling, or technical replicates.
This tutorial provides a concise guide to:
Load and validate aggregated multi-slice datasets
Perform differential expression across biological conditions
In this tutorial, we use two mouse brains (C04042E2 and C04042E3) analyzed in Bin 20 as an example. They are stained with ssDNA and H&E, respectively.
Select .stereo file from SAW aggr . Browse Dataset Information to learn about the basic statistical information of the sample.
Multi-slice visualization & consistency check
In Projection dropdown, you can switch to each slice's Spatial view. And a UMAP view, which includes all slices.
Cross-slice consistency assessment
In UMAP view, select Slice group in the sidebar. Points are intermixed across slices. If there are structured separations, it’s essential to distinguish whether the observed separation between slices arises from true biological differences or technical artifacts.
UMAP after batch correction in SAW aggr Re-color the points based on a biological annotation or SAW-generated clusters. In this example, you can discover that the common clusters have coherence that persists across slices.
Cross-Sample differential expression
You can compare identical regions across experimental conditions. Click 
and select CSV for differential expression to open a DE mode dialog.
Select Across Multiple Samples, and you see a form. Configure the analysis parameters:
Experimental condition:
Select the group that defines your experimental condition (e.g., treatment with condition case/control). The analysis will compare gene expression between these conditions.
Target region:
Choose the biological or cell-type annotation group that defines the region of interest (e.g., ROI with label HIP). The analysis will be restricted to only those spots/cells belonging to this region in all selected slices.
Slice scope:
StereoMap automatically identifies and lists the slices that contain both the specified experimental conditions and the target region. Only slices with valid data for all required categories are included, ensuring a fair and meaningful comparison.
Click Run differential expression and export a CSV file. Input this file into SAW renalyze diffExp and obtain the result.
