Spatial transcriptomics tutorials with omicverse

Spatial preprocessing involves cropping, rotating, and aligning slides, then segmenting cells using Cellpose. OmicVerse workflows handle coordinate manipulation, quality control metrics, and tile segmentation for Visium HD datasets, producing preprocessed AnnData objects ready for downstream analysis.

Deconvolution infers cell-type proportions from spatial data using methods like Tangram, cell2location, and Starfysh. These pipelines map reference transcriptomics to spatial coordinates, resolving cell types at the tissue level across Visium, Stereo-seq, and Slide-seq platforms.

Yes. OmicVerse enables multi-slice integration for combined analysis, spatial clustering across samples, and trajectory modeling. Cell communication analysis reconstructs interaction networks between identified cell populations using spatial proximity and expression data.

No. OmicVerse tutorials guide cell segmentation configuration and execution without requiring deep image processing expertise. Cellpose handles Visium HD tile segmentation automatically within the workflow, producing segmentation masks for deconvolution downstream.

Workflows load Visium, Visium HD, Stereo-seq, and Slide-seq datasets, importing raw spatial matrices and images. OmicVerse handles coordinate mapping, stores intermediate results as AnnData objects, and exports visualizations and processed data in standard bioinformatics formats.

Trajectory analysis traces developmental or functional paths through spatial cell populations, identifying differentiation or transition sequences. OmicVerse integrates trajectory methods with spatial coordinates, revealing directional cell state changes within tissue architecture.