This section provides step-by-step walkthroughs for the most common analysis scenarios in ariadne.ai SPATIAL. Each guide takes you through a complete workflow from start to finish, with practical advice on parameter choices and result interpretation.
For a reference description of individual jobs and their parameters, see Submitting Jobs. For a description of the underlying methods, see Statistical analysis.
The foundational workflow for any new dataset. Covers every step from uploading your raw images to obtaining a quantitative per-cell expression table ready for spatial analysis — including registration, cell segmentation, intensity mapping, cell population identification, and spatial neighborhood analysis.
Suitable for: any multiplexed immunofluorescence experiment (CODEX, Phenocycler, SignalStar), spatial transcriptomics (Xenium, Visium HD), or serial IHC dataset.
A targeted workflow for answering one of the most common questions in spatial biology: are two populations of interest spatially associated, and where in the tissue does this co-localization occur? Covers defining cell populations, running Neighborhood Enrichment for quantitative co-localization scoring, and using Recurrent Cellular Neighborhoods to map microenvironments in the tissue.
Suitable for: immuno-oncology (e.g. immune cell infiltration into tumour), inflammation research (e.g. macrophage proximity to necrotic tissue), and any experiment where spatial co-localization between two defined populations is the key question.
More guides are in preparation. If you have a specific analysis question that is not covered here, get in touch — we are happy to help you find the right approach for your data.