Build interaction networks from your data to reveal hub genes, modules, and the key drivers organizing the response.
Identify the biomarkers in your data that indicate a specific biological state, process, or treatment response.
Discover subgroups within your population from their molecular profiles to stratify samples and guide decisions.
Map your spatial architecture of a tissue, identifying distinct regions and how their cell types and pathways differ.
Infer how your cell populations signal to one another, identifying the ligand-receptor interactions in your dataset.
Order cells along developmental or regenerative trajectories, using pseudotime to trace how states emerge, diverge, and transition.
Uncover the mechanism behind your signature with topology-aware impact analysis: perturbed pathways and the upstream regulators driving them.
Match your disease gene signature to drugs known to reverse it, or to drugs that produce a similar molecular effect.
Annotate single cells in your sample, automatically or manually (by cluster), to define which cell populations are present.