Research question
Does the target population contain distinct states? Do treatment-associated changes reflect shifts in subpopulation proportions, within-cell expression, or both?
AI VIRTUAL CELL · CELL STATE PREDICTION
Characterize differences. Explore state transitions.
Study differentiation, disease-associated states and cellular heterogeneity to understand how cells change across conditions.
Cell state prediction: scope and outputs
Study differentiation, disease-associated states and cellular heterogeneity to understand how cells change across conditions.
Input data
Cell expression or imaging data, sample annotations, controls and timing. Dynamic inference also requires longitudinal or time-series evidence suited to the study design.
Expected outputs
State representations, subgroup comparisons, candidate markers and trajectory hypotheses. Distances in an embedding are not equivalent to elapsed biological time.
Validation approach
Use independent samples, marker assays or functional experiments to test state interpretations and investigate batch effects and cellular composition changes.
Define the scope using the available data and validation conditions. Generalization to unseen cell types, perturbations or experimental systems requires separate evaluation.
Explore methods and data preparation ↗START WITH A QUESTION
Begin with a cellular question.
Clarify the objective, data and validation pathway so predictions can inform the next experiment.