Inferring Cooperativity From Pooled Measurements

In many modern experiments, latent interactions drive multicomponent stochastic systems, yet the data are available only as pooled measurements that obscure these dependencies.

Whether such interactions can be identified and inferred from aggregate signals remains largely unexplored.

Motivated by multi-channel electrophysiological recordings, we address this problem by introducing sum-dependent Markov chains, a class of finite-state continuous-time multivariate Markov processes whose transition rates encode interactions through the aggregate state.

Under natural structural conditions, we establish identifiability of the latent dynamic parameters from the aggregate process.

We define a cooperativity index that distinguishes positive cooperativity, negative cooperativity and independence, and construct its consistent estimators.

For discretely and noisily observed pooled data, we develop likelihood-based inference through a hidden Markov model, address the associated embedding problem, and prove consistency and asymptotic normality.

We further propose a stepdown test for cooperativity with asymptotic size control and power guarantees.

Simulations and real-data analyses, demonstrate the scope and effectiveness of the methodology.