Model-based classification in biology: tales from statistical inference

Uncovering hidden data structures from large scale experiments is a common problem across many areas of biology from the analysis of sequencing data to the recording of neuronal populations.

Standard methods of dimensionality reduction and clustering based on abstract data representations are often insufficient to capture biological complexity and are difficult to validate with statistical confidence.

We will discuss model-based approaches to data classification in three biological contexts: deregulation patterns in RNA sequencing data, analysis of neuronal subtypes and the detection of synchronous neuronal populations.

In all these contexts Bayesian inference can be used to support data classification with rigorous statistical evidence.