microbiotasplits

Microbiotasplits is a term used in microbiome research to describe a pattern in community composition where a microbiota partitions into two or more discrete, relatively stable states rather than following a smooth, continuous trajectory. In longitudinal data, this can appear as branches or distinct clusters in the temporal trajectory of samples, indicating state transitions that separate assemblages into different community configurations.

Detection and analysis rely on standard microbiome methods. Researchers compute beta-diversity distances such as Bray-Curtis or UniFrac, apply dimensionality reduction, and use clustering or time-series approaches to identify separable states. Change-point analysis, hierarchical clustering, topic models, or Dirichlet-mixture models can be employed to infer state boundaries and transitions. Once defined, researchers examine state occupancy, transition probabilities, and the timing of shifts relative to perturbations.

Biological drivers of microbiotasplits include diet changes, antibiotic exposure, infection, hormonal shifts, aging, and environmental perturbations. Interactions among taxa can create feedbacks that stabilize a given state, leading to abrupt shifts when perturbations exceed a threshold. While splits may resemble enterotype-like partitions, the emphasis is on dynamic state changes over time rather than static classifications.

Applications and implications involve understanding the stability and resilience of microbial communities, predicting responses to treatments, and informing targeted interventions such as diet modifications or probiotics. Caution is warranted due to issues common to compositional data, sampling depth, and the risk of overfitting cluster structures in complex, high-dimensional datasets.

Related concepts include microbial community states, enterotypes, beta diversity, and ecological stability. Microbiotasplits remains an area of active methodological development and interpretation in longitudinal microbiome studies.