A guide to Z-score-based methods, with illustrations from biological data sets

The increase in the collection of biological data allows for the individual and longitudinal monitoring of hematological or urine biomarkers.However, identifying abnormal behavior in these biological sequences is not trivial.Moreover, the complexity of the biological data (correlation between biomarkers, seasonal effects, etc.) is also an issue.This work details a statistical framework for handling biological sequences using three custom Z-score methods in the intra-individual variability scope.These methods can detect abnormal samples in the longitudinal sequences with respect to the seasonality, chronological time or correlation between biomarkers.We illustrate the use of the framework on the longitudinal data of 3,936 professional soccer players (5 biomarkers) and 1,683 amateur or professional cyclists (10 biomarkers).The results show that a particular Z-score method, designed to detect a change in a series of consecutive observations, measured a high proportion of abnormal values in the ferritin and IGF1 biomarkers for both data sets.The proposed framework and methods could be applied in other contexts, such as the clinical patient follow-up in monitoring abnormal values of biological markers.The methods are flexible enough to include more complex biological features, which can be directly incorporated into the model design.