A powerful study design in the fields of genomics and metabolomics is the
'replicated time course experiment' where individual time series are observed
for a sample of biological units, such as human patients, termed replicates.
Standard practice for analysing these data sets is to fit each variable (e.g.
gene transcript) independently with a functional mixed-effects model to account
for between-replicate variance. However, such an independence assumption is
biologically implausible given that the variables are known to be highly
correlated.