  The novel data analytical platform General Effect Modelling (GEM), is an
umbrella platform covering different data analytical methods that handle data
with multiple design variables (or pseudo design variables) and multivariate
responses. GEM is here demonstrated in an analysis of proteome data from
cerebrospinal fluid (CSF) from two independent previously published datasets,
one data set comprised of persons with relapsing-remitting multiple sclerosis,
persons with other neurological disorders and persons without neurological
disorders, and one data set had persons with clinically isolated syndrome
(CIS), which is the first clinical symptom of MS, and controls. The primary aim
of the present publication is to use these data to demonstrate how patient
stratification can be utilised by GEM for multivariate analysis. We also
emphasize how the findings shed light on important aspects of the molecular
mechanism of MS that may otherwise be lost. We identified proteins involved in
neural development as significantly lower for MS/CIS than for their respective
controls. This information was only seen after stratification of the persons
into two groups, which were found to have different inflammatory patterns and
the utilisation of this by GEM. Our conclusion from the study of these data is
that disrupted neural development may be an early event in CIS and MS.
