ABSTRACT
Exploratory factor analysis (EFA) has been developed as a powerful statistical procedure in psychological research. EFA's purpose is to identify the nature and number of latent constructs (= factors) underlying a set of observed variables. Since the research goal of EFA is to determine what causes the observed responses, EFA is ideal for hypothesis-based studies, such as identifying the number and nature of latent factors (e.g., cause, risk factors, etc.). However, the application of EFA in the biomedical field has been limited. Guillain-Barré syndrome (GBS) is peripheral neuropathy, in which the presence of antibodies to glycolipids has been associated with clinical signs. Although the precise mechanism for the generation of anti-glycolipid antibodies is unclear, we hypothesized that latent factors, such as distinct autoantigens and microbes, could induce different sets of anti-glycolipid antibodies in subsets of GBS patients. Using 55 glycolipid antibody titers from 100 GBS and 30 control sera obtained by glycoarray, we conducted EFA and extracted four factors related to neuroantigens and one potentially suppressive factor, each of which was composed of the distinct set of anti-glycolipid antibodies. The four groups of anti-glycolipid antibodies categorized by unsupervised EFA were consistent with experimental and clinical findings reported previously. Therefore, we proved that unsupervised EFA could be applied to biomedical data to extract latent factors. Applying EFA for other biomedical big data may elucidate latent factors of other diseases with unknown causes or suppressing/exacerbating factors, including COVID-19.