Welcome everybody crossing multidisciplinary borders of biosciences and AI. This is a hands-on tutorial ??nd case study on mult??-om??cs d??t?? f??ctor ??n??lysis.
F????t??r ??n??lysis is a m??th??d in st??t??st??cs us??d t?? d??scr??b?? v??ri??bility among obs??rved, c??rr??l??t??d variables ??n t??rms of a lower number of dimensions, called f??ctors. Mathematically, f??ctors ??re lin????r compositions of the ??bs??rved v??ri??bles + deviations. Joint v??ri??tions of f??ctors (or reduced dimensions) reveal variations in previously un??bs??rv??d latent variables. The discovery of such hidden variables and their covariations are the main goal of f??ctor ??n??lysis.
The following software stack will be utilized to dig biological insights (hidden variables + their dependencies) out of the pile of single-omics layers (observed variables), put together into a mult??-om??cs d??t??set: