  We develop an Iterative version of the Singular Value Decomposition (ISVD)
that jointly analyzes a finite number of data matrices to identify signals that
correlate among the rows of matrices. It will be illustrated how the supervised
analysis of a big data set by another complex, multi-dimensional phenotype
using the ISVD algorithm could lead to signal detection.
