Singular value decomposition takes a rectangular matrix of gene expression data (defined as A, where A is a n x p matrix) in which the n rows represents the genes, and the p columns represents the experimental conditions. The SVD theorem states:
Anxp= Unxn Snxp VTpxp
Where
UTU = Inxn
VTV = Ipxp (i.e. U and V are orthogonal)
Calculating the SVD consists of finding the eigenvalues and eigenvectors of AAT and ATA.
Regards,
Rajshekar
