Fig. 1
From: A computational approach for the functional classification of the epigenome
Non-negative matrix factorization of epigenetic data. The scheme gives an intuitive representation of how NMF can be used to approximate a multivariate epigenetic signal in a pre-defined number of signal patterns. The algorithm takes as input a data-matrix (V) with rows corresponding to a series of genomic intervals (or loci) and columns corresponding to different epigenetic tracks for the marks. Each cell in the matrix defines the normalized/background corrected signal of a given epigenetic mark (y) in a given locus (x) (a). As result, a standard NMF procedure yields two sparse matrices W (the weight matrix) and H (the coefficient matrix) describing the contribution of each code/profile to single loci and single marks respectively (b)