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[Preprint]. 2023 Feb 24:2023.02.24.529899. [Version 2] doi: 10.1101/2023.02.24.529899

Figure 2: Outline of the TranNet method.

Figure 2:

Input matrices X and Y represent the expressions of genes in control (X, panel (A1)) and tumor (Y, panel (A2)) samples across p patients (rows). Genes gi,i=1,n which are differentially expressed between control and tumor samples (referred to as tumor genes) are represented by individual nodes (columns from 1 to n) and the rest of the genes are represented by principal components of their expression (columns from pc1 to pck) preserving major trends of the expression. The principal components which are differentiated between normal and tumor tissue samples are considered as additional variables in the multi-variate analysis. Distribution trend of expression of a differentially expressed gene gi is visualized in the left of the yellow panel. As illustrated in a 2-dimensional space in the right of the yellow panel, although neither gˆl nor gˆt is differentially expressed between control and tumor tissue samples, their joint distribution is differentiated along the axis of principal component pc1 showing a similar trend to the DE genes while pc2 is not differentiated (B). The transition map is a linear operator defined by matrix M computed to minimise Y’s representation error subject to a sparsity constraint explained in the main text (C). The result is summarised as a bipartite network representing regulatory influences from control samples to tumors (D). Regulatory potential of a gene represents its total contribution to the transition network (E).