Author: Noh, Heeju; Shoemaker, Jason E; Gunawan, Rudiyanto
Title: Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection Document date: 2018_4_6
ID: j80hnhpb_23
Snippet: where C k and S k are the 1 × m vectors of log 2 FC expressions and time-derivatives of gene k across m samples, the subscript R k refers to the set of (n TF,k +n P,k ) protein regulators of gene k in the cell type-specific PGRN, C R k and S R k denote the (n TF +n P,k ) × m matrices of log 2 FCs and their slopes across m samples, A k is the 1 × (n TF +n P ) vector of weights for edges in the PGRN pointing to gene k, and P k is the 1 × m vect.....
Document: where C k and S k are the 1 × m vectors of log 2 FC expressions and time-derivatives of gene k across m samples, the subscript R k refers to the set of (n TF,k +n P,k ) protein regulators of gene k in the cell type-specific PGRN, C R k and S R k denote the (n TF +n P,k ) × m matrices of log 2 FCs and their slopes across m samples, A k is the 1 × (n TF +n P ) vector of weights for edges in the PGRN pointing to gene k, and P k is the 1 × m vector of dysregulation impacts of gene k over m samples. In ProTINA, the vectors A k and P k for each gene k in Equations (6) and (7) were estimated by ridge regression. The ridge regression provides a solution to an underdetermined linear regression problem of the standard form: y = Xβ + ε, using a penalized least square objective function:
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