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_21
Snippet: a * kj c ji + p ki (4) where c ki denotes the log 2 FC expression for gene k in sample i. The variable p ki represents the part of log 2 FC of gene k expression in sample i that cannot be accounted for by the log 2 FC of its protein regulators. In other words, p ki indicates the perturbations to the expression of gene k. As detailed below, ProTINA relies on the magnitude and directions of such network perturbations (dysregulations) to identify pr.....
Document: a * kj c ji + p ki (4) where c ki denotes the log 2 FC expression for gene k in sample i. The variable p ki represents the part of log 2 FC of gene k expression in sample i that cannot be accounted for by the log 2 FC of its protein regulators. In other words, p ki indicates the perturbations to the expression of gene k. As detailed below, ProTINA relies on the magnitude and directions of such network perturbations (dysregulations) to identify proteins with altered gene regulatory activity. The dynamical information contained in time-series gene expression profiles could greatly improve the inference of the edge weights above. But, the pseudo steady-state assumption hinders the application of the linear regression in Equation (4) to time-series data. As previously described in (11), time-series information could be accounted for by adding the following linear constraint on the linear regression problem:
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