Causal network perturbations for instance-specific analysis of single cell and disease samples.

ABSTRACT:

Motivation

Complex diseases involve perturbation in multiple pathways and a major challenge in clinical genomics is characterizing pathway perturbations in individual samples. This can lead to patient-specific identification of the underlying mechanism of disease thereby improving diagnosis and personalizing treatment. Existing methods rely on external databases to quantify pathway activity scores. This ignores the data dependencies and that pathways are incomplete or condition-specific.

Results

ssNPA is a new approach for subtyping samples based on deregulation of their gene networks. ssNPA learns a causal graph directly from control data. Sample-specific network neighborhood deregulation is quantified via the error incurred in predicting the expression of each gene from it

SUBMITTER: Buschur KL 

PROVIDER: S-EPMC7178399 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature