If many genes are perturbed in a population of cells, this can lead to diseases like cancer. The perturbations can happen in different ways, e.g. via mutations, copy number abberations or methylation. However, not all perturbations are observed in all samples.

Nested Effects Model-based perturbation inference (NEM$\pi$) uses observed perturbation profiles and gene expression data to infer unobserved perturbations and augment observed ones. The causal network of the perturbed genes (P-genes) is modelled as an adjacency matrix $\phi$ and the genes with observed gene expression (E-genes) are modelled with the attachment $\theta$ with $\theta_{ij}=1$, if E-gene $j$ is attached to S-gene $i$. If E-gene $j$ is attached to P-gene $i$, $j$ shows an effect for a perturbation of P-gene $i$. Hence, $\phi\theta$ predicts gene expression profiles, which can be compared to the real data. NEM$\pi$ iteratively infers a network $\phi$ based on gene expression profiles and a perturbation profile, and the perturbation profile based on a network $\phi$.


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Pirkl M, Beerenwinkel N (2021). "Inferring perturbation profiles of cancer samples." Bioinformatics.

cbg-ethz/nempi documentation built on May 4, 2021, 9:44 a.m.