Introduction

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$\pi \backslash pi$) 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 $\varphi \backslash phi$ and the genes with observed gene expression (E-genes) are modelled with the attachment $\theta \backslash theta$ with ${\theta }_{ij}=1\backslash theta\_\{ij\}=1$, if E-gene $jj$ is attached to S-gene $ii$. If E-gene $jj$ is attached to P-gene $ii$, $jj$ shows an effect for a perturbation of P-gene $ii$. Hence, $\varphi \theta \backslash phi\backslash theta$ predicts gene expression profiles, which can be compared to the real data. NEM$\pi \backslash pi$ iteratively infers a network $\varphi \backslash phi$ based on gene expression profiles and a perturbation profile, and the perturbation profile based on a network $\varphi \backslash phi$.

Install:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("nempi")

Most recent (devel) version:

install.packages("devtools")

library(devtools)

install_github("cbg-ethz/nempi")

library(nempi)

For the reproduction of the publication see the script in the other directory.

Reference

Pirkl M, Beerenwinkel N (2021). “Inferring perturbation profiles of cancer samples.” Bioinformatics. https://doi.org/10.1093/bioinformatics/btab113.