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