DepInfeR-package: DepInfeR for inferring sample-specific protein dependencies

In Huber-group-EMBL/DepInfeR: Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling

DepInfeR for inferring sample-specific protein dependencies

Description

DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864.

Details

The main functions are:

• runLASSORegression - perform inference of target importance

• processTarget - pre-process drug-protein affinity dataset

For detailed information on usage, see the package vignette, by typing vignette("DepInfeR").

All software-related questions should be posted to the Bioconductor Support Site:

https://support.bioconductor.org

The code can be viewed at the GitHub repository. https://github.com/Huber-group-EMBL/DepInfeR

Author(s)

Alina Batzilla, Junyan Lu

References

Batzilla, A. and Lu, J. et al. (2022) Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling. https://www.biorxiv.org/content/10.1101/2022.01.11.475864v1

Huber-group-EMBL/DepInfeR documentation built on April 7, 2023, 7:40 a.m.