Analyzing gene perturbation screens with nested effects models in R and bioconductor

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Nested Effects Models (NEMs) are probabilistic models used to analyze gene perturbation screens.
• These models are crucial for understanding high-dimensional phenotypes like microarrays and cell morphology.
• NEMs help reverse-engineer upstream/downstream relationships in cellular signaling cascades.

Purpose of the Study:

• To introduce the open-source R package 'nem' for efficient inference of NEMs.
• To provide a software tool for reconstructing gene regulatory pathways from experimental data.
• To implement state-of-the-art methods for NEM analysis.

Main Methods:

• The 'nem' package utilizes various search algorithms for model fitting.
• It accepts candidate pathway genes and phenotypic profiles as input.
• The software is designed for broad applicability across different data types and representations.

Main Results:

• The package enables efficient inference of NEMs from complex biological data.
• It facilitates the reconstruction of pathway structures explaining observed perturbation effects.
• The implemented methods represent the current state-of-the-art in NEM analysis.

Conclusions:

• The 'nem' R package offers a powerful and flexible tool for systems biology research.
• It democratizes the application of advanced NEMs for pathway inference.
• The software is freely available via the Bioconductor project.