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Exploring the Effects of Spaceflight on Mouse Physiology using the Open Access NASA GeneLab Platform

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MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

Shuo Zhang^1,2, Benjamin J Heil³, Weiguang Mao⁴

¹Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104.

|May 24, 2024

Summary

This summary is machine-generated.

We developed mousiPLIER, a machine learning tool trained on extensive mouse brain data. This method enhances the interpretability of gene expression, revealing biological pathways linked to brain aging.

Keywords:

aging gene expression machine learning

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Area of Science:

Computational biology
Neuroscience
Genomics

Background:

Gene expression profiling offers insights but struggles with network complexity.
Machine learning can integrate biological knowledge to interpret gene expression data.
Pathway-Level Information Extractor (PLIER) uses pathway gene sets (latent variables) to reduce dimensionality and improve interpretability.

Purpose of the Study:

To develop and validate the first mouse-specific PLIER model (mousiPLIER) using a large-scale dataset.
To apply mousiPLIER to analyze microglia and astrocyte gene expression during mouse brain aging.
To provide a user-friendly web server for exploring learned latent variables.

Main Methods:

Trained the mousiPLIER model on 190,111 mouse brain RNA-sequencing samples.
Validated mousiPLIER in a study of aging-related gene expression in mouse microglia and astrocytes.
Utilized k-means clustering to identify relevant studies for a specific latent variable (LV41).

Main Results:

mousiPLIER successfully reduced dimensionality and improved interpretability of mouse brain transcriptomic data.
Identified significant biological pathways associated with aging, including LV41 related to striatal signals.
LV41 analysis confirmed relevance to striatum and aging across scientific literature.

Conclusions:

mousiPLIER is an effective method for uncovering meaningful biological processes in mouse brain transcriptomic studies.
The developed model and web server facilitate the exploration of latent variables in mouse brain research.
This approach enhances the understanding of complex gene regulatory networks in the context of aging.