MousiPLIER: A Mouse Pathway-Level Information Extractor Model.
Shuo ZhangBenjamin J HeilWayne MaoCasey S GreeneElizabeth A HellerPublished in: bioRxiv : the preprint server for biology (2023)
Analysis of RNA-sequencing data commonly generates differential expression of individual genes across conditions. However, genes are regulated in complex networks, not as individual entities. Machine learning models that incorporate prior biological information are a powerful tool to analyze human gene expression. However, such models are lacking for mouse despite the vast number of mouse RNA-seq datasets. We trained a mouse pathway-level information extractor model (mousiPLIER). The model reduced the data dimensionality from over 10,000 genes to 196 latent variables that map to prior pathway and cell marker gene sets. We demonstrated the utility of mousiPLIER by applying it to mouse brain aging data and developed a web server to facilitate the use of the model by the scientific community.
Keyphrases
- rna seq
- single cell
- gene expression
- machine learning
- genome wide
- electronic health record
- big data
- genome wide identification
- endothelial cells
- dna methylation
- health information
- mental health
- artificial intelligence
- bioinformatics analysis
- data analysis
- mesenchymal stem cells
- cell therapy
- genome wide analysis
- social media