Supervised Deep Learning with Gene Annotation for Cell Classification

Lin, Z.; Sun, W.

doi:10.1101/2024.07.15.603527

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bioRxiv · 10.1101/2024.07.15.603527

Supervised Deep Learning with Gene Annotation for Cell Classification

Lin, Z.·Sun, W.

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Abstract

Gene-by-gene differential expression analysis is a widely used supervised approach for interpreting single-cell RNA-sequencing (scRNA-seq) data. However, modern scRNA-seq datasets often contain large numbers of cells, which can produce numerous differentially expressed genes with exceedingly small p-values but minimal effect sizes, and thus making biological interpretation difficult. To overcome this challenge, we developed Supervised Deep learning with gene ANnotation (SDAN), a method that integrates gene-annotation information with gene-expression profiles using a graph neural network. SDAN identifies functionally coherent gene sets that best classify cells, and the resulting cell-level classification scores can be aggregated to make individual-level predictions. We evaluated SDAN and two representative existing methods in three real-data applications to identify gene sets associated with severe COVID-19, dementia, and immunotherapy response in cancer. SDAN consistently outperformed alternative approaches by achieving two key objectives simultaneously: accurate classification of outcomes and unambiguous assignment of genes to gene sets of functionally related genes.

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Lin, Z., Sun, W.. 2024-07-15. Supervised Deep Learning with Gene Annotation for Cell Classification. https://doi.org/10.1101/2024.07.15.603527

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