Unsupervised generative AI discovers pan-leukocyte dysregulated pathways in single-cell lupus data

Abstract

Comparisons of single-cell RNA-sequencing (scRNA-seq) data from healthy and diseased tissues are widely used to identify cell-type-specific dysregulated genes, pathways, and processes. Accordingly, many sophisticated methods have been developed for this purpose. However, such tools generally require considerable user expertise for optimal performance. Here, we show that unsupervised application of a linearly-decoded Variational Auto Encoder (a generative AI model) to scRNA-seq data recapitulates and extends findings from a seminal recent lupus study and leads to new insights. Thanks to existing software libraries, our approach is straightforward to implement, computationally efficient, methodologically robust, and produces consistent and reproducible results.