Guided-topic modelling of single-cell transcriptomes enables joint cell-type-specific and disease-subtype deconvolution of bulk transcriptomes with a focus on cancer studies

Cell-type composition is an important indicator of health. We developed a method called Guided Topic Model for deconvolution (GTM-decon) to automatically learn cell-type-specific gene signatures from single-cell RNA-seq data based on their cell labels to deconvolve bulk RNA-seq data. GTM-decon confers accurate cell-type deconvolution throughout comprehensive experiments on simulated and real data. GTM-decon is versatile: using cell-type-specific guides to identify cell-type of origin in pancreatic and breast cancer, phenotype-specific guides to segregate breast cancer subtypes, and a nested-guided design with breast cancer subtype at the primary level and cell type at the secondary level to identify cell-type-specific differentially expressed genes.