Single-cell RNA-seq has become a powerful tool for researchers to study biologically significant characteristics at explicitly high resolution, but its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce TAPE, a deep learning method that connects bulk RNA-seq and single-cell RNA-seq to balance the demands of big data and precision. By taking advantage of constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with existing methods on several benchmarking datasets, TAPE is more accurate (up to 40% performance improvement on the real bulk data) and faster than previous methods. For example, only TAPE can predict the tendency of increasing monocytes-to-lymphocytes (MLR) ratio in COVID-19 patients from mild to serious symptoms, whose estimated indices are consistent with laboratory data. More importantly, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. Combining with single-sample gene set enrichment analysis (ssGSEA), TAPE also provides valuable clues for people to investigate the immune response in different virus-infected patients. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.
Chen, Y.; Wang, Y.; Chen, Y.; Wei, Y.; Li, Y.; Chan, T.-F.; Li, Y.