Surface protein imputation from single cell transcriptomes by deep neural networks

While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single cell Transcriptome to Protein prediction with deep neural network (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources. Cell-surface proteins serve as phenotypic cell markers and in many cases are more indicative of cellular function than the transcriptome. Here, the authors introduce a transfer learning framework to impute surface protein abundances from scRNA-seq data.