Deep Domain Adversarial Neural Network for the Deconvolution of Cell Type Mixtures in Tissue Proteome Profiling

Deconvolution is a computational method for the determination/resolution of cell type proportions from bulk RNA sequence data. This technique is frequently used for the analysis of divergent cell types in tumor tissue samples. However, deconvolution technology is still in its infancy for the analysis of cell types using proteomic data sets due to challenges with repeatability/reproducibility, variable reference standards and the issues relating to single-cell proteomic reference data sets. Here, we developed a novel deep learning-based deconvolution method (scpDeconv) tailored to single-cell proteomic data sets. scpDeconv uses an autoencoder to leverage the information from bulk proteomic data to improve the quality of single-cell proteomic data, and employs a domain adversarial architecture to bridge the single-cell and bulk data distributions and transfer labels from single-cell data set to bulk data set. Extensive experiments validated the performance of scpDeconv in the deconvolution of proteomic data from various species/sources and produced using different proteomic technologies. This method should find broad applicability to areas including tumor microenvironment interpretation and clinical diagnosis/classification.