Deep domain adversarial neural network for the deconvolution of cell type mixtures in tissue proteome profiling

Abstract

Cell type deconvolution is a computational method for the determination/resolution of cell type proportions from bulk sequencing data, and is frequently used for the analysis of divergent cell types in tumour tissue samples. However, deconvolution technology is still in its infancy for the analysis of cell types using proteomic data due to challenges with repeatability/reproducibility, variable reference standards and the lack of single-cell proteomic reference data. Here we develop a deep-learning-based deconvolution method (scpDeconv) specifically designed for proteomic data. 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 to bulk data. Extensive experiments validate the performance of scpDeconv in the deconvolution of proteomic data produced from various species/sources and different proteomic technologies. This method should find broad applicability to areas including tumour microenvironment interpretation and clinical diagnosis/classification. Deconvolution of cell types in tissue proteomic data is a challenging computational task for the bioinformatics community. A deep-learning method termed scpDeconv is introduced that makes efficient use of single-cell proteomics data to deconvolve cell types and states from bulk proteomics measurements.