Semi-supervised consistency regularization for accurate cell type fraction and gene expression estimation

Robin Khatri, Pierre Machart, Stefan Bonn

Keywords: Cell deconvolution, consistency regularization

Abstract: Cell deconvolution is the estimation of cell type fractions and cell type-specific gene expression from mixed data with unknown composition. In biomedical research, cell deconvolution, which is a source separation task, is used to obtain mechanistic and diagnostic insights into human diseases. An unmet challenge in cell deconvolution, however, is the scarcity of realistic training data and the strong domain shift observed in synthetic training data that is used in contemporary methods. Here, we hypothesize that simultaneous consistency regularization of the target and training domains will improve deconvolution performance. By adding this biologically motivated consistency loss to two novel deep learning-based deconvolution algorithms, we achieve state-of-the-art performance on both cell fraction and gene expression estimation. Our method, DISSECT, outperforms competing algorithms across several biomedical gene expression datasets and can be easily adapted to deconvolve other biomedical data types, as exemplified by our spatial expression deconvolution experiments.

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