Deep Learning Based Deconvolution of Gene Expression Data of Human Glioblastomas for Improving Future Immunotherapy

Numerous approaches to cell-type deconvolution have been proposed, yet very few take advantage of the emerging discipline of deep learning and most approaches are limited to input data regarding the expression profiles of the cell types in question.

We have developed DECODE, a deep learning method for the task that is data-driven and does not depend on input expression profiles.

DECODE builds on a deep unfolded non-negative matrix factorization technique. It is shown to outperform previous approaches on a range of synthetic and real data sets, producing abundance estimates that are closer to and better correlated with the real values.