Deconvolution of omics data in Python with Deconomix -- cellular compositions, cell-type specific gene regulation, and background contributions
Journal:
bioRxiv
Published Date:
Mar 24, 2026
Abstract
Background: Gene expression profiles derived from heterogeneous bulk samples contain signals from various cell populations. Cell-type deconvolution approaches are computational tools to reverse engineer the composition of bulks in term of cell populations. Accurate estimates of cell compositions are crucial for identifying cell populations relevant for disease. Moreover, analyses, such as the identification of differentially expressed genes, can be confounded by cellular composition, as differences in gene expression may arise from both variations in cellular composition and gene regulation. Results: We present Deconvolution of omics data (Deconomix) - a comprehensive toolbox for the cell-type deconvolution of bulk transcriptomics data, available as a Python package and standalone graphical user interface. Deconomix stands apart from competing solutions with rich functionality and highly efficient implementations. It facilitates (A) the inference of cellular compositions from bulk transcriptomics data, (B) the machine learning-based optimization of gene weights to resolve small cell populations and to disentangle phenotypically related cells, (C) the inference of background contributions which otherwise would deteriorate cell-type deconvolution, and (D) population estimates of cell-type specific gene regulation. To showcase the application of Deconomix, we present a case study on breast cancer data from TCGA, highlighting subtype-specific cellular compositions and cell-type-specific gene-regulatory programs. Conclusion: We present Deconomix, a comprehensive Python package including a graphical user interface for the inference of cellular compositions, cell-type-specific gene regulation, and background contributions from bulk transcriptomics data. Key words: Cell-type deconvolution, Bulk transcriptomics, Gene regulation, Gene expression analysis, Machine learning, Cellular composition inference
