Constantin Zackl

This paper benchmarks second-generation single-cell-informed deconvolution tools for bulk RNA-seq, revealing performance differences based on data characteristics and providing an ecosystem called omnideconv.

The paper introduces a mathematical model, RaCInG, to infer cell-cell interaction networks in the tumor microenvironment using bulk RNA-seq data, revealing patient-specific features and immune response associations.

This paper develops a signature, iHet, to capture tumor microenvironment heterogeneity linked to immune activity and evasion using transcriptomic data from lung cancer.