Semi-Supervised Deconvolution of Bulk RNA-Seq Data with Hyperparameter Optimization // Performs semi-supervised deconvolution of bulk RNA sequencing (RNA-seq) data. Known cell-type proportions are estimated using supervised methods -- 'CIBERSORTx' (CSx), 'CIBERSORT' (CS), 'FARDEEP' (Fast And Robust DEconvolution of Expression Profiles), and 'DCQ' (Digital Cell Quantifier) -- while unknown components are inferred using non-negative matrix factorization ('NMF') with limited-memory Broyden-Fletcher-Goldfarb-Shanno with bounds ('L-BFGS-B') optimization. Hyperparameters are selected automatically using a Pareto-frontier-based approach with knee- point detection, allowing application when the reference signature matrix is incomplete. More details about 'DICEpro' can be found in Ba et al. (2026) doi:10.64898/2026.06.17.732876.