We ascertained alternative computation deconvolution algorithms implemented in the CellMix package (ver 1.6). Based on accuracy and robustness evaluation results, we compared and reported the following three algorithms that outperformed the others: Digital Sorting Algorithm (named “DSA”) [27], which employs linear modeling to infer cell distributions; the method population-specific expression analysis (PSEA, also named meanProfile in CellMix implementation) [29] that calculates estimated expression profiles relative to the average of the marker gene list for each cell type [29]; and a semi-supervised learning method that employs non-negative matrix factorization (ssNMF in CellMix implementation) [57]. We employed a leave-one-out cross-validation (LOOCV) procedure to evaluate the accuracy provided by each method. The best performing algorithm ssNMF integrates cell-type marker genes to resolve the drawbacks of completely unsupervised standard non-negative matrix factorization. We followed the standard procedure described in the CellMix package, which included the extraction of marker genes from the reference samples (function extractMarkers from the CellMix package), and the posterior invocation of the function ged to infer cellular population from the gene expression of bulk RNA-seq data. Besides, we tested additional methods
