Evaluating reference-mixture matching in cell-type deconvolution with single-cell RNA-seq references

Accurate estimation of cell-type composition from bulk RNA sequencing (RNA-seq) is critical for dissecting the cellular basis of disease. Single-cell RNA sequencing (scRNA-seq) has emerged as a preferred reference for cell-type deconvolution. However, in practice, scRNA-seq references often differ from the target bulk samples in terms of clinical condition or cohort composition, potentially degrading performance. Here, we systematically evaluate deconvolution methods under varying reference matching conditions, using a scRNA-seq dataset of peripheral blood mononuclear cells (PBMCs) from managed lupus patients and healthy controls. We constructed four scRNA-seq references: (i) 20 lupus patients, (ii) 20 healthy controls, (iii) 10 lupus patients + 10 controls, and (iv) 20 lupus patients + 20 controls. We simulated bulk RNA-seq mixtures with known cell-type proportions from scRNA-seq generated on independent lupus patients and healthy controls from the same study and evaluated the performance of seven cell-type deconvolution algorithms (CIBERSORTx S-mode, CIBERSORTx NS-mode, MuSiC2, InstaPrism, BLADE, DISSECT, Scaden) when using matched, mis-matched, and mixed scRNA-seq references. We also evaluated the performance on a publicly available bulk RNA-seq PBMC dataset with cell-type proportions estimated by flow cytometry. Performance is assessed via root-mean-squared error, Pearson correlation, and Lin's concordance correlation coefficients. Our results show that the choice of method has a greater impact on deconvolution accuracy than the degree of reference matching. DISSECT consistently achieved the best performance. Reference-matching effects were more pronounced for regression-based methods such as CIBERSORTx and MuSiC2. Overall, we recommend using robust methods like DISSECT and employing matched references when available.