Table 1 Selected deconvolution methods. The six reference-based deconvolution methods selected for the benchmark analysis rely on different mathematical approaches and marker gene selection strategies. Also noted is the software availability and other benchmark studies where these methods were noted as top performers
Method | Citation | Approach | Marker gene selection | Availability | Top benchmark performance |
|---|---|---|---|---|---|
DWLS (Dampened weighted least-squares) | Tsoucas et al., Nature Comm, 2019 [5] | Weighted least squares | - | R package on CRAN | Cobos et al. [18] |
Bisque | Jew et al., Nature Comm, 2020 [6] | Bias correction: Assay | - | R package on GitHub | Dai et al. [17] |
MuSiC (Multi-subject single-cell) | Wang et al., Nature Communications, 2019 [7] | Bias correction: Source | Weights Genes | R package GitHub | Jin et al. [20] |
BayesPrism | Chu et al., Nature Cancer, 2022 [8] | Bayesian | Pairwise t-test | Webtool R package on GitHub | Hippen et al. [22] |
hspe (dtangle) (hybrid-scale proportion estimation) | High collinearity adjustment | Multiple options- default “ratio” 1vALL mean expression ratio | R package on GitHub | Dai et al. [17] | |
CIBERSORTx | Newman et al., Nat Biotech, 2019 [11] | Machine Learning | Differential Gene expression | Webtool, Docker Image | Jin et al. [20] |