TABLE 1.

The benchmarked deconvolution methods.

Algorithm	Regression	Reference
Bisque	Non‐negative least squares	Jew et al. (2020)
CIBERSORTx	Support vector regression	Newman et al. (2019)
EV‐origin	Support vector regression	Li et al. (2020)
DeconRNASeq	Non‐negative least squares	Gong and Szustakowski (2013)
dtangle	Linear mixing model	Hunt et al. (2018)
DWLS a	Dampened weighted least squares	Tsoucas et al. (2019)
DWLSj a	Dampened weighted least squares b	Tsoucas et al. (2019)
DWLS OLS a	Ordinary least squares	Tsoucas et al. (2019)
DWLS SVR a	Support vector regression	Tsoucas et al. (2019)
MuSiC	Weighted non‐negative least squares	Wang et al. (2019)
MuSiC nnls	Non‐negative least squares	Wang et al. (2019)

^a The DWLS‐based methods use the same algorithm to identify signature genes for tissue/cell types.

^b Unlike DWLS, DWLSj does not iterate the proportion estimation to converge on a solution.