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. 2019 Jul 5;35(14):i436–i445. doi: 10.1093/bioinformatics/btz363

Table 2.

Guidelines for method selection

Cell type Recommended methods Overall performance Absolute score No background predictions
B cell EPIC ++ ++ +
MCP-counter ++
T cell CD4+ EPIC ++ ++
xCell ++ ++
T cell CD4+ non-regulatory quanTIseq + ++ +
xCell + ++
T cell regulatory quanTIseq ++ ++
xCell ++ ++
T cell CD8+ quanTIseq ++ ++
EPIC ++ ++
MCP-counter ++
xCell + ++
Natural Killer Cell EPIC ++ ++ +
MCP-counter ++
Macrophage / Monocyte xCell ++
EPIC + ++ +
MCP-counter ++
Cancer-associated fibroblast EPIC ++ ++ +
MCP-counter ++
Endothelial Cell EPIC ++ ++ +
xCell ++ ++
Dentricic cell None of the methods can be recommended to estimate overall DC content. MCP-counter and quanTIseq can be used to profile mDCs.

Note: For each cell type, we recommend which method to use, highlighting advantages and possible limitations. Overall performance: indicates how well predicted fractions correspond to known fractions in the benchmark. Absolute score: the method provides an absolute score that can be interpreted as a cell fraction. Methods that do not provide an absolute score only support inter-sample comparisons within the same experimental dataset, i.e. the score is only meaningful in relation to another sample. Background predictions: indicates, if a method tends to predict a cell type although it is absent.