PD1highCD8Tscore: Quantify PD-1hiCD8+ T cells in tumor environment

Description Usage Arguments Details Examples

To explore the association between a marker and ICI therapy in datasets.

1 2	ICIResponse(marker, Gide = Gide, Riaz = Riaz, Kim = Kim, Cho = Cho, Jung = Jung, background = background)

`marker`	if marker was a vector, we calculated the average of the marker genes;if marker was a list of signature genes, we use singscore method calculated the signature score. Your marker should contain a list of Upregulated and Downregulated (if necessary) gene-set names.
`Gide`	expression matrix and clinical information from Gide et al, see Gide for more details.
`Riaz`	expression matrix and clinical information from Riaz et al, see Riaz for more details.
`Kim`	expression matrix and clinical information from Kim et al, see Kim for more details.
`Cho`	expression matrix and clinical information from Cho et al, see Cho for more details.
`Jung`	expression matrix and clinical information from Jung et al, see Jung for more details.
`background`	background gene sets for getSigScore, see background for more detail.

We return a list. It contains the relationship to ICI therapy response (a dataframe contains mean scores in response/non-response group, pvalues in Wilcox comparison and AUCs) and cox proportional model (continous variable).

data(PD1hiCD8T_Sig)
data(Gide)
data(Riaz)
data(Kim)
data(Jung)
data(Cho)
data(background)
ICIResponseRes <- ICIResponse(list(PD1hiCD8T_Sig$Gene.Name), Gide = Gide, Riaz = Riaz,
 Kim = Kim,  Cho = Cho, Jung = Jung, background = background)