importanceADT

A function to calculate the importance score of ADT

importanceADT(
  sce,
  altExp_name = "ADT",
  exprs_value = "logcounts",
  method = c("randomForest", "PCA"),
  group = NULL,
  subsample = TRUE,
  times = 10,
  prop = 0.8,
  k_pca = 5,
  remove_first_PC = TRUE,
  ...
)

Arguments

sce	A singlecellexperiment object
altExp_name	A character indicates which expression matrix is used. by default is none (i.e. RNA).
exprs_value	A character indicates which expression value in assayNames is used.
method	A character indicates the method of ADT importance calculation, either randomForest or PCA
group	A vector indicates the grouping of the data (for random forest)
subsample	Whether perform subsampling (for random forest)
times	A numeric indicates the times of subsampling is performed (for random forest)
prop	A numeric indicates the proportion of cells are subsampled from the whole data (for random forest)
k_pca	Number of principal component will be used to calculate the loading scores (for PCA)
remove_first_PC	A logical input indicates whether the first component will be removed from calculation (for PCA).
...	other arguments to `randomForest()` or `prcomp()` function

Value

A SingleCellExperiment object

Details

For random forest, the importance scores are based on features importance. For PCA, it implements the method proposed in Levin et al (based on the loading of features).

References

Levine, J.H., Simonds, E.F., Bendall, S.C., Davis, K.L., El-ad, D.A., Tadmor, M.D., Litvin, O., Fienberg, H.G., Jager, A., Zunder, E.R. and Finck, R., 2015. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell, 162(1), pp.184-197.

Examples

data("sce_control_subset", package = "CiteFuse")
sce_control_subset <- importanceADT(sce_control_subset,
group = sce_control_subset$SNF_W_louvain,
subsample = TRUE)