View source: R/importanceADT.R
importanceADT | R Documentation |
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, ... )
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 |
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).
A SingleCellExperiment object
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.
data("sce_control_subset", package = "CiteFuse") sce_control_subset <- importanceADT(sce_control_subset, group = sce_control_subset$SNF_W_louvain, subsample = TRUE)
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