ICAcomputing | R Documentation |
We introduced independent component analysis (ICA) into single cell clustering to decompose the gene expression matrix into a number of independent components. Each independent component was characterized by a co-expression pattern and was associated with certain meaningful biological pathway.
ICAcomputing( obj, center = TRUE, scale = FALSE, RMT = TRUE, nc.vec = NULL, nc.global.mode = NULL, global.mode = FALSE, ICA.type = "JADE", nbIt = 10, funClus = "hclust", two.stage = TRUE, two.stage.ICA = "JADE" )
obj |
a seurat object which contain the gene expression matrix of all batches, meanwhile it also contain the batch ID which document the batch label information |
center |
a boolean variable to determine whether perform centralization to each batch |
scale |
a boolean variable to determine whether perform scaling to each batch |
RMT |
a boolean variable to determine whether perform random matrix theory based number of components estimation |
nc.vec |
a numrical vector to determine the number of independent components for each batch |
nc.global.mode |
a numeric vector to determine the number of components of all batches when running global mode. |
global.mode |
a boolean variable to determine whether running global mode based ICA |
ICA.type |
an optional character string given a method for computing independent components. One of "JADE" (default), "FastICA", or "MineICA": can be abbreviated |
nbIt |
The number of iterations of FastICA in MineICA |
funClus |
The clustering function to be used to cluster the estimates in MineICA. One of "hclust"(default),"kmeans", "pam", "agnes" can be abbreviated |
two.stage |
Whether running ICA+RMT based components number estimation |
two.stage.ICA |
Which type of ICA methods shall be applied in ICA+RMT estimate. One can select "FastICA" or "JADE" |
a list object which containing the ica-components
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