| runDimReduce | R Documentation |
Generic Wrapper function for running dimensionality reduction
runDimReduce(
inSCE,
method = c("scaterPCA", "seuratPCA", "seuratICA", "scanpyPCA", "rTSNE", "seuratTSNE",
"scaterUMAP", "seuratUMAP", "scanpyUMAP", "scanpyTSNE"),
useAssay = NULL,
useReducedDim = NULL,
useAltExp = NULL,
reducedDimName = method,
nComponents = 20,
useFeatureSubset = NULL,
scale = FALSE,
seed = 12345,
...
)
inSCE |
Input SingleCellExperiment object. |
method |
One from |
useAssay |
Assay to use for computation. If |
useReducedDim |
The low dimension representation to use for embedding
computation. Default |
useAltExp |
The subset to use for computation, usually for the
selected variable features. Default |
reducedDimName |
The name of the result matrix. Required. |
nComponents |
Specify the number of dimensions to compute with the selected method in case of PCA/ICA and the number of components to use in the case of TSNE/UMAP methods. |
useFeatureSubset |
Subset of feature to use for dimension reduction. A
character string indicating a |
scale |
Logical scalar, whether to standardize the expression values.
Default |
seed |
Random seed for reproducibility of results.
Default |
... |
The other arguments for running a specific algorithm. Please refer to the one you use. |
Wrapper function to run one of the available dimensionality
reduction algorithms integrated within SCTK from scaterPCA,
runSeuratPCA, runSeuratICA, runTSNE,
runSeuratTSNE, runUMAP and
runSeuratUMAP. Users can use an assay by specifying
useAssay, use the assay in an altExp by specifying both
useAltExp and useAssay, or use a low-dimensionality
representation by specifying useReducedDim.
The input SingleCellExperiment object with
reducedDim updated with the result.
data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- runNormalization(sce, useAssay = "counts",
outAssayName = "logcounts",
normalizationMethod = "logNormCounts")
sce <- runDimReduce(inSCE = sce, method = "scaterPCA",
useAssay = "logcounts", scale = TRUE,
reducedDimName = "PCA")
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