RunTSNE: Barnes-Hut implementation of t-Distributed Stochastic...
In ILoReg: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data
Description
Usage
Arguments
Value
Examples
Description
Run nonlinear dimensionality reduction using t-SNE with the
PCA-transformed consensus matrix as input.
Usage
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RunTSNE.SingleCellExperiment(object, perplexity)
## S4 method for signature 'SingleCellExperiment'
RunTSNE(object, perplexity = 30)
Arguments
object
of SingleCellExperiment class
perplexity
perplexity of t-SNE
Value
object of SingleCellExperiment class
Examples
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library(SingleCellExperiment)
sce <- SingleCellExperiment(assays = list(logcounts = pbmc3k_500))
sce <- PrepareILoReg(sce)
## These settings are just to accelerate the example, use the defaults.
sce <- RunParallelICP(sce,L=2,threads=1,C=0.1,k=5,r=1)
sce <- RunPCA(sce,p=5)
sce <- RunTSNE(sce)
ILoReg documentation built on Nov. 8, 2020, 8:20 p.m.
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Description Usage Arguments Value Examples
Description
Run nonlinear dimensionality reduction using t-SNE with the PCA-transformed consensus matrix as input.
Usage
1 2 3 4 | RunTSNE.SingleCellExperiment(object, perplexity)
## S4 method for signature 'SingleCellExperiment'
RunTSNE(object, perplexity = 30)
|
Arguments
object |
of |
perplexity |
perplexity of t-SNE |
Value
object of SingleCellExperiment class
Examples
1 2 3 4 5 6 7 | library(SingleCellExperiment)
sce <- SingleCellExperiment(assays = list(logcounts = pbmc3k_500))
sce <- PrepareILoReg(sce)
## These settings are just to accelerate the example, use the defaults.
sce <- RunParallelICP(sce,L=2,threads=1,C=0.1,k=5,r=1)
sce <- RunPCA(sce,p=5)
sce <- RunTSNE(sce)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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