pickDimReduction,matrix-method {netSmooth} | R Documentation

## Pick the dimensionality reduction method for a dataset that gives the 2D embedding with the highest entropy

### Description

Pick the dimensionality reduction method for a dataset that gives the 2D embedding with the highest entropy

### Usage

    ## S4 method for signature 'matrix'
    pickDimReduction(x, flavors = c("pca", "tsne",
      "umap"), is.counts = TRUE)

    ## S4 method for signature 'SummarizedExperiment'
    pickDimReduction(x)

    ## S4 method for signature 'Matrix'
    pickDimReduction(x, flavors = c("pca", "tsne",
      "umap"), is.counts = TRUE)

    ## S4 method for signature 'DelayedMatrix'
    pickDimReduction(x, flavors = c("pca", "tsne",
      "umap"), is.counts = TRUE)

### Arguments

`x` | matrix or SummarizedExperiment object [GENES x SAMPLES]
`flavors` | list of dimensionality reduction algorithms to try. Currently the options are "pca", "tsne" and "umap"
`is.counts` | logical: is exprs count data

### Value

name of dimensionality reduction method that gives the highest 2d entropy

### Examples

    x <- matrix(rnbinom(60000, size=1, prob = .1), ncol=100)
    pickDimReduction(x)

[Package netSmooth version 1.6.0 Index]