RunDimensionReduction {IRISFGM} | R Documentation

## RunDimensionReduction

### Description

This function is based on the Seurat package to perform dimension reduction. The input matirx is the LTMG signaling matrix.

### Usage

    RunDimensionReduction(object, ...)

    .runDimensionReduction(
      object,
      mat.source = c("LTMG", "UMImatrix"),
      reduction = "umap",
      dims = seq_len(15),
      perplexity = 15,
      seed = 1
    )

    ## S4 method for signature 'IRISFGM'
    RunDimensionReduction(
      object,
      mat.source = c("LTMG", "UMImatrix"),
      reduction = "umap",
      dims = seq_len(15),
      perplexity = 15,
      seed = 1
    )

### Arguments

`object` | Input IRIS-FGM object.
`...` | other arguments passed to methods
`mat.source` | choose source data for running this function either from LTMG signal matrix or from processed data. Values of this parameter are 'LTMG' and 'UMImatrix'
`reduction` | select a method for dimension reduction, including umap, tsne, and pca.
`dims` | select the number of PCs from PCA results to perform the following dimension reduction and cell clustering.
`perplexity` | Perplexity parameter as optimal number of neighbors.
`seed` | Set the seed of R‘s random number generator, which is useful for creating simulations or random objects that can be reproduced.

### Value

This function will generate pca, tsne, or umap dimension reduction results.

### Examples

    data(example_object)
    example_obejct <- RunDimensionReduction(example_object,
       mat.source= 'LTMG',
       reduction = 'umap',
       dims = 1:15 ,
       perplexity = 15,
       seed = 1)

[Package IRISFGM version 1.2.0 Index]