plotManifold {CellTrails} | R Documentation

## Visualize the learned manifold

### Description

Method visualizes an approximation of the manifold in the latent space in two dimensions.

### Usage

    plotManifold(sce, color_by = c("phenoName", "featureName"), name,
      perplexity = 30, recalculate = FALSE)

### Arguments

`sce` | A `SingleCellExperiment` object
`color_by` | Indicates if nodes are colorized by a feature expression ('featureName') or phenotype label ('phenoName')
`name` | A character string specifying the featureName or phenoName
`perplexity` | Perplexity parameter for tSNE computation (default: 30)
`recalculate` | Indicates if tSNE should be recalcuated and results returned (default: FALSE)

### Details

Visualizes the learned lower-dimensional manifold in two dimensions using an approximation obtained by Barnes-Hut implementation of t-Distributed Stochastic Neighbor Embedding (tSNE; van der Maaten and Hinton 2008). Each point in this plot represents a sample. Points can be colorized according to feature expression or experimental metadata. The points' coloration can be defined via the attributes `color_by` and `name`, respectively. A previously computed tSNE visualization will be reused if it was set accordingly (see `manifold2D<-`). The parameter `perplexity` is used for the tSNE calculation.

### Value

A `ggplot` object

### Author(s)

Daniel C. Ellwanger

### References

van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605.

### See Also

`Rtsne` `latentSpace` `manifold2D`

### Examples

    # Example data
    data(exSCE)

    plotManifold(exSCE, color_by="featureName", name="feature_10")
    gp <- plotManifold(exSCE, color_by="phenoName", name="age",
                      recalculate=TRUE)
    manifold2D(exSCE) <- gp

[Package CellTrails version 1.0.1 Index]