plotManifold: Visualize the learned manifold
In elldc/CellTrails: Reconstruction, visualization and analysis of branching trajectories

Description Usage Arguments Details Value Author(s) References See Also Examples

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

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

`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)

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.

A ggplot object

Daniel C. Ellwanger

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.

Rtsne latentSpace manifold2D

# 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