plotDimensionalReduction: Plot Dimensional Reduction

In roryk/bcbioSinglecell: Single-Cell RNA-Seq Utilities

Description

• t-SNE: t-distributed Stochastic Neighbor Embedding.

• PCA: Principal Component Analysis.

• UMAP: Uniform Manifold Approximation and Projection.

Usage

plotTSNE(object, ...)

plotUMAP(object, ...)

## S4 method for signature 'SingleCellExperiment'
plotTSNE(object, interestingGroups = "ident",
  color = NULL, pointsAsNumbers = FALSE,
  pointSize = getOption("pointSize", 0.75),
  pointAlpha = getOption("pointAlpha", 0.75), label = TRUE,
  labelSize = getOption("labelSize", 6L), dark = FALSE, grid = FALSE,
  legend = TRUE, aspectRatio = 1L, title = NULL)

## S4 method for signature 'seurat'
plotTSNE(object, interestingGroups = "ident",
  color = NULL, pointsAsNumbers = FALSE,
  pointSize = getOption("pointSize", 0.75),
  pointAlpha = getOption("pointAlpha", 0.75), label = TRUE,
  labelSize = getOption("labelSize", 6L), dark = FALSE, grid = FALSE,
  legend = TRUE, aspectRatio = 1L, title = NULL)

## S4 method for signature 'SingleCellExperiment'
plotUMAP(object, interestingGroups = "ident",
  color = NULL, pointsAsNumbers = FALSE,
  pointSize = getOption("pointSize", 0.75),
  pointAlpha = getOption("pointAlpha", 0.75), label = TRUE,
  labelSize = getOption("labelSize", 6L), dark = FALSE, grid = FALSE,
  legend = TRUE, aspectRatio = 1L, title = NULL)

## S4 method for signature 'seurat'
plotUMAP(object, interestingGroups = "ident",
  color = NULL, pointsAsNumbers = FALSE,
  pointSize = getOption("pointSize", 0.75),
  pointAlpha = getOption("pointAlpha", 0.75), label = TRUE,
  labelSize = getOption("labelSize", 6L), dark = FALSE, grid = FALSE,
  legend = TRUE, aspectRatio = 1L, title = NULL)

## S4 method for signature 'SingleCellExperiment'
plotPCA(object, interestingGroups = "ident",
  color = NULL, pointsAsNumbers = FALSE,
  pointSize = getOption("pointSize", 0.75),
  pointAlpha = getOption("pointAlpha", 0.75), label = TRUE,
  labelSize = getOption("labelSize", 6L), dark = FALSE, grid = FALSE,
  legend = TRUE, aspectRatio = 1L, title = NULL)

## S4 method for signature 'seurat'
plotPCA(object, interestingGroups = "ident",
  color = NULL, pointsAsNumbers = FALSE,
  pointSize = getOption("pointSize", 0.75),
  pointAlpha = getOption("pointAlpha", 0.75), label = TRUE,
  labelSize = getOption("labelSize", 6L), dark = FALSE, grid = FALSE,
  legend = TRUE, aspectRatio = 1L, title = NULL)

Arguments

object	Object.
...	Additional arguments.
interestingGroups	Character vector of interesting groups. Must be formatted in camel case and intersect with sampleData() colnames.
color	Desired ggplot color scale. Must supply discrete values. When set to NULL, the default ggplot2 color palette will be used. If manual color definitions are desired, we recommend using ggplot2::scale_color_manual().
pointsAsNumbers	Plot the points as numbers (TRUE) or dots (FALSE).
pointSize	Cell point size.
pointAlpha	Alpha transparency level. Useful when there many cells in the dataset, and some cells can be masked.
label	Overlay a cluster identitiy label on the plot.
labelSize	Size of the text label.
dark	Plot against a dark background using basejump::theme_midnight().
grid	Show major grid lines but hide axis lines.
legend	Include plot legend.
aspectRatio	Aspect ratio.
title	Plot title.

Value

ggplot.

Note

plotUMAP() requires the Python dependency umap-learn. We recommend installing this with conda: conda install -c conda-forge umap-learn.

Author(s)

Michael Steinbaugh, Rory Kirchner

See Also

• Seurat::DimPlot().

• UMAP GitHub repo.

• Seurat Mouse Cell Atlas vignette.

Other Clustering Functions: cellTypesPerCluster, knownMarkersDetected, plotCellTypesPerCluster, plotFeatureTSNE, plotKnownMarkersDetected, plotPCElbow, sanitizeMarkers, topMarkers

Examples

# seurat ====
object <- seurat_small

# t-SNE
plotTSNE(object)
plotTSNE(object, pointsAsNumbers = TRUE)
plotTSNE(object, dark = TRUE)

# UMAP
plotUMAP(object)

# PCA
plotPCA(object)

roryk/bcbioSinglecell documentation built on May 27, 2019, 10:44 p.m.