plotReducedDim: Plot reduced dimensions

In acidgenomics/minimalism: Acid Genomics Plot Functions and Themes

Plot reduced dimensions

Description

Plot cell-level dimensionality reduction.

Usage

plotPca(object, ...)

plotReducedDim(object, ...)

plotTsne(object, ...)

plotUmap(object, ...)

## S4 method for signature 'SingleCellExperiment'
plotReducedDim(
  object,
  reduction = "UMAP",
  dims = 1:2,
  interestingGroups = NULL,
  color = getOption(x = "acid.discrete.color", default =
    AcidPlots::acid_scale_color_synesthesia_d()),
  pointSize = getOption(x = "acid.point.size", default = 1L),
  pointAlpha = getOption(x = "acid.point.alpha", default = 0.9),
  pointsAsNumbers = getOption(x = "acid.points.as.numbers", default = FALSE),
  label = getOption(x = "acid.label", default = FALSE),
  labelSize = getOption(x = "acid.label.size", default = 6L),
  dark = getOption(x = "acid.dark", default = FALSE),
  legend = getOption(x = "acid.legend", default = TRUE),
  labels = list(title = NULL, subtitle = NULL)
)

## S4 method for signature 'SingleCellExperiment'
plotPca(object, ...)

## S4 method for signature 'SummarizedExperiment'
plotPca(
  object,
  assay = 1L,
  interestingGroups = NULL,
  ntop = 500L,
  label = getOption(x = "acid.label", default = FALSE),
  pointSize = getOption(x = "acid.point.size", default = 3L),
  labels = list(title = "PCA", subtitle = NULL)
)

## S4 method for signature 'SingleCellExperiment'
plotTsne(object, ...)

## S4 method for signature 'SingleCellExperiment'
plotUmap(object, ...)

Arguments

object	Object.
reduction	vector(1). Dimension reduction name or index position.
dims	integer. Vector of length 2 that denotes the columns from the reduced dimension matrix to use for centerX and centerY column calculations. Defaults the first and second dimensions.
interestingGroups	character. Groups of interest to use for visualization. Corresponds to factors describing the columns of the object.
color	ScaleDiscrete. Desired ggplot2 color scale. Must supply discrete values. When set NULL, the default ggplot2 color palette will be used. If manual color definitions are desired, we recommend using ggplot2::scale_color_manual(). To set the discrete color palette globally, use: options("acid.color.discrete" = ggplot2::scale_color_viridis_d())
pointSize	numeric(1). Point size for dots in the plot. In the range of 1-3 is generally recommended.
pointAlpha	numeric(1) (0-1). Alpha transparency level.
pointsAsNumbers	logical(1). Plot the points as numbers (TRUE) or dots (FALSE).
label	logical(1). Superimpose sample text labels on the plot.
labelSize	integer(1). Size of the text label.
dark	logical(1). Plot against a dark background using the acid_theme_light() ggplot2 theme.
legend	logical(1). Include plot legend.
labels	list. ggplot2 labels. See ggplot2::labs() for details.
...	Additional arguments.
assay	vector(1). Assay name or index position.
ntop	integer(1) or Inf. Number of most variable genes to plot. Use Inf to include all genes (not recommended).

Details

For SingleCellExperiment, colors using ident column defined in colData() by default.

Value

ggplot.

Reduction types

• PCA: Principal Component Analysis.

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

• UMAP: Uniform Manifold Approximation and Projection.

Principal component analysis (plotPca)

PCA (Jolliffe, et al., 2002) is a multivariate technique that allows us to summarize the systematic patterns of variations in the data. PCA takes the expression levels for genes and transforms it in principal component space, reducing each sample into one point. Thereby, we can separate samples by expression variation, and identify potential sample outliers. The PCA plot is a way to look at how samples are clustering.

We're using a modified version of the DESeqTransform method here.

UMAP calculation

UMAP calculation in R requires the Python module umap-learn. The UMAP module can be loaded in R using reticulate.

Note

Updated 2022-03-07.

Author(s)

Michael Steinbaugh, Rory Kirchner

References

Jolliffe, et al., 2002.

See Also

• DESeq2::plotPca().

• Seurat::DimPlot().

• monocle3::plot_cells().

Examples

data(
    RangedSummarizedExperiment,
    SingleCellExperiment_Seurat,
    package = "AcidTest"
)

## SummarizedExperiment ====
object <- RangedSummarizedExperiment
plotPca(object)

## SingleCellExperiment ====
object <- SingleCellExperiment_Seurat
plotReducedDim(object, reduction = "UMAP")

acidgenomics/minimalism documentation built on April 1, 2024, 10:34 a.m.