Plot reduced dimensions

Plot cell-level dimensionality reduction.

Usage

plotPca(object, ...)

plotReducedDim(object, ...)

plotTsne(object, ...)

plotUmap(object, ...)

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

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

# S4 method for 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).

Value

ggplot.

Details

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

Note

Updated 2022-03-07.

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.

References

Jolliffe, et al., 2002.

Author

Michael Steinbaugh, Rory Kirchner

Examples

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

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


## SingleCellExperiment ====
object <- SingleCellExperiment_Seurat
plotReducedDim(object, reduction = "UMAP")
#> reduction: UMAP
#> dims: 1:2