Visualize Joint-RPCA results

Source: R/AllGenerics.R, R/plotJointRPCA.R

Creates a two-dimensional ordination plot from Joint-RPCA results stored in a SingleCellExperiment or MultiAssayExperiment. In addition to the sample ordination, feature loadings can be visualized as vectors.

plotJointRPCA(x, ...)

# S4 method for class 'MultiAssayExperiment'
plotJointRPCA(x, dimred, ...)

# S4 method for class 'SingleCellExperiment'
plotJointRPCA(x, dimred, add.vectors = TRUE, ...)

Arguments

x

A SummarizedExperiment or MultiAssayExperiment object containing Joint-RPCA results.

...

Additional arguments passed to plotOrdination and to the feature-vector plotting. Commonly used Joint-RPCA-specific arguments include:

  • ntop: integer(1) or NULL. Maximum number of loading vectors shown per data layer. The vectors with the largest lengths are retained. Set to NULL to display all vectors. (Default: 10)

dimred

character(1): Specifies the name of the Joint-RPCA result to plot.

add.vectors

logical(1) or character. If TRUE, feature loading vectors are added. Alternatively, a character vector can be supplied to display only features whose names match the given pattern(s). (Default: TRUE)

Value

A ggplot2 object.

Details

This function is a wrapper around plotOrdination for Joint-RPCA results. Consequently, most graphical parameters are passed directly to plotOrdination(), including options for colouring, grouping, faceting and adding ellipses. See plotOrdination for a complete description of these arguments.

Feature loadings are plotted as vectors originating from the origin. By default, only the longest loading vectors are shown for each data layer to improve readability.

See also

Examples

data("HintikkaXOData")
mae <- HintikkaXOData

mae[[1]] <- transformAssay(
    mae[[1]],
    assay.type = "counts",
    method = "rclr",
    impute = FALSE
)
mae[[2]] <- transformAssay(
    mae[[2]],
    assay.type = "nmr",
    method = "log10"
)

mae <- addJointRPCA(
    mae,
    experiments = c(1, 2),
    assay.types = c("rclr", "log10")
)
#> Warning: 'experiments' dropped; see 'drops()'
#> harmonizing input:
#>   removing 40 sampleMap rows not in names(experiments)

# Basic Joint-RPCA plot
plotJointRPCA(mae, "JointRPCA")


# Colour samples by metadata
plotJointRPCA(mae, "JointRPCA", colour.by = "Fat")


# Add confidence ellipses
plotJointRPCA(
    mae,
    "JointRPCA",
    colour.by = "Fat",
    add.ellipse = TRUE
)


# Show all loading vectors
plotJointRPCA(
    mae,
    "JointRPCA",
    ntop = NULL
)


# Display only selected feature vectors
plotJointRPCA(
    mae,
    "JointRPCA",
    add.vectors = c("Bacteroides", "Roseburia")
)


# Use boxed labels without repelling
plotJointRPCA(
    mae,
    "JointRPCA",
    text.labels = FALSE,
    repel.labels = FALSE
)