plotDR {CATALYST} | R Documentation

## Plot reduced dimensions

### Description

Dimension reduction plot colored by expression, cluster, sample or group ID.

### Usage

    plotDR(
      x,
      dr = NULL,
      color_by = "condition",
      facet_by = NULL,
      ncol = NULL,
      assay = "exprs",
      scale = TRUE,
      q = 0.01,
      dims = c(1, 2),
      k_pal = CATALYST:::.cluster_cols,
      a_pal = hcl.colors(10, "Viridis")
    )

### Arguments

`x` | a `SingleCellExperiment`.
`dr` | character string specifying which dimension reduction to use. Should be one of `reducedDimNames(x)`; default to the 1st available.
`color_by` | character string specifying the color coding; valid values are `rownames(sce)` and `names(colData(x))`.
`facet_by` | character string specifying a non-numeric cell metadata column to facet by; valid values are `names(colData(x))`.
`ncol` | integer scalar specifying number of facet columns; ignored unless coloring by multiple features without facetting or coloring by a single feature with facetting.
`assay` | character string specifying which assay data to use when coloring by marker(s); valid values are `assayNames(x)`.
`scale` | logical specifying whether `assay` data should be scaled between 0 and 1 using lower (1%) and upper (99%) expression quantiles; ignored if `!all(color_by %in% rownames(x))`.
`q` | single numeric in [0,0.5) determining the quantiles to trim when `scale = TRUE`.
`dims` | length 2 numeric specifying which dimensions to plot.
`k_pal` | character string specifying the cluster color palette; ignored when `color_by` is not one of `names(cluster_codes(x))`. If less than `nlevels(cluster_ids(x, k))` are supplied, colors will be interpolated via `colorRampPalette`.
`a_pal` | character string specifying the `assay` data palette when coloring by feature(s), i.e. `all(color_by %in% rownames(x))`.

### Value

a `ggplot` object.

### Author(s)

Helena L Crowell helena.crowell@uzh.ch

### References

Nowicka M, Krieg C, Crowell HL, Weber LM et al. CyTOF workflow: Differential discovery in high-throughput high-dimensional cytometry datasets. F1000Research 2017, 6:748 (doi: 10.12688/f1000research.11622.1)

### Examples

    # construct SCE & run clustering
    data(PBMC_fs, PBMC_panel, PBMC_md)
    sce <- prepData(PBMC_fs, PBMC_panel, PBMC_md)

    # run clustering & dimension reduction
    sce <- cluster(sce)
    sce <- runDR(sce, dr = "UMAP", cells = 100)

    # color by single marker, split by sample
    plotDR(sce, color_by = "CD7", facet_by = "sample_id", ncol = 4)

    # color by a set of markers using custom color palette
    cdx <- grep("CD", rownames(sce), value = TRUE)
    plotDR(sce, color_by = cdx, ncol = 4,
      a_pal = rev(hcl.colors(10, "Spectral")))

    # color by scaled expression for
    # set of markers, split by condition
    plotDR(sce,
      scale = TRUE,
      facet_by = "condition",
      color_by = sample(rownames(sce), 4))

    # color by 8 metaclusters using custom
    # cluster color palette, split by sample
    p <- plotDR(sce,
      color_by = "meta8",
      facet_by = "sample_id",
      k_pal = c("lightgrey", "cornflowerblue", "navy"))
    p$facet$params$ncol <- 4; p

[Package CATALYST version 1.14.1 Index]