pbMDS {CATALYST} | R Documentation

## Pseudobulk-level MDS plot

### Description

Pseudobulk-level Multi-Dimensional Scaling (MDS) plot computed on median marker expressions in each sample.

### Usage

    pbMDS(
      x,
      by = c("sample_id", "cluster_id", "both"),
      k = "meta20",
      features = NULL,
      assay = "exprs",
      fun = c("median", "mean", "sum"),
      color_by = switch(by, sample_id = "condition", "cluster_id"),
      label_by = if (by == "sample_id") "sample_id" else NULL,
      shape_by = NULL,
      size_by = is.null(shape_by),
      pal = if (color_by == "cluster_id") .cluster_cols else NULL
    )

### Arguments

`x` | a `SingleCellExperiment`.
`by` | character string specifying whether to aggregate by `sample_id`, `cluster_id` or `both`.
`k` | character string specifying which clustering to use when `by != "sample_id"`; valid values are `names(cluster_codes(x))`.
`features` | character string specifying which features to include for computation of reduced dimensions; valid values are `"type"/"state"` for `type/state_markers(x)` if `rowData(x)$marker_class` have been specified; a subset of `rownames(x)`; NULL to use all features.
`assay` | character string specifying which assay data to use; valid values are `assayNames(x)`.
`fun` | character string specifying which summary statistic to use.
`color_by` | character string specifying a non-numeric cell metadata column to color by; valid values are `names(colData(x))`.
`label_by` | character string specifying a non-numeric cell metadata column to label by; valid values are `names(colData(x))`.
`shape_by` | character string specifying a non-numeric cell metadata column to shape by; valid values are `names(colData(x))`.
`size_by` | logical specifying whether points should be sized by the number of cells that went into aggregation; i.e., the size of a give sample, cluster or cluster-sample instance.
`pal` | character vector of colors to use; NULL for default `ggplot2` colors.

### 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

    data(PBMC_fs, PBMC_panel, PBMC_md)
    sce <- prepData(PBMC_fs, PBMC_panel, PBMC_md)
    sce <- cluster(sce)

    # sample-level pseudobulks
    # including state-markers only
    pbMDS(sce, by = "sample_id", features = "state")

    # cluster-level pseudobulks
    # including type-features only
    pbMDS(sce, by = "cluster_id", features = "type")

    # pseudobulks by cluster-sample
    # including all features
    pbMDS(sce, by = "both", k = "meta12",
      shape_by = "condition", size_by = TRUE)

[Package CATALYST version 1.14.1 Index]