plotSignatures {scDataviz} | R Documentation

## Find enriched markers per identified cluster and visualise these as a custom corrplot.

### Description

Find enriched markers per identified cluster and visualise these as a custom corrplot.

### Usage

    plotSignatures(
      indata,
      assay = "scaled",
      clusterAssign = metadata(indata)[["Cluster"]],
      funcSummarise = function(x) mean(x, na.rm = TRUE),
      col = colorRampPalette(brewer.pal(9, "RdPu"))(100),
      labCex = 1,
      legendCex = 1,
      labDegree = 90,
      verbose = TRUE
    )

### Arguments

`indata` | A data-frame or matrix, or `SingleCellExperiment` object. If a data-frame or matrix, this should relate to expression data (cells as columns; genes as rows). If a `SingleCellExperiment` object, data will be extracted from an assay component named by `assay`.
`assay` | Name of the assay slot in `indata` from which data will be taken, assuming `indata` is a `SingleCellExperiment` object.
`clusterAssign` | A vector of cell-to-cluster assignments. This can be from any source but must align with your cells / variables. There is no check to ensure this when `indata` is not a `SingleCellExperiment` object.
`funcSummarise` | A mathematical function used to summarise expression per marker, per cluster.
`col` | colorRampPalette to be used for shading low-to-high expression.
`labCex` | cex (size) of the main plot labels.
`legendCex` | cex (size) of the legend labels.
`labDegree` | Rotation angle of the main plot labels.
`verbose` | Boolean (TRUE / FALSE) to print messages to console or not.

### Details

Find enriched markers per identified cluster and visualise these as a custom corrplot. `plotSignatures` first collapses your input data's expression profiles from the level of cells to the level of clusters based on a mathematical function specified by `funcSummarise`. It then centers and scales the data range to be between -1 and +1 for visualisation purposes.

### Value

A `corrplot` object.

### Author(s)

Kevin Blighe <kevin@clinicalbioinformatics.co.uk>

### Examples

    # create random data that follows a negative binomial
    mat <- jitter(matrix(
      MASS::rnegbin(rexp(1000, rate=.1), theta = 4.5),
      ncol = 20))
    colnames(mat) <- paste0('CD', 1:ncol(mat))
    rownames(mat) <- paste0('cell', 1:nrow(mat))

    u <- umap::umap(mat)$layout
    colnames(u) <- c('UMAP1','UMAP2')
    rownames(u) <- rownames(mat)
    clus <- clusKNN(u)

    plotSignatures(t(mat), clusterAssign = clus)

[Package scDataviz version 1.0.0 Index]