plotGeneCount {tradeSeq} | R Documentation

## Plot gene expression in reduced dimension.

### Description

Plot the gene in reduced dimensional space.

### Usage

    plotGeneCount(curve, ...)

    ## S4 method for signature 'SlingshotDataSet'
    plotGeneCount(
      curve,
      counts = NULL,
      gene = NULL,
      clusters = NULL,
      models = NULL,
      title = NULL
    )

    ## S4 method for signature 'PseudotimeOrdering'
    plotGeneCount(
      curve,
      counts = NULL,
      gene = NULL,
      clusters = NULL,
      models = NULL,
      title = NULL
    )

    ## S4 method for signature 'SingleCellExperiment'
    plotGeneCount(
      curve,
      counts = NULL,
      gene = NULL,
      clusters = NULL,
      models = NULL,
      title = NULL
    )

### Arguments

`curve` | One of the following: A `PseudotimeOrdering` or `SlingshotDataSet` object. The output from trajectory inference using Slingshot. A `SingleCellExperiment` object. The output from trajectory inference using Slingshot. A `CellDataset` object.
`...` | parameters including:
`counts` | The count matrix, genes in rows and cells in columns. Only needed if the input is of the type `PseudotimeOrdering` or `SlingshotDataSet` and the `gene` argument is not `NULL`.
`gene` | The name of gene for which you want to plot the count or the row number of that gene in the count matrix. Alternatively, one can specify the `clusters` argument.
`clusters` | The assignation of each cell to a cluster. Used to color the plot. Either `clusters` or `gene` and `counts` must be supplied.
`models` | The fitted GAMs, typically the output from `fitGAM`. Used to display the knots. Does not work with a `CellDataset` object as input.
`title` | Title for the plot.

### Details

If both `gene` and `clusters` arguments are supplied, the plot will be colored according to gene count level. If none are provided, the function will fail.

### Value

A `ggplot` object

### Examples

    set.seed(97)
    library(slingshot)
    data(crv, package="tradeSeq")
    data(countMatrix, package="tradeSeq")
    rd <- slingReducedDim(crv)
    cl <- kmeans(rd, centers = 7)$cluster
    lin <- getLineages(rd, clusterLabels = cl, start.clus = 4)
    crv <- getCurves(lin)
    counts <- as.matrix(countMatrix)
    gamList <- fitGAM(counts = counts,
     pseudotime = slingPseudotime(crv, na = FALSE),
     cellWeights = slingCurveWeights(crv))
    plotGeneCount(crv, counts, gene = "Mpo")

[Package tradeSeq version 1.8.0 Index]