plotDimReduceFeature: Plotting feature expression on a dimension reduction plot
In celda: CEllular Latent Dirichlet Allocation

Description Usage Arguments Value Examples

Create a scatterplot for each row of a normalized gene expression matrix where x and y axis are from a data dimension reduction tool. The cells are colored by expression of the specified feature.

plotDimReduceFeature(x, ...)

## S4 method for signature 'SingleCellExperiment'
plotDimReduceFeature(
  x,
  reducedDimName,
  dim1 = NULL,
  dim2 = NULL,
  useAssay = "counts",
  altExpName = "featureSubset",
  features,
  headers = NULL,
  normalize = FALSE,
  zscore = TRUE,
  exactMatch = TRUE,
  trim = c(-2, 2),
  limits = c(-2, 2),
  size = 1,
  xlab = "Dimension_1",
  ylab = "Dimension_2",
  colorLow = "blue4",
  colorMid = "grey90",
  colorHigh = "firebrick1",
  midpoint = 0,
  ncol = NULL,
  decreasing = FALSE
)

## S4 method for signature 'matrix'
plotDimReduceFeature(
  x,
  dim1,
  dim2,
  features,
  headers = NULL,
  normalize = FALSE,
  zscore = TRUE,
  exactMatch = TRUE,
  trim = c(-2, 2),
  limits = c(-2, 2),
  size = 1,
  xlab = "Dimension_1",
  ylab = "Dimension_2",
  colorLow = "blue4",
  colorMid = "grey90",
  colorHigh = "firebrick1",
  midpoint = 0,
  ncol = NULL,
  decreasing = FALSE
)

`x`	Numeric matrix or a SingleCellExperiment object with the matrix located in the assay slot under `useAssay`. Rows represent features and columns represent cells.
`...`	Ignored. Placeholder to prevent check warning.
`reducedDimName`	The name of the dimension reduction slot in `reducedDimNames(x)` if `x` is a SingleCellExperiment object. Ignored if both `dim1` and `dim2` are set.
`dim1`	Numeric vector. First dimension from data dimension reduction output.
`dim2`	Numeric vector. Second dimension from data dimension reduction output.
`useAssay`	A string specifying which assay slot to use if `x` is a SingleCellExperiment object. Default "counts".
`altExpName`	The name for the altExp slot to use. Default "featureSubset".
`features`	Character vector. Features in the rownames of counts to plot.
`headers`	Character vector. If 'NULL', the corresponding rownames are used as labels. Otherwise, these headers are used to label the features.
`normalize`	Logical. Whether to normalize the columns of 'counts'. Default `FALSE`.
`zscore`	Logical. Whether to scale each feature to have a mean 0 and standard deviation of 1. Default `TRUE`.
`exactMatch`	Logical. Whether an exact match or a partial match using `grep()` is used to look up the feature in the rownames of the counts matrix. Default TRUE.
`trim`	Numeric vector. Vector of length two that specifies the lower and upper bounds for the data. This threshold is applied after row scaling. Set to NULL to disable. Default `c(-1,1)`.
`limits`	Passed to scale_colour_gradient2. The range of color scale.
`size`	Numeric. Sets size of point on plot. Default 1.
`xlab`	Character vector. Label for the x-axis. Default "Dimension_1".
`ylab`	Character vector. Label for the y-axis. Default "Dimension_2".
`colorLow`	Character. A color available from 'colors()'. The color will be used to signify the lowest values on the scale.
`colorMid`	Character. A color available from 'colors()'. The color will be used to signify the midpoint on the scale.
`colorHigh`	Character. A color available from 'colors()'. The color will be used to signify the highest values on the scale.
`midpoint`	Numeric. The value indicating the midpoint of the diverging color scheme. If `NULL`, defaults to the mean with 10 percent of values trimmed. Default `0`.
`ncol`	Integer. Passed to facet_wrap. Specify the number of columns for facet wrap.
`decreasing`	logical. Specifies the order of plotting the points. If `FALSE`, the points will be plotted in increasing order where the points with largest values will be on top. `TRUE` otherwise. If `NULL`, no sorting is performed. Points will be plotted in their current order in `x`. Default `FALSE`.

The plot as a ggplot object

data(sceCeldaCG)
sce <- celdaTsne(sceCeldaCG)
plotDimReduceFeature(x = sce,
  reducedDimName = "celda_tSNE",
  normalize = TRUE,
  features = c("Gene_99"),
  exactMatch = TRUE)
library(SingleCellExperiment)
data(sceCeldaCG)
sce <- celdaTsne(sceCeldaCG)
plotDimReduceFeature(x = counts(sce),
  dim1 = reducedDim(altExp(sce), "celda_tSNE")[, 1],
  dim2 = reducedDim(altExp(sce), "celda_tSNE")[, 2],
  normalize = TRUE,
  features = c("Gene_99"),
  exactMatch = TRUE)