plotSCEScatter: Dimension reduction plot tool for all types of data
In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description Usage Arguments Value Examples

Plot results of reduced dimensions data of counts stored in any slot in the SingleCellExperiment object.

plotSCEScatter(
  inSCE,
  slot = NULL,
  sample = NULL,
  annotation,
  feature = NULL,
  groupBy = NULL,
  shape = NULL,
  reducedDimName = NULL,
  conditionClass = NULL,
  xlab = NULL,
  ylab = NULL,
  axisSize = 10,
  axisLabelSize = 10,
  dim1 = NULL,
  dim2 = NULL,
  bin = NULL,
  binLabel = NULL,
  dotSize = 2,
  transparency = 1,
  colorLow = "white",
  colorMid = "gray",
  colorHigh = "blue",
  defaultTheme = TRUE,
  title = NULL,
  titleSize = 15,
  labelClusters = TRUE,
  legendTitle = NULL,
  legendTitleSize = 12,
  legendSize = 10,
  combinePlot = "none",
  plotLabels = NULL
)

`inSCE`	Input SingleCellExperiment object with saved dimension reduction components or a variable with saved results. Required
`slot`	Desired slot of SingleCellExperiment used for plotting. Possible options: "assays", "colData", "metadata", "reducedDims". Default NULL.
`sample`	Character vector. Indicates which sample each cell belongs to.
`annotation`	Desired vector within the slot used for plotting. Default NULL.
`feature`	name of feature stored in assay of SingleCellExperiment object. Will be used only if "assays" slot is chosen. Default NULL.
`groupBy`	Group by a condition(any column of the annotation data). Default NULL.
`shape`	add shapes to each condition.
`reducedDimName`	saved dimension reduction name in the SingleCellExperiment object. Required.
`conditionClass`	class of the annotation data used in colorBy. Options are NULL, "factor" or "numeric". If NULL, class will default to the original class. Default NULL.
`xlab`	Character vector. Label for x-axis. Default NULL.
`ylab`	Character vector. Label for y-axis. Default NULL.
`axisSize`	Size of x/y-axis ticks. Default 10.
`axisLabelSize`	Size of x/y-axis labels. Default 10.
`dim1`	1st dimension to be used for plotting. Can either be a string which specifies the name of the dimension to be plotted from reducedDims, or a numeric value which specifies the index of the dimension to be plotted. Default is NULL.
`dim2`	2nd dimension to be used for plotting. Can either be a string which specifies the name of the dimension to be plotted from reducedDims, or a numeric value which specifies the index of the dimension to be plotted. Default is NULL. Default is second PCA component for PCA data and NULL otherwise.
`bin`	Numeric vector. If single value, will divide the numeric values into the 'bin' groups. If more than one value, will bin numeric values using values as a cut point.
`binLabel`	Character vector. Labels for the bins created by the 'bin' parameter. Default NULL.
`dotSize`	Size of dots. Default 2.
`transparency`	Transparency of the dots, values will be 0-1. Default 1.
`colorLow`	Character. A color available from 'colors()'. The color will be used to signify the lowest values on the scale. Default 'white'.
`colorMid`	Character. A color available from 'colors()'. The color will be used to signify the midpoint on the scale. Default 'gray'.
`colorHigh`	Character. A color available from 'colors()'. The color will be used to signify the highest values on the scale. Default 'blue'.
`defaultTheme`	adds grid to plot when TRUE. Default TRUE.
`title`	Title of plot. Default NULL.
`titleSize`	Size of title of plot. Default 15.
`labelClusters`	Logical. Whether the cluster labels are plotted.
`legendTitle`	title of legend. Default NULL.
`legendTitleSize`	size of legend title. Default 12.
`legendSize`	size of legend. Default 10.
`combinePlot`	Must be either "all", "sample", or "none". "all" will combine all plots into a single .ggplot object, while "sample" will output a list of plots separated by sample. Default "none".
`plotLabels`	labels to each plot. If set to "default", will use the name of the samples as the labels. If set to "none", no label will be plotted.

a ggplot of the reduced dimensions.

plotSCEScatter(
  inSCE = mouseBrainSubsetSCE, legendTitle = NULL,
  slot = "assays", annotation = "counts", feature = "Apoe",
  reducedDimName = "TSNE_counts", labelClusters = FALSE
)