plotDimReduceFeature {celda}R Documentation

Plotting feature expression on a dimensionality reduction plot

Description

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

Usage

plotDimReduceFeature(dim1, dim2, counts, features, normalize = TRUE,
  exactMatch = TRUE, trim = c(-2, 2), size = 1,
  xlab = "Dimension_1", ylab = "Dimension_2", colorLow = "grey",
  colorMid = NULL, colorHigh = "blue")

Arguments

dim1

Numeric vector. First dimension from data dimensionality reduction output.

dim2

Numeric vector. Second dimension from data dimensionality reduction output.

counts

Integer matrix. Rows represent features and columns represent cells.

features

Character vector. Uses these genes for plotting.

normalize

Logical. Whether to normalize the columns of 'counts'. 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(-2,2).

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. Default 'grey'.

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. Default 'blue'.

Value

The plot as a ggplot object

Examples


data(celdaCGSim, celdaCGMod)
celdaTsne <- celdaTsne(counts = celdaCGSim$counts,
    celdaMod = celdaCGMod)
plotDimReduceFeature(dim1 = celdaTsne[, 1],
    dim2 = celdaTsne[, 2],
    counts = celdaCGSim$counts,
    features = c("Gene_99"),
    exactMatch = TRUE)


[Package celda version 1.0.1 Index]