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 1.0.4 Index]