Draws a heatmap focusing on a principal component. Both cells and genes are sorted by their principal component scores. Allows for nice visualization of sources of heterogeneity in the dataset.
DimHeatmap(
object,
dims = 1,
nfeatures = 30,
cells = NULL,
reduction = "pca",
disp.min = -2.5,
disp.max = NULL,
balanced = TRUE,
projected = FALSE,
ncol = NULL,
fast = TRUE,
raster = TRUE,
slot = "scale.data",
assays = NULL,
combine = TRUE
)PCHeatmap(object, ...)
No return value by default. If using fast = FALSE, will return a
patchworked ggplot object if combine = TRUE, otherwise
returns a list of ggplot objects
Seurat object
Dimensions to plot
Number of genes to plot
A list of cells to plot. If numeric, just plots the top cells.
Which dimensional reduction to use
Minimum display value (all values below are clipped)
Maximum display value (all values above are clipped); defaults to 2.5
if slot is 'scale.data', 6 otherwise
Plot an equal number of genes with both + and - scores.
Use the full projected dimensional reduction
Number of columns to plot
If true, use image to generate plots; faster than using ggplot2, but not customizable
If true, plot with geom_raster, else use geom_tile. geom_raster may look blurry on some viewing applications such as Preview due to how the raster is interpolated. Set this to FALSE if you are encountering that issue (note that plots may take longer to produce/render).
Data slot to use, choose from 'raw.data', 'data', or 'scale.data'
A vector of assays to pull data from
Combine plots into a single patchworked
ggplot object. If FALSE, return a list of ggplot objects
Extra parameters passed to DimHeatmap
data("pbmc_small")
DimHeatmap(object = pbmc_small)
Run the code above in your browser using DataLab