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.
PCHeatmap(object, pc.use = 1, cells.use = NULL, num.genes = 30,
use.full = FALSE, disp.min = -2.5, disp.max = 2.5, do.return = FALSE,
col.use = pyCols, use.scale = TRUE, do.balanced = FALSE,
remove.key = FALSE, label.columns = NULL, ...)Seurat object
Principal components to use
A list of cells to plot. If numeric, just plots the top cells.
Number of genes to return
Use the full PCA (projected PCA). Default i s FALSE
Minimum display value (all values below are clipped)
Maximum display value (all values above are clipped)
Default is TRUE: plot scaled data. If FALSE, plot raw data on the heatmap.
Return an equal number of genes with both + and - PC scores.
Removes the color key from the plot.
Whether to label the columns. Default is TRUE for 1 PC, FALSE for > 1 PC
If do.return==TRUE, a matrix of scaled values which would be passed to heatmap.2. Otherwise, no return value, only a graphical output