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.
ICHeatmap(object, ic.use = 1, cells.use = NULL, num.genes = 30,
disp.min = -2.5, disp.max = 2.5, do.return = FALSE, col.use = pyCols,
use.scale = TRUE, do.balanced = FALSE, ...)Seurat object
Independent components to use
Cells to include in the heatmap (default is all cells)
Number of genes to return
Minimum display value (all values below are clipped)
Maximum display value (all values above are clipped)
Default is FALSE. If TRUE, return a matrix of scaled values which would be passed to heatmap.2
Color palette to use
Default is TRUE: plot scaled data. If FALSE, plot raw data on the heatmap.
Return an equal number of genes with both + and - IC scores.
Additional parameters to heatmap.2. Common examples are cexRow and cexCol, which set row and column text sizes
If do.return==TRUE, a matrix of scaled values which would be passed to heatmap.2. Otherwise, no return value, only a graphical output