Similar to FeaturePlot, however, also splits the plot by visualizing each identity class separately.
FeatureHeatmap(object, features.plot, dim.1 = 1, dim.2 = 2,
idents.use = NULL, pt.size = 2, cols.use = rev(heat.colors(10)),
pch.use = 16, reduction.use = "tsne")Seurat object
Vector of features to plot
Dimension for x-axis (default 1)
Dimension for y-axis (default 2)
Which identity classes to display (default is all identity classes)
Adjust point size for plotting
Ordered vector of colors to use for plotting. Default is heat.colors(10).
Pch for plotting
Which dimensionality reduction to use. Default is "tsne", can also be "pca", or "ica", assuming these are precomputed.
No return value, only a graphical output
Particularly useful for seeing if the same groups of cells co-exhibit a common feature (i.e. co-express a gene), even within an identity class. Best understood by example.