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 = c("grey", "red"),
pch.use = 16, reduction.use = "tsne", group.by = NULL,
sep.scale = FALSE, do.return = FALSE, min.exp = -Inf, max.exp = Inf,
rotate.key = FALSE, plot.horiz = FALSE, key.position = "right")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.
Group cells in different ways (for example, orig.ident)
Scale each group separately. Default is FALSE.
Return the ggplot2 object
Min cutoff for scaled expression value, supports quantiles in the form of 'q##' (see FeaturePlot)
Max cutoff for scaled expression value, supports quantiles in the form of 'q##' (see FeaturePlot)
rotate the legend
rotate the plot such that the features are columns, groups are the rows
position of the legend ("top", "right", "bottom", "left")
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.