plotFeature | R Documentation |
Generates one plot for each dataset, colored by chosen feature (column) from cell.data slot. Feature can be categorical (factor) or continuous. Can also plot all datasets combined with by.dataset = FALSE.
plotFeature(
object,
feature,
by.dataset = TRUE,
discrete = NULL,
title = NULL,
pt.size = 0.3,
text.size = 3,
do.shuffle = TRUE,
rand.seed = 1,
do.labels = FALSE,
axis.labels = NULL,
do.legend = TRUE,
legend.size = 5,
option = "plasma",
cols.use = NULL,
zero.color = "#F5F5F5",
return.plots = FALSE
)
object |
|
feature |
Feature to plot (should be column from cell.data slot). |
by.dataset |
Whether to generate separate plot for each dataset (default TRUE). |
discrete |
Whether to treat feature as discrete; if left NULL will infer from column class in cell.data (if factor, treated like discrete) (default NULL). |
title |
Plot title (default NULL). |
pt.size |
Controls size of points representing cells (default 0.3). |
text.size |
Controls size of plot text (cluster center labels) (default 3). |
do.shuffle |
Randomly shuffle points so that points from same dataset are not plotted one after the other (default TRUE). |
rand.seed |
Random seed for reproducibility of point shuffling (default 1). |
do.labels |
Print centroid labels for categorical features (default FALSE). |
axis.labels |
Vector of two strings to use as x and y labels respectively. |
do.legend |
Display legend on plots (default TRUE). |
legend.size |
Size of legend spots for discrete data (default 5). |
option |
Colormap option to use for ggplot2's scale_color_viridis (default 'plasma'). |
cols.use |
Vector of colors to form gradient over instead of viridis colormap (low to high). Only applies to continuous features (default NULL). |
zero.color |
Color to use for zero values (no expression) (default '#F5F5F5'). |
return.plots |
Return ggplot plot objects instead of printing directly (default FALSE). |
List of ggplot plot objects (only if return.plots TRUE, otherwise prints plots to console).
ligerex <- createLiger(list(ctrl = ctrl, stim = stim))
ligerex <- normalize(ligerex)
ligerex <- selectGenes(ligerex)
ligerex <- scaleNotCenter(ligerex)
ligerex <- optimizeALS(ligerex, k = 5, max.iter = 1)
ligerex <- quantile_norm(ligerex)
ligerex <- runTSNE(ligerex)
plotFeature(ligerex, "nUMI", pt.size = 1)
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