data_type : str
One of the names of the data types, e.g. “expression” or
“splicing” (default “expression”)
x_pc : int, optional
Which principal component to plot on the x-axis (default 1)
y_pc : int, optional
Which principal component to plot on the y-axis (default 2)
sample_subset : str or None
Which subset of the samples to use, based on some phenotype
column in the experiment design data. If None, all samples are
used. (default None)
feature_subset : str or None
Which subset of the features to used, based on some feature type
in the expression data (e.g. “variant”). If None, all features
are used. (default None)
title : str, optional
Title of the reduced space plot (default ‘’)
featurewise : bool, optional
If True, the features are reduced on the samples, and the plotted
points are features, not samples. (default False)
plot_violins : bool
Whether or not to make the violinplots of the top features. This
can take a long time, so to save time you can turn it off if you
just want a quick look at the PCA. (default False)
show_point_labels : bool, optional
Whether or not to show the labels of the points. If this is
samplewise (default), then this labels the samples. If this is
featurewise, then this labels the features. (default False)
reduce_kwargs : dict, optional
Keyword arguments to the reducer (default None)
color_samples_by : str, optional
Instead of coloring the samples by their phenotype, color them by
this column in the metadata. (default None)
bokeh : bool, optional
If True, plot a javascripty/interactive bokeh plot instead of a
static printable figure (default False)
most_variant_features : bool, optional
If True, then only take the most variant of the provided features.
The most variant are determined by taking the features whose
variance is ``std_multiplier``standard deviations away from the
mean feature variance (default False)
std_multiplier : float, optional
If most_variant_features is True, then use this as a cutoff
for the minimum variance of a feature to be included (default 2)
scale_by_variance : bool, optional
If True, then scale the x- and y-axes by the explained variance
ratio of the principal component dimensions. Only valid for PCA
and its variations, not for NMF or tSNE. (default True)
kwargs : other keyword arguments
All other keyword arguments are passed to
DecomopsitionViz.plot()
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