flotilla.Study.plot_pca¶

Study.plot_pca(data_type='expression', x_pc=1, y_pc=2, sample_subset=None, feature_subset=None, title='', featurewise=False, plot_violins=True, show_point_labels=False, reduce_kwargs=None, **kwargs)¶

Performs DataFramePCA on both expression and splicing study_data

Parameters:

Parameters:	data_type : str One of the names of the data types, e.g. “expression” or “splicing” x_pc : int Which principal component to plot on the x-axis y_pc : int Which principal component to plot on the y-axis 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. 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. title : str The title of the plot 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. show_point_labels : bool 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.

data_type : str

One of the names of the data types, e.g. “expression” or “splicing”

x_pc : int

Which principal component to plot on the x-axis

y_pc : int

Which principal component to plot on the y-axis

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.

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.

title : str

The title of the plot

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.

show_point_labels : bool

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.