reduced_space : pandas.DataFrame
A (n_samples, n_dimensions) DataFrame of the post-dimensionality
reduction data
components_ : pandas.DataFrame
A (n_features, n_dimensions) DataFrame of how much each feature
contributes to the components (trailing underscore to be
consistent with scikit-learn)
explained_variance_ratio_ : pandas.Series
A (n_dimensions,) Series of how much variance each component
explains. (trailing underscore to be consistent with scikit-learn)
feature_renamer : function, optional
A function which takes the name of the feature and renames it,
e.g. from an ENSEMBL ID to a HUGO known gene symbol. If not
provided, the original name is used.
groupby : mapping function | dict, optional
A mapping of the samples to a label, e.g. sample IDs to
phenotype, for the violinplots. If None, all samples are treated
the same and are colored the same.
singles : pandas.DataFrame, optional
For violinplots only. If provided and ‘plot_violins’ is True,
will plot the raw (not reduced) measurement values as violin plots.
pooled : pandas.DataFrame, optional
For violinplots only. If provided, pooled samples are plotted as
black dots within their label.
outliers : pandas.DataFrame, optional
For violinplots only. If provided, outlier samples are plotted as
a grey shadow within their label.
featurewise : bool, optional
If True, then the “samples” are features, e.g. genes instead of
samples, and the “features” are the samples, e.g. the cells
instead of the gene ids. Essentially, the transpose of the
original matrix. If True, then violins aren’t plotted. (default
False)
order : list-like, optional
The order of the labels for the violinplots, e.g. if the data is
from a differentiation timecourse, then this would be the labels
of the phenotypes, in the differentiation order.
violinplot_kws : dict, optional
Any additional parameters to violinplot
data_type : ‘expression’ | ‘splicing’, optional
For violinplots only. The kind of data that was originally used
for the reduction. (default ‘expression’)
label_to_color : dict, optional
A mapping of the label, e.g. the phenotype, to the desired
plotting color (default None, auto-assigned with the groupby)
label_to_marker : dict, optional
A mapping of the label, e.g. the phenotype, to the desired
plotting symbol (default None, auto-assigned with the groupby)
scale_by_variance : bool, optional
{x,y}_pc : str, optional
Principal component to plot on the x- and y-axis. (default “pc_1”
and “pc_2”)
n_vectors : int, optional
Number of vectors to plot of the principal components. (default 20)
distance : ‘L1’ | ‘L2’, optional
The distance metric to use to plot the vector lengths. L1 is
“Cityblock”, i.e. the sum of the x and y coordinates, and L2 is
the traditional Euclidean distance. (default “L1”)
n_top_pc_features : int, optional
THe number of top features from the principal components to plot.
(default 50)
max_char_width : int, optional
Maximum character width of a feature name. Useful for crazy long
feature IDs like MISO IDs
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