singlet.dataset.plot

class singlet.dataset.plot.Plot(dataset)

Bases: singlet.dataset.plugins.Plugin

Plot gene expression and phenotype in single cells

clustermap(cluster_samples=False, cluster_features=False, phenotypes_cluster_samples=(), phenotypes_cluster_features=(), annotate_samples=False, annotate_features=False, labels_samples=True, labels_features=True, orientation='horizontal', colorbars=False, **kwargs)

Samples versus features / phenotypes.

Parameters:

• cluster_samples (bool or linkage) – Whether to cluster samples and show the dendrogram. Can be either, False, True, or a linkage from scipy.cluster.hierarchy.linkage.
• cluster_features (bool or linkage) – Whether to cluster features and show the dendrogram. Can be either, False, True, or a linkage from scipy.cluster.hierarchy.linkage.
• phenotypes_cluster_samples (iterable of strings) – Phenotypes to add to the features for joint clustering of the samples. If the clustering has been precomputed including phenotypes and the linkage matrix is explicitely set as cluster_samples, the same phenotypes must be specified here, in the same order.
• phenotypes_cluster_features (iterable of strings) – Phenotypes to add to the features for joint clustering of the features and phenotypes. If the clustering has been precomputed including phenotypes and the linkage matrix is explicitely set as cluster_features, the same phenotypes must be specified here, in the same order.
• annotate_samples (dict, or False) – Whether and how to annotate the samples with separate colorbars. The dictionary must have phenotypes or features as keys. For qualitative phenotypes, the values can be palette names or palettes (with at least as many colors as there are categories). For quantitative phenotypes and features, they can be colormap names or colormaps.
• annotate_features (dict, or False) – Whether and how to annotate the featues with separate colorbars. The dictionary must have features metadata as keys. For qualitative annotations, the values can be palette names or palettes (with at least as many colors as there are categories). For quantitative annotatoins, the values can be colormap names or colormaps. Keys must be columns of the Dataset.featuresheet, except for the key ‘mean expression’ which is interpreted to mean the average of the counts for that feature.
• labels_samples (bool) – Whether to show the sample labels. If you have hundreds or more samples, you may want to turn this off to make the plot tidier.
• labels_features (bool) – Whether to show the feature labels. If you have hundreds or more features, you may want to turn this off to make the plot tidier.
• orientation (string) – Whether the samples are on the abscissa (‘horizontal’) or on the ordinate (‘vertical’).
• tight_layout (bool or dict) – Whether to call matplotlib.pyplot.tight_layout at the end of the plotting. If it is a dict, pass it unpacked to that function.
• colorbars (bool) – Whether to add colorbars. One colorbar refers to the heatmap. Moreover, if annotations for samples or features are shown, a colorbar for each of them will be shown as well.
• **kwargs – named arguments passed to seaborn.clustermap.

Returns:

A seaborn ClusterGrid instance.

plot_coverage(features='total', kind='cumulative', ax=None, tight_layout=True, legend=False, **kwargs)

Plot number of reads for each sample

Parameters:

• features (list or string) – Features to sum over. The string ‘total’ means all features including spikeins and other, ‘mapped’ means all features excluding spikeins and other, ‘spikeins’ means only spikeins, and ‘other’ means only ‘other’ features.
• kind (string) – Kind of plot (default: cumulative distribution).
• ax (matplotlib.axes.Axes) – The axes to plot into. If None (default), a new figure with one axes is created. ax must not strictly be a matplotlib class, but it must have common methods such as ‘plot’ and ‘set’.
• tight_layout (bool or dict) – Whether to call matplotlib.pyplot.tight_layout at the end of the plotting. If it is a dict, pass it unpacked to that function.
• legend (bool or dict) – If True, call ax.legend(). If a dict, pass as **kwargs to ax.legend.
• **kwargs – named arguments passed to the plot function.

Returns:

matplotlib.axes.Axes with the axes contaiing the plot.

plot_distributions(features, kind='violin', ax=None, tight_layout=True, legend=False, orientation='vertical', sort=False, bottom=0, grid=None, **kwargs)

Plot distribution of spike-in controls

Parameters:

• features (list or string) – List of features to plot. If it is the string ‘spikeins’, plot all spikeins, if the string ‘other’, plot other features.
• kind (string) – Kind of plot, one of ‘violin’ (default), ‘box’, ‘swarm’.
• ax (matplotlib.axes.Axes) – Axes to plot into. If None (default), create a new figure and axes.
• tight_layout (bool or dict) – Whether to call matplotlib.pyplot.tight_layout at the end of the plotting. If it is a dict, pass it unpacked to that function.
• legend (bool or dict) – If True, call ax.legend(). If a dict, pass as **kwargs to ax.legend. Notice that legend has a special meaning in these kinds of seaborn plots.
• orientation (string) – ‘horizontal’ or ‘vertical’.
• sort (bool or string) – True or ‘ascending’ sorts the features by median, ‘descending’ uses the reverse order.
• bottom (float or string) – The value of zero-count features. If you are using a log axis, you may want to set this to 0.1 or any other small positive number. If a string, it must be ‘pseudocount’, then the CountsTable.pseudocount will be used.
• grid (bool or None) – Whether to add a grid to the plot. None defaults to your existing settings.
• **kwargs – named arguments passed to the plot function.

Returns:

The axes with the plot.

Return type:

matplotlib.axes.Axes

scatter_reduced_samples(vectors_reduced, color_by=None, color_log=None, cmap='viridis', ax=None, tight_layout=True, **kwargs)

Scatter samples after dimensionality reduction.

Parameters:

• vectors_reduced (pandas.Dataframe) – matrix of coordinates of the samples after dimensionality reduction. Rows are samples, columns (typically 2 or 3) are the component in the low-dimensional embedding.
• color_by (string or None) – color sample dots by phenotype or expression of a certain feature.
• color_log (bool or None) – use log of phenotype/expression in the colormap. Default None only logs expression, but not phenotypes.
• cmap (string or matplotlib colormap) – color map to use for the sample dots.
• ax (matplotlib.axes.Axes) – The axes to plot into. If None (default), a new figure with one axes is created. ax must not strictly be a matplotlib class, but it must have common methods such as ‘plot’ and ‘set’.
• tight_layout (bool or dict) – Whether to call matplotlib.pyplot.tight_layout at the end of the plotting. If it is a dict, pass it unpacked to that function.
• **kwargs – named arguments passed to the plot function.

Returns:

matplotlib.axes.Axes with the axes containing the plot.

scatter_statistics(features='mapped', x='mean', y='cv', ax=None, tight_layout=True, legend=False, grid=None, **kwargs)

Scatter plot statistics of features.

Parameters:

• features (list or string) – List of features to plot. The string ‘mapped’ means everything excluding spikeins and other, ‘all’ means everything including spikeins and other.
• x (string) – Statistics to plot on the x axis.
• y (string) – Statistics to plot on the y axis.
• ax (matplotlib.axes.Axes) – The axes to plot into. If None (default), a new figure with one axes is created. ax must not strictly be a matplotlib class, but it must have common methods such as ‘plot’ and ‘set’.
• tight_layout (bool or dict) – Whether to call matplotlib.pyplot.tight_layout at the end of the plotting. If it is a dict, pass it unpacked to that function.
• legend (bool or dict) – If True, call ax.legend(). If a dict, pass as **kwargs to ax.legend.
• grid (bool or None) – Whether to add a grid to the plot. None defaults to your existing settings.
• **kwargs – named arguments passed to the plot function.

Returns:

matplotlib.axes.Axes with the axes contaiing the plot.