Visualization basics

AlphaGenome predicts a variety of output types with different data shapes and biological interpretations (table). We provide alphagenome.visualization to generate matplotlib figures from model API outputs, which we outline here.

Tip

See the visualizing predictions tutorial for worked examples of plotting different modalities.

Plot

The key function, plot(), takes as input a list of components and returns a matplotlib.figure.Figure.

Components

A component is a light wrapper around a model output (such as predicted genomic tracks, splice junctions, etc) and specifies plot aesthetics. Each component maps to one vertically stacked subplot in the final figure (see blue text in the figure). Each component has an independent y-axis but shares a common x-axis, corresponding to the length of the DNA interval, in base pairs (bp).

Several default components are available, each designed to best visually represent different modalities and data shapes returned by the model API (see table).

Annotations

Additional figure elements specific to the DNA interval, but outside of components – such as locations of promoters or variants – can be overlaid via a list of annotations that are passed to plot().

Custom plotting

For users interested in configuring novel components, extend the AbstractComponent() and AbstractAnnotation() base classes.

Any other data supplied by the user can be visualized using this library as is, as long as it is provided to plot_components in the format required e.g. TrackData for Tracks.

Illustrative diagram of visualization library. Blue text indicates plot_components classes, and purple text indicates arguments to plot_components that adjust figure-wide aesthetics

Plotting components and annotation classes.

Component name plot_components.*	Description	Example figure	Data shape supported	Recommended model outputs	Good for visualising variants?
Tracks	A line-plot visualizing a scalar value at each genomic position (or coarser resolution) e.g. predictions of RNA_SEQ for a specific	Colab cell	1D	All except SPLICE_JUNCTIONS; CONTACT_MAPS	No
OverlaidTracks	A line-plot as for Tracks, but with two separate lines on the same axis with different colors e.g. predictions of RNA_SEQ for the Reference and Alternative sequence defined by a variant.	Colab cell	1D x 2	All except SPLICE_JUNCTIONS; CONTACT_MAPS	Yes
Sashimi	A series of arcs, each representing a scalar value for a pair of genomic positions (e.g. splice junctions). The thickness of the arcs are determined by the relative sizes of the scalars.	Colab cell	2D (sparse)	SPLICE_JUNCTIONS	Yes
SeqLogo	A sequence of letters (bases) with heights corresponding to a single scalar value per genomic position (e.g. from contribution scores).	Colab cell	1D + sequence	ISM contribution scores	Yes
ContactMaps	A heatmap visualizing a matrix of scalars (e.g. predicted DNA-DNA contacts), one for each pair of genomic positions in an interval.	Colab cell	2D	CONTACT_MAPS	No
ContactMapsDiff	A heatmap as for ContactMaps, but with a diverging color map centered on zero (white) to represent values derived from differences (e.g. ALT - REF)	Colab cell	2D	CONTACT_MAPS	Yes
TranscriptAnnotation	Horizontal lines representing locations of transcripts. Exons, introns, untranslated regions, and direction of transcription are indicated by differences in line thickness.	Colab cell	Interval(s)	N/A	No
VariantAnnotation	A semi-transparent rectangle (or vertical line if a variant) spanning all plot components, indicating the location of an interval (or variant). The interval (variant) is optionally labeled.	Colab cell	Interval(s) or Variant(s)	N/A	Yes
AbstractComponent	This is an abstract class, which is the parent class of most plot_components.*. A user can define their own component class, provided it adheres to the structure specified by AbstractComponent. The workhorse method is plot_ax(), which populates a matplotlib.axes.Axes object with visuals defined by the input data.	N/A	N/A	N/A	N/A