jdgenometracks 0.2.1

A Python package for plotting genomic data.

jdgenometracks

Overview

jdgenometracks is a Python package designed to simplify the visualization of genomic data using Plotly and Matplotlib. The package supports visualizing different track types such as BED, BEDGRAPH, and axis tracks, making it ideal for managing and plotting large genomic datasets with customizable visual styles.

At the core of the package is the TrackFactory, which allows users to easily create and manage genomic tracks, enabling the use of both Plotly and Matplotlib with the same set of track definitions.

Key Features

• TrackFactory for easy creation of tracks (e.g., BED, BEDGRAPH, axis, and spacer tracks).
• Supports Plotly and Matplotlib visualization.
• Customizable plotting options (e.g., colors, markers, lines).
• Easily handle large genomic datasets and visualize them in multiple columns or rows.

Installation

pip install jdgenometracks

Getting Started

Example: Creating and Plotting Tracks with Plotly

import jdgenometracks as jdg

# Define a set of tracks to visualize
tracks = [
    jdg.TrackFactory.create_track(
        file_path="example_data/track1.bedgraph",
        track_name="Track 1",
        track_type="bedgraph",
        plot_type="points",
        plotly_options={
            "marker_color": "blue",
            "marker_size": 4
        }
    ),
    jdg.TrackFactory.create_track(
        file_path="example_data/track2.bed",
        track_name="Track 2",
        track_type="bed",
        plotly_options={
            "fillcolor": "red",
            "line_color": "black"
        }
    ),
    jdg.TrackFactory.create_track(
        track_name="Bottom Axis",
        track_type="axis",
        axis_type="verbose"
    )
]

# Now, pass these tracks to a Plotly plotting utility (example):
plotter = jdg.PlotlyPlotter(tracks, total_height=800)
fig = plotter.plot_all_tracks(column_titles=["Sample Data"])
fig.show()

Example: Creating and Plotting Tracks with Matplotlib

import jdgenometracks as jdg

# Define a set of tracks for Matplotlib
tracks = [
    jdg.TrackFactory.create_track(
        file_path="example_data/track1.bedgraph",
        track_name="Track 1",
        track_type="bedgraph",
        plot_type="lines",
        mpl_plot_options={
            "color": "blue",
            "linewidth": 2
        }
    ),
    jdg.TrackFactory.create_track(
        file_path="example_data/track2.bed",
        track_name="Track 2",
        track_type="bed",
        mpl_rect_options={
            "color": "red",
            "linewidth": 2
        },
        mpl_text_options={
            "va": "center"
        },
        mpl_text_alignment="right"
    ),
    jdg.TrackFactory.create_track(
        track_name="Bottom Axis",
        track_type="axis",
        axis_type="verbose"
    )
]

# Use a Matplotlib plotter
plotter = jdg.MPLPlotter(tracks, total_height=8)
fig, axes = plotter.plot_all_tracks(plot_title="Genomic Data")
plt.show()

Example: Displaying Two Columns of Tracks with Plotly

import jdgenometracks as jdg
import numpy as np

# Define two sets of tracks to display in two columns
tracks = [
    # First Column (Column 1)
    jdg.TrackFactory.create_track(
        file_path="example_data/track1_column1.bedgraph",
        track_name="Track 1 - Column 1",
        track_type="bedgraph",
        plot_type="points",
        plotly_options={
            "marker_color": "blue",
            "marker_size": 4
        }
    ),
    jdg.TrackFactory.create_track(
        file_path="example_data/track2_column1.bed",
        track_name="Track 2 - Column 1",
        track_type="bed",
        plotly_options={
            "fillcolor": "red",
            "line_color": "black"
        }
    ),
    jdg.TrackFactory.create_track(
        track_name="Bottom Axis - Column 1",
        track_type="axis",
        axis_type="verbose"
    ),

    # Second Column (Column 2)
    jdg.TrackFactory.create_track(
        file_path="example_data/track1_column2.bedgraph",
        track_name="Track 1 - Column 2",
        track_type="bedgraph",
        plot_type="lines",
        plotly_options={
            "line_color": "green",
            "line_width": 2
        }
    ),
    jdg.TrackFactory.create_track(
        file_path="example_data/track2_column2.bed",
        track_name="Track 2 - Column 2",
        track_type="bed",
        plotly_options={
            "fillcolor": "purple",
            "line_color": "black"
        }
    ),
    jdg.TrackFactory.create_track(
        track_name="Bottom Axis - Column 2",
        track_type="axis",
        axis_type="verbose"
    )
]

# Reshape the tracks to 2 columns (first 3 tracks in column 1, next 3 in column 2)
tracks = np.array(tracks).reshape(-1, 2)

# Plot using PlotlyPlotter
plotter = jdg.PlotlyPlotter(tracks, total_height=800)
fig = plotter.plot_all_tracks(
    column_titles=["Column 1", "Column 2"],  # Titles for each column
    height_props=[1, 1, 0.1],  # Proportions for each row (tracks and axis)
    width_props=[1, 1]  # Equal width for both columns
)

# Display the figure
fig.show()

TrackFactory Usage

The TrackFactory allows you to create different types of tracks. Here are some examples of how to use the factory to create tracks:

BEDGRAPH Track

jdg.TrackFactory.create_track(
    file_path="path/to/file.bedgraph",
    track_name="Example BEDGRAPH",
    track_type="bedgraph",
    plot_type="lines",
    plotly_options={
        "line_color": "green",
        "line_width": 2
    }
)

BED Track

jdg.TrackFactory.create_track(
    file_path="path/to/file.bed",
    track_name="Example BED",
    track_type="bed",
    plotly_options={
        "fillcolor": "purple",
        "line_width": 2
    }
)

Axis Track

jdg.TrackFactory.create_track(
    track_name="X-Axis",
    track_type="axis",
    axis_type="verbose"
)

Customizing Plots

Each track can be customized using Plotly keyword arguments or Matplotlib keyword arguments. Example options include:

• Colors: Set line and marker colors.
• Line Styles: Adjust line widths and types.
• Markers: Control marker styles (e.g., size, symbol).
• Axes: Customize axis labels and ticks.

Advanced Usage

For more advanced use cases, you can extend the package to support new track types or customize existing ones by modifying the TrackFactory to handle additional parameters or features.