Manhattan Plot in Python

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Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Manhattan Plot¶

ManhattanPlot allows you to visualize genome-wide association studies (GWAS) efficiently. Using WebGL under the hood, you can interactively explore overviews of massive datasets comprising hundreds of thousands of points at once, or take a closer look at a small subset of your data. Hover data and click data are accessible from within the Dash app.

In [1]:

import pandas as pd
import dash_bio


df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/manhattan_data.csv')


dash_bio.ManhattanPlot(
    dataframe=df,
)

Highlighted points color, and colors of the suggestive line and the genome-wide line¶

Change the color of the points that are considered significant.

In [2]:

import pandas as pd
import dash_bio


df = pd.read_csv('https://raw.githubusercontent.com/plotly/dash-bio-docs-files/master/manhattan_data.csv')

dash_bio.ManhattanPlot(
    dataframe=df,
    highlight_color='#00FFAA',
    suggestiveline_color='#AA00AA',
    genomewideline_color='#AA5500'
)

ManhattanPlot with Dash¶

Out[3]:

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter