CARVIEW |
Select Language
HTTP/2 200
accept-ranges: bytes
access-control-allow-origin: *
age: 1
cache-control: max-age=600
content-encoding: gzip
content-type: text/html; charset=utf-8
date: Mon, 28 Jul 2025 08:46:06 GMT
etag: W/"687e6266-1bd00fb"
expires: Mon, 28 Jul 2025 08:56:05 GMT
last-modified: Mon, 21 Jul 2025 15:53:10 GMT
server: Vercel
strict-transport-security: max-age=63072000
vary: Accept-Encoding
via: 1.1 varnish
x-cache: MISS
x-cache-hits: 0
x-fastly-request-id: 4feb667a176e47db42e1bbc591aec87698df94b6
x-github-request-id: 7FAE:3B09BF:1DFBD3:23A31D:688738CC
x-origin-cache: HIT
x-proxy-cache: MISS
x-robots-tag: index
x-served-by: cache-bom-vanm7210064-BOM
x-timer: S1753692365.361826,VS0,VE787
x-vercel-cache: MISS
x-vercel-id: bom1::vdwbw-1753692365348-76421ffd9ab9
content-length: 5982451
Manhattan plot in Python
Manhattan Plot in Python
Plotly Studio: Transform any dataset into an interactive data application in minutes with AI. Sign up for early access now.
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,
)