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https://github.com/shujiahuang/qmplot
A Python package for creating high-quality manhattan and Q-Q plots from GWAS results.
https://github.com/shujiahuang/qmplot
genomics-data-visualization gwas gwas-tools matplotlib python visualization
Last synced: about 2 months ago
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A Python package for creating high-quality manhattan and Q-Q plots from GWAS results.
- Host: GitHub
- URL: https://github.com/shujiahuang/qmplot
- Owner: ShujiaHuang
- License: gpl-3.0
- Created: 2021-02-20T06:25:40.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2023-12-27T01:37:20.000Z (12 months ago)
- Last Synced: 2024-09-23T02:08:33.827Z (3 months ago)
- Topics: genomics-data-visualization, gwas, gwas-tools, matplotlib, python, visualization
- Language: Python
- Homepage:
- Size: 2.36 MB
- Stars: 37
- Watchers: 3
- Forks: 11
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
QMplot: A Python tool for creating high-quality manhattan and Q-Q plots from GWAS results.
==========================================================================================
[](https://zenodo.org/badge/latestdoi/340579658)
**qmplot** is a handy, user-friendly tool and Python library that allows
for quick and flexible of publication-ready manhattan and Q-Q plots
directly from PLINK association results files or any data frame with
columns containing chromosomal name, chromosomal position, P-value and
optionally the SNP name(e.g. rsID in dbSNP).
This library is inspired by
[r-qqman](https://github.com/stephenturner/qqman), but it\'s much more
convenient than *r-qqman* that the column of chromosomal name doesn\'t
have to be numeric any more, which means you can **keep the raw name of
chromosomal** and don\'t have to covert X, Y, MT, etc to be 23, 24, 25,
etc.
Dependencies
------------
qmplot supports Python 3.6+ and no longer supports Python 2.
Instatllation requires [numpy](https://numpy.org/),
[scipy](https://www.scipy.org/), [pandas](https://pandas.pydata.org/)
and [matplotlib](https://matplotlib.org/).
Installation
------------
**qmplot** is written by Python and release in PyPI. The latest stable
release can be installed by running the following command:
``` {.bash}
pip install qmplot
```
Quick Start
-----------
We use a PLINK2.x association output data
"[gwas_plink_result.tsv](tests/data/gwas_plink_result.tsv)" which is
in `tests/data` directory, as the input for the plots below. Here is the
format preview of \"gwas_plink_result.tsv\":
----------------------------------------------------------------------------------------------------------------
\#CHROM POS ID REF ALT A1 TEST OBS_CT BETA SE T_STAT P
--------- --------- ----------- ----- ----- ---- ------ -------- ------------ ---------- ----------- -----------
1 904165 1_904165 G A A ADD 282 -0.0908897 0.195476 -0.464967 0.642344
1 1563691 1_1563691 T G G ADD 271 0.447021 0.422194 1.0588 0.290715
1 1707740 1_1707740 T G G ADD 283 0.149911 0.161387 0.928888 0.353805
1 2284195 1_2284195 T C C ADD 275 -0.024704 0.13966 -0.176887 0.859739
1 2779043 1_2779043 T C T ADD 272 -0.111771 0.139929 -0.79877 0.425182
1 2944527 1_2944527 G A A ADD 276 -0.054472 0.166038 -0.32807 0.743129
1 3803755 1_3803755 T C T ADD 283 -0.0392713 0.128528 -0.305547 0.760193
1 4121584 1_4121584 A G G ADD 279 0.120902 0.127063 0.951511 0.342239
1 4170048 1_4170048 C T T ADD 280 0.250807 0.143423 1.74873 0.0815274
----------------------------------------------------------------------------------------------------------------
**qmplot** apply two ways to generate manhattan and Q-Q plots:
### 1. Commandline options
This is the simplest way to plot manhattan and QQ plots if you already
have PLINK2.x association output. You can directly type `qmplot --help`
and will find all the options below:
```bash
usage: qmplot [-h] -I INPUT -O OUTPREFIX [-T TITLE] [-P SIGN_PVALUE] [-M M_ID]
[--open-gui]
qmplot: Creates high-quality manhattan and QQ plots from PLINK association
output (or any dataframe with chromosome, position, and p-value).
optional arguments:
-h, --help show this help message and exit
-I INPUT, --input INPUT
Input file
-O OUTPREFIX, --outprefix OUTPREFIX
The prefix of output file
-T TITLE, --title TITLE
Title of figure
-P SIGN_PVALUE, --sign-mark-pvalue SIGN_PVALUE
Genome wide significant p-value sites. [1e-6]
-M M_ID, --top-sign-signal-mark-id M_ID
A string denoting the column name for which you want
to annotate the Top Significant SNPs. Default: "ID"(PLINK2.x)
--display Set to be GUI backend, which can show the figure.
```
The following command will give you the two png plots with 300 dpi
resolution:
```bash
$ qmplot -I data/gwas_plink_result.tsv -T Test -M ID --dpi 300 -O test
```
The manhattan plot looks like:
The Q-Q plot looks like:
Note: You can only modify the plots throught `qmplot` commandline
options which is a big limitation when you want to make more change.
### 2. Python library
This is the most flexible way. You can use qmplot as a library in you
Python code and create the plots by your mind.
### Manhattan plot with default parameters
The `manhattanplot()` function in **qmplot** takes a data frame with
columns containing the chromosomal name/id, chromosomal position,
P-value and optionally the name of SNP(e.g. rsID in dbSNP).
By default, `manhattanplot()` looks for column names corresponding to
those outout by the plink2 association results, namely, "#CHROM",
"POS", "P", and "ID", although different column names can be
specificed by user. Calling `manhattanplot()` function with a data frame
of GWAS results as the single argument draws a basic manhattan plot,
defaulting to a darkblue and lightblue color scheme.
```python
import pandas as pd
import matplotlib.pyplot as plt
from qmplot import manhattanplot
if __name__ == "__main__":
df = pd.read_table("tests/data/gwas_plink_result.tsv", sep="\t")
df = df.dropna(how="any", axis=0) # clean data
# generate manhattan plot and set an output file.
ax = manhattanplot(data=df)
plt.savefig("output_manhattan_plot.png")
```
Rotate the x-axis tick label by setting `xticklabel_kws` to avoid label
overlap:
```python
ax = manhattanplot(data=df,
# set vertical or any other degrees as you like.
xticklabel_kws={"rotation": "vertical"})
```
Or rotate the labels 45 degrees by setting
`xticklabel_kws={"rotation": 45}`.
When run with default parameters, the `manhattanplot()` function draws
horizontal lines drawn at $-log_{10}{(10^{-5})}$ for "suggestive"
associations and $-log_{10}{(5×10^{-8})}$ for the "genome-wide significant"
threshold. These can be move to different locations or turned off completely
with the arguments `suggestiveline` and `genomewideline`, respectively.
```python
ax = manhattanplot(data=df,
suggestiveline=None, # Turn off suggestiveline
genomewideline=None, # Turn off genomewideline
xticklabel_kws={"rotation": "vertical"},
is_show=True) # display the plot in screen
```
The behavior of the `manhattanplot` function changes slightly when
results from only a single chromosome is used. Here, instead of plotting
alternating colors and chromosome ID on the x-axis, the SNP\'s position
on the chromosome is plotted on the x-axis:
```python
# plot only results of chromosome 8.
manhattanplot(data=df, CHR="chr8", xlabel="Chromosome 8")
plt.savefig("output_chr8_manhattan_plot.png")
```
`manhattanplot()` funcion has the ability to highlight SNPs with
significant GWAS signal and annotate the Top SNP, which has the lowest
P-value:
```python
ax = manhattanplot(data=df,
sign_marker_p=1e-6, # highline the significant SNP with ``sign_marker_color`` color.
is_annotate_topsnp=True, # annotate the top SNP
xticklabel_kws={"rotation": "vertical"})
```
Additionally, highlighting SNPs of interest can be combined with
limiting to a single chromosome to enable \"zooming\" into a particular
region containing SNPs of interest.
### An example for a better Manhattan plot
Futher graphical parameters can be passed to the `manhattanplot()`
function to control things like plot title, point character, size,
colors, etc. Here is the example:
```python
import pandas as pd
from qmplot import manhattanplot
if __name__ == "__main__":
# loading data from local file
df = pd.read_table("tests/data/gwas_plink_result.tsv", sep="\t")
df = df.dropna(how="any", axis=0) # clean data
# defined the plot style
f, ax = plt.subplots(figsize=(12, 4), facecolor='w', edgecolor='k')
xtick = set(['chr' + i for i in list(map(str, range(1, 10))) + ['11', '13', '15', '18', '21', 'X']])
manhattanplot(data=data,
marker=".",
sign_marker_p=1e-6, # Genome wide significant p-value
sign_marker_color="r",
snp="ID",
title="Test",
xtick_label_set=xtick,
xlabel="Chromosome",
ylabel=r"$-log_{10}{(P)}$",
sign_line_cols=["#D62728", "#2CA02C"],
hline_kws={"linestyle": "--", "lw": 1.3},
is_annotate_topsnp=True,
ld_block_size=500000, # 500000 bp
text_kws={"fontsize": 12, # The fontsize of annotate text
"arrowprops": dict(arrowstyle="-", color="k", alpha=0.6)},
ax=ax)
plt.savefig("better.manhattan.png")
```
Find more details about the parameters by typing `manhattanplot?` in
IPython console.
### QQ plot with defualt parameters
The `qqplot()` function can be used to generate a Q-Q plot to visualize
the distribution of association \"P-value\". The `qqplot()` function
takes a vector of P-values as its the only required argument.
``` {.python}
import pandas as pd
from qmplot import qqplot
if __name__ == "__main__":
df = pd.read_table("tests/data/gwas_plink_result.tsv", sep="\t")
df = df.dropna(how="any", axis=0) # clean data
ax = qqplot(data=df["P"])
```
### A better QQ plot
Futher graphical parameters can be passed to `qqplot()` to control the
plot title, axis labels, point characters, colors, points sizes, etc.
Here is the example:
``` {.python}
import pandas as pd
import matplotlib.pyplot as plt
from qmplot import qqplot
if __name__ == "__main__":
df = pd.read_table("tests/data/gwas_plink_result.tsv", sep="\t")
df = df.dropna(how="any", axis=0) # clean data
# Create a Q-Q plot
f, ax = plt.subplots(figsize=(6, 6), facecolor="w", edgecolor="k")
qqplot(data=data["P"],
marker="o",
title="Test",
xlabel=r"Expected $-log_{10}{(P)}$",
ylabel=r"Observed $-log_{10}{(P)}$",
ax=ax)
plt.savefig("test.QQ.png")
```
Find more details about the parameters by typing `qqplot?` in IPython
console.