qmplot 0.0.5

qmplot: Create high-quality manhattan and QQ plots for PLINK association output (or any dataframe with chromosome, position, and p-value).

qmplot is a handy tool and Python package for creating high-quality manhattan and Q-Q plots from PLINK association output or any dataframe with chromosome, position and p-value.

This library is inspired by r-qqman.

Dependencies

qmplot supports Python 3.6+ and no longer supports Python 2.

Instatllation requires numpy, scipy, pandas and matplotlib.

Installation

qmplot is written by Python and release in PyPI. The latest stable release can be installed by running the following command:

pip install qmplot

Quick Start

We use a PLINK2.x association output 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 associtation output. You can directly type qmplot --help and will find all the options below:

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:
                        PLINK2.x's "ID"
  --open-gui            Set to be GUI backend, which can show the figure.

The following command will give you the two png plots with 300 dpi resolution:

$ 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 whill be a big limitation when you want to make more change.

2. Python package

This is the most flexible way. You can use qmplot as a package in you Python code and create the plots by your mind.

Manhattan plot with default parameters:

import pandas as pd
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
    ax = manhattanplot(data=df, figname="output_manhattan_plot.png")

A better Manhattan plot

import pandas as pd
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

    # Create a manhattan plot
    f, ax = plt.subplots(figsize=(12, 4), facecolor='w', edgecolor='k')
    xtick = set(list(map(str, range(1, 15))) + ['16', '18', '20', '22', '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,  # CHR='8', # specific showing the chromosome 8th
                  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=50000,  # 50000 bp
                  annotext_kws={"size": 12,  # The fontsize of annotate text
                                "xycoords": "data",
                                "xytext": (15, +15),
                                "textcoords": "offset points",
                                "bbox": dict(boxstyle="round", alpha=0.2),
                                "arrowprops": dict(arrowstyle="->",  # "-|>"
                                                   connectionstyle="angle,angleA=0,angleB=80,rad=10",
                                                   alpha=0.6, relpos=(0, 0))},

                  dpi=300,
                  figname="output_manhattan_plot.png",
                  ax=ax)

Find more detail about the parameters by typing manhattanplot? in IPython console.

QQ plot with defualt parameters.

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=list(df["P"]), figname="output_QQ_plot.png")

A better QQ plot

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
    # Create a Q-Q plot
    f, ax = plt.subplots(figsize=(6, 6), facecolor="w", edgecolor="k")
    qqplot(data=list(data["P"]),
           marker="o",
           title="Test",
           xlabel=r"Expected $-log_{10}{(P)}$",
           ylabel=r"Observed $-log_{10}{(P)}$",
           dpi=300,
           figname="output_QQ_plot.png",
           ax=ax)

Find more detail about the parameters by typing qqplot? in IPython console.