Metadata-Version: 1.2
Name: qmplot
Version: 0.1.5
Summary: qmplot: Create high-quality manhattan and QQ plots for PLINK association output (or any dataframe with chromosome, position, and p-value).
Home-page: https://github.com/ShujiaHuang/qmplot
Author: Shujia Huang
Author-email: huangshujia9@gmail.com
Maintainer: Shujia Huang
Maintainer-email: huangshujia9@gmail.com
License: BSD (3-clause)
Download-URL: https://github.com/ShujiaHuang/qmplot
Description: QMplot: A Python tool for creating high-quality manhattan and Q-Q plots from GWAS results.
        ==========================================================================================
        
        **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:
        
        .. code:: 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:
        
        .. code:: 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:
        
        .. code:: bash
        
                $ qmplot -I data/gwas_plink_result.tsv -T Test -M ID --dpi 300 -O test
        
        The manhattan plot looks like:
        
        .. figure:: tests/test.manhattan.png
        
        
        The Q-Q plot looks like:
        
        .. figure:: tests/test.QQ.png
        
        
        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.
        
        
        .. code:: python
        
            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
        
                # generate manhattan plot and set an output file.
                ax = manhattanplot(data=df, figname="output_manhattan_plot.png")
        
        .. figure:: tests/output_manhattan_plot.png
           :alt: output\_manhattan\_plot.png
        
        Rotate the x-axis tick label by setting ``xticklabel_kws`` to avoid label overlap:
        
        .. code:: python
        
            ax = manhattanplot(data=df,
                               xticklabel_kws={"rotation": "vertical"},  # set vertical or any other degrees as you like.
                               figname="output_manhattan_plot.png")
        
        .. figure:: tests/output_manhattan_plot_xviertical.png
        
        Or rotate the labels 45 degrees by setting ``xticklabel_kws={"rotation": 45}``.
        
        The parameters of ``manhattanplot()`` defined the name of output figure file 
        and the format of the figure file depending on the file suffix, which could
        be ".png", ".jpg", or ".pdf".
        
        When run with default parameters, the ``manhattanplot()`` function draws 
        horizontal lines drawn at $-log_{10}{(1e-5)}$ for "suggestive" associations 
        and $-log_{10}{(5e-8)}$ for the "genome-wide significant" threshold. These 
        can be move to different locations or turned off completely with the arguments 
        ``suggestiveline`` and ``genomewideline``, respectively.
        
        .. code:: 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
                               figname="output_manhattan_plot.png")
        
        .. figure:: tests/output_manhattan_plot_xviertical_noline.png
        
        The behavior of the ``manhattanplot`` function changes slightly when results 
        from only a single chromosome are 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:
        
        .. code:: python
        
            # plot only results of chromosome 8.
            manhattanplot(data=df, CHR="chr8", xlabel="Chromosome 8",
                          figname="output_chr8_manhattan_plot.png")
        
        .. figure:: tests/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:
        
        
        .. code:: 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"},
                               figname="output_manhattan_anno_plot.png")
        
        .. figure:: tests/output_manhattan_anno_plot.png
        
        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:
        
        .. code:: 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)},
                              dpi=300,
                              figname="output_manhattan_plot.png",
                              ax=ax)
        
        .. figure:: tests/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.
        
        .. code:: 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"], figname="output_QQ_plot.png")
        
        .. figure:: tests/output_QQ_plot.png
        
        
        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:
        
        .. code:: 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
                    # 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 $-log_{10}{(P)}$",
                           dpi=300,
                           figname="output_QQ_plot.png",
                           ax=ax)
        
        .. figure:: tests/test.QQ.png
        
        
        Find more details about the parameters by typing ``qqplot?`` in IPython console.
        
        
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
