geneview: A python package for visualizing genomics data

geneview is a library for making attractive and informative genomics graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures. And now it is actively developed.

Some of the features that geneview offers are:

• High-level abstractions for structuring grids of plots that let you easily build complex visualizations.
• Functions for visualizing general genomics plots.

Installation

To install the released version, just do

pip install geneview

This command will install geneview and all the dependencies.

Quick start

Manhattan and Q-Q plot

We use a PLINK2.x association output data gwas.csv which is in geneview-data directory, as the input for the plots below. Here is the format preview of gwas:

#CHROM	POS	ID	REF	ALT	A1	TEST	OBS_CT	BETA	SE	T_STAT	P
chr1	904165	1_904165	G	A	A	ADD	282	-0.0908897	0.195476	-0.464967	0.642344
chr1	1563691	1_1563691	T	G	G	ADD	271	0.447021	0.422194	1.0588	0.290715
chr1	1707740	1_1707740	T	G	G	ADD	283	0.149911	0.161387	0.928888	0.353805
chr1	2284195	1_2284195	T	C	C	ADD	275	-0.024704	0.13966	-0.176887	0.859739
chr1	2779043	1_2779043	T	C	T	ADD	272	-0.111771	0.139929	-0.79877	0.425182
chr1	2944527	1_2944527	G	A	A	ADD	276	-0.054472	0.166038	-0.32807	0.743129
chr1	3803755	1_3803755	T	C	T	ADD	283	-0.0392713	0.128528	-0.305547	0.760193
chr1	4121584	1_4121584	A	G	G	ADD	279	0.120902	0.127063	0.951511	0.342239
chr1	4170048	1_4170048	C	T	T	ADD	280	0.250807	0.143423	1.74873	0.0815274
chr1	4180842	1_4180842	C	T	T	ADD	277	0.209195	0.146122	1.43165	0.153469
chr1	6053630	1_6053630	T	G	G	ADD	269	-0.210917	0.129069	-1.63414	0.103503
chr1	7569602	1_7569602	C	T	C	ADD	281	-0.136834	0.13265	-1.03154	0.303249
chr1	7575666	1_7575666	T	C	C	ADD	277	-0.231278	0.159448	-1.45049	0.14815

Manhattan plot with default parameters

The manhattanplot() function in geneview 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.

import matplotlib.pyplot as plt
import geneview as gv

# load data
df = gv.load_dataset("gwas")
# Plot a basic manhattan plot with horizontal xtick labels and the figure will display in screen.
ax = gv.manhattanplot(data=df)
plt.show()

Rotate the x-axis tick label by setting xticklabel_kws to avoid label overlap:

ax = manhattanplot(data=df, 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 $-log_{10}{(1e-5)}$ 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.

ax = manhattanplot(data=df,
                   suggestiveline=None,  # Turn off suggestiveline
                   genomewideline=None,  # Turn off genomewideline
                   xticklabel_kws={"rotation": "vertical"})

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:

# plot only results of chromosome 8.
manhattanplot(data=df, CHR="chr8", xlabel="Chromosome 8")

manhattanplot() funcion has the ability to highlight SNPs with significant GWAS signal and annotate the Top SNP, which has the lowest P-value:

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.

Show a better manhattan plot

Futher graphical parameters can be passed to the manhattanplot() function to control thing like plot title, point character, size, colors, etc. Here is the example:

import matplotlib.pyplot as plt
import geneview as gv

# common parameters for plotting
plt_params = {
    "pdf.fonttype": 42,
    "font.sans-serif": "Arial",
    "legend.fontsize": 14,
    "axes.titlesize": 18,
    "axes.labelsize": 16,
    "xtick.labelsize": 14,
    "ytick.labelsize": 14
}
plt.rcParams.update(plt_params)

# Create a manhattan plot
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"]])
_ = gv.manhattanplot(data=df,
                     marker=".",
                     sign_marker_p=1e-6,  # Genome wide significant p-value
                     sign_marker_color="r",
                     snp="ID",  # The column name of annotation information for top SNPs.

                     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=50000,  # 50000 bp
                     text_kws={"fontsize": 12,
                               "arrowprops": dict(arrowstyle="-", color="k", alpha=0.6)},
                     ax=ax)

QQ plot with default 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.

import matplotlib.pyplot as plt
import geneview as gv

# load data
df = gv.load_dataset("gwas")
# Plot a basic manhattan plot with horizontal xtick labels and the figure will display in screen.
ax = gv.qqplot(data=df["P"])
plt.show()

Show 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:

import matplotlib.pyplot as plt
import geneview as gv

f, ax = plt.subplots(figsize=(6, 6), facecolor="w", edgecolor="k")
_ = gv.qqplot(data=df["P"],
              marker="o",
              title="Test",
              xlabel=r"Expected $-log_{10}{(P)}$",
              ylabel=r"Observed $-log_{10}{(P)}$",
              ax=ax)

• More tutorials about GWAS

Admixture plot

Generate Admixture plot from the raw admixture output result:

simple example for admixtureplot

import matplotlib.pyplot as plt
from geneview import load_dataset
from geneview import admixtureplot

f, ax = plt.subplots(1, 1, figsize=(14, 2), facecolor="w", constrained_layout=True, dpi=300)
admixtureplot(data=load_dataset("admixture_output.Q"), 
              population_info=load_dataset("admixture_population.info"),
              ylabel_kws={"rotation": 45, "ha": "right"},
              ax=ax)

or

import matplotlib.pyplot as plt
import geneview as gv

admixture_output_fn = gv.load_dataset("admixture_output.Q")
population_group_fn = gv.load_dataset("admixture_population.info")

# define the order for population to plot
pop_group_1kg = ["KHV", "CDX", "CHS", "CHB", "JPT", "BEB", "STU", "ITU", "GIH", "PJL", "FIN", 
                 "CEU", "GBR", "IBS", "TSI", "PEL", "PUR", "MXL", "CLM", "ASW", "ACB", "GWD", 
                 "MSL", "YRI", "ESN", "LWK"]

f, ax = plt.subplots(1, 1, figsize=(14, 2), facecolor="w", constrained_layout=True, dpi=300)
gv.popgen.admixtureplot(data=admixture_output_fn, 
                        population_info=population_group_fn,
                        edgewidth=2.0,
                        group_order=pop_group_1kg,
                        shuffle_popsample_kws={"frac": 0.5},
                        ylabel_kws={"rotation": 45, "ha": "right"},
                        ax=ax)

• The format of input files and more details about admixtureplot

Venn plots

Venn diagrams for 2, 3, 4, 5, 6 sets.

Minimal venn plot example

import geneview as gv

table = {
    "Dataset 1": {"A", "B", "D", "E"},
    "Dataset 2": {"C", "F", "B", "G"},
    "Dataset 3": {"J", "C", "K"}
}
ax = gv.venn(table) 

Manual adjustment of petal labels

If necessary, the labels on the petals (i.e., various intersections in the Venn diagram) can be adjusted manually.

For this, generate_petal_labels() can be called first to get the petal_labels dictionary, which can be modified.

After modification, pass petal_labels to functions venn().

from numpy.random import choice
import geneview as gv

dataset_dict = {
    name: set(choice(1000, 250, replace=False))
    for name in list("ABCD")
}

petal_labels = gv.generate_petal_labels(dataset_dict.values(), fmt="{logic}\n({percentage:.1f}%)") 
ax = gv.venn(data=petal_labels, names=list(dataset_dict.keys()), legend_use_petal_color=True)

• More tutorials about venn

Dependencies

Geneview only supports Python 3 and no longer supports Python 2.

Installation requires numpy, scipy, pandas, and matplotlib. Some functions will use statsmodels.

We need the data structures: DataFrame and Series in pandas. It's easy and worth to learn, click here to see more detail tutorial for these two data type.

License

Released under a GPL-3.0 license