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Geneview: A python package for genomics data visualization.

Project description

geneview: A python package for genomics data visualization

PyPI Version License Code Coverage

geneview is a library for making attractive and informative genomic 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:

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


To install the released version, just do

pip install geneview

You may instead want to use the development version from Github, by running

pip install git+git://

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:

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


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 = {
    "font.sans-serif": "Arial",
    "legend.fontsize": 14,
    "axes.titlesize": 18,
    "axes.labelsize": 16,
    "xtick.labelsize": 14,
    "ytick.labelsize": 14

# 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,
                     sign_marker_p=1e-6,  # Genome wide significant p-value
                     snp="ID",  # The column name of annotation information for top SNPs.


                     sign_line_cols=["#D62728", "#2CA02C"],
                     hline_kws={"linestyle": "--", "lw": 1.3},

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


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.utils.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"])


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"],
              xlabel=r"Expected $-log_{10}{(P)}$",
              ylabel=r"Observed $-log_{10}{(P)}$",

Admixture plot

Generate Admixture plot from the raw admixture output result:

simple example for admixtureplot

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

f, ax = plt.subplots(1, 1, figsize=(14, 2), facecolor="w", constrained_layout=True, dpi=300)
              ylabel_kws={"rotation": 45, "ha": "right"},



import matplotlib.pyplot as plt
import geneview as gv

admixture_output_fn = gv.utils.load_dataset("admixture_output.Q")
population_group_fn = gv.utils.load_dataset("")

# 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)
                        shuffle_popsample_kws={"frac": 0.5},
                        ylabel_kws={"rotation": 45, "ha": "right"},


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)



Geneview supports Python 3.7+ 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.


Released under a BSD (3-clause) license

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