geneview: A python package for genomics data visualization
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:
- High-level abstractions for structuring grids of plots that let you easily build complex visualizations.
- Functions for visualizing general genomic plots.
Installation
To install the released version, just do
pip install geneviewThis 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.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)
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 = {
"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.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"])
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)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)
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.utils.load_dataset("admixture_output.Q")
population_group_fn = gv.utils.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,
group_order=pop_group_1kg,
shuffle_popsample_kws={"frac": 0.5},
ylabel_kws={"rotation": 45, "ha": "right"},
ax=ax)
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)
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 BSD (3-clause) license