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

bash 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.

```python 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:

python 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.

python 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:

```python

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:

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"})

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:

```python import matplotlib.pyplot as plt import geneview as gv

common parameters for plotting

pltparams = { "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(pltparams)

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=".", signmarkerp=1e-6, # Genome wide significant p-value signmarkercolor="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.

```python

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:

```python 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

```python 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", constrainedlayout=True, dpi=300) admixtureplot(data=loaddataset("admixtureoutput.Q"), populationinfo=loaddataset("admixturepopulation.info"), ylabel_kws={"rotation": 45, "ha": "right"}, ax=ax) ```

or

```python import matplotlib.pyplot as plt import geneview as gv

admixtureoutputfn = gv.loaddataset("admixtureoutput.Q") populationgroupfn = gv.loaddataset("admixturepopulation.info")

define the order for population to plot

popgroup1kg = ["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", constrainedlayout=True, dpi=300) gv.popgen.admixtureplot(data=admixtureoutputfn, populationinfo=populationgroupfn, edgewidth=2.0, grouporder=popgroup1kg, shufflepopsamplekws={"frac": 0.5}, ylabelkws={"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

```python 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().

```python from numpy.random import choice import geneview as gv

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

petallabels = gv.generatepetallabels(datasetdict.values(), fmt="{logic}\n({percentage:.1f}%)") ax = gv.venn(data=petallabels, names=list(datasetdict.keys()), legendusepetal_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