pykegg 0.1.1

Visualizing and analyzing KEGG information in Python

pykegg

Analyze and visualize KEGG information using network approach.

Using biopython, igraph and plotnine, parse the KGML into igraph and easily plot the relevant information contained in KEGG Pathway in Python environment.

import requests_cache
import numpy as np
from PIL import Image
import pykegg
import matplotlib as mpl

## Be sure to cache all the downloaded files to avoid recursive querying
requests_cache.install_cache('pykegg_cache')
g = pykegg.KGML_graph(pid="hsa03460")

## Export as igraph
gra = g.get_graph()
print(gra)

## Overlay to raw image
nds = g.get_nodes()
nds = nds[nds.original_type=="gene"]
nds["lfc"] = np.arange(-2,2,0.01)[0:nds.shape[0]]
cmap_grad = mpl.colors.LinearSegmentedColormap.from_list("cmap_grad", ["yellow","green"])
norm = mpl.colors.Normalize(vmin=min(nds.lfc), vmax=max(nds.lfc))
nds["color"] = [
        mpl.colors.to_hex(cmap_grad(norm(x))) if x is not None else None
        for x in nds.lfc
    ]
Image.fromarray(pykegg.overlay_opencv_image(nds, pid="hsa03460"))

## Plot using plotnine
# options.figure_size = (7,5)
pykegg.plot_kegg_pathway_plotnine(g, node_x_nudge=25, label_size=5, show_label="gene")

Documentation

https://pykegg.readthedocs.io/

TODO

• The function for converting identifiers (especially for ENTREZID <-> SYMBOL) without connection to servers, like using genomic-features.
• Parallel edge support by nudging x and y position