How to plot a KEGG pathway map with MetaboGlobe

First, download a KEGG pathway map in KGML format from the KEGG website. For example, the Glycolysis pathway map for humans can be downloaded from https://rest.kegg.jp/get/hsa00010/kgml . Then, you can load it using the load_kegg_map function:

import metaboglobe.kegg_pathway
# load in the KEGG map for glycolysis
kegg_map = metaboglobe.kegg_pathway.load_kegg_map("path/to/glycolysis_map.xml")

Plotting the map itself can be done as follows:

import metaboglobe.kegg_plotting
from matplotlib import pyplot as plt

kegg_map = ...  # See above

fig, ax = plt.subplots(figsize=(10, 10))
metaboglobe.kegg_plotting.plot_double_arrows(ax, kegg_map)
plt.show()

How to use with a data table

Let's say you have a pandas DataFrame like this:

import pandas

data_frame = pandas.DataFrame({
    "reactions": ["1.00 * Citrate [c] --> 1.00 * Isocitrate [c] ACO1; IREB2", 
                "1.00 * Isocitrate [c] --> 1.00 * alpha-Ketoglutarate [c] ACO1; IREB2",
                "1.00 * alpha-Ketoglutarate [c] --> 1.00 * Succinyl-CoA [c] OGDH; DLST"],
    "flux_values": [0.5, 1.0, 0.2]
})

Then you can add the flux values to the KEGG map using the insert_values_in_map function:

import metaboglobe.tabular_data

kegg_map = ...  # See above
data_frame = ...  # See above

metaboglobe.tabular_data.insert_values_in_map(kegg_map, data_frame, reaction_col="reactions", value_col="flux_values")

And then plot like above.

Normally your data would not be hardcoded, but come from a data file such as a CSV file. If you're unfamiliar with Pandas, it's easy to load from a CSV file. For example, if you have a CSV file with the same columns as above, you can load it like this:

import pandas
data_frame = pandas.read_csv("path/to/your/data.csv")

How to use with Compass data

Compass is a tool to predict metabolic fluxes from single-cell RNA-seq data. This package provides a convenient way to visualize Compass results on the KEGG metabolic pathway maps.

First, load in the scRNAseq data into an AnnData object, and make sure you have ran Compass on the package. Then, load in your results:

import metaboglobe.compass_data

adata = ...  # load in your AnnData object
metaboglobe.compass_data.add_compass_output(adata, "path/to/compass output/CENTRAL_CARBON_ENERGY", obsm_key="CENTRAL_CARBON_ENERGY")

This folder is expected to have a reactions.tsv file and a model.json.gz file in it. By default, the reaction fluxes will be stored under adata.obsm[folder_name], with folder_name = CENTRAL_CARBON_ENERGY in this example. You can specify a different key using the obsm_key argument.

Second, load in the KEGG pathway map you want to plot (see above), and apply the coloring to the map:

import metaboglobe.compass_data

kegg_map = ...  # load in the KEGG map for glycolysis
adata = ...  # load in your AnnData object with Compass results

model = metaboglobe.compass_data.load_compass_model("path/to/compass output/CENTRAL_CARBON_ENERGY")
comparison = metaboglobe.compass_data.setup_comparison_to_single_cells(adata, model, groupby="condition",
                                                                       obsm_key="CENTRAL_CARBON_ENERGY",
                                                                       min_percentile=20, max_percentile=80)
comparison.insert_values_in_map(kegg_map, group="condition_a")

# Plot the kegg_map as shown above. It should now be colored according to the Compass fluxes for "condition_a".