Visualizing time series metabolome profile

by Kozo Nishida (Riken, Japan)

Software Requirments

Please install the following software packages to run this workflow:

Background

This is a sample workflow to automate complex Cytoscape data integaration/visualization process. Please read the following document for more background:

https://github.com/idekerlab/KEGGscape/wiki/How-to-visualize-time-series-metabolome-profile



In [10]:

    
import json
import requests
import pandas as pd

PORT_NUMBER = 1234
BASE_URL = "http://localhost:" + str(PORT_NUMBER) + "/v1/"
HEADERS = {'Content-Type': 'application/json'}

Load a KGML pathway data file with KEGGscape REST API



In [11]:

    
requests.get("http://localhost:1234/keggscape/v1/ath00020")
res = requests.get("http://localhost:1234/v1/networks/currentNetwork")
result = json.loads(res.content)
pathway_suid = result["data"]["networkSUID"]
print("Pathway SUID = " + str(pathway_suid))









    



Pathway SUID = 71718

Load table data file as Pandas DataFrame



In [12]:

    
profile_csv = "https://raw.githubusercontent.com/idekerlab/KEGGscape/develop/wiki/data/light-dark-20.csv"
profile_df = pd.read_csv(profile_csv)
profile_df.head()

Convert the DataFrame to JSON and send it to Cytoscape



In [13]:

    
profile = json.loads(profile_df.to_json(orient="records"))
# print(json.dumps(profile, indent=4))



In [14]:

    
new_table_data = {
    "key": "KEGG_NODE_LABEL",
    "dataKey": "KEGG",
    "data": profile
}

update_table_url =  BASE_URL + "networks/" + str(pathway_suid) + "/tables/defaultnode"
requests.put(update_table_url, data=json.dumps(new_table_data), headers=HEADERS)









    Out[14]:





<Response [200]>

Set values to the chart column



In [15]:

    
chart_entry = 'barchart: attributelist="ld20t14,ld20t16,ld20t20,ld20t24,ld20t28,ld20t32,ld20t36,ld20t40,ld20t44,ld20t48,ld20t52,ld20t56,ld20t60,ld20t64,ld20t68,ld20t72" colorlist="up:red,zero:red,down:red" showlabels="false"'

target_row_url = BASE_URL + "networks/" + str(pathway_suid) + "/tables/defaultnode/columns/KEGG"
res2 = requests.get(target_row_url)
matched = json.loads(res2.content)["values"]

df2 = pd.DataFrame(columns=["id", "chart"]);
df2["id"] = matched
df2["chart"] = chart_entry

data = json.loads(df2.to_json(orient="records"))
    
chart_data = {
    "key": "KEGG",
    "dataKey": "id",
    "data": data
}

requests.put(update_table_url, data=json.dumps(chart_data), headers=HEADERS)









    Out[15]:





<Response [200]>

Create Visual Style for Custom Mapping



In [16]:

    
custom_graphics_mapping = {
    "mappingType" : "passthrough",
    "mappingColumn" : "chart",
    "mappingColumnType" : "String",
    "visualProperty" : "NODE_CUSTOMGRAPHICS_1"
}

style_url = BASE_URL + "styles/KEGG Style/mappings"
requests.post(style_url, data=json.dumps([custom_graphics_mapping]), headers=HEADERS)









    Out[16]:





<Response [201]>

Uncheck the Fit Custom Graphics to node box, and bundle edges from menubar.

You will see

	Metabolite	KEGG	ld20t14	ld20t16	ld20t20	ld20t24	ld20t28	ld20t32	ld20t36	ld20t40	ld20t44	ld20t48	ld20t52	ld20t56	ld20t60	ld20t64	ld20t68	ld20t72
0	Alanine	C00041	-0.051007	0.040631	-0.165335	-0.126382	-0.093879	-0.102625	0.009509	0.051824	-0.137530	-0.140400	-0.116827	-0.060302	0.027001	0.081365	-0.060153	-0.097988
1	Valine	C00183	-0.025403	-0.003585	-0.058986	-0.069280	-0.129784	-0.092210	0.009129	-0.068003	-0.105905	-0.097984	-0.095181	-0.021911	-0.021224	-0.028314	-0.032834	-0.025435
2	Leucine	C00123	-0.005918	0.058287	-0.049900	-0.075853	-0.098339	-0.003847	0.056037	-0.104399	-0.122298	-0.098051	-0.082529	0.027760	-0.006289	-0.048610	-0.042620	-0.000897
3	Isoleucine	C00407	0.079870	0.101324	-0.103993	-0.041534	-0.020148	0.066486	0.091006	-0.027547	-0.193494	-0.144676	-0.015375	0.070540	0.118164	0.013795	-0.060418	-0.042314
4	Glycine	C00037	-0.056155	-0.185757	-0.857195	-0.725952	-0.051028	0.069038	0.042867	0.098185	-0.445921	-0.893788	-0.201299	0.000000	-0.064322	-0.090597	-0.637518	-0.874438