Visualize time series metabolome profile

In [3]:

    

import json
import requests
import pandas as pd

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

# Make sure Cytoscape  RESTful API App is running!
cytoscape_version = requests.get(BASE_URL + 'version')
print("Cytoscape RESTful API module is ready: version " +  json.loads(cytoscape_version.text)["cytoscapeVersion"])


    

    




Cytoscape RESTful API module is ready: version 3.1.1

In [4]:

    

pathway_location = "http://rest.kegg.jp/get/ath00020/kgml"
res1 = requests.post(BASE_URL + "networks?source=url", data=json.dumps([pathway_location]), headers=HEADERS)
result = json.loads(res1.content)
pathway_suid = result[0]["networkSUID"][0]
print("Pathway SUID = " + str(pathway_suid))


    

    




Pathway SUID = 52

In [5]:

    

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()


    

    
Out[5]:






  

    

      

      
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
    
  

5 rows × 18 columns

In [6]:

    

profile = json.loads(profile_df.to_json(orient="records"))

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[6]:





<Response [204]>

In [7]:

    

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[7]:





<Response [204]>

In [8]:

    

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[8]:





<Response [204]>