February 25st, 2019
This Jupyter Notebook contains an interactive tutorial for running the Expression2Kinases (X2K) API using Python 3.
The notebook contains the following sections:
The X2K API allows for programmatic analysis of an input gene list.
The run_X2K() function displayed below can be used to analyze a gene list and load the results in a Python dictionary by performing a POST request.
The function requires only one input, input_genes, a list of gene symbols to be analyzed. Additional optional parameters can be specified with the options parameters.
# Import modules
import http.client
import json
##### Function to run X2K
### Input: a Python list of gene symbols
### Output: a dictionary containing the results of X2K, ChEA, G2N, KEA.
def run_X2K(input_genes, options={}):
# Open HTTP connection
conn = http.client.HTTPConnection("amp.pharm.mssm.edu")
# Set default options
default_options = {'text-genes': '\n'.join(input_genes),
'included_organisms': 'both',
'TF-target gene background database used for enrichment': 'ChEA & ENCODE Consensus',
'sort transcription factors by': 'p-value',
'min_network_size': 10,
'number of top TFs': 10,
'path_length': 2,
'min_number_of_articles_supporting_interaction': 0,
'max_number_of_interactions_per_protein': 200,
'max_number_of_interactions_per_article': 100,
'enable_BioGRID': True,
'enable_IntAct': True,
'enable_MINT': True,
'enable_ppid': True,
'enable_Stelzl': True,
'kinase interactions to include': 'kea 2018',
'sort kinases by': 'p-value'}
# Update options
for key, value in options.items():
if key in default_options.keys() and key != 'text-genes':
default_options.update({key: value})
# Get payload
boundary = "----WebKitFormBoundary7MA4YWxkTrZu0gW"
payload = ''.join(
['--' + boundary + '\r\nContent-Disposition: form-data; name=\"{key}\"\r\n\r\n{value}\r\n'.format(**locals())
for key, value in default_options.items()]) + '--' + boundary + '--'
# Get Headers
headers = {
'content-type': "multipart/form-data; boundary=" + boundary,
'cache-control': "no-cache",
}
# Initialize connection
conn.request("POST", "/X2K/api", payload, headers)
# Get response
res = conn.getresponse()
# Read response
data = res.read().decode('utf-8')
# Convert to dictionary
x2k_results = {key: json.loads(value) if key != 'input' else value for key, value in json.loads(data).items()}
# Clean results
x2k_results['ChEA'] = x2k_results['ChEA']['tfs']
x2k_results['G2N'] = x2k_results['G2N']['network']
x2k_results['KEA'] = x2k_results['KEA']['kinases']
x2k_results['X2K'] = x2k_results['X2K']['network']
# Return results
return x2k_results
# Get input genes
input_genes = ['Nsun3', 'Polrmt', 'Nlrx1', 'Sfxn5', 'Zc3h12c', 'Slc25a39', 'Arsg', 'Defb29', 'Ndufb6', 'Zfand1',
'Tmem77', '5730403B10Rik', 'Tlcd1', 'Psmc6', 'Slc30a6', 'LOC100047292', 'Lrrc40', 'Orc5l', 'Mpp7',
'Unc119b', 'Prkaca', 'Tcn2', 'Psmc3ip', 'Pcmtd2', 'Acaa1a', 'Lrrc1', '2810432D09Rik', 'Sephs2', 'Sac3d1',
'Tmlhe', 'LOC623451', 'Tsr2', 'Plekha7', 'Gys2', 'Arhgef12', 'Hibch', 'Lyrm2', 'Zbtb44', 'Entpd5',
'Rab11fip2', 'Lipt1', 'Intu', 'Anxa13', 'Klf12', 'Sat2', 'Gal3st2', 'Vamp8', 'Fkbpl', 'Aqp11', 'Trap1',
'Pmpcb', 'Tm7sf3', 'Rbm39', 'Bri3', 'Kdr', 'Zfp748', 'Nap1l1', 'Dhrs1', 'Lrrc56', 'Wdr20a', 'Stxbp2',
'Klf1', 'Ufc1', 'Ccdc16', '9230114K14Rik', 'Rwdd3', '2610528K11Rik', 'Aco1', 'Cables1', 'LOC100047214',
'Yars2', 'Lypla1', 'Kalrn', 'Gyk', 'Zfp787', 'Zfp655', 'Rabepk', 'Zfp650', '4732466D17Rik', 'Exosc4',
'Wdr42a', 'Gphn', '2610528J11Rik', '1110003E01Rik', 'Mdh1', '1200014M14Rik', 'AW209491', 'Mut',
'1700123L14Rik', '2610036D13Rik', 'Cox15', 'Tmem30a', 'Nsmce4a', 'Tm2d2', 'Rhbdd3', 'Atxn2', 'Nfs1',
'3110001I20Rik', 'BC038156', 'LOC100047782', '2410012H22Rik', 'Rilp', 'A230062G08Rik', 'Pttg1ip', 'Rab1',
'Afap1l1', 'Lyrm5', '2310026E23Rik', 'C330002I19Rik', 'Zfyve20', 'Poli', 'Tomm70a', 'Slc7a6os', 'Mat2b',
'4932438A13Rik', 'Lrrc8a', 'Smo', 'Nupl2', 'Trpc2', 'Arsk', 'D630023B12Rik', 'Mtfr1', '5730414N17Rik',
'Scp2', 'Zrsr1', 'Nol7', 'C330018D20Rik', 'Ift122', 'LOC100046168', 'D730039F16Rik', 'Scyl1',
'1700023B02Rik', '1700034H14Rik', 'Fbxo8', 'Paip1', 'Tmem186', 'Atpaf1', 'LOC100046254', 'LOC100047604',
'Coq10a', 'Fn3k', 'Sipa1l1', 'Slc25a16', 'Slc25a40', 'Rps6ka5', 'Trim37', 'Lrrc61', 'Abhd3', 'Gbe1',
'Parp16', 'Hsd3b2', 'Esm1', 'Dnajc18', 'Dolpp1', 'Lass2', 'Wdr34', 'Rfesd', 'Cacnb4', '2310042D19Rik',
'Srr', 'Bpnt1', '6530415H11Rik', 'Clcc1', 'Tfb1m', '4632404H12Rik', 'D4Bwg0951e', 'Med14', 'Adhfe1',
'Thtpa', 'Cat', 'Ell3', 'Akr7a5', 'Mtmr14', 'Timm44', 'Sf1', 'Ipp', 'Iah1', 'Trim23', 'Wdr89', 'Gstz1',
'Cradd', '2510006D16Rik', 'Fbxl6', 'LOC100044400', 'Zfp106', 'Cd55', '0610013E23Rik', 'Afmid', 'Tmem86a',
'Aldh6a1', 'Dalrd3', 'Smyd4', 'Nme7', 'Fars2', 'Tasp1', 'Cldn10', 'A930005H10Rik', 'Slc9a6', 'Adk',
'Rbks', '2210016F16Rik', 'Vwce', '4732435N03Rik', 'Zfp11', 'Vldlr', '9630013D21Rik', '4933407N01Rik',
'Fahd1', 'Mipol1', '1810019D21Rik', '1810049H13Rik', 'Tfam', 'Paics', '1110032A03Rik', 'LOC100044139',
'Dnajc19', 'BC016495', 'A930041I02Rik', 'Rqcd1', 'Usp34', 'Zcchc3', 'H2afj', 'Phf7', '4921508D12Rik',
'Kmo', 'Prpf18', 'Mcat', 'Txndc4', '4921530L18Rik', 'Vps13b', 'Scrn3', 'Tor1a', 'AI316807', 'Acbd4',
'Fah', 'Apool', 'Col4a4', 'Lrrc19', 'Gnmt', 'Nr3c1', 'Sip1', 'Ascc1', 'Fech', 'Abhd14a', 'Arhgap18',
'2700046G09Rik', 'Yme1l1', 'Gk5', 'Glo1', 'Sbk1', 'Cisd1', '2210011C24Rik', 'Nxt2', 'Notum', 'Ankrd42',
'Ube2e1', 'Ndufv1', 'Slc33a1', 'Cep68', 'Rps6kb1', 'Hyi', 'Aldh1a3', 'Mynn', '3110048L19Rik', 'Rdh14',
'Proz', 'Gorasp1', 'LOC674449', 'Zfp775', '5430437P03Rik', 'Npy', 'Adh5', 'Sybl1', '4930432O21Rik',
'Nat9', 'LOC100048387', 'Mettl8', 'Eny2', '2410018G20Rik', 'Pgm2', 'Fgfr4', 'Mobkl2b', 'Atad3a',
'4932432K03Rik', 'Dhtkd1', 'Ubox5', 'A530050D06Rik', 'Zdhhc5', 'Mgat1', 'Nudt6', 'Tpmt', 'Wbscr18',
'LOC100041586', 'Cdk5rap1', '4833426J09Rik', 'Myo6', 'Cpt1a', 'Gadd45gip1', 'Tmbim4', '2010309E21Rik',
'Asb9', '2610019F03Rik', '7530414M10Rik', 'Atp6v1b2', '2310068J16Rik', 'Ddt', 'Klhdc4', 'Hpn', 'Lifr',
'Ovol1', 'Nudt12', 'Cdan1', 'Fbxo9', 'Fbxl3', 'Hoxa7', 'Aldh8a1', '3110057O12Rik', 'Abhd11', 'Psmb1',
'ENSMUSG00000074286', 'Chpt1', 'Oxsm', '2310009A05Rik', '1700001L05Rik', 'Zfp148', '39509', 'Mrpl9',
'Tmem80', '9030420J04Rik', 'Naglu', 'Plscr2', 'Agbl3', 'Pex1', 'Cno', 'Neo1', 'Asf1a', 'Tnfsf5ip1',
'Pkig', 'AI931714', 'D130020L05Rik', 'Cntd1', 'Clec2h', 'Zkscan1', '1810044D09Rik', 'Mettl7a', 'Siae',
'Fbxo3', 'Fzd5', 'Tmem166', 'Tmed4', 'Gpr155', 'Rnf167', 'Sptlc1', 'Riok2', 'Tgds', 'Pms1', 'Pitpnc1',
'Pcsk7', '4933403G14Rik', 'Ei24', 'Crebl2', 'Tln1', 'Mrpl35', '2700038C09Rik', 'Ubie', 'Osgepl1',
'2410166I05Rik', 'Wdr24', 'Ap4s1', 'Lrrc44', 'B3bp', 'Itfg1', 'Dmxl1', 'C1d']
# Run X2K results
x2k_results = run_X2K(input_genes)
x2k_results.keys()
| Parameter | Step | Description | Notes |
|---|---|---|---|
| **input_genes** (required) | X2K | Contains the input gene set for the X2K analysis. | A list of strings representing the input gene symbols. |
| *organism* (optional) | ChEA | The organism from which TF-target interaction data should be integrated. | One of `('human_only', 'mouse_only', 'both')`. Default `'both'`. |
| *TF-target gene background database used for enrichment* (optional) | ChEA | The database from which TF-target interaction data should be integrated, | One of `('ChEA 2015', 'ENCODE 2015', 'ChEA & ENCODE Consensus', 'Transfac and Jaspar', 'ChEA 2016', 'ARCHS4 TFs Coexp', 'CREEDS', 'Enrichr Submissions TF-Gene Coocurrence')` Default `'ChEA & ENCODE Consensus')`. |
| *sort transcription factors by* (optional) | ChEA | The method used to sort the top Transcription Factors identified by ChEA. | One of `('p-value', 'rank', 'combined score')`. Default `'p-value'`. |
| *path_length* (optional) | G2N | The maximum Protein-Protein Interaction path length for the network expansion. | Integer, default `2`. |
| *minimum_network_size* (optional) | G2N | The minimum size of the Protein-Protein interaction network generated using Genes2Networks. | Integer, default `50`. |
| *min_number_of_articles_supporting_interaction* (optional) | G2N | The minimum number of published articles supporting a Protein-Protein Interaction for the expanded subnetwork. | Integer, default `2`. |
| *max_number_of_interactions_per_protein* (optional) | G2N | The maximum number of physical interactions allowed for the proteins in the expanded subnetwork. | Integer, default `200`. |
| *max_number_of_interactions_per_article* (optional) | G2N | The maximum number of physical interactions reported in each published article | Integer, default `100`. |
| enable_Biocarta (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_BioGRID (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'true'`. |
| enable_BioPlex (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_DIP (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_huMAP (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_InnateDB (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_IntAct (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'true'`. |
| enable_KEGG (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_MINT (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'true'`. |
| enable_ppid (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'true'`. |
| enable_SNAVI (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_iREF (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_Stelzl (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'true'`. |
| enable_vidal (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_BIND (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_figeys (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| enable_HPRD (optional) | G2N | The Protein-Protein Interaction databases to integrate for generation of the expanded subnetwork. | Either `'true'` or `'false'`. Default `'false'`. |
| *number_of_results* (optional) | G2N | The maximum network size of the expanded network generated using Genes2Networks. | Integer, default `50`. |
| *kinase interactions to include* | KEA | Kinase interactions databases to include. | One of `('p-value', 'rank', 'combined score')`. Default `'p-value'`. |
| *sort_kinases_by* (optional) | KEA | The method used to sort the top Transcription Factors identified by KEA. | One of `('kea 2018', 'ARCHS4', 'iPTMnet', 'NetworkIN', 'Phospho.ELM', 'Phosphopoint', 'PhosphoPlus', 'MINT')`. Default `'kea 2018'`. |
The run_X2K() function returns results as dict containing four keys, whose contents are described below.
| Key | Notes | Contents |
|---|---|---|
| **ChEA** | Contains the results of the **Transcription Factor Enrichment Analysis**, generated using ChEA. | A `list` of `dict`s containing information on the top TFs predicted to regulate the input genes. |
| **G2N** | Contains the results of the **Protein-Protein Interaction Expansion**, generated using Genes2Networks (G2N). | A `dict` containing two keys:
|
| **KEA** | Contains the results of the **Kinase Enrichment Analysis**, generated using KEA. | A `list` of `dict`s containing information on the top kinases predicted to regulate the subnetwork identified by G2N. |
| **X2K** | Contains the **Expression2Kinases network**, generated by integrating the results of ChEA, G2N and KEA. | A `dict` containing two keys:
|
# Import pandas
import pandas as pd
# Read results
chea_dataframe = pd.DataFrame(x2k_results['ChEA'])
chea_dataframe.head()
Table 1 | Results of the ChEA analysis. Each row represents a transcription factor predicted to regulate the input gene list.
The results for the G2N analysis can be accessed in x2k_results['G2N'].
The results are stored in a dictionary containing two keys:
edgesinteractions# G2N nodes dataframe
g2n_nodes_dataframe = pd.DataFrame(x2k_results['G2N']['nodes']).drop('pvalue', axis=1)
g2n_nodes_dataframe.head()
Table 2 | Nodes of the Genes2Networks expanded subnetwork. Each row represents a node in the expanded subnetwork. The type column indicates whether the node is a Transcription Factor identified by ChEA, or an intermediate protein.
# G2N edges dataframe
g2n_edges_dataframe = pd.DataFrame(x2k_results['G2N']['interactions'])
g2n_edges_dataframe.head()
Table 3 | Edges of the Genes2Networks expanded subnetwork. Each row represents an edge in the expanded subnetwork generated by G2N on the top transcription factors identified by ChEA.
The results for the KEA analysis can be accessed in x2k_results['KEA'].
# KEA Results
kea_dataframe = pd.DataFrame(x2k_results['KEA'])
kea_dataframe.head()
Table 4 | Results of the KEA analysis. Each row represents a protein kinase predicted to regulate the expanded subnetwork generated by G2N.
The results for the X2K analysis can be accessed in x2k_results['X2K'].
The results are stored in a dictionary containing two keys:
nodesinteractions# X2K nodes dataframe
x2k_nodes_dataframe = pd.DataFrame(x2k_results['X2K']['nodes']).drop('pvalue', axis=1)
x2k_nodes_dataframe.head()
Table 5 | Nodes of the final Expression2Kinases network. Each row represents a node in the final X2K network network. The type column indicates whether the node is a Transcription Factor identified by ChEA, an intermediate protein identified by G2N, or a protein kinase identified by KEA.
# X2K edges dataframe
x2k_edges_dataframe = pd.DataFrame(x2k_results['X2K']['interactions'])
x2k_edges_dataframe.head()
Table 6 | Edges of the final Expression2Kinases subnetwork. Each row represents an edge in the final network identified by integrating the results of ChEA, G2N, and KEA.