pyscnet.NetEnrich

pyscnet.NetEnrich.graph_toolkit

Created on Sun Jun 16 20:50:39 2019

@author: mwu

pyscnet.NetEnrich.graph_toolkit.buildnet(gnetdata, key_links, top=None)[source]
Parameters
  • gnetdata – Gnetdata object.

  • key_links – str, key of links referring which linkage table for buidling graph

  • top – int, default None. top ranked links

Returns

Gnetdata object with graph added into NetAttrs

pyscnet.NetEnrich.graph_toolkit.detect_community(gnetdata, **kwargs)[source]
Parameters
  • gnetdata – Gnetdata object.

  • kwargs – additional parameters passed to community_louvain.best_partition()

Returns

Gnetdata object with ‘communities’ added into NetAttrs

pyscnet.NetEnrich.graph_toolkit.find_consensus_graph(gnetdata, link_key='all', method='intersection', toprank=100, threshold=None, **kwargs)[source]
Parameters
  • gnetdata – Gnetdata object.

  • link_key – str, default all. key referring to linkage table.

  • method – str, default intersection. methods for detecting consensus links. Note: intersection is recommended when there are less than 3 linkage tables.

  • toprank – int, default 100. top ranked edges for intersection method.

  • threshold – int, default None. set threshold for ensemble method.

Returns

Gnetdata object with consensus links added into NetAttrs.

pyscnet.NetEnrich.graph_toolkit.get_centrality(gnetdata)[source]
Parameters

gnetdata – Gnetdata object.

Returns

gnetData object with ‘centralities’ added into NetAttrs

pyscnet.NetEnrich.graph_toolkit.graph_merge(link_1, link_2, toprank=None, method='union')[source]
Parameters
  • link_1 – dataframe. linkage table of graph_1

  • link_2 – dataframe. linkage table of graph_2

  • toprank – int, default None. top edges from each methods for graph_merge

  • method – str, default union. methods:[union, intersection, snf]. snf refers to similarity network fusion.

Returns

dataframe, merged linkage

pyscnet.NetEnrich.graph_toolkit.graph_traveral(graph, start, threshold, method='bfs')[source]
Parameters
  • graph – network graph object.

  • start (str) – str. starting point of graph.

  • threshold – int. the depth-limit

  • method – str. bfs or dfs

Returns

explored graph.

pyscnet.NetEnrich.graph_toolkit.path_merge(path_1, path_2, k_mer=3, path='Eulerian')[source]

TODO: perform de bruijn graph mapping for ginve two path lists

pyscnet.NetEnrich.graph_toolkit.random_walk(gnetdata, start, supervisedby, steps)[source]
Parameters
  • gnetdata – Gnetdata object

  • start – str, starting point of graph.

  • supervisedby – str, ‘betweenness’, ‘closeness’, ‘degree’ or ‘pageRank’

  • steps – int, number of steps.

Returns

a list of travelled nodes.