This repository contains the Python package called triaction, which enables the detection of triadic interactions. It also provides visualization capabilities to visualise triadic interaction network.
The package has been created by Anthony Baptista and Ginestra Bianconi, with the help of Jun Yamamoto and Marta Niedostatek.
arXiv link to the preprint: https://arxiv.org/abs/2404.14997
The latest version of triaction has been moved to Trim: https://github.com/anthbapt/trim
$ python setup.py installContinuous data:- Gene expression
Scatter plot of the results of the Triaction algorithm on AML gene expression data. Each data point shows the information-theoretic measures for a triple of nodes
from triaction.analysis import decision_tree_val, visualisation_conditioned_val
gene_expression = pd.read_csv('data/reduce_gene_expression.tsv', sep = '\t', index_col=0)
name_X = 'GATA1'
name_Y = 'TAL1'
name_Z = 'KLF5'
X = np.array(gene_expression.T[name_X])
Y = np.array(gene_expression.T[name_Y])
Z = np.array(gene_expression.T[name_Z])
timeseries = np.zeros((3,len(X)))
timeseries[0,:] = X
timeseries[1,:] = Y
timeseries[2,:] = Z
num = 5
tlen = len(X)
I = [0,1,2]
visualisation_conditioned_val(timeseries, I, num, tlen, name = save_folder + '/' + 'good', cond = [th1,th2])from triaction.triadic_vis import triadic_network_vis_from_data_and_graph
ppi = pd.read_csv('data/reduce_ppi.tsv', sep = '\t')
graph_ppi = nx.from_pandas_edgelist(ppi, source = '0', target = '1')
short_range = pd.read_csv('data/short_range.txt', sep = '\t')
sub_triadic = pd.concat([short_range[short_range['P']<0.001], long_range[long_range['P']<0.001]]).reset_index()
sub_sub_triadic = sub_triadic.sort_values(by='Theta', ascending=False).reset_index()
sub_sub_triadic = sub_sub_triadic[0:10]
set_nodes = set(sub_sub_triadic['reg']).union(set(sub_sub_triadic['node1'])).union(set(sub_sub_triadic['node2']))
graph_ppi.remove_edges_from(list(nx.selfloop_edges(graph_ppi)))
sub_graph = nx.Graph(graph_ppi.subgraph(list(set_nodes)))
sub_final = sub_triadic[sub_triadic['node1'].isin(set_nodes)]
sub_final = sub_final[sub_final['node2'].isin(set_nodes)]
sub_final = sub_final[sub_final['reg'].isin(set_nodes)]
sub_final = sub_final.reset_index(drop=True)
top = len(sub_final)
triadic_network_vis_from_data_and_graph(sub_graph, sub_final, top)