bigants 0.0.9

BiGAnts - a package for network-constrained biclustering of omics data

BiGAnts: network-constrained biclustering of patients and multi-omics data

PyPI package for conjoint clustering of networks and omics data

An application example is given in the file script_main.py in the project's GitHub.

To install the package please run: pip install bigants

Data input

The algorithm needs as an input one CSV matrix with gene expression/methylation/any other numerical data and one TSV file with a network.

Numerical data

Numerical data is accepted in the following format:

• genes as rows.
• patients as columns.
• first column - genes IDs (can be any IDs).

For instance:

	Unnamed: 0	GSM748056	GSM748059	...	GSM748278	GSM748279	GSM1465989
0	1454	0.053769	0.117412	...	-0.392363	-1.870838	-1.432554
1	201931	-0.618279	0.278637	...	0.803541	-0.514947	2.361925
2	8761	0.215820	-0.343865	...	0.700430	0.073281	-0.977656
3	2703	-0.504701	1.295049	...	1.861972	0.601808	0.191013
4	26207	-0.626415	-0.646977	...	2.331724	2.339122	-0.100924

There are 2 examples of gene expression datasets that can be placed in the "data" folder

• GSE30219 - a Non-Small Cell Lung Cancer dataset from GEO for patients with either adenocarcinoma or squamous cell carcinoma.
• TCGA pan-cancer dataset with patients that have luminal or basal breast cancer. Both can be found here

Network

An interaction network should be present as a TSV table with two columns that represent two interacting genes. Without a header!

For instance:

	6416	2318
0	6416	5371
1	6416	351
2	6416	409
3	6416	5932
4	6416	1956

In the data folder (on the GitHub page of the project) there is an example of a PPI network from Bioigrid with experimentally validated interactions.

Functions

1. bigants.data_preprocessing(path_expr, path_net, log2 = False, size = 2000)

Parameters:

• path_to_expr: string, path to the numerical data
• path_to_net: string, path to the network file
• log2: bool, (default = False), indicates if log2 transformation should be applied to the data
• size: int, optional (default = 2000) determines the number of genes that should be pre-selected by variance for the analysis. Shouldn't be higher than 5000.

Returns:

• GE: pandas data frame, processed expression data
• G: networkX graph, processed network data
• labels: dict, for mapping between real genes/patients IDs and the internal ones
• rev_labels: dict, additional dictionary for mapping between real genes/patients IDs and the internal ones

2. bigants.BiGAnts(GE,G,L_g_min,L_g_max) creates a model for the given data:

Parameters:

• GE: pandas dataframe, processed expression data
• G: networkX graph, processed network data
• L_g_min: int, minimal solution subnetwork size
• L_g_max: int, maximal solution subnetwork size

Methods:

bigants.BiGAnts.run(self, n_proc = 1, K = 20, evaporation = 0.5, show_plot = False)

• K: int, default = 20, number of ants. Fewer ants - less space exploration. Usually set between 20 and 50
• n_proc: int, default = 1, number of processes that should be used
• evaporation, float, default = 0.5, the rate at which pheromone evaporates
• show_plot: bool, default = False, set true if convergence plots should be during the analysis