COMBO: COmbing Multi-Bio Omics

Ilaria Cosentini, Vincenza Barresi, Daniele Filippo Condorelli, Alfredo Ferro, Alfredo Pulvirenti, and Salvatore Alaimo. Combo: A computational framework to analyze rna-seq and methylation data through heterogeneous multi-layer networks. In Hocine Cheri, Rosario Nunzio Mantegna, Luis M. Rocha, Chantal Cheri, and Salvatore Miccich�e, editors, Complex Networks and Their Applications XI, pages 251{264, Cham, 2023. Springer International Publishing. ISBN 978-3-031-21127-0.

COMBO is a novel pipeline for multi-layer network inference and analysis to identify relevant pathways in the studied case. Taking advantage of the Boolean implication method, both transcriptomic and epigenomic data were analyzed through StepMiner[1] and BooleanNet systems [2]. The goal was to identify the implication between the different transcripts and methylated CpGs. The obtained results were used to generate heterogeneous multi-layer graphs. Subsequently, Neo4J was exploited to query the multi-layer network with properly defined Cypher queries.

USAGE

bash COMBO.bash -h

Usage: COMBO.bash [options]

## Mandatory augument:
-E               expression matrix
-M               methylation matrix
-i               input file with sample information
-c               file with comparison between samples
-h               Print this Help
-o               output folder path

## Optional arguments:
# StepMiner and BooleanNet
-d <int>         delta threshold StepMiner (default=0.5)
-s <int>         statistic of an implication to be considered significant in BooleanNet (default=6.0)
-P <int>         maximum p-value P of an implication to be considered significant (default=0.01)

# Multilayer network creation
-w <string>      metapathway, can be KEGG or KEGG_Reactome (default=KEGG)
-f <int>         logFC threshold for expression data (default=|0.8|)
-x <int>         logFC threshold for methylation data (default=0)
-j <int>         adjusted pvalue threshold for methylation and expression data (default=0.05)
-n <string>      annotation name (mutation, protein expression)
-a               annotation table

# Number of CPU threads
-T <int>         threads number

References

[1] Sahoo, D.,Dill, D.L., Tibshirani, R., Plevritis, S.K.: Extracting binary signals from microarray time-course data. Nucl. Acids Res., 3705–12 (2007)

[2] Sahoo, D., Dill, D.L., Gentles, A.J., Tibshirani, R., Plevritis, S.K.: Boolean implication networks derived from large scale, whole genome microarray datasets. Genome Biol. 9, R157 (2008)