A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation.
Analyzing gene expression patterns is a mainstay to gain functional insights of biological systems. A plethora of tools exist to identify significant enrichment of pathways for a set of differentially expressed genes. Most tools analyze gene overlap between gene sets and are therefore severely hampered by the current state of pathway annotation, yet at the same time they run a high risk of false assignments. A way to improve both true positive and false positive rates (FPRs) is to use a functional association network and instead look for enrichment of network connections between gene sets. We present a new network crosstalk analysis method BinoX that determines the statistical significance of network link enrichment or depletion between gene sets, using the binomial distribution. This is a much more appropriate statistical model than previous methods have employed, and as a result BinoX yields substantially better true positive and FPRs than was possible before. A number of benchmarks were performed to assess the accuracy of BinoX and competing methods. We demonstrate examples of how BinoX finds many biologically meaningful pathway annotations for gene sets from cancer and other diseases, which are not found by other methods. BinoX is available at http://sonnhammer.org/BinoX.
Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden christoph.ogris@scilifelab.se.
Christoph
C
Ogris
Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.
Dimitri
D
Guala
Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.
Thomas
T
Helleday
Stockholm Bioinformatics Center, Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121 Solna, Sweden.
Erik L L
EL
Sonnhammer
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D006801:Humans; D053858:Metabolic Networks and Pathways; D015398:Signal Transduction; D012984:Software
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