Statistical assessment of crosstalk enrichment between gene groups in biological networks.
BACKGROUND
Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets.


RESULTS
Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA).


METHODS
CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.
Stockholm Bioinformatics Centre, Science for Life Laboratory, Solna, Sweden.
Theodore
T
McCormack
Oliver
O
Frings
Andrey
A
Alexeyenko
Erik L L
EL
Sonnhammer
D019295:Computational Biology / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D015398:Signal Transduction; D012984:Software