  Despite recent technological advances in genomic sciences, our understanding
of cancer progression and its driving genetic alterations remains incomplete.
Here, we introduce TiMEx, a generative probabilistic model for detecting
patterns of various degrees of mutual exclusivity across genetic alterations,
which can indicate pathways involved in cancer progression. TiMEx explicitly
accounts for the temporal interplay between the waiting times to alterations
and the observation time. In simulation studies, we show that our model
outperforms previous methods for detecting mutual exclusivity. On large-scale
biological datasets, TiMEx identifies gene groups with strong functional
biological relevance, while also proposing many new candidates for biological
validation. TiMEx possesses several advantages over previous methods, including
a novel generative probabilistic model of tumorigenesis, direct estimation of
the probability of mutual exclusivity interaction, computational efficiency, as
well as high sensitivity in detecting gene groups involving low-frequency
alterations. R code is available at www.cbg.bsse.ethz.ch/software/TiMEx.
