Abstract
Models of cancer progression provide insights on the order of accumulation of genetic alterations during cancer development. Algorithms to infer such models from the currently available mutational profiles collected from different cancer patients (cross-sectional data) have been defined in the literature since late the 90s. These algorithms differ in the way they extract a graphical model of the
events modelling the progression, e.g., somatic mutations or copy-number alterations.
TRONCO is an R package for TRanslational ONcology which provides a series of functions to assist the user in the analysis of cross-sectional genomic data and, in particular, it implements algorithms that aim to model cancer progression by means of the notion of selective advantage. These algorithms are proved to outperform the current state-of-the-art in the inference of cancer progression
models. TRONCO also provides functionalities to load input cross-sectional data, set up the execution of the algorithms, assess the statistical confidence in the results, and visualize the models.
events modelling the progression, e.g., somatic mutations or copy-number alterations.
TRONCO is an R package for TRanslational ONcology which provides a series of functions to assist the user in the analysis of cross-sectional genomic data and, in particular, it implements algorithms that aim to model cancer progression by means of the notion of selective advantage. These algorithms are proved to outperform the current state-of-the-art in the inference of cancer progression
models. TRONCO also provides functionalities to load input cross-sectional data, set up the execution of the algorithms, assess the statistical confidence in the results, and visualize the models.
| Original language | English |
|---|---|
| Pages (from-to) | 39-59 |
| Number of pages | 21 |
| Journal | The R Journal |
| Volume | 8 |
| Issue number | 2 |
| Early online date | 21 Oct 2016 |
| DOIs | |
| Publication status | E-pub ahead of print - 21 Oct 2016 |