Inferring tree causal models of cancer progression with probability raising

Loes Olde Loohuis, Loes Olde Loohuis, Giulio Caravagna, Alex Graudenzi, Daniele Ramazzotti, Giancarlo Mauri, Marco Antoniotti, Bud Mishra

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper, we define a novel theoretical framework called CAPRESE (CAncer PRogression Extraction with Single Edges) to reconstruct such models based on the notion of probabilistic causation defined by Suppes. We consider a general reconstruction setting complicated by the presence of noise in the data due to biological variation, as well as experimental or measurement errors. To improve tolerance to noise we define and use a shrinkage-like estimator. We prove the correctness of our algorithm by showing asymptotic convergence to the correct tree under mild constraints on the level of noise. Moreover, on synthetic data, we show that our approach outperforms the state-of-the-art, that it is efficient even with a relatively small number of samples and that its performance quickly converges to its asymptote as the number of samples increases. For real cancer datasets obtained with different technologies, we highlight biologically significant differences in the progressions inferred with respect to other competing techniques and we also show how to validate conjectured biological relations with progression models.

Original languageEnglish
Number of pages30
JournalPLoS ONE
Issue number10
Publication statusPublished - 2014

Keywords / Materials (for Non-textual outputs)

  • Algorithms
  • Disease Progression
  • Humans
  • Image Processing, Computer-Assisted
  • Models, Theoretical
  • Neoplasms
  • Neoplastic Processes
  • Phylogeny
  • Probability


Dive into the research topics of 'Inferring tree causal models of cancer progression with probability raising'. Together they form a unique fingerprint.

Cite this