Inferring tree causal models of cancer progression with probability raising

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

Research output: Contribution to journalArticle

Abstract

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 (US)
Article numbere108358
JournalPLoS One
Volume9
Issue number10
DOIs
StatePublished - Oct 9 2014

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Causality
neoplasms
Noise
Neoplasms
Measurement errors
shrinkage
Technology
sampling
methodology
Datasets

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Loohuis, L. O., Caravagna, G., Graudenzi, A., Ramazzotti, D., Mauri, G., Antoniotti, M., & Mishra, B. (2014). Inferring tree causal models of cancer progression with probability raising. PLoS One, 9(10), [e108358]. https://doi.org/10.1371/journal.pone.0108358

Inferring tree causal models of cancer progression with probability raising. / Loohuis, Loes Olde; Caravagna, Giulio; Graudenzi, Alex; Ramazzotti, Daniele; Mauri, Giancarlo; Antoniotti, Marco; Mishra, Bhubaneswar.

In: PLoS One, Vol. 9, No. 10, e108358, 09.10.2014.

Research output: Contribution to journalArticle

Loohuis, LO, Caravagna, G, Graudenzi, A, Ramazzotti, D, Mauri, G, Antoniotti, M & Mishra, B 2014, 'Inferring tree causal models of cancer progression with probability raising', PLoS One, vol. 9, no. 10, e108358. https://doi.org/10.1371/journal.pone.0108358
Loohuis LO, Caravagna G, Graudenzi A, Ramazzotti D, Mauri G, Antoniotti M et al. Inferring tree causal models of cancer progression with probability raising. PLoS One. 2014 Oct 9;9(10). e108358. https://doi.org/10.1371/journal.pone.0108358
Loohuis, Loes Olde ; Caravagna, Giulio ; Graudenzi, Alex ; Ramazzotti, Daniele ; Mauri, Giancarlo ; Antoniotti, Marco ; Mishra, Bhubaneswar. / Inferring tree causal models of cancer progression with probability raising. In: PLoS One. 2014 ; Vol. 9, No. 10.
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