Inferring tree causal models of cancer progression with probability raising.

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 Extra...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Loes Olde Loohuis, Giulio Caravagna, Alex Graudenzi, Daniele Ramazzotti, Giancarlo Mauri, Marco Antoniotti, Bud Mishra
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7b96cb7021714f50b638ff3db535e60c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:7b96cb7021714f50b638ff3db535e60c
record_format	dspace
spelling	oai:doaj.org-article:7b96cb7021714f50b638ff3db535e60c2021-11-25T05:57:14ZInferring tree causal models of cancer progression with probability raising.1932-620310.1371/journal.pone.0108358https://doaj.org/article/7b96cb7021714f50b638ff3db535e60c2014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0108358https://doaj.org/toc/1932-6203Existing 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.Loes Olde LoohuisGiulio CaravagnaAlex GraudenziDaniele RamazzottiGiancarlo MauriMarco AntoniottiBud MishraPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 10, p e108358 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Loes Olde Loohuis Giulio Caravagna Alex Graudenzi Daniele Ramazzotti Giancarlo Mauri Marco Antoniotti Bud Mishra Inferring tree causal models of cancer progression with probability raising.
description	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.
format	article
author	Loes Olde Loohuis Giulio Caravagna Alex Graudenzi Daniele Ramazzotti Giancarlo Mauri Marco Antoniotti Bud Mishra
author_facet	Loes Olde Loohuis Giulio Caravagna Alex Graudenzi Daniele Ramazzotti Giancarlo Mauri Marco Antoniotti Bud Mishra
author_sort	Loes Olde Loohuis
title	Inferring tree causal models of cancer progression with probability raising.
title_short	Inferring tree causal models of cancer progression with probability raising.
title_full	Inferring tree causal models of cancer progression with probability raising.
title_fullStr	Inferring tree causal models of cancer progression with probability raising.
title_full_unstemmed	Inferring tree causal models of cancer progression with probability raising.
title_sort	inferring tree causal models of cancer progression with probability raising.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/7b96cb7021714f50b638ff3db535e60c
work_keys_str_mv	AT loesoldeloohuis inferringtreecausalmodelsofcancerprogressionwithprobabilityraising AT giuliocaravagna inferringtreecausalmodelsofcancerprogressionwithprobabilityraising AT alexgraudenzi inferringtreecausalmodelsofcancerprogressionwithprobabilityraising AT danieleramazzotti inferringtreecausalmodelsofcancerprogressionwithprobabilityraising AT giancarlomauri inferringtreecausalmodelsofcancerprogressionwithprobabilityraising AT marcoantoniotti inferringtreecausalmodelsofcancerprogressionwithprobabilityraising AT budmishra inferringtreecausalmodelsofcancerprogressionwithprobabilityraising
_version_	1718414349601603584

Inferring tree causal models of cancer progression with probability raising.

Ejemplares similares