CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.

We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by...

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Autores principales:	Lillian R Thistlethwaite, Varduhi Petrosyan, Xiqi Li, Marcus J Miller, Sarah H Elsea, Aleksandar Milosavljevic
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d8935c88ca1248f9bd4404aed5f9f659
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spelling	oai:doaj.org-article:d8935c88ca1248f9bd4404aed5f9f6592021-12-02T19:57:26ZCTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.1553-734X1553-735810.1371/journal.pcbi.1008550https://doaj.org/article/d8935c88ca1248f9bd4404aed5f9f6592021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008550https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, "Connect the Dots", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.Lillian R ThistlethwaiteVarduhi PetrosyanXiqi LiMarcus J MillerSarah H ElseaAleksandar MilosavljevicPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 1, p e1008550 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Lillian R Thistlethwaite Varduhi Petrosyan Xiqi Li Marcus J Miller Sarah H Elsea Aleksandar Milosavljevic CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.
description	We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, "Connect the Dots", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.
format	article
author	Lillian R Thistlethwaite Varduhi Petrosyan Xiqi Li Marcus J Miller Sarah H Elsea Aleksandar Milosavljevic
author_facet	Lillian R Thistlethwaite Varduhi Petrosyan Xiqi Li Marcus J Miller Sarah H Elsea Aleksandar Milosavljevic
author_sort	Lillian R Thistlethwaite
title	CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.
title_short	CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.
title_full	CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.
title_fullStr	CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.
title_full_unstemmed	CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.
title_sort	ctd: an information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d8935c88ca1248f9bd4404aed5f9f659
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CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.

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