Discretization of gene expression data revised

Gallo, Cristian; Cecchini, Rocío L.; Carballido, Jessica Andrea; Micheletto, Sandra; Ponzoni, Ignacio

doi:10.1093/bib/bbv074

Cited by 57 publications

(43 citation statements)

References 28 publications

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“…While discretization always implies a loss of information, it also simplifies the state space of the problem considerably and it can deflate noisy data. Advantages and disadvantages of using discretized expression data are discussed in [32], for example.…”

Section: Methodsmentioning

confidence: 99%

Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare

Will

Helms

2017

BMC Syst Biol

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BackgroundDifferential analysis of cellular conditions is a key approach towards understanding the consequences and driving causes behind biological processes such as developmental transitions or diseases. The progress of whole-genome expression profiling enabled to conveniently capture the state of a cell’s transcriptome and to detect the characteristic features that distinguish cells in specific conditions. In contrast, mapping the physical protein interactome for many samples is experimentally infeasible at the moment. For the understanding of the whole system, however, it is equally important how the interactions of proteins are rewired between cellular states. To overcome this deficiency, we recently showed how condition-specific protein interaction networks that even consider alternative splicing can be inferred from transcript expression data. Here, we present the differential network analysis tool PPICompare that was specifically designed for isoform-sensitive protein interaction networks.ResultsBesides detecting significant rewiring events between the interactomes of grouped samples, PPICompare infers which alterations to the transcriptome caused each rewiring event and what is the minimal set of alterations necessary to explain all between-group changes. When applied to the development of blood cells, we verified that a reasonable amount of rewiring events were reported by the tool and found that differential gene expression was the major determinant of cellular adjustments to the interactome. Alternative splicing events were consistently necessary in each developmental step to explain all significant alterations and were especially important for rewiring in the context of transcriptional control.ConclusionsApplying PPICompare enabled us to investigate the dynamics of the human protein interactome during developmental transitions. A platform-independent implementation of the tool PPICompare is available at https://sourceforge.net/projects/ppicompare/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0400-x) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare

Will

Helms

2017

BMC Syst Biol

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“…On the absence of such knowledge that is normally difficult to be objectively determined and assessed, automated and statistically driven approaches are followed. Discretization of gene expression values is already followed in many microarray studies and respective data analysis approaches [112], [113]. …”

Section: Methodsmentioning

confidence: 99%

MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways

et al. 2016

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Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes.

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“…A necessary step for the formalization of data is to be able to express them in the terms of discrete activity levels [7]. The usual problem is to decide for a component, what is its threshold concentration, i.e.…”

Section: Measurementsmentioning

confidence: 99%

“…Here, we rely on an assumption that a fold change of two or more is significant, which is, to the best of our knowledge, a common practice and in our case produces a good separation. However, it might be worthwhile to revisit these experiments and evaluated how different discretization methods [7] would compare.…”

Section: Egfr Modelmentioning

confidence: 99%