Analysis of Megavariate Data in Functional Genomics

Mosleth, Ellen Færgestad; McLeod, Anette; Rud, Ida; Axelsson, Lars; Solberg, Lars Erik; Moen, Birgitte; Gilman, Krista Marie Erickson; Færgestad, Eline Mosleth; Lysenko, Artem; Rawlings, Chris; Dankel, Simon N.; Mellgren, Gunnar; Barajas‐Olmos, Francisco; Orozco, Lorena; Sæbø, Solve; Gidskehaug, Lars; Janbu, Astrid Oust; Köhler, Achim; Martens, Harald; Liland, Kristian Hovde

doi:10.1016/b978-044452701-1.00011-9

Cited by 17 publications

(16 citation statements)

References 61 publications

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“…For wheat, the Affymetrix arrays comprise approximately 60 000 features (gene probe sets corresponding to slightly fewer unique transcripts), which may show separate or coordinated patterns of expression. The multivariate nature of functional genomics data must therefore be taken into account (Faergestad et al, 2009). One family of multivariate methods is based on the projection of the original variables onto new variables defined as linear combinations of the original variables [i.e.…”

Section: Discussionmentioning

confidence: 99%

A novel approach to identify genes that determine grain protein deviation in cereals

Mosleth

Wan

Lysenko

et al. 2014

Plant Biotechnology Journal

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SummaryGrain yield and protein content were determined for six wheat cultivars grown over 3 years at multiple sites and at multiple nitrogen (N) fertilizer inputs. Although grain protein content was negatively correlated with yield, some grain samples had higher protein contents than expected based on their yields, a trait referred to as grain protein deviation (GPD). We used novel statistical approaches to identify gene transcripts significantly related to GPD across environments. The yield and protein content were initially adjusted for nitrogen fertilizer inputs and then adjusted for yield (to remove the negative correlation with protein content), resulting in a parameter termed corrected GPD. Significant genetic variation in corrected GPD was observed for six cultivars grown over a range of environmental conditions (a total of 584 samples). Gene transcript profiles were determined in a subset of 161 samples of developing grain to identify transcripts contributing to GPD. Principal component analysis (PCA), analysis of variance (ANOVA) and means of scores regression (MSR) were used to identify individual principal components (PCs) correlating with GPD alone. Scores of the selected PCs, which were significantly related to GPD and protein content but not to the yield and significantly affected by cultivar, were identified as reflecting a multivariate pattern of gene expression related to genetic variation in GPD. Transcripts with consistent variation along the selected PCs were identified by an approach hereby called one-block means of scores regression (one-block MSR).

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Section: Discussionmentioning

confidence: 99%

A novel approach to identify genes that determine grain protein deviation in cereals

Mosleth

Wan

Lysenko

et al. 2014

Plant Biotechnology Journal

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“…2009 [30], that a three gene epistatic model can be simulated by GEMANOVA (Generalised Multiplicative Analysis of Variance) based on a classical genetic description of traits on the morphological level. However, on the molecular level the dosage effect of genes that cannot be visually distinguished by phenotypic traits can in principle be revealed.…”

Section: Experimental Confirmation Of the Autonomy In Soft Modelling mentioning

confidence: 99%

“…as from spectra in Figure 6A, subsection 4.2.1), which comes prior to induction that is inferring from populations of signs (as in a PCA or PLS). Chemometrics and the Megavariate Revolution [2,30] make inductive modelling scientific by identifying outliers [5]. Chemometrics and gentle models for data inspection guided by the conceptual validation [24] of the chemistry and biology behind data should qualify as the mathematics of semiotics.…”

Section: Implementation Of Chemometrics and Soft Modelling In Sciencementioning

confidence: 99%

A physiochemical theory on the applicability of soft mathematical models—experimentally interpreted

Munck

Jespersen

Rinnan

et al. 2010

Journal of Chemometrics

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a An extension of chemometric theory was experimentally explored to explain the physiochemical basis of the very high efficiency of soft modelling of data from nature. Soft modelling in self-organisation was interpreted by studying the unique chemical patterns of mutants in an isogenic barley model on endosperm development. Extremely reproducible, differential Near Infrared (NIR) spectral patterns specifically overviewed the effect on cell composition of each mutant cause. Extended Canonical Variates Analysis (ECVA) classified spectra in wild type, starch and protein mutants. The spectra were interpreted by chemometric data analysis and by pattern inspection to morphological, genetic, molecular and chemical information. Deterministic chemical reactions were defined in the glucan pathway. A drastic mutation in a gene controlling the starch/ß-glucan composition changed water activity that introduced a diffusive, stochastic effect on the catalysis of all active enzymes. 'Decision making' in self-organisation is autonomous and performed by the soft modelling of the chemical deterministic and stochastic reactions in the endosperm cell as a whole. Uncertainty in the analysis of endosperm emergence was experimentally delimited as the 'indeterminacy' in local molecular path modelling 'bottom up' and the 'irreducibility' of the phenomenological NIR spectra 'top down' . The experiment confirmed Ilya Prigogine's interpretation of self-organisation by his dynamic computer model programmed with a self-modeled non-local extension of quantum mechanics (QM). The significance of selforganisation explained by Prigogine here interpreted as physiochemical soft modelling introduces a paradigm shift in macroscopic science that forwards a major argument for soft mathematical modelling and chemometrics to obtain full scientific legitimacy.

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“…The term ‘metabolome’, first coined in 1998, refers to the complete collection of low‐molecular‐weight molecules (metabolites, <1500 Da) present within a cell or biological sample, including amino acids, sugars, carbohydrates, nucleotides, and organic acids 20‐22 . Biochemical reactions, catalysed by proteome constituents, determine the metabolome's composition and subsequently the biological structure and function of the organism's phenotype.…”

Section: Metabolomicsmentioning

confidence: 99%

Science‐in‐brief: Proteomics and metabolomics in equine veterinary science

Anderson

2022

Equine Veterinary Journal

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Analysis of Megavariate Data in Functional Genomics

Cited by 17 publications

References 61 publications

A novel approach to identify genes that determine grain protein deviation in cereals

A novel approach to identify genes that determine grain protein deviation in cereals

A physiochemical theory on the applicability of soft mathematical models—experimentally interpreted

Science‐in‐brief: Proteomics and metabolomics in equine veterinary science

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