Interpretation of analysis of variance models using principal component analysis to assess the effect of a maternal anticancer treatment on the mineralization of rat bones

Concurrence of osteonecrosis and steroid myopathy secondary to oral steroid therapy in a patient with ABCB1 gene polymorphisms: A case report

Glucocorticoids (GCs) are widely used in various autoimmune diseases. Side effects may occur in patients with long-term or high-dose GC usage. Among them, steroid myopathy and osteonecrosis are two severe forms. We report a patient with pemphigus vulgaris on GC-treatment who developed muscle weakness when a cumulative dose of methylprednisolone reached about 20g (14-80mg/d for 2.5 years). Laboratory tests showed slightly elevated lactate dehydrogenase and hydroxybutyrate dehydrogenase. MRI revealed osteonecrosis in the femoral head, distal femur, and proximal tibia of both legs. The biopsy of the right quadriceps revealed atrophy of type II myofiber without leukocyte infiltration, which was suggestive of steroid myopathy. Genotyping of the patient showed 5G/5G genotype of the PAI-1 gene and CC genotype of the ABCB1 gene (C3435T), suggesting she was sensitive to GCs. The patient's lesions were considered to be GC-induced adverse events, which were improved with tapering GC. Therefore, it is important to recognize steroid-induced musculoskeletal side effects and genotyping favors personalized medication.

ANOVA simultaneous component analysis: A tutorial review

When analyzing experimental chemical data, it is often necessary to incorporate the structure of the study design into the chemometric/statistical models to effectively address the research questions of interest. ANOVA-Simultaneous Component Analysis (ASCA) is one of the most prominent methods to include such information in the quantitative analysis of multivariate data, especially when the number of variables is large. This tutorial review intends to explain in a simple way how ASCA works, how it is operated and how to correctly interpret ASCA results, with approachable mathematical and visual descriptions. Two examples are given: the first, a simulated chemical reaction, serves to illustrate the ASCA steps and the second, from a real chemical ecology data set, the interpretation of results. An overview of methods closely related to ASCA is also provided, pointing out their differences and scope, to give a wide-ranging picture of the available options to build multivariate models that take experimental design into account.

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ASCA is a multivariate method for analysis of multi-factor data.

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An overview of the (mathematical) principles of ASCA is presented.

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Key aspects for practical application of ASCA are discussed.

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Detailed explanation of ASCA output in terms of score and loading plots is given.

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Literature review of other multivariate techniques for analysis of multi-factor data.

Separating common from distinctive variation

Background

Joint and individual variation explained (JIVE), distinct and common simultaneous component analysis (DISCO) and O2-PLS, a two-block (X-Y) latent variable regression method with an integral OSC filter can all be used for the integrated analysis of multiple data sets and decompose them in three terms: a low(er)-rank approximation capturing common variation across data sets, low(er)-rank approximations for structured variation distinctive for each data set, and residual noise. In this paper these three methods are compared with respect to their mathematical properties and their respective ways of defining common and distinctive variation.

Results

The methods are all applied on simulated data and mRNA and miRNA data-sets from GlioBlastoma Multiform (GBM) brain tumors to examine their overlap and differences. When the common variation is abundant, all methods are able to find the correct solution. With real data however, complexities in the data are treated differently by the three methods.

Conclusions

All three methods have their own approach to estimate common and distinctive variation with their specific strength and weaknesses. Due to their orthogonality properties and their used algorithms their view on the data is slightly different. By assuming orthogonality between common and distinctive, true natural or biological phenomena that may not be orthogonal at all might be misinterpreted.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1037-2) contains supplementary material, which is available to authorized users.

Chemometric evaluation of different experimental conditions on wheat (Triticum aestivum L.) development using liquid chromatography mass spectrometry (LC-MS) profiles of benzoxazinone derivatives

Different chemometric techniques have been used to evaluate the effect of distinct experimental conditions and factors on Triticum aestivum L. plant development. The study was conducted using three wheat varieties, Astron, Ritmo and Stakado. These varieties were grown under organic and conventional cultivation systems. Samples were collected at five growth stages. Shoots and roots of each plant at these stages were analysed. Three replicates of each analysed sample were performed to improve representativeness and to allow for the evaluation of natural variability and interaction effects. All samples were analysed using Liquid Chromatography Mass-Spectrometry (LC-MS), and the Total Ion Current (TIC) profiles of benzoxazinone derivatives obtained for each sample were investigated. Qualitative and quantitative assessments of these TIC profiles and of their changes in the analysed samples were carried out using different chemometric techniques. Estimation of main effects, and of their possible interaction, was performed by means of Analysis of Variance combined to Principal Component Analysis (ANOVA-PCA) and of Analysis of Variance combined to Simultaneous Component Analysis (ASCA).

Generic framework for high-dimensional fixed-effects ANOVA

In functional genomics it is more rule than exception that experimental designs are used to generate the data. The samples of the resulting data sets are thus organized according to this design and for each sample many biochemical compounds are measured, e.g. typically thousands of gene-expressions or hundreds of metabolites. This results in high-dimensional data sets with an underlying experimental design. Several methods have recently become available for analyzing such data while utilizing the underlying design. We review these methods by putting them in a unifying and general framework to facilitate understanding the (dis-)similarities between the methods. The biological question dictates which method to use and the framework allows for building new methods to accommodate a range of such biological questions. The framework is built on well known fixed-effect ANOVA models and subsequent dimension reduction. We present the framework both in matrix algebra as well as in more insightful geometrical terms. We show the workings of the different special cases of our framework with a real-life metabolomics example from nutritional research and a gene-expression example from the field of virology.