•We propose a new method named Angle Distribution of Loading Subspaces (ADLS) for the estimation of chemical rank.•ADLS is compared with a well-known method CORCONDIA and the comparision shows that ADLS is more robust.•The method is tested on simulated datasets, constructed with different levels of ‘non-trilinear’ structure.•The method is also tested on real datasets, including flurencence and HPLD-DAD datasets.Three-way analysis is increasingly popular because of the unique decomposition of the trilinear model, clear physical/chemical interpretation, and “second-order advantage,” which means the possible quantification of the interesting components in complex system with unknown interferents. The estimation of chemical rank is a crucial step in three-way analysis. In this paper, we propose a new method for the estimation of the chemical rank in three-way analysis, namely, angle distribution of loading subspace (ADLS). The stability of the three-way analysis model is tested by the variation of the angle distribution of the loading subspaces using bootstrapping. The largest number of components related to a stable model is selected as a chemical rank. We compared the results of ADLS and core consistency diagnostic (CORCONDIA), which is a well-known method for the estimation of chemical rank, based on two three-way analysis algorithms parallel factor analysis (PARAFAC) and self-weighted alternating trilinear decomposition (SWATLD). Using simulated and real data sets with “non-trilinear” structure, e.g., noise, the coherence and the presence of component with low sensitivity (s), the comparison shows that ADLS is more robust than CORCONDIA, and furthermore, the results of ADLS based on SWATLD are clearer than its results based PARAFAC.

•We present a novel method for the analysis of Multicolour Flow Cytometry data that uses concepts from the NOC approach in industrial process monitoring•The method may simultaneously diagnose immunological responses from the multivariate distributions of surface marker expressions on immunological cells.•The model may reveal the underlying immunological response mechanisms from the response-associated multivariate patterns of variability.•The method may thirdly identify the specific single cells that are associated to the response.Multicolour Flow Cytometry (MFC) is widely used for single-cell analysis and employs a vastly increasing number of markers. It can be used for disease diagnosis, research of disease mechanisms and the identification and isolation of individual cells based on their surface marker profile. However, data analysis methods exploiting all these advantages are lacking. Our novel FLow cytometric Orthogonal Orientation for Diagnosis (FLOOD) method reveals disease specific marker patterns. The method constructs a benchmark from surface marker abundances that is used to highlight deviations of challenged from unchallenged individuals. We demonstrate its power in an in vivo study of the response of healthy humans to lipopolysaccharide (LPS) challenge. FLOOD reveals a reproducible pattern of challenge specific markers on blood neutrophils. The method both provides new mechanistic insights and confirms established knowledge on LPS-response, which demonstrates the high potential of FLOOD for both clinical and research application.

Micro-organismal interspecies competition induces highly complex ecological interactions. Its associated biochemistry is an extremely rich source for bioactive molecules, that can be evaluated by comparing assays of separated species to ‘co-cultures’ in which they compete. The untargeted view that metabolomics provides, gives unprecedented insight into the wealth of involved metabolites. Currently used multivariate data analysis methods in metabolomics, like principal component analysis, do not focus upon up- and down-regulation of constitutive metabolite pools during competition. This severely limits the interpretation of competition mechanisms and the associated metabolites from the extremely information-rich metabolomics data. Projected orthogonal chemical encounter monitoring (POCHEMON) is a novel multivariate data analysis method that reveals all competition-related biochemical changes from the co-cultures: both up- or down-regulated, and de novo synthesised metabolites. It describes the metabolite composition of a co-culture assay by a mixture of the metabolites expressed in both separated species. Aspects of the co-culture metabolism that cannot be described in this way, are present only in the co-cultures and therefore likely associated to interspecies competition. We highlight the potential of POCHEMON by a study on fungal interactions in onychomycosis, a nail infection that may severely affect immuno-suppressed individuals. The resulting model reveals many unexpected or as yet unknown metabolites involved in the competition, that can be specifically identified as up- or down-regulated or de novo produced upon competition.

Many metabolomics studies aim to find ‘biomarkers’: sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment. Properly understanding individual differences is crucial for generating knowledge in fields like personalised medicine, evolution and ecology. We propose to use simultaneous component analysis with individual differences constraints (SCA-IND), a data analysis method from psychology that focuses on these differences. This method constructs axes along the natural biochemical differences between biological replicates, comparable to principal components. The model may shed light on changes in the individual differences between experimental groups, but also on whether these differences correspond to, e.g., responders and non-responders or to distinct chemotypes. Moreover, SCA-IND reveals the individuals that respond most to a manipulation and are best suited for further experimentation. The method is illustrated by the analysis of individual differences in the metabolic response of cabbage plants to herbivory. The model reveals individual differences in the response to shoot herbivory, where two ‘response chemotypes’ may be identified. In the response to root herbivory the model shows that individual plants differ strongly in response dynamics. Thereby SCA-IND provides a hitherto unavailable view on the chemical diversity of the induced plant response, that greatly increases understanding of the system.

•‘Target group heterogeneity’ is a novel source of acceptance information in conjoint studies.•Consumer group heterogeneity may vary systematically between groups, a new objective for marketing.Acceptance of a product by a consumer may result from a convoluted interplay between product attributes and individual characteristics of that consumer. Different methods that systematically combine product properties with consumer groups segmented on such characteristics have provided unprecedented insight, but ignore heterogeneity in acceptance within each consumer group. Although such knowledge is invaluable for targeted marketing, dedicated methods for consumer group heterogeneity are lacking. The authors aim to fill this gap by the Individual Differences (InD) method, which models differences between consumers within the same target group. The method scores the ‘diffusion’ within each group, shows how much each consumer contributes to that, and relates this information to product properties. Thereby also novel groups may be discovered, with attributes not covered in the consumer segmentation. The illustrative consumer study on apple juice reveals how young women differ in their price-consciousness and their acceptance on specific preparation technologies more than older women. Although men exhibit heterogeneity on the same product attributes, their mutual variability is considerably lower and they thereby form more homogeneous target groups.

•‘Target group heterogeneity’ is a novel source of acceptance information in conjoint studies.•Consumer group heterogeneity may vary systematically between groups, a new objective for marketing.Acceptance of a product by a consumer may result from a convoluted interplay between product attributes and individual characteristics of that consumer. Different methods that systematically combine product properties with consumer groups segmented on such characteristics have provided unprecedented insight, but ignore heterogeneity in acceptance within each consumer group. Although such knowledge is invaluable for targeted marketing, dedicated methods for consumer group heterogeneity are lacking. The authors aim to fill this gap by the Individual Differences (InD) method, which models differences between consumers within the same target group. The method scores the ‘diffusion’ within each group, shows how much each consumer contributes to that, and relates this information to product properties. Thereby also novel groups may be discovered, with attributes not covered in the consumer segmentation. The illustrative consumer study on apple juice reveals how young women differ in their price-consciousness and their acceptance on specific preparation technologies more than older women. Although men exhibit heterogeneity on the same product attributes, their mutual variability is considerably lower and they thereby form more homogeneous target groups.