Extracting meaningful information from metabonomic data using multivariate statistics.

Lipidomic differentiation between human kidney tumors and surrounding normal tissues using HILIC-HPLC/ESI-MS and multivariate data analysis.

Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

Metabolic markers are the core of metabonomic surveys. Hence selection of differential metabolites is of great importance for either biological or clinical purpose. Here, a feature selection method was developed for complex metabonomic data set. As an effective tool for metabonomics data analysis, support vector machine (SVM) was employed as the basic classifier. To find out meaningful features effectively, support vector machine recursive feature elimination (SVM-RFE) was firstly applied. Then, genetic algorithm (GA) and random forest (RF) which consider the interaction among the metabolites and independent performance of each metabolite in all samples, respectively, were used to obtain more informative metabolic difference and avoid the risk of false positive. A data set from plasma metabonomics study of rat liver diseases developed from hepatitis, cirrhosis to hepatocellular carcinoma was applied for the validation of the method. Besides the good classification results for 3 kinds of liver diseases, 31 important metabolites including lysophosphatidylethanolamine (LPE) C16:0, palmitoylcarnitine, lysophosphatidylethanolamine (LPC) C18:0 were also selected for further studies. A better complementary effect of the three feature selection methods could be seen from the current results. The combinational method also represented more differential metabolites and provided more metabolic information for a “global” understanding of diseases than any single method. Further more, this method is also suitable for other complex biological data sets.

Naphthalene (NAP) is found as a pollutant in water, soil, and air, and adsorption is the most prominent removal process of this compound, among the methods studied. A study concerning the types of adsorbents and the parameters with the greatest influence on the adsorption process is interesting to direct future works on new adsorbents. The use of multivariate data analysis tools becomes an appealing way to compile data obtained from bibliographic reviews and to establish a behavior in NAP adsorption. This work aims to evaluate the parameters with greater influence on NAP adsorption process regarding adsorption capacity (qeexp) with the principal component analysis (PCA), and to group common NAP adsorbents by chemical characteristics through hierarchical cluster analysis (HCA). The variables qeexp, S, [NAP]0, T, CT, and [Ads] were used to perform PCA with correlation matrix. For the HCA, the variables S, [NAP]0, T, CT, and [Ads] with average linkage method (UPGMA) and Euclidean distance were used. Through PCA, it is possible to infer that S and [NAP]0 are the factors with greater influence in qeexp of NAP, while T, CT, and [Ads] have little correlation. PCA also shows that activated charcoal is the adsorbent with higher qeexp. HCA grouped the adsorbents into four groups by their chemical classes, except group A. Both PCA and HCA methods show themselves as potential tools to evaluate a data set of NAP adsorption processes.

Microorganisms are closely associated with human diseases and health. Understanding the composition and function of microbial communities requires extensive research. Metaproteomics has recently become an important method for throughout and in-depth study of microorganisms. However, major challenges in terms of sample processing, mass spectrometric data acquisition, and data analysis limit the development of metaproteomics owing to the complexity and high heterogeneity of microbial community samples. In metaproteomic analysis, optimizing the preprocessing method for different types of samples and adopting different microbial isolation, enrichment, extraction, and lysis schemes are often necessary. Similar to those for single-species proteomics, the mass spectrometric data acquisition modes for metaproteomics include data-dependent acquisition (DDA) and data-independent acquisition (DIA). DIA can collect comprehensive peptide information from a sample and holds great potential for future development. However, data analysis for DIA is challenged by the complexity of metaproteome samples, which hinders the deeper coverage of metaproteomes. The most important step in data analysis is the construction of a protein sequence database. The size and completeness of the database strongly influence not only the number of identifications, but also analyses at the species and functional levels. The current gold standard for metaproteome database construction is the metagenomic sequencing-based protein sequence database. A public database-filtering method based on an iterative database search has been proven to have strong practical value. The peptide-centric DIA data analysis method is a mainstream data analysis strategy. The development of deep learning and artificial intelligence will greatly promote the accuracy, coverage, and speed of metaproteomic analysis. In terms of downstream bioinformatics analysis, a series of annotation tools that can perform species annotation at the protein, peptide, and gene levels has been developed in recent years to determine the composition of microbial communities. The functional analysis of microbial communities is a unique feature of metaproteomics compared with other omics approaches. Metaproteomics has become an important component of the multi-omics analysis of microbial communities, and has great development potential in terms of depth of coverage, sensitivity of detection, and completeness of data analysis.

Monitoring of forest pests and diseases is resource-intensive, requiring individual woodlands and trees to be visited and assessed for symptoms. Climate change and increased global connectivity are amplifying the scale of the monitoring challenge, with the number of new plant biosecurity threats increasing each year. Citizen science can increase the scale of pest and disease surveys. However, it is argued that citizen science data can be biased and inaccurate. This study examines potential biases in citizen science data by focusing on the case study of acute oak decline (AOD), a disease syndrome impacting native oaks within the UK associated with the beetle Agrilus biguttatus. Analysis was performed using two contrasting citizen science data set sources: the National Biodiversity Network (NBN) Atlas, which is a repository for citizen science data sets, and Tree Alert, a targeted citizen science project that encouraged landowners and the public to report the occurrence of AOD. For both data sets, detection was more likely in locations with higher Coleoptera reports, suggesting that there are hubs of recorder activity. For the NBN data set, A. biguttatus was more likely to be found in areas where historic parks and gardens were present. For the Tree Alert data set, A. biguttatus was less likely to be found on open access land, indicating that the programme was successful in engaging private landowners. These results indicate that understanding sources of bias within reporting schemes is an important step in data analysis and that the inclusion of structured survey designs would enable the extent of biases to be documented.

Patents are an important source of technological knowledge, but the amount of existing patents is vast and quickly growing. This makes development of tools and methodologies for quickly revealing patterns in patent collections important. In this paper, we describe how structured chemometric principles of multivariate data analysis can be applied in the context of text analysis in a novel combination with common machine learning preprocessing methodologies. We demonstrate our methodology in 2 case studies. Using principal component analysis (PCA) on a collection of 12338 patent abstracts from 25 companies in big pharma revealed sub‐fields which the companies are active in. Using PCA on a smaller collection of patents retrieved by searching for a specific term proved useful to quickly understand how patent classifications relate to the search term. By using orthogonal projections to latent structures (O‐PLS) on patent classification schemes, we were able to separate patents on a more detailed level than using PCA. Lastly, we performed multi‐block modeling using OnPLS on bag‐of‐words representations of abstracts, claims, and detailed descriptions, respectively, showing that semantic variation relating to patent classification is consistent across multiple text blocks, represented as globally joint variation. We conclude that using machine learning to transform unstructured data into structured data provide a good preprocessing tool for subsequent chemometric multivariate data analysis and provides an easily interpretable and novel workflow to understand large collections of patents. We demonstrate this on collections of chemical and pharmaceutical patents.

A multivariate analysis method, orthogonal partial least squares to latent structures (OPLS), was used to discriminate Alzheimer's disease (AD), early and late mild cognitive impairment (EMCI and LMCI) from cognitively normal control (CN) using MRI and PET measures. FreeSurfer 5.1 generated 271 MRI features including 49 subcortical volumes, 68 cortical volumes, 68 cortical thicknesses, 70 surface areas and 16 hippocampus subfields. Subjects with all aforementioned MRI measures passing quality control and valid Fludeoxyglucose (18F) (FDG) and Florbetapir (18F) PET scans were selected from ADNI database, resulting in a total of 524 participants (137 CN, 214 EMCI, 103 LMCI and 70 AD) for the study. Altogether 286 features including 15 significant PET uptake features (7 for FDG and 8 for AV-45) were utilized for OPLS analysis. Predictive power was evaluated by Q2(Y), a quantifier of the statistical significance for class separation. The results show that MRI features (Q2(Y) =0.645), and PET features (Q2(Y) = 0.636) has comparable predictive power in separating AD from CN, and MRI features are better predictor of LMCI (Q2(Y) = 0.282) than PET (Q2(Y) = 0.294). Combination of PET and MRI has the most predictive power for LMCI and AD with Q2(Y) of 0.294 and 0.721, respectively. While for EMCI, cortical thickness was found to be the best predictor with a Q2(Y) of 0.108, suggesting cortical thickness may be the first structural change ahead of others and should be prioritized in prediction of very mild cognitive impairment.

A multivariate approach for evaluation and monitoring of water quality in mining and minerals processing industry

In the normal casework of Forensic Document Examiners (FDEs), a common type of forgery is the alteration of the date of creation of a document through the erasure and addition of another date with ballpoint pen of color similar to the original. The chronology of manuscripts in a document has been a concern within the FDE community. Recently, Infrared spectroscopy (IR) and multivariate statistical analysis have been used to differentiate pens, but this paper aimed to develop an IR method that could also date a manuscript by multivariate statistic. Ballpoint pen lines from different brands made in different kinds of papers (office white and recycled), with variable ages, were analyzed by Infrared Spectroscopy with Total Attenuated Reflectance Accessory. The spectra obtained were analyzed with multivariate statistical analysis, especially Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). This study concludes that different kinds of paper do not influence the characterization of the pens by IR-ATR. The method was able to characterize the variables that were responsible for the discrimination of ballpoint pens from different brands/models and for differentiation of samples of different ages. The HCA and PCA were able to group pens with similar ages, and differentiate lines recently wrote from older lines. Within the loadings that are responsible for the differentiation, it was possible to identify absorption peaks from 2-phenoxyethanol, a solvent whose evaporation from the ink could indicate the line age. Further studies regarding other pen samples and multivariate statistical approaches could be conducted to confirm these findings.

<p>This thesis investigates three research problems which arise in multivariate data and censored regression. The first is the identification of outliers in multivariate data. The second is a dissimilarity measure for clustering purposes. The third is the diagnostics analysis for the Buckley-James method in censored regression. Outliers can be defined simply as an observation (or a subset of observations) that is isolated from the other observations in the data set. There are two main reasons that motivate people to find outliers; the first is the researcher's intention. The second is the effects of an outlier on analyses, i.e. the existence of outliers will affect means, variances and regression coefficients; they will also cause a bias or distortion of estimates; likewise, they will inflate the sums of squares and hence, false conclusions are likely to be created. Sometimes, the identification of outliers is the main objective of the analysis, and whether to remove the outliers or for them to be down-weighted prior to fitting a non-robust model. This thesis does not differentiate between the various justifications for outlier detection. The aim is to advise the analyst of observations that are considerably different from the majority. Note that the techniques for identification of outliers introduce in this thesis is applicable to a wide variety of settings. Those techniques are performed on large and small data sets. In this thesis, observations that are located far away from the remaining data are considered to be outliers. Additionally, it is noted that some techniques for the identification of outliers are available for finding clusters. There are two major challenges in clustering. The first is identifying clusters in high-dimensional data sets is a difficult task because of the curse of dimensionality. The second is a new dissimilarity measure is needed as some traditional distance functions cannot capture the pattern dissimilarity among the objects. This thesis deals with the latter challenge. This thesis introduces Influence Angle Cluster Approach (iaca) that may be used as a dissimilarity matrix and the author has managed to show that iaca successfully develops a cluster when it is used in partitioning clustering, even if the data set has mixed variables, i.e. interval and categorical variables. The iaca is developed based on the influence eigenstructure. The first two problems in this thesis deal with a complete data set. It is also interesting to study about the incomplete data set, i.e. censored data set. The term 'censored' is mostly used in biological science areas such as a survival analysis. Nowadays, researchers are interested in comparing the survival distribution of two samples. Even though this can be done by using the logrank test, this method cannot examine the effects of more than one variable at a time. This difficulty can easily be overcome by using the survival regression model. Examples of the survival regression model are the Cox model, Miller's model, the Buckely James model and the Koul- Susarla-Van Ryzin model. The Buckley James model's performance is comparable with the Cox model and the former performs best when compared both to the Miller model and the Koul-Susarla-Van Ryzin model. Previous comparison studies proved that the Buckley-James estimator is more stable and easier to explain to non-statisticians than the Cox model. Today, researchers are interested in using the Cox model instead of the Buckley-James model. This is because of the lack of function of Buckley-James model in the computer software and choices of diagnostics analysis. Currently, there are only a few diagnostics analyses for Buckley James model that exist. Therefore, this thesis proposes two new diagnostics analyses for the Buckley-James model. The first proposed diagnostics analysis is called renovated Cook's distance. This method produces comparable results with the previous findings. Nevertheless, this method cannot identify influential observations from the censored group. It can only detect influential observations from the uncensored group. This issue needs further investigation because of the possibility of censored points becoming influential cases in censored regression. Secondly, the local influence approach for the Buckley-James model is proposed. This thesis presents the local influence diagnostics of the Buckley-James model which consist of variance perturbation, response variable perturbation, censoring status perturbation, and independent variables perturbation. The proposed diagnostics improves and also challenge findings of the previous ones by taking into account both censored and uncensored data to have a possibility to become an influential observation.</p>

Multivariate data analysis for Raman imaging of a model pharmaceutical tablet

High-density lipoprotein (HDL) functionality has been reported to be associated with coronary artery disease (CAD). However, little is known about the impact of HDL functionality on coronary atherosclerosis. Thirty-eight type 2 diabetic patients with CAD who underwent percutaneous coronary intervention were examined. Coronary atheroma burden and plaque composition of the culprit lesions were assessed using conventional gray-scale and integrated backscatter intravascular ultrasound. HDL-mediated cholesterol efflux capacity (HDL-CEC) and HDL antioxidant capacity, estimated as HDL inflammatory index (HII), were examined. The associations between HDL functionality and coronary plaques were analyzed using multivariate data analysis, including principal components analysis and orthogonal partial least squares (OPLS) models. Percent atheroma volume was correlated with HDL-CEC (r = 0.34, p = 0.04) but not with HII (p = 0.65). The OPLS model demonstrated that the percentage lipid volume was significantly associated with HDL functionality [coefficient (95% confidence interval); HDL-CEC: -0.26 (-0.49, -0.04); HII: 0.34 (0.08, 2.60), respectively]. HII exhibited the highest variable importance in projection score, indicating the greatest contribution. HDL functionality was associated with coronary plaque composition, a key component of plaque vulnerability. Our findings highlight the potential importance of HDL functionality for coronary plaque stabilization.

The post-experiment processing of X-ray Diffraction Microscopy data is often time-consuming and difficult. This is mostly due to the fact that even if a preliminary result has been reconstructed, there is no definitive answer as to whether or not a better result with more consistently retrieved phases can still be obtained. We show here that the first step in data analysis, the assembly of two-dimensional diffraction patterns from a large set of raw diffraction data, is crucial to obtaining reconstructions of highest possible consistency. We have developed software that automates this process and results in consistently accurate diffraction patterns. We have furthermore derived some criteria of validity for a tool commonly used to assess the consistency of reconstructions, the phase retrieval transfer function, and suggest a modified version that has improved utility for judging reconstruction quality.

Compressing movement information via principal components analysis (PCA): Contrasting outcomes from the time and frequency domains