The diagnosis and treatment of prostate cancer (PCa) is a major health-care concern worldwide. This cancer can manifest itself in many distinct forms and the transition from clinically indolent PCa to the more invasive aggressive form remains poorly understood. It is now universally accepted that glycan expression patterns change with the cellular modifications that accompany the onset of tumorigenesis. The aim of this study was to investigate if differential glycosylation patterns could distinguish between indolent, significant, and aggressive PCa. Whole serum N-glycan profiling was carried out on 117 prostate cancer patients’ serum using our automated, high-throughput analysis platform for glycan-profiling which utilizes ultra-performance liquid chromatography (UPLC) to obtain high resolution separation of N-linked glycans released from the serum glycoproteins. We observed increases in hybrid, oligomannose, and biantennary digalactosylated monosialylated glycans (M5A1G1S1, M8, and A2G2S1), bisecting glycans (A2B, A2(6)BG1) and monoantennary glycans (A1), and decreases in triantennary trigalactosylated trisialylated glycans with and without core fucose (A3G3S3 and FA3G3S3) with PCa progression from indolent through significant and aggressive disease. These changes give us an insight into the disease pathogenesis and identify potential biomarkers for monitoring the PCa progression, however these need further confirmation studies.

This chapter presents an introduction to Markovian modelling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapters, Markovian models are probabilistic models, focusing on the transitions between states instead of studying sequences as a whole. The chapter provides an introduction to this method and differentiates between its most common variations: first-order Markov models, hidden Markov models, mixture Markov models, and mixture hidden Markov models. In addition to a thorough explanation and contextualisation within the existing literature, the chapter provides a step-by-step tutorial on how to implement each type of Markovian model using the R package seqHMM. The chapter also provides a complete guide to performing stochastic process mining with Markovian models as well as plotting, comparing and clustering different process models.

Clustering is a collective term which refers to a broad range of techniques aimed at uncovering patterns and subgroups within data. Interest lies in partitioning heterogeneous data into homogeneous groups, whereby cases within a group are more similar to each other than cases assigned to other groups, without foreknowledge of the group labels. Clustering is also an important component of several exploratory methods, analytical techniques, and modelling approaches and therefore has been practiced for decades in education research. In this context, finding patterns or differences among students enables teachers and researchers to improve their understanding of the diversity of students—and their learning processes—and tailor their supports to different needs. This chapter introduces the theory underpinning dissimilarity-based clustering methods. Then, we focus on some of the most widely-used heuristic dissimilarity-based clustering algorithms; namely,K-means,K-medoids, and agglomerative hierarchical clustering. TheK-means clustering algorithm is described including the outline of the arguments of the relevant R functions and the main limitations and practical concerns to be aware of in order to obtain the best performance. We also discuss the relatedK-medoids algorithm and its own associated concerns and function arguments. We later introduce agglomerative hierarchical clustering and the related R functions while outlining various choices available to practitioners and their implications. Methods for choosing the optimal number of clusters are provided, especially criteria that can guide the choice of clustering solution among multiple competing methodologies—with a particular focus on evaluating solutions obtained using different dissimilarity measures—and not only the choice of the number of clustersKfor a given method. All of these issues are demonstrated in detail with a tutorial in R using a real-life educational data set.

Sequence analysis is an increasingly popular approach for analysing life courses represented by ordered collections of activities experienced by subjects over time. Here, we analyse a survey data set containing information on the career trajectories of a cohort of Northern Irish youths tracked between the ages of 16 and 22. We propose a novel, model-based clustering approach suited to the analysis of such data from a holistic perspective, with the aims of estimating the number of typical career trajectories, identifying the relevant features of these patterns, and assessing the extent to which such patterns are shaped by background characteristics. Several criteria exist for measuring pairwise dissimilarities among categorical sequences. Typically, dissimilarity matrices are employed as input to heuristic clustering algorithms. The family of methods we develop instead clusters sequences directly using mixtures of exponential-distance models. Basing the models on weighted variants of the Hamming distance metric permits closed-form expressions for parameter estimation. Simultaneously allowing the component membership probabilities to depend on fixed covariates and accommodating sampling weights in the clustering process yields new insights on the Northern Irish data. In particular, we find that school examination performance is the single most important predictor of cluster membership.