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DNA methylation analysis using bisulfite sequencing data
Published in Altuna Akalin, Computational Genomics with R, 2020
Setting the plot=FALSE will return a dendrogram object which can be manipulated by users or fed in to other user functions that can work with dendrograms.
Acinetobacter — Microbiology
Published in E. Bergogne-Bénézin, M.L. Joly-Guillou, K.J. Towner, Acinetobacter, 2020
Patterns can be based on minimal inhibitory concentrations or on inhibition zone diameters determined by disc or tablet diffusion. Results based on inhibition zones are usually transformed to ‘resistant’, or ‘susceptible’ depending on breakpoints. This implies that useful information, i.e., the actual size of the zones, is not used. In addition, classification can be difficult if many antibiotics are used. An alternative approach that uses the full information of inhibition zones has been applied in several studies (Dijkshoorn et al., 1993b; Weernink et al., 1995; Horrevorts et al., 1995). In this method (see Appendix IV), diameters of inhibition zones are used for cluster analysis. Thus, isolates are grouped according to their similarity in the antibiogram pattern. The grouping depicted in a dendrogram can be compared to other typing characteristics and the epidemiology of the strains. Application of this method to a study of 25 isolates from five outbreaks (Dijkshoorn et al., 1993b) showed a striking grouping of isolates according to their origin except for one isolate (Figure 3.1). Furthermore, the grouping correlated well with other typing features of the isolates.
Cluster Analysis
Published in Nusrat Rabbee, Biomarker Analysis in Clinical Trials with R, 2020
Hierarchical clustering is another powerful approach to partition clusters for identifying groups in the data set. It is often used to cluster life sciences data that often has hierarchy built-in. Hierarchical clustering has the distinct advantage that any valid measure of distance can be used. In fact, the observations themselves are not required: all that is used is a matrix of distances. It does not require one to prespecify the number of clusters. The result of hierarchical clustering is a tree-based representation of the objects, which is known as dendrogram. Observations can be subdivided into groups by cutting the dendrogram at a desired similarity level resulting into a user-chosen number of clusters. A popular way of doing cluster analysis is to determine k=the # of cluster through PAM or k means and then using Hierarchical Clustering to group the data matrix columns into k clusters.
Hierarchical clustering of prolonged post-concussive symptoms after 12 months: symptom-centric analysis and association with functional impairments
Published in Brain Injury, 2023
Ali Alim-Marvasti, Narayan Kuleindiren, Federico Tiersen, Monika Johal, Aaron Lin, Hamzah Selim, Raphael Rifkin-Zybutz, Mohammad Mahmud
These five clusters represent similarities between the RPQ questions as assessed by similarities in severity perceived by participants, not as intended by the questionnaire or interpreted by the physician. The clusters and their constituent symptoms were obtained unsupervised. The height of each node in the dendrogram in Figure 1 is inversely proportional to the similarity between its constituent symptoms. Therefore, the headache-relatedcluster (in green) and sensitivity-cluster(brown) were associated with the most variance amongst their symptoms, while the three cognitive symptoms (yellow) were the most similar. As migraines are most likely responsible for the symptoms reported in the headache-related and sensitivity clusters, one interpretation of this is that while the severity of the various symptoms that constitute migraines were variable, cognitive symptoms such as forgetfulness, taking longer to think, and lack of concentration were amongst the most semantically equivalent RPQ-16 symptoms.
Applications of monitoring and tracing the evolution of clustering solutions in dynamic datasets
Published in Journal of Applied Statistics, 2023
Muhammad Atif, Muhammad Shafiq, Friedrich Leisch
The hierarchical algorithm either works from top to bottom approach, where initially all observations are grouped into one cluster and then successively split them. Alternatively, consider the individual item as a separate cluster and then merge the most similar ones. Every single step of the HCA can be visualized on a graph known as a Dendrogram. A Dendrogram is a tree-like layout that keeps the memory of the sequence of merging and splitting of the clusters at each step. The y-axis of Dendrogram shows the distance between clusters whereas x-axis represents members of the corresponding cluster. A mere inspection of this plot can determine the optimal number of clusters by drawing a vertical line at an appropriate height [43]. Deciding the number of clusters from the Dendrogram is relatively simple to understand and implement, yet at the cost of its strongest criticism. The Dendrogram does not provide a clear picture in case of large datasets.
Identification of candidate blood biomarkers for the diagnosis of septicaemic melioidosis based on WGCNA
Published in Artificial Cells, Nanomedicine, and Biotechnology, 2022
Li Yin, Yuanyuan Chen, Tingting Fu, Lin Liu, Qianfeng Xia
The adjacency matrix for a scale-free topology network was defined by choosing the soft threshold power 9 which is performed using the pickSoftThreshold function in WGCNA as shown in Figure 1. This value directly affected the construction of the module and the division of genes within the module. This was the lowest value closest to the scale-free network. In our study, the dynamic tree cut method was employed to identify these kinds of genes with similar expression patterns as well as their relevant biological processes and pathways. Next, the modules were clustered according to their eigengenes which stands for their correlations. Here, the cut height of 0.2 is chosen to merge the similar modules (Figure 2(A)). The dendrogram was generated using hierarchical clustering, in which the short vertical line corresponded a gene and the branches corresponded the co-expressed genes (Figure 2(B)).