Rabies positive cells were assigned to one of the two components (i.e., superficial and deep layers) created by the clustering algorithm. Data is presented as the proportion of all rabies labelled cells in each
creating a weighted connectivity matrix to visualize brain-wide projection patterns (Fig.2c, Source Data Fig. 2). Outputs from MOp-ul predominantly target isocortex, striatum and thalamus (44.9, 29.0 and 8.1% of total axon density, respectively) with less axon in midbrain, medulla ...
With the popularity of unsupervised machine learning methods, the classical DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm (Ester et al., 1996) has recently drawn much attention. In practice, the DBSCAN algorithm has been successfully applied in cloud identific...
The development and application of next-generation sequencing (NGS) technology and high-density SNP chips, as well as advanced statistical methods and bio-informatics tools have substantially improved the ability to detect genomic selected regions in livestock and poultry breeds. Selective signal detection...
The clustering phenomenon observed by EM after chemical fixation in DS LCLs and fibroblasts was very rare after HPF, indicating that it could be an artefact due to increased density of endosomes, and to the use of fixative that has been shown to favor the clustering of synaptic vesicles [48,...
Fig. 21. Scatterplot of A2u parameters of monoporphyrin-lanthanoid coordination compounds (blue) and bis-porphyrinoids (green and orange), overlaid on a kernel density estimation plot. The increased density and clustering of similar compounds indicates that there are three distinct conformational cl...
Several of these constructions have good stability properties or good asymptotic behavior. However, as explained in [6], all of the known 1-parameter persistence strategies for handling outliers or variations in density share certain disadvantages: First, they all depend on a choice of a parameter...
Since the number of dimensions in our resultant matrix is high (780 features), we use a technique called HDBSCAN (hierarchical density-based spatial clustering of applications with noise) that identifies key clusters in the kinship space (given in Table 1). We set two parameters in the HDBSCAN...
miRNAs and gene set scores were clustered using the hierarchical clustering algorithm, using Euclidean distance as distance metrics. The stability and statistical significance of the clusters were evaluated using the bootstrapping analysis (n = 10.000) implemented in th pvclust R package. ...
Using a diffusion map embedding algorithm, we identified gradient components, which estimated the low-dimensional embedding from the high-dimensional connectivity matrix. The algorithm is controlled by parameters α and t, where α controls the influence of the density of sampling points on the ...