BACKGROUND: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for investigating cell abundance changes during tissue regeneration and remodeling processes. Differential cell abundance supports the initial clustering of all cells; then, the number of cells per cluster and sample are evaluated, and the dependence of these counts concerning the phenotypic covariates of the samples is studied. Analysis heavily depends on the clustering method. Partitioning Around Medoids (PAM or k-medoids) represents a well-established clustering procedure that leverages the downstream interpretation of clusters by pinpointing real individuals in the dataset as cluster centers (medoids) without reducing dimensions. Of note, PAM suffers from high computational costs and memory requirements. RESULTS: This paper proposes a method for differential abundance analysis using PAM as a clustering method and negative binomial regression as a statistical model to relate covariates to cluster/cell counts. We used this approach to study the differential cell abundance of human endometrial cell types throughout the natural secretory phase of the menstrual cycle. We developed a new R package -scellpam-, that incorporates an efficient parallel C++ implementation of PAM, and applied this package in this study. We compared the PAM-BS clustering method with other methods and evaluated both the computational aspects of its implementation and the quality of the classifications obtained using distinct published datasets with known subpopulations that demonstrate promising results. CONCLUSIONS: The implementation of PAM-BS, included in the scellpam package, exhibits robust performance in terms of speed and memory usage compared to other related methods. PAM allowed quick and robust clustering of sets of cells with a size ranging from 70,000 to 300,000 cells. https://cran.r-project.org/web/packages/scellpam/index.html . Finally, our approach provides important new insights into the transient subpopulations associated with the fertile time frame when applied to the study of changes in the human endometrium during the secretory phase of the menstrual cycle.

BACKGROUND: Partitioning around medoids (PAM) is one of the most widely used and successful clustering method in many fields. One of its key advantages is that it only requires a distance or a dissimilarity between the individuals, and the fact that cluster centers are actual points in the data set means they can be taken as reliable representatives of their classes. However, its wider application is hampered by the large amount of memory needed to store the distance matrix (quadratic on the number of individuals) and also by the high computational cost of computing such distance matrix and, less importantly, by the cost of the clustering algorithm itself. RESULTS: Therefore, new software has been provided that addresses these issues. This software, provided under GPL license and usable as either an R package or a C++ library, calculates in parallel the distance matrix for different distances/dissimilarities ([Formula: see text], [Formula: see text], Pearson, cosine and weighted Euclidean) and also implements a parallel fast version of PAM (FASTPAM1) using any data type to reduce memory usage. Moreover, the parallel implementation uses all the cores available in modern computers which greatly reduces the execution time. Besides its general application, the software is especially useful for processing data of single cell experiments. It has been tested in problems including clustering of single cell experiments with up to 289,000 cells with the expression of about 29,000 genes per cell. CONCLUSIONS: Comparisons with other current packages in terms of execution time have been made. The method greatly outperforms the available R packages for distance matrix calculation and also improves the packages that implement the PAM itself. The software is available as an R package at https://CRAN.R-project.org/package=scellpam and as C++ libraries at https://github.com/JdMDE/jmatlib and https://github.com/JdMDE/ppamlib The package is useful for single cell RNA-seq studies but it is also applicable in other contexts where clustering of large data sets is required.

This works deals with the concept of liver segmentation by using a priori information based on probabilistic atlases and segmentation learning based of previous steps. A probabilistic atlas is here understood as a probability or membership map that tells how likely is that a point belongs to a shape drawn from the shape distribution at hand. We devise a procedure to segment Perfusion Magnetic Resonance liver images that combines both: a probabilistic atlas of the liver and a segmentation algorithm based on global information of previous simpler segmentation steps, local information from close segmented slices and finally a mathematical morphology procedure, namely viscous reconstruction, to fill the shape. Preliminary results of the algorithm are provided.

Image retrieval has become an interesting and active field due to the increasing necessity of searching and browsing very large image repositories. Images are represented using several kinds of low-level descriptors from which convenient similarity or score functions are computed. Recent work deals with different ways of combining these measures to improve the overall performance of the retrieval system. This paper builds upon previous ideas taken from different contexts to deploy a convenient combination framework that takes into account learning data directly gathered from the users that are supposed to end using the system. The proposal is empirically evaluated and compared to other ways of combining the same measures.

This paper presents a method for detecting abnormalities in spatial arrangements of cells within any tissue that can be described by different sets of relevant points. The method has been applied to the detection of subtle abnormalities in corneal endothelia. Images of this type of tissue can be characterized by two types of points: cell centroids and triple points associated with the apical intersections as it was proposed by Díaz.7 Both types of points jointly considered are modeled using a bivariate spatial point process; then a statistical analysis based on certain distributional descriptors proposed by Doguwa4,9 is carried out to discriminate severe and subtle abnormalities from controls. According to the results, the method constitutes a valid alternative to currently used techniques, which involve statistical descriptions of cell density, cell area and hexagonality, that fail in the detection of these subtle abnormalities.

This paper proposes new descriptors for binary and gray-scale images based on newly defined spatial size distributions (SSD). The main idea consists of combining a granulometric analysis of the image with a comparison between the geometric covariograms for binary images or the auto-correlation function for gray-scale images of the original image and its granulometric transformation; the usual granulometric size distribution then arises as a particular case of this formulation. Examples are given to show that in those cases in which a finer description of the image is required, the more complex descriptors generated from the SSD could be advantageously used. It is also shown that the new descriptors are probability distributions so their intuitive interpretation and properties can be appropriately studied from the probabilistic point of view. The usefulness of these descriptors in shape analysis is illustrated by some synthetic examples and their use in texture analysis is studied. Various cases of SSD and several former methods for texture classification are compared.

This article describes a method to detect and characterise the independent movement of several rigid objects in a sequence of images. No model is assumed for the moving objects, meanwhile a linear model in the image plane is accepted for the pixels of each object, which can be changed without losing the generality. The approach works on each pair of images. Relevant points are detected on them and then each moving object is identified as two clusters of similar relevant points, one per image, that perform a consistent movement. The consistent movement constraint is incorporated into the cluster building phase, which is different from previous approaches. The main property of the proposed method is its robustness, since minimal assumptions are assumed. Results are provided with tests on two different kinds of scenes: a laboratory one and a natural traffic scene.