Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

15 pages, 6 figures.-- 19604360 [PubMed]BACKGROUND: The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure. RESULTS: This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset. CONCLUSION: The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologiesResearch supported by grant BIO2008-05067 (Programa Nacional de Biotecnología; Ministerio de Ciencia e Innovación. Spain), awarded to IM. AM was a FPI fellow from Ministerio de Educación y Ciencia (Spain).Peer reviewe

Spatial analysis for the distribution of cells in tissue sections

Spatial analysis, playing an essential role in data mining, is applied in a considerable number of fields. It is because of its broad applicability that dealing with the interdisciplinary issues is becoming more prevalent. It aims at exploring the underlying patterns of the data. In this project, we will employ the methodology that we utilize to tackle spatial problems to investigate how the cells distribute in the infected tissue sections and if there are clusters existing among the cells. The cells that are neighboring to the viruses are of interest. The data were provided by the Medetect Company in the form of 2-dimensional point data. We firstly adopted two common spatial analysis methods, clustering methods and proximity methods. In addition, a method for constructing a 2-dimensional hull was developed in order to delineate the compartments in tissue sections. A binomial test was conducted to evaluate the results. It is detectable that the clusters do exist among cells. The immune cells would accumulate around the viruses. We also found different patterns near and far away from viruses. This study implicates that the cells are interactive with each other and thus present the spatial patterns. However, our analyses are restricted in a planar circumstance instead of treating them in 3-dimensional space. For the further study, the spatial analysis could be carried out in three dimensions.It has been popular to utilize the heuristic methods or the existing methods to discover new findings and explain the mysterious phenomena in other subjects. And it is known that everything in nature relates to each other. In this sense, we could assume that the entire distribution of objects is relative to the locations of individuals. The idea of my work is attempting to explore this spatial relationship existing among cells. In my project, the relationships between individual cells or groups of cells are interesting. Our data is presented like the point cloud. It is doubted that if there are any groups existing among these points and if the viruses have neighbors. The methods are mainly categorized into three parts. The first method is to integrate the similar objects into groups. Here the similar objects could be the ones that are close to each other. The second method analyzes the degree of closeness between objects and looks for the neighbors of viruses. The last method can be used to draw the border of a point cloud, which seems like constructing the boundary of districts. Within each method, we carried out the corresponding case studies. Since similar objects can be grouped together, it is interesting to look into the details of each group. Thus we can know which two objects are similar in the same group. Basically, different types of cells in the same group can be checked and studied. In the closeness analysis, we found that some cells are indeed closer to each other. The constructed border could help us know the shape of point clouds. It can be concluded that the spatial relationship does exist among the cells. Groups of cells can be identified at a large extent. And one certain type of cells could be more attracted by some cells from a local level. However, this study is carried out in a 2D space. Actually, we neglect the real shape of cells which have heights. This could be a more interesting topic in the future