Analysis and Association in Microbiota Network (SparCC, Ccrepe, MB y Glasso)

Evolutionary studies of microbial associations currently highlight the relevant techniques that characterize the degree of correlation and interaction that exist within microbiotic communities, where traditional correlation and clustering techniques fail due to the compositional correlation and high dimensionality of the count that exists in the data and whose representation is given through sparse matrices; for this we intend to examine through real data different methods for the construction and analysis of microbiotic networks through modern techniques (SparCC, Ccrepe, MB and Glasso) that allow us to create associations in high dimensional data containing compositional correlations, which allow us to identify the real degree of correlation, concurrence and segmentation between the different communities as well as describe the type of complex network that exists at the microbiota level; in such a way that we can characterize and infer graphically the internal interaction at the microbiotic level between different types of patients at the level of their intestinal microbiota and also determine which microorganism has a greater connectivity at the level of any disease or if there is any pattern of association.