Integrative microbiomics is a tool that allows merging of microbiome datasets from the same set of samples and/or individuals to provide a more holistic view of intra-kingdom interaction. In addition, the tool implements spectral clustering following data integration that allows for a cluster analysis on the merged microbiome datasets.
A similarity metric is a measure or function that defines distances and/or similarities between samples. For example, Bray-Curtis Similarity is a common metric applied in ecology or to human microbiomes.
An algorithm for data merging is a method or process through which the respective datasets are integrated.
The default value is automatically computed based on your uploaded dataset
The below table represents the average silhouette width for different numbers of clusters. The silhouette value represents a measure of how similar an object or individual is to its own cluster (cohesion) when compared to other clusters (separation). Silhouette width ranges from −1 to +1, where higher values indicate that the object or individual is well matched with its own cluster and poorly matched to neighbouring clusters. We recommend not solely depending on average silhouette width (as illustrated below) when selecting your optimal cluster number. Instead, one should consider average silhouette width along with best eigen gap and rotation cost (see optimal number of clusters section) in choosing the optimal cluster number for your dataset.
As a default, an ensemble-based voting of three differing methodologies (1) Best Eigen Gap (2) Rotation cost and (3) Average silhouette width has been used by the tool (as indicated above) to determine the optimal (best) number of clusters for your dataset (see: Supplementary methods from Mac Aogáin, Narayana et al).
This webtool has been developed to allow an integration of microbiome data. When you have used this tool to analyze your data, please cite this methodology as: link.