Clustering software tools | Mass spectrometry-based untargeted metabolomics
Clustering is a well-established technique in the context of gene expression analysis and coexpression studies. Intensity-based clustering by analogy aims to group similar intensity profiles in order to identify interesting groups of marker candidates and visualize them in a convenient way. A major problem with the application of clustering algorithms is that an adequate number of clusters can often not be inferred automatically.
Provides a web-based analytical pipeline for high-throughput metabolomics studies. MetaboAnalyst aims to offer a variety of commonly used procedures for metabolomic data processing, normalization, multivariate statistical analysis, as well as data annotation. The current implementation focuses on exploratory statistical analysis, functional interpretation, and advanced statistics for translational metabolomics studies. This tool is also available as desktop version.
An adaptive multi-seeds based heuristic clustering method that avoids the large memory need for storing seeds and/or distance matrix. MSClust uses a greedy heuristic strategy to build one cluster at a time. Each cluster is expanded from a limited initial set with multi-seeds, where the initial multi-seeds are generated based on an adaptive strategy. Unassigned sequences are then compared to the seeds sequentially. A new sequence is added to the current cluster and removed from the input if the average distance between the sequence and seeds is smaller than the user-defined threshold; otherwise, the sequence is marked as unassigned.
Aims to integrate and analyze metabolomics experiment data. MeltDB is a program that can be applied for the description and analysis of metabolomic experiments. This program hosts over 30 experiments predominantly from gas chromatography-mass spectrometry (GC/MS) measurements. Moreover, this tool includes an API allowing users to evaluate novel methods and algorithms for the preprocessing of metabolomic datasets.
Aims users to detect metabolites by annotation of pathways from cross-omics data. MarVis-Suite serves especially for the extraction, clustering, and visualization of metabolic markers from data originating of non-targeted experiments. It provides interactive desktop user interfaces for interactive inspection of data clusters, and supplies specialized functions for the analysis of data from non-targeted mass spectrometry (MS) experiments.
Integrates algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. CAMERA integrates multiple methods for grouping related features, and uses a dynamic rule table for the annotation of ion species. It is designed to post-process XCMS feature lists, and to collect all features related to a compound into a compound spectrum. For this, a set of algorithms has been implemented in CAMERA, such as the fast retention time-based grouping, but also a graph-based algorithm to integrate the peak shape analysis, isotopic information and intensity correlation across samples. The automatic sample selection avoids poor results if compounds have a low intensity (or are absent) in some samples. The ion species annotation uses a dynamic rule set, and a new strategy to combine spectral information from samples measured in positive and negative ion mode.
Permits users to realize autonomous and real-time analysis of metabolomic data. SimExTargId is an open source R package that provides an autonomous workflow that can also calculate data preprocessing in real-time, thereby alerting the user to signal degradation or loss. This method also facilitates real-time monitoring of liquid chromatography-mass spectrometry (LC-MS) data acquisition.
Extracts pure ion chromatogram (PIC), quantifies metabolites and recognizes pattern. KPIC is based on k-means clustering and takes intensity into consideration. It avoids estimating mass difference tolerance of ions and reduce the number of split signals. This tool shows satisfactory results of feature detection, alignment, grouping, quantitation and pattern recognition. It takes full advantage of profile-based method for alignment.