Metabolomics Data Analysis Services
Metabolomics research leads to the handling of complex data sets, which include hundreds to thousands of metabolites. Their comprehensive evaluation requires specialized data analysis that includes cheminformatics, bioinformatics and statistics aspects. Moreover, to better understand the role of each metabolite in the studied condition, metabolomics data must be interpreted. This requires that every chemical information derived from metabolomics analysis must be related to both biochemical causes and physiological consequences[1]. Errors in data analysis may result in inaccurate identification of metabolites, poor quantitation, and ultimately introduce a statistical error and affect the efficiency of the data analysis methods applied.
Thus, strict quality control and preprocessing of data generated from the metabolomics analysis platforms are necessary to retrieve meaningful information before actual data analysis.
Alfa Chemistry has a team of highly skilled Ph.D. and M.S. biochemists with rich research and development experience as well as professional metabolites knowledge. At the same time, Alfa Chemistry has the world’s leading metabolomics data analysis tools to provide customers with professional and reliable services.
Our Service Scope
With our accumulated experience in metabolomics data analysis, Alfa Chemistry is passionate about metabolomics data preprocessing and data statistics.
Data preprocessing mainly includes nuclear magnetic resonance spectroscopy (NMR) data preprocessing and mass spectrometry (MS) data preprocessing. Generally, these preprocessing include noise reduction, retention time correction, peak alignment, peak filtering, peak identification and chromatogram alignment. Alfa Chemistry has acquired many commercial NMR data preprocessing tools, such as Mestrelab Research NMR tool, Amix, and Chenomx NMR Suite. In addition, Alfa Chemistry provides powerful tools for MS data preprocessing, such as MZmine.
A typical metabolomics data statistics consists of two phases: initially, different univariate and multivariate methods are used to generate an overview of the considered data sets and to identify the metabolites that show significant changes under the studied conditions; then, data mining techniques are used to discriminate groups of functionally related metabolites. Typical univariate statistical tools of Alfa Chemistry include: t-test, variance analysis, Wilcoxon rank-sum test, Kruskal-Wallis test or others. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are two of the most frequently used multivariate methods by Alfa Chemistry in metabolomics data statistics.
Our Software Tools
Powerful software tools are essential to address the vast amount and variety of data generated by metabolomics analysis. Alfa Chemistry has many software tools for metabolomics data analysis, including MetaCoreTM, MetaboAnalyst, InCroMAP and 3Omics. Our software tools have the following capabilities.
- Processing of raw spectral data.
- Statistical analysis to find significantly expressed metabolites.
- Connection to metabolites databases for metabolites identification.
- Integration and analysis of multiple heterogeneous “omics” data.
- Bioinformatics analysis and visualization of molecular interaction networks.
Our Advantages
Alfa Chemistry has been deeply engaged in the field of metabolomics data analysis for many years. Our advantages include, but are not limited to the following.
- Alfa Chemistry has competitive service prices to reduce your research costs.
- Alfa Chemistry has a professional technical team to provide high quality services.
- Alfa Chemistry has advanced software tools to make metabolomics data analysis easy.
- Alfa Chemistry has reliable pre-sales and after-sales service that is responsible for the data to the end.
Please contact us if you have questions about metabolomics data analysis. We will provide you with professional and high quality services.
Ordering Process
Reference
- Cambiaghi, A.; et al. Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Briefings in Bioinformatics, 2016, 3(18): 498-510.
Please kindly note that our products and services are for research use only.
