Bioinformatics Analysis of Metabolomics
CD ComputaBio offers bioinformatics analysis of metabolomics to meet the specific needs of different customers. The basic principle of bioinformatics data processing is to convert raw data files into easily interpretable parameters, including ion retention time and ion intensity in each raw data file Measurement values and so on. In addition to these basic features, data processing can also extract other information, such as the isotope distribution of ions. The bioinformatics methods of metabolome provided by CD ComputaBio mainly include analysis of variance, correlation analysis, PLS-DA/OPLS-DA analysis, etc.
Overall solutions
- Metabolomics data quality analysis
CD ComputaBio uses two methods, QC sample spectrum comparison and principal component analysis, to analyze and evaluate the QC sample data in the project experiment. We will analyze the UPLC-QTOF-MS total ion chromatogram of the QC sample obtained from the analysis, and compare the overlapped spectra.
- Cluster Analysis of Differential Metabolites
In order to evaluate the rationality of candidate metabolites, and at the same time to display the relationship between samples more comprehensively and intuitively and the differences in the expression patterns of metabolites in different samples, CD ComputaBio uses qualitatively significant differences in metabolite expression levels to classify each group of samples. Hierarchical Clustering can help us accurately screen marker metabolites and study changes in related metabolic processes.
- Principal component analysis (PCA)
Principal Component Analysis (PCA) is an unsupervised data analysis method in which several comprehensive variables are selected from the analyzed problem to make them reflect as much information of the original variables as possible, so as to achieve the purpose of dimensionality reduction.
- KEGG differential metabolite pathway analysis
CD ComputaBio enriches the obtained differential metabolites for metabolic pathways, and uses the KEGG database as a background to analyze related pathways. Select all metabolites of the same species as the background and analyze the metabolic pathways with P value <0.05.
The univariate analysis method is the simplest and most commonly used experimental data analysis method, which can visually display the significance of metabolite changes between two samples, thereby helping us to screen for potential marker metabolites.
Our services
| Project name |
Bioinformatics analysis of metabolomics |
| Our services |
The high-throughput methods used in metabolomics research will generate a large amount of metabolic analysis-related data, which requires the use of bioinformatics methods for processing. CD ComputaBio provide you with the following services but not limited to:
- Metabolomics data quality analysis
- Cluster Analysis of Differential Metabolites
- Principal component analysis (PCA)
-
KEGG differential metabolite pathway analysis
|
| Sample requirements |
Our bioinformatics analysis of metabolomics service requires you to provide specific requirements. |
| Screening cycle |
Decide according to your needs. |
| Deliverables |
We provide you with raw data and analysis service. |
| Price |
Inquiry |
CD ComputaBio' bioinformatics analysis of metabolomics service can significantly reduce the cost and labor of the subsequent experiments. Bioinformatics analysis of metabolomics service is a personalized and customized innovative scientific research service. Each project needs to be evaluated before the corresponding analysis plan and price can be determined. If you want to know more about service prices or technical details, please feel free to contact us.
* For Research Use Only.
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