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Metabolomics Data Analysis

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Metabolomic data analysis is a specialized field of bioinformatics and data analysis that focuses on the study of metabolites within biological systems. Metabolites are small molecules, such as sugars, amino acids, and lipids, that serve as the end products of cellular processes and provide a snapshot of an organism's biochemical state. The goal of metabolomic data analysis is to understand the complex interplay of these molecules within a biological system, whether it's a cell, tissue, organism, or even an ecosystem.

Creative Proteomics provide metabolomic data analysis service. Our services go beyond mere analysis; we offer a holistic approach that includes data integration, stringent quality control, customization, and robust data reporting. We understand that each research project is unique, and we are dedicated to tailoring our services to meet your specific objectives. Whether you're delving into metabolic pathways, seeking biomarkers, or conducting time series analyses, our versatile solutions empower you to uncover the hidden secrets within your metabolomic data.

We leverage cutting-edge bioinformatics software and adhere to the highest quality standards, ensuring that the results are both accurate and reliable. We support a wide range of sample types and maintain compliance with regulatory standards for the secure handling of sensitive data.

Workflow of Metabolomic Data Analysis Services

1. Sample Collection and Preparation

Our process begins with the collection of biological samples, including tissues, blood, urine, and more. We employ meticulous sample preparation techniques to ensure the isolation of metabolites while removing unwanted contaminants, ensuring the integrity of your samples.

2. Mass Spectrometry Data Acquisition

Utilizing high-throughput mass spectrometry instruments, such as liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS), we perform precise measurements of metabolites. This technology provides detailed information about the molecular weight and structure of metabolites.

3. Data Preprocessing

Raw data obtained from mass spectrometry instruments are often noisy and contain variations. Our data preprocessing techniques, including baseline correction, normalization, and filtering, ensure the reliability and quality of your data for further analysis.

4. Metabolite Identification

Identifying detected metabolites is a crucial step. Our expert team employs bioinformatics tools and databases to match experimental data to known metabolites, utilizing spectral matching, accurate mass matching, and fragmentation pattern analysis for accurate identification.

5. Quantification

Following identification, we quantify the concentration of each identified metabolite in your samples. This step is essential for understanding the relative abundances of metabolites and their biological significance.

6. Statistical Analysis

Our statistical analysis techniques help identify differences between sample groups and highlight metabolites with significant changes. We provide comprehensive statistical analysis to enable you to draw meaningful conclusions and identify potential biomarkers or key metabolites of interest.

7. Pathway and Network Analysis

To provide a holistic view, we integrate your metabolomic data with other omics data, such as genomics and proteomics. Pathway analysis tools are used to interpret how changes in metabolite levels impact biological processes, allowing you to gain deeper insights into the biological context.

8. Data Visualization

Our tools for visualizing data, which encompass heatmaps, scatterplots, and pathway maps, serve the purpose of rendering results in a visually comprehensible manner. These visual representations are instrumental in simplifying the communication of intricate discoveries and patterns.

9. Validation and Interpretation

Finally, we ensure the validity of your results through additional experiments or cross-referencing with existing knowledge. Our experts help you interpret the data within a biological context.

Metabolomics Data Analysis

Metabolomic Data Analysis Provided by Creative Proteomics

Basic Metabolomic Data Analysis

Analysis TypeDescription
Data Preprocessing- Data normalization and scaling
- Handling missing values
- Data transformation (e.g., log transformation)
- Batch correction (if applicable)
Univariate Analysis- Identification of significant differences between sample groups
- Common tests: t-tests, ANOVA, Wilcoxon tests, etc.
- Visualization with box plots and volcano plots
Multivariate Analysis- Exploring patterns within the entire dataset
- Methods: Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA)
Pathway Analysis- Identifying metabolic pathways affected by changes in metabolites
- Assessing pathway enrichment and impact on cellular processes

Advanced Metabolomic Data Analysis

Analysis TypeDescription
Feature Selection- Identifying relevant metabolites for classification or regression models
- Methods: Recursive Feature Elimination (RFE), L1 regularization (Lasso), etc.
Clustering Analysis- Grouping samples with similar metabolite profiles
- Methods: Hierarchical clustering, k-means clustering, etc.
Time Series Analysis- Investigating changes in metabolite profiles over time
- Visualizing trends and patterns through time course analysis
Network Analysis- Constructing metabolic networks from metabolite data
- Identifying key metabolites and their interactions within the network
Correlation and Regression- Assessing relationships between metabolites and external factors (e.g., clinical variables)
- Linear and nonlinear regression, partial correlation analysis, etc.
Machine Learning- Building predictive models for classification and regression tasks
- Methods: Support Vector Machines (SVM), Random Forest, Neural Networks, etc.
Pathway Impact Analysis- Evaluating the impact of metabolite changes on specific pathways
- Assessing the biological relevance of detected metabolites in the context of pathways
Metabolite Set Enrichment- Identifying sets of metabolites that are significantly altered in specific conditions or pathways
- Assessing enrichment of metabolite sets for biological insights
Biomarker Discovery- Identifying specific metabolites as potential biomarkers for diseases or conditions
- Receiver Operating Characteristic (ROC) analysis, cross-validation, etc.

Principal Component AnalysisPrincipal Component Analysis

Volcano PlotVolcano Plot

Heatmap AnalysisHeatmap Analysis

1. Q: How to do further screening for differential metabolites?

A: Screening for differential metabolites is generally performed using a combination of thresholds such as P-value < 0.05 vip=" ">1.

P-values are derived from unidimensional statistical analysis (e.g., T-test). VIP values are derived from multivariate statistical analysis (e.g., OPLS-DA) characterizing the value of the contribution of that variable to the difference between the two groups.

There is also a practice of using P-values <0.05 logfc="">1 or logFC<-1) [FC=Fold change] for differential screening, but both P-values and FC values are sourced from univariate statistical analysis.

To do further screening on this basis, there are several approaches.

(1) P-value < 0.05, ranking the VIP values (the larger the VIP value, the more significant the differential metabolite).

(2) VIP>1, to rank the P values (the smaller the P value, the more significant the differential metabolite).

(3) Within the range of P values <0.05 vip=" ">1, do ranking on logFC values (the larger the logFC greater than 1, the more significant; the smaller the logFC less than -1, the more significant).

Restricted to stricter base screening conditions, e.g., p-value <0.01& vip=" ">2.

2. Q: What to do if you can't find differential metabolites during screening?

If common screening criteria (VIP > 1 and P < 0.05) don't yield any differential metabolites, you can start by widening the threshold, such as VIP > 1 and P < 0.1. If you still can't identify differential metabolites, you can perform univariate statistical analysis on the detected substances, using criteria like FC > 1.5 and P < 0.05. This analysis compares the expression values of individual metabolites among different sample groups and visualizes the differences using a volcano plot.

3. Q: Once you've identified differential metabolites, how do you verify their functions?

First, you need to conduct qualitative and quantitative validation. This typically involves using triple quadrupole mass spectrometry for targeted validation to obtain absolute quantitative information. Additionally, based on preliminary metabolic pathway analysis, if you know which regulatory pathway the differential metabolite belongs to, you can perform functional validation on the molecules within that pathway. Furthermore, you can complement the analysis by incorporating data from other omics approaches such as transcriptomics and proteomics.

4. Q: What is the volcano chart and what is it for?

A: The volcano map mainly shows the information of two dimensions, P-value and Fold-Change. All these information is closely related to the screening of differential metabolites, so it shows the distribution profile of differential metabolites in all substances.

5. Q: What is a Venn diagram? What is the use of it?

A: A Venn diagram is used to show all possible logical relationships between a finite set of different.

6. Q: What do R2 and Q2 mean respectively?

A: R2X (for PCA) or R2Y (for PLS-DA) indicates the proportion of the data variance or variance that can be explained by the current model, indicating the goodness of fit (the fit). Q2 indicates the proportion of the data variance that can be predicted by the current model, i.e., the prediction rate, indicating the predictive power of the current model.

7. Q: What is the difference between PLS-DA and OPLS-DA models?

A: OPLS-DA has an additional positive exchange calculation than PLS-DA, which filters out signals that are not relevant to the model classification. For example, if the difference between groups is relatively small and the difference within groups is relatively large, the PLS-DA VIP is used to screen out what may be the within-group difference variable, which is easily misleading. OPLS-DA is an upgraded version of PLS-DA.

8. Q: Permutation test judgment criteria?

A: The replacement test is the randomization test or re-randomization test. The usual criteria are R2<0.3 and Q2<0.05, but sometimes the sample biology is too small to meet the requirements, so only the slope of the regression line needs to be positive.

9. Q: Why is the comparative analysis only two by two?

A: Because in the search for differentials it is determined whether the substance is a differential based on the difference in content. The amount of a substance in one group rises/declines relative to the other group, while the amount of change relative to the other two groups cannot be calculated at the same time.

10. Q: Can the sample sizes be different for the two comparison groups?

A: Yes, only the minimum number of biological replicates in each group is required to be met.

11. Q: Can data from multiple platforms be integrated into one piece for PCA?

A: Yes.

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