Analysis
Step by Step Analysis
Preprocessing
To perform quantitative and qualitative analysis of LC-MS data, users convert LC-MS raw data into a two-dimensional table through methods such as peak extraction, peak alignment, and RT correction of mass spectrometry data by "Quantitative analysis" function. Annotate metabolites from public metabolite libraries (HMDB, METLIN, MASSBANK, etc.) be used the "Annotation". According to the user's data content, primary identification (m/z) and secondary identification (MS/MS) are performed.
Data clean
To perform data cleaning on the two-dimensional tables (m/z, rt, intensity, etc.) during the quantitative process, including background removal; NA removal and filling; data normalization; RSD filtering; batch correction and outliers detection. Users select corresponding analysis methods and parameters based on their own data characteristics.
Statistical Analysis
Statistical Analysis plays a pivotal role in finding key metabolomic signatures and distinguishing between sample groups, and providing insights into the underlying biological variations contributing to the observed metabolic shifts. Univariate analysis, including two-sample and multi-sample comparisons, enables the identification of individual metabolites exhibiting significant changes. On the other hand, multivariate analysis such as Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) offer a holistic perspective by capturing patterns across the entire metabolomic profile.
Function Analysis
Functional Analysis encompasses various methods to explore the biological significance of metabolic changes. This includes correlation analysis to identify relationships between metabolites, linear regression analysis to model quantitative associations, logistic regression analysis for categorical outcomes, survival analysis employing Kaplan-Meier (K-M) analysis and Cox proportional hazards models analysis , and pathway enrichment analysis methods such as Over-Representation Analysis (ORA) and Quantitative Enrichment Analysis (QEA).
One Click Analysis
Data Clean + Statistical Analysis
To perform quantitative and qualitative analysis of LC-MS data, users convert LC-MS raw data into a two-dimensional table through methods such as peak extraction, peak alignment, and RT correction of mass spectrometry data by "Quantitative analysis" function. Annotate metabolites from public metabolite libraries (HMDB, METLIN, MASSBANK, etc.) be used the "Annotation". According to the user's data content, primary identification (m/z) and secondary identification (MS/MS) are performed.