MatrixAnalyzer allows you to view the individual target component abundances in each of your processed samples.
The data can be normalized to an internal standard component and/or to sample weight information if required. Results can be reported based on component abundance derived from height or area and from the sum of the component ions, specified component ions or base peak ion depending on specific requirements. The data can be viewed in a number of ways. A standard tabular view includes delta RT/RIs, average values, standard deviation, a component coverage percent and p-values. Further processing and reporting of principal component analysis is also available. MatrixAnalyzer is used extensively in metabolomics but the underlying workflow can be used in many other applications where sample differences are being investigated.
Class information can be incorporated in to your data processing sequence to assist with the interpretation of the PCA results and the component information including abundance can be exported to third party software for further processing. As an example, the PCA report shown below represents 12 derivatization batches for a mixture of metabolite standards. The PCA shows that batch 4 is clearly distinct from the other batches but with a total explained variance of 9.5% for PCs 1 and 2, the batches appear fairly similar.
PCA is used in many applications were differences between samples are being investigated. Additional visualization tools in MatrixAnalyzer allow for comparison of response and retention times for components across all samples and classes further assisting with the interpretation of the overall results.
Figure 1. PCA score plot from results with log transformation