How to Perform Normalization and Peak Alignment Using XCMS for Metabolomics Data Preprocessing
When performing metabolomics data preprocessing with XCMS, normalization and peak alignment are critical steps. Normalization eliminates technical variation between samples, allowing for comparison and analysis of metabolite peak intensities across different samples. Peak alignment addresses the drift and shift of metabolite peaks across samples, ensuring consistent peak positions for the same metabolite in different samples. The general steps are as follows:
1. First, use XCMS to extract peaks from raw metabolomics data, obtaining the metabolite peak list and peak intensities for each sample.
2. Next, select an appropriate normalization method based on experimental design and data characteristics, such as TIC normalization or IS normalization.
3. For TIC normalization, calculate the total ion intensity for each sample and divide it by the average or median total ion intensity across all samples.
4. For IS normalization, choose a suitable internal standard and calculate the intensity of each metabolite peak divided by the intensity of the internal standard.
5. Then, choose an appropriate peak alignment method based on experimental design and data characteristics, such as retcor, obiwarp, or groupcorr.
6. For retcor and obiwarp, use XCMS functions to perform peak alignment and correct the peak positions.
7. For groupcorr, group the samples, perform peak alignment for each group, and then apply the peak alignment results to other samples.
8. Finally, perform subsequent statistical and bioinformatics analyses on the normalized and peak-aligned data.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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