QCs can be defined in different ways, MetaboAnalyst can use QC data based on different design purposes
- QCs are technical replicates from a pooled sample (combining small aliquots from all samples)
- Used for parameter optimization (spectral processing)
- Used for data filtering (i.e. removing features with high variance or low repeatability in QC data)
- Used for quality checking (i.e. PCA in statistical analysis)
- QCs are internal standards
- Used for sample-specific normalization (QCs contain volumes, dry weights, or cell counts of individual samples); You can do this manually using “Sample-specific normalization”, or include these values as a new feature (i.e. Weights) in the data, and use “Normalization by reference feature” option, then choose “Weights” from the drop down list;
- Used for feature-specific normalization (QCs contain internal standards for each feature). In this case, you need to include these values as new sample(s) in the data, and use "Normalization by a reference sample (if just a single QC). If there are multiple QCs, make sure you put mutiple QCs into one group (i.e. QC), then choose “Normalization by a pooled sample from group:” and then choose “QC” from the drop-down list
- QCs are long-term reference (LTR) samples
- Used for batch effect removal from multiple batches. This function is offered in “Utility”=>" Batch Effect Correction"