In statistics, correlation is a bivariate analysis that measures the strengths of association between two variables. Correlation analysis is a simple and useful univariate method to test whether two variables are related.
Creative Proteomics performs the following two correlation analysis:
- Identification of features similar to a known biomarker. In this case, researchers are looking for features (metabolites or peaks) showing similarities in their intensity or concentration changes to a feature of interest (co-expression). Correlation analysis can be directly performed against the target feature to identify those peaks or metabolites that are either positively or negatively correlated. Hierarchical clustering method can also be used. Features located in the same cluster as the target feature are most similar in terms of intensity or concentration changes.
- Identification of features following a particular pattern. In this case, researchers are looking for features that have shown particular patterns of changes under multiple (>2) conditions or through a range (>2) of time points. A template matching approach was used to address this situation. Template matching is part of the correlation analysis suite. Either a pre-defined pattern or a new pattern can be used to perform template matching. The template patterns must be specified as a series of numbers corresponding to concentration levels expected in different groups or at different time points.
Creative Proteomics supports many similarity measures, including:
- Euclidean distance,
- Pearson’s correlation,
- Spearman’s rank correlation
- Kendall’s τ-test
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