When performing multi-omics integration or building correlation networks, a common instinct is to split a complex dataset into smaller, pairwise comparisons (e.g., Control vs. Treatment A, Control vs. Treatment B) to analyze them separately.
However, reducing your data to two groups for correlation analysis is often detrimental. Here is a breakdown of why keeping your groups together provides much stronger, more reliable biological insights.
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Correlation Relies on Co-Variation
Correlation analysis evaluates how features (such as a microbial abundance and a gene expression level) move together across different states. To confidently determine if two features are truly correlated, you need to observe their behavior across a wide spectrum of biological variation. Multi-group datasets capture a full gradient of response (e.g., Control, Mild Treatment, Severe Treatment, or multiple time points). If you subset the data down to just two groups, you drastically restrict this variance, making it much harder to detect true, robust biological trends. -
More Conditions = Stronger Statistical Evidence
Having more samples spread across diverse treatments or time points provides far greater evidence of a true relationship. If Feature X and Feature Y correlate cleanly across Control, Half-Dose, and Full-Dose treatments, that correlation is highly robust. If you only look at Control vs. Full-Dose, you lose the resolution of the intermediate states that validate the trend, reducing your overall statistical power. -
How This Impacts multi-omics Integration
Algorithms implemented in OmicsAnalyst (DIABLO, MCIA and MOFA) all fit a single, integrated model across all groups at once. DIABLO extracts features that best discriminate all the study groups simultaneously. Splitting the dataset into separate pairwise analyses forces the algorithm to create fragmented, independent models. This defeats the purpose of the global model and will not properly reflect the overarching multi-group study design.
Best practices
If your goal is to see what is unique to a specific comparison, do not split the dataset for the global integration model. Instead, perform differential expression/abundance analysis for each specific pairwise contrast first. You can then use those uniquely significant features as your feature selection method for targeted downstream network analyses, rather than chopping up the underlying global data structure.