This is a follow-up for the post: Making Sense of Fermentation Data With Correlation Matrix Heat Maps

Correlation matrix heat maps are powerful—but only if you know what you’re looking at. In this follow-up to our post on using heat maps in fermentation, we’re diving into super quick examples of good practices and common misinterpretations.

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Good Example 1: Strong Biological Insight A clear negative correlation between glucose residual and final titer (-0.78) suggests carbon limitation is affecting productivity. This aligns with expectations: more glucose consumed = more product formed.

Takeaway: When the correlation matches your understanding of the biology or process, it’s a green flag.

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Misleading Example 1: Spurious Correlation A strong positive correlation between agitation speed and OD600 may look meaningful—but it might just reflect increased aeration improving DO levels, which in turn supports growth. If you don’t account for confounding variables (like DO), you may overattribute causality.

Fix: Pair with multivariate regression or DoE to parse the true drivers of the relationship.

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Good Example 2: Data Reduction You notice that airflow and DO are almost perfectly correlated (r = 0.95). This suggests you may not need both variables in your models or plots.

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Misleading Example 2: Non-Linear Relationship Looks Weak A metabolite may peak at one phase in the fermentation and drop later. Correlation with time = 0.02, suggesting no relationship. But in reality, it’s a non-linear trend missed by Pearson’s r.

Fix: Use Spearman correlation or visualize the trend with time series plots.

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Good Example 3: Hypothesis Generation A surprising positive correlation between acetate and OD600 hints at overflow metabolism—possibly a stress response at high cell density.

Takeaway: Use unexpected correlations as clues to investigate further, not definitive conclusions.

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Misleading Example 3: Influence of Outliers One bioreactor run crashed, dropping titer and skewing correlations with multiple process variables. The matrix shows artificially strong or weak r values as a result.

Fix: Pair heatmaps with scatterplots or use robust correlation methods to spot and handle outliers.

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Final Thoughts Correlation heat maps are great exploratory tools, but they’re not the whole story. Think of them as a first pass—a map of where to look next, not where to stop.

Next up: We’ll walk through how to build better multivariate models by using heat maps, PCA, and DoE in tandem.

Keywords: correlation matrix, heat map, fermentation data, interpretation pitfalls, multivariate analysis, Spearman, PCA, DoE