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The Problem
Microbiome differences often correlate with age, diet, medication, and geography. If those are also correlated with disease or outcome, false conclusions arise.
Why It Happens
Because real human cohorts are messy. You can’t always control everything. But without proper modeling, associations become spurious.
How It Shows Up
Example: patients and controls differ - but also come from different hospitals or regions. Microbiome signal actually reflects geography or BMI, not disease.
Common Mistakes
- No covariate adjustment in models
- Underpowered subgroup analysis
- Drawing conclusions from confounded comparisons
What We Do Instead
We model covariates explicitly - using mixed effects or multivariate models. We do subgroup analysis when possible. And we’re cautious when groups differ in multiple axes.
The Problem
Microbiome data are sparse, high-dimensional, and compositional. Machine learning models often show high accuracy in training - but fail to generalize.
Why It Happens
People use small cohorts (e.g. n=30) with thousands of taxa. Cross-validation is poorly implemented. Feature selection leaks into test data.
How It Shows Up
AUC of 0.95 in published paper. But when retried on new data, accuracy drops to random.
Common Mistakes
- Not separating feature selection from validation
- Reporting inflated metrics from repeated CV
- Ignoring compositional nature of features
What We Do Instead
We use rigorous nested CV. We prefer interpretable models. And we always validate findings against known biology, not just metrics.
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The Problem
Linking microbiome data to host transcriptome or proteome is complex. Naive correlation produces misleading results.
Why It Happens
People often ignore that microbiome data is compositional, and host data is continuous. Without proper transformation and model design, spurious links emerge.
How It Shows Up
Studies claim “this gene correlates with Bacteroides,” but don’t adjust for data structure, batch effects, or sparsity.
Common Mistakes
- Using Pearson/Spearman correlations directly
- No control for batch or covariatesV
- Forcing links when alignment is poor (e.g. different sample sets)
What We Do Instead
We use multivariate models (e.g. sPLS-DA, MOFA), carefully transformed inputs, and matched designs. Integration is only meaningful if metadata, preprocessing, and normalization are aligned.
The Problem
Microbiome studies are prone to overinterpretation. A marginal increase in a low-abundance genus becomes the headline.
Why It Happens
Pressure to find a “story.” Reviewers and editors expect named taxa, mechanisms, and associations - even when data is ambiguous.
How It Shows Up
Papers claiming Lactobacillus protects against depression, or Enterococcus causes obesity - from small, observational cohorts
Common Mistakes
- Ignoring effect size and uncertainty
- Reporting uncorrected p-values
- Overstating correlation as causation
What We Do Instead
We report uncertainty clearly. We avoid causality claims unless experimentally supported. And we prefer to say “no strong association found” than force a narrative.
All of these pitfalls are real. We’ve seen them in papers, in conference talks, and in datasets clients sent us with reviewer comments in panic. Some are technical, some are statistical, and some are psychological - wanting to see a story where there’s only noise.
Our job as bioinformaticians is not to push buttons and draw charts. It is to ask: what does this result actually mean? Where could it go wrong? And would I still believe it if it were someone else’s paper?
That mindset - not the tool or the pipeline - is what prevents failure in microbiome data analysis and metagenomic interpretation.
Ready to avoid microbiome analysis traps? Our senior bioinformaticians partner with researchers to build pipelines that stand up to scrutiny and deliver actionable insights. Request a free consultation →