Impact of Pre-Processing on Analyses

The same Analysis Module with the same set of parameters can produce widely different results depending on the pre-processing settings. Pre-processing parameters can affect diversity values, distance matrices, clustering patterns, feature importance, and statistical comparisons.

What to look for when setting pre-processing parameters

Several properties of the Data Table determine how strongly pre-processing affects your results. How much these properties matter depends mainly on the type of sequencing data (long-read or short-read 16S, or WGS), the profile type (taxonomic or functional), and the complexity and variability of the sampled environment (e.g., soil samples tend to be more complex, whereas vaginal samples tend to be less complex):

Sparsity: a feature is sparse across the Data Table when it is not detected in most samples of the cohort. Sparse features are typically taxa or genes with extremely low prevalence in the Data Table.
Sequencing depth: how many reads each sample has, and how much this differs between samples in the cohort. Uneven sequencing depth introduces technical bias into comparisons and can be mistaken for a real biological difference in the variable of interest.

Sparsity and Sequencing depth variability depending on the Cosmos-Hub Profiling Workflow

	Suggested Taxonomic Rank (if applicable)	Notes on sparsity depending on profile type	Seq. depth variability per sequencing technology
16S SR Amplicon-sequencing ITS Amplicon Classification	`Genus`	High sparsity Rare and sparse features (OTU, ASVs) might be spurious (PCR, copy-number bias)	Often large, driven by PCR amplification bias and primer efficiency
Long-read Amplicon Profiling	`Species`	High sparsity but rare feature calls have higher confidence Features in the Data Table depend on the EMU database used for classification	Moderate
CHAMP Human Microbiome Taxonomic and Functional Profiling KEPLER Host-Agnostic Taxonomic Profiling Host-Agnostic Functional Profiling AMR/VF Profiling	`Species`	High sparsity for CHAMP and KEPLER taxonomic profiles Lower sparsity for functional profiles generated from module-based databases (GMM, GBM, MetaCyc) Higher sparsity for gene-based profiles (CAZymes, GO, Enzyme Commission, AMR/VF)	Moderate, driven by host-DNA content and input biomass

Pre-processing impact on Analysis Module outcomes

Analysis	Sensitivity	Pre-processing suggestions	Data-type note
Alpha diversity	High	Control for uneven depth when comparing Observed Richness. Rarefaction is not needed when comparing Alpha-diversity evenness indexes (Shannon, Simpson) between different groups.	Functional profiles are often more stable than taxonomic ones because of functional redudancy across taxa. ASV-level 16S richness can be inflated by spurious rare taxa.
Beta diversity / PCA Explorer	Medium to High	Rarefaction is suggested when using Beta diversity metrics that are computed on presence-absence data (e.g: Jaccard)	Functional profiles often separate more subtly than taxonomic ones in the ordination plots, reflecting functional redundancy across taxa.
Heatmap / Stacked Bar Charts	Low	Pre-processing not needed.
ML Classification & regression	Medium to High	Filtering for Prevalence can reduce the risk of ML model learning from noise and sparse features.	High-dimensional WGS functional data usually benefits most from filtering and aggregation.
Univariate comparison	High	Filtering for Prevalence reduces the multiple-testing burden arising from sparse features and can improve power.	Sparse taxonomic features are usually more affected by pre-processing than broader, module-based functional profiles

References

Zhou R et al. Data pre-processing for analyzing microbiome data. Brief Bioinform / NIH review. 2023;24(6)
Busato S et al. Compositionality, sparsity, spurious heterogeneity, and other pitfalls in microbiome machine learning. Patterns. 2022;3(12)

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Analysis Projects

Impact of Pre-Processing on Analyses

What to look for when setting pre-processing parameters

Sparsity and Sequencing depth variability depending on the Cosmos-Hub Profiling Workflow

Pre-processing impact on Analysis Module outcomes

References

​What to look for when setting pre-processing parameters

​Sparsity and Sequencing depth variability depending on the Cosmos-Hub Profiling Workflow

​Pre-processing impact on Analysis Module outcomes

​References

What to look for when setting pre-processing parameters

Sparsity and Sequencing depth variability depending on the Cosmos-Hub Profiling Workflow

Pre-processing impact on Analysis Module outcomes

References