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Feature table pre-processing is the preliminary step shared by all analysis modules. It defines how the feature table data are prepared before running any analysis module of Cosmos-Hub 2.0. Pre-processing affects:
  • Which samples and features are included
  • How microbial features are grouped
  • How technical factors, such as sequencing depth or low-abundance noise, are handled.

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Why is it useful? Is it necessary?

Microbiome feature tables are often sparse, noisy, and affected by technical variation:
  • Some features are detected at very low abundance or in very few samples.
  • Some samples have much higher or lower sequencing depth than others.
  • The reliability of taxonomic or functional assignments depends on the sequencing technology and reference database used.
Not sure if you need it? Check these first.
  • Sequencing depth: are some samples much lower than the rest?
  • Sparsity: what fraction of the table is zeros?
  • Rare features: how many features occur in only a few samples?
Pre-processing is a trade-off:
Done well: more robust, easier-to-interpret results.
Done poorly: loss of real signal or biased results (for example, by removing truly informative rare features or keeping low-depth samples that distort comparisons).

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Pre-processing the microbial feature table

In Cosmos-Hub 2.0, the feature table representing microbial composition in a cohort can be expressed with different metrics:
  • Counts: the number of (adjusted) sequencing reads mapped to an annotated reference, such as a microbial genome, gene, or functional module.
  • Relative abundances: the proportion of reads assigned to a feature out of the total reads in the sample, often expressed as a percentage. Relative abundances within a single profile are compositional, because they sum to 100%.
  • Presence / absence: a binary value indicating only whether a feature is considered detected in a sample, without quantifying how much is present. Presence calls typically depend on a minimum relative abundance threshold.
Feature tables have a simple structure:
  • Rows are features (taxa, genes, or pathways).
  • Columns are samples.
  • Each cell is a value (count, relative abundance, or presence/absence).
Data Table pre-processing acts on all three, and it helps to know which operation changes which component of the feature table: Each analysis module applies its own requirements on top of these pre-processing settings (for example, rarefaction depth for diversity, or specific transformations for machine learning), but they all start from the same pre-processed feature table.