TaxaNorm

TaxaNorm implements a feature-wise normalization method for microbiome sequencing data based on a zero-inflated negative binomial model that accounts for taxon-specific effects of sequencing depth.

Key Features:

• Taxon-specific normalization: Normalizes taxa counts using size factors that vary across both samples and taxa, rather than applying uniform sample-specific scaling.
• Zero-inflated negative binomial model: Incorporates zero-inflation to better capture the nature of microbiome data, assuming effects of sequencing depth on mean and dispersion vary across taxa.
• Diagnostic tests: Provides two corresponding diagnosis tests to validate the varying sequencing depth effect.

Scientific Applications:

• Microbiome data normalization: Removes variability due to differential sequencing depth before downstream analysis while avoiding over- or under-correction for specific taxa.
• Technical bias correction: Improves performance when correcting technical bias in real datasets compared to existing normalization methods.
• Downstream statistical analysis: Balances power and false discovery control in downstream analyses such as differential abundance testing.

Methodology:

TaxaNorm uses a zero-inflated negative binomial regression framework that introduces sample- and taxon-specific bias correction. The method assumes that sequencing depth effects on mean and dispersion parameters vary across taxa, and incorporates a zero-inflation component to model excess zeros in microbiome count data.