Dirichlet-multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data

Area of Science:

• Molecular ecology
• Statistical modeling
• Bioinformatics

Background:

• Molecular ecology studies often involve analyzing count data representing relative feature abundances, such as microbial taxa or gene transcripts.
• The multinomial distribution models the sampling process, while replicate samples inform an underlying Dirichlet distribution, forming a Dirichlet-multinomial model (DMM).
• While DMM has been described, its performance and limitations require thorough evaluation.

Purpose of the Study:

• To assess the efficacy of Dirichlet-multinomial modeling (DMM) in detecting differential abundance in molecular ecology count data.
• To compare the performance of three computational methods (Hamiltonian Monte Carlo, variational inference, Gibbs Markov chain Monte Carlo) for implementing DMM.
• To evaluate DMM's ability to identify shifts in relative abundances between experimental groups.

Main Methods:

• Simulated count data were used to quantify DMM's power in detecting proportion differences between treatment and control groups.
• Three computational approaches—Hamiltonian Monte Carlo (HMC), variational inference (VI), and Gibbs Markov chain Monte Carlo—were employed to implement DMM.
• The sensitivity of DMM was further tested using a published dataset of lung microbiome compositions.

Main Results:

• DMM demonstrated superior ability to detect shifts in relative abundances compared to analogous statistical tools, with a low false positive rate.
• Among the implementation methods, HMC yielded the most accurate abundance estimates, while VI offered the highest computational efficiency.
• Analysis of lung microbiome data revealed DMM identified potentially pathogenic bacterial taxa more abundant in children who aspirated foreign material, differences missed by other methods.

Conclusions:

• Dirichlet-multinomial modeling (DMM) shows significant potential as a robust statistical method for molecular ecology, enhancing the detection of subtle changes in compositional data.
• HMC and VI are viable computational strategies for DMM, offering trade-offs between accuracy and efficiency.
• DMM's application to real-world data, such as lung microbiomes, highlights its capacity to uncover biologically relevant microbial shifts that may be overlooked by conventional approaches.