Quantifying the impact of tree choice in metagenomics differential abundance studies with

Antoine Bichat1,2, Mahendra Mariadassou3, Jonathan Plassais2 and Christophe Ambroise1

1. LaMME - Université d’Évry-Val-d’Essonne; 2. Enterome; 3. MaIAGE - INRA

Microbiota

Data: taxonomy and abundance

Phylum Class Order Family Genus S001 S002 S003 S004 S005
Actinobacteria Coriobacteriia Coriobacteriales Atopobiaceae Atopobium 84 0 12 54 0
Actinobacteria Coriobacteriia Eggerthellales Eggerthellaceae Eggerthella 2 0 0 7 0
Bacteroidetes Bacteroidia Bacteroidales Prevotellaceae Prevotella 525 7 134 753 0
Firmicutes Bacilli Lactobacillales Lactobacillaceae Lactobacillus 88 1770 1490 119 2136
Firmicutes Bacilli Lactobacillales Streptococcaceae Streptococcus 0 0 138 4 0
Firmicutes Negativicutes Veillonellales Veillonellaceae Dialister 152 4 2 192 0
Firmicutes Negativicutes Veillonellales Veillonellaceae Megasphaera 402 0 4 102 0
Fusobacteria Fusobacteriia Fusobacteriales Leptotrichiaceae Sneathia 302 0 35 272 0

Objectives

Workflow

Hierarchical False Discovery Rate

The z-scores \(\mathbf{z} = \Phi^{-1}(\mathbf{p})\) are smoothed using the following hierarchical model:

\[\mathbf{z} | \mathbb{\mu} \sim \mathcal{N}_n \left( \mathbb{\mu}, \sigma^2 \mathbf{I}_m \right) \qquad \mathbf{\mu} \sim \mathcal{N}_m\left(\gamma \mathbf{1} , \tau^2 \mathbf{C}_{\rho} \right)\]

where \(\mathbf{C}_{\rho} = \left(\text{exp} (−2\rho \mathbf{D}_{i,j} )\right)\) with \(\mathbf{D}\) the patristic distance matrix between taxa from the tree. By applying Bayes’s formula:

\[\mathbf{z} \sim \mathcal{N}_m \left(\gamma \mathbf{1},\tau^2 \mathbf{C}_{\rho} + \sigma^2 \mathbf{I}_m\right)\]

\[\mathbb{\mu}^* = \left(\mathbf{I}_m + \frac{\sigma_0^2}{\tau_0^2} \mathbf{C}_{\rho_0}^{-1}\right)^{-1}\left(\frac{\sigma_0^2}{\tau_0^2} \mathbf{C}_{\rho_0}^{-1}\gamma_0 \mathbf{1} + \mathbf{z}\right)\] Finally, a permutation-based FDR control is applied on \(\mathbb{\mu}^*\)

Take-home message

Results

Poster made with pagedown

References

📄 Xiao, Jian, Hongyuan Cao, and Jun Chen. False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing. Bioinformatics 33.18 (2017): 2873-2881.

📄 Bokulich, Nicholas A., et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Science translational medicine 8.343 (2016): 343ra82-343ra82.

📄 Opstelten, Jorrit L., et al. Gut microbial diversity is reduced in smokers with Crohn’s disease. Inflammatory bowel diseases 22.9 (2016): 2070-2077.

Contact Information



abichat.github.io
antoinebichat
@_abichat
@abichat