Correlation Tree Analysis

This repository contains analysis done in the paper Incorporating Phylogenetic Information in Microbiome Differential Abundance Studies Has No Effect on Detection Power and FDR Control (Frontiers in Microbiology).

Some results might be slightly different from those in the article due to seed choice or limited number of replications in simulations.

Structure of the repository

Forest

Each subfolder is named after the studied dataset and contains several files:

a script that performs the analysis (analysis_dataset.R),
several .png figures corresponding to different visualizations and distances,
saved intermediary results to avoid long recomputation (.rds),
evetually biom (.biom) or newick (.nwk) files for input data such as abundnace tables or phylogeny.

Each R script:

loads the data,
generates the forest of trees,
computes pairwise distances between trees (with BHV and RF),
performs PCoAs (one per distance),
draws individual plots:
1. distance from each tree to the correlation tree,
2. projection of the forest on the two first axis.

Real Datasets

This folder contains scripts to do differentialy abundance studies on datasets Chaillou, Chlamydiae and Zeller (genus and MSP level).

As previously, it contains the R script, plots (.png), intermediary results (.rds) and eventually biological input data (.biom and .nwk).

Each script compares correlation and taxonomy (or phylogeny) in term of detected species. It uses hierarchical FDR (from structSSI) for both Chaillou and Chlamydiae datasets, and z-scores smoothing (from StructFDR) for datasets from Zeller.

Simulations

This folder contains scripts that simulates datasets according to parametric (P) and non parametric (NP) schemes.

The parametric simulation mimics the scheme used in Xiao, Cao, and Chen (2017). It fits a negative-binomial Dirichlet-Multinomial (DM) distibution on Wu dataset and generates new differentially abundance datasets.

The non-parametric simulation uses a real dataset from Brito et al. (2016). It generates differentially abundant species by applying specified fold-change in half of the samples.

For each set of parameters, at least 600 replication were done. As this is really time consumming, preprocessed data are saved in .rds files.

Figures

This folder contains the scripts used to produce every figure in the article. Each script takes its input in the folders of the repository and is named after the Figure number in the article (Figure_1.R, Figure_S1.R, etc).

Datasets

Dataset	Biome	Rank	Taxa	Samples	Analysis	Publication
Chlamydiae	Varied	OTU	21	26	Tree & DA	Caporaso et al. (2011)
Ravel	Vaginal	Genus	40	396	Tree	Ravel et al. (2011)
Wu	Gut	OTU	400	98	Simulations	Wu et al. (2011)
Zeller	Gut	Genus	119	199	Tree & DA	Zeller et al. (2014)
Zeller MSP	Gut	MSP	878	199	DA	Zeller et al. (2014)
Chaillou	Food	OTU	499/97	64	Tree & DA	Chaillou et al. (2015)
Brito	Gut	OTU	77	112	Simulations	Brito et al. (2016)

Reproducibility and packages

Analysis ran under R version 3.6.1.

Package	Version
ape	5.3
biomformat	1.12.0
broom	0.5.4
correlationtree	0.0.1
cowplot	1.0.0
curatedMetagenomicData	1.14.1
distory	1.4.3
dplyr	0.8.4
evabic	0.0.1
forcats	0.4.0
furrr	0.1.0
ggplot2	3.2.1
ggstance	0.3.3
ggtree	1.16.6
glue	1.3.1.9000
igraph	1.2.4.2
janitor	1.2.1
phyloseq	1.28.0
purrr	0.3.3
readr	1.3.1
scales	1.1.0
stringr	1.4.0
StructFDR	1.3
structSSI	1.1.1
tidyr	1.0.2
tidyverse	1.3.0
yatah	0.1.0

To install non-CRAN packages, run these lines:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("biomformat")
BiocManager::install("curatedMetagenomicData")
BiocManager::install("ggtree")
BiocManager::install("phyloseq")
BiocManager::install("multtest") # dependency for structSSI

if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")
remotes::install_github("abichat/correlationtree")
remotes::install_url("https://cran.r-project.org/src/contrib/Archive/structSSI/structSSI_1.1.1.tar.gz") # archived from CRAN

Bibliography

Brito, I. L., S. Yilmaz, K. Huang, L. Xu, S. D. Jupiter, A. P. Jenkins, W. Naisilisili, et al. 2016. “Mobile Genes in the Human Microbiome Are Structured from Global to Individual Scales.” Nature 535 (7612): 435–39. https://doi.org/10.1038/nature18927.

Caporaso, J. G., C. L. Lauber, W. A. Walters, D. Berg-Lyons, C. A. Lozupone, P. J. Turnbaugh, N. Fierer, and R. Knight. 2011. “Global Patterns of 16S rRNA Diversity at a Depth of Millions of Sequences Per Sample.” Proceedings of the National Academy of Sciences 108 (Supplement_1): 4516–22. https://doi.org/10.1073/pnas.1000080107.

Chaillou, Stéphane, Aurélie Chaulot-Talmon, Hélène Caekebeke, Mireille Cardinal, Souad Christieans, Catherine Denis, Marie Hélène Desmonts, et al. 2015. “Origin and Ecological Selection of Core and Food-Specific Bacterial Communities Associated with Meat and Seafood Spoilage.” The ISME Journal 9 (5): 1105–18. https://doi.org/10.1038/ismej.2014.202.

Ravel, J., P. Gajer, Z. Abdo, G. M. Schneider, S. S. K. Koenig, S. L. McCulle, S. Karlebach, et al. 2011. “Vaginal Microbiome of Reproductive-Age Women.” Proceedings of the National Academy of Sciences 108 (Supplement_1): 4680–7. https://doi.org/10.1073/pnas.1002611107.

Wu, G. D., J. Chen, C. Hoffmann, K. Bittinger, Y.-Y. Chen, S. A. Keilbaugh, M. Bewtra, et al. 2011. “Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes.” Science 334 (6052): 105–8. https://doi.org/10.1126/science.1208344.

Xiao, Jian, Hongyuan Cao, and Jun Chen. 2017. “False Discovery Rate Control Incorporating Phylogenetic Tree Increases Detection Power in Microbiome-Wide Multiple Testing.” Edited by Oliver Stegle. Bioinformatics 33 (18): 2873–81. https://doi.org/10.1093/bioinformatics/btx311.

Zeller, Georg, Julien Tap, Anita Y Voigt, Shinichi Sunagawa, Jens Roat Kultima, Paul I Costea, Aurélien Amiot, et al. 2014. “Potential of Fecal Microbiota for Early‐stage Detection of Colorectal Cancer.” Molecular Systems Biology 10 (11): 766. https://doi.org/10.15252/msb.20145645.

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