README.md

In SIAMCAT: Statistical Inference of Associations between Microbial Communities And host phenoTypes

SIAMCAT

Overview

SIAMCAT is a pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes. A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots).

SIAMCAT is developed in the Zeller group and is part of the suite of computational microbiome analysis tools hosted at EMBL.

Starting with SIAMCAT

Installation

In order to start with SIAMCAT, you need to install it from Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("SIAMCAT", version = "3.8")

Alternatively, you can install the current development version via devtools:

require("devtools")
devtools::install_github(repo = 'zellerlab/siamcat')

Quick start

There are a few manuals that will kick-start you and help you analyse your data with SIAMCAT. You can find links to those on the Bioconductor website of SIAMCAT or you can type into R:

browseVignettes("SIAMCAT")
# Please Note:
# `browseVignettes` only works if `SIAMCAT` has been installed via Bioconductor

Contact

If you run into any issue: - create an issue in this repository or - mail Georg Zeller or - ask at the SIAMCAT support group

If you found SIAMCAT useful, please consider giving us feedback.

Citation

If you use SIAMCAT, please cite us by using

citation("SIAMCAT")

or by

Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, Bork P, Sunagawa S, Zeller G (2020) SIAMCAT: user-friendly and versatile machine learning workflows for statistically rigorous microbiome analyses bioRxiv 2020.02.06.931808; doi: https://doi.org/10.1101/2020.02.06.931808

Examples of primary package output

To give you a small preview about the primary package output, here are some example plots taking from the main SIAMCAT vignette.

In this vignette, we use an example dataset which is also included in the SIAMCAT package. The dataset is taken from the publication of Zeller et al, which demonstrated the potential of microbial species in fecal samples to distinguish patients with colorectal cancer (CRC) from healthy controls.

Association testing

The result of the check.associations function is an association plot. For significantly associated microbial features, the plot shows: - the abundances of the features across the two different classes (CRC vs. controls) - the significance of the enrichment calculated by a Wilcoxon test (after multiple hypothesis testing correction) - the generalized fold change of each feature - the prevalence shift between the two classes, and - the Area Under the Receiver Operating Characteristics Curve (AU-ROC) as non-parametric effect size measure.

Model interpretation plot

After statistical models have been trained to distinguish cancer cases from controls, the models can be investigated by the function model.interpretation.plot. The plots shows: - the median relative feature weight for selected features (barplot on the left) - the robustness of features (i.e. in how many of the models the specific feature has been selected) - the distribution of selected features across samples (central heatmap) - which proportion of the weight of all different models are shown in the plot (boxplot on the right), and - distribution of metadata across samples (heatmap below).

Where SIAMCAT has been used already

Several publications already used SIAMCAT (or previous versions thereof).

• Potential of fecal microbiota for early-stage detection of colorectal cancer Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR, Costea PI, Amiot A, Böhm J, Brunetti F, Habermann N, Hercog R, Koch M, Luciani A, Mende DR, Schneider MA, Schrotz-King P, Tournigand C, Tran Van Nhieu J, Yamada T, Zimmermann J, Benes V, Kloor M, Ulrich CM, von Knebel Doeberitz M, Sobhani I, Bork P Molecular Systems Biology, (2014) 10, 766

  Original Publication that inspired SIAMCAT

• Gut Microbiota Linked to Sexual Preference and HIV Infection Noguera-Julian M, Rocafort M, Guillén Y, Rivera J, Casadellà M, Nowak P, Hildebrand F, Zeller G, Parera M, Bellido R, Rodríguez C,Carrillo J, Mothe B, Coll J, Bravo I, Estany C, Herrero C, Saz J, Sirera G, Torrela A, Navarro J, Crespo M, Brander C, Negredo E, Blanco J, Guarner F, Calle ML, Bork P, Sönnerborgd A, Clotet B, Paredes R EBioMedicine 5 (2016) 135-146

  See Figure 5

• Extensive transmission of microbes along the gastrointestinal tract Schmidt TSB, Hayward MR, Coelho LP, Li SS, Costea PI, Voigt AY, Wirbel J, Maistrenko OM, Alves RJC, Bergsten E, de Beaufort C, Sobhani I, Heintz-Buschart A, Sunagawa S, Zeller G, Wilmes P, Bork P eLife, (2019) 8:e42693

  See Figure 3 - figure supplement 1

• Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer Wirbel J, Pyl PT, Kartal E, Zych K, Kashani A, Milanese A, Fleck JS, Voigt AY, Palleja A, Ponnudurai R, Sunagawa S, Coelho LP, Schrotz-King P, Vogtmann E, Habermann N, Niméus E, Thomas AM, Manghi P, Gandini S, Serrano D, Mizutani S, Shiroma H, Shiba S, Shibata T, Yachida S, Yamada T, Waldron L, Naccarati A, Segata N, Sinha R, Ulrich CM, Brenner H, Arumugam M, Bork P, Zeller G Nature Medicine, (2019) [Epub ahead of print]

  In this publication, SIAMCAT is used extensively for holdout testing

If you used SIAMCAT in your publication, let us know!

SIAMCAT documentation built on Nov. 8, 2020, 5:14 p.m.