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# SIAMCAT <img src="man/figures/logo.png" align="right" width="120" /> [![Build Status](]( ## 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). <a href=''> <img src="man/figures/embl_microbiome_tools_logo.png" align="right" width="200"> </a> `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: ```R if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("SIAMCAT") ``` Alternatively, you can install the current development version via `devtools`: ```R 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`: ```R browseVignettes("SIAMCAT") # Please Note: # `browseVignettes` only works if `SIAMCAT` has been installed via Bioconductor ``` ## Feedback and Contact If you have any question about `SIAMCAT`, if you run into any issue, or if you would like to make a feature request, please: - create an [issue in this repository]( or - mail [Georg Zeller]( or - ask at the [SIAMCAT support group](!forum/siamcat-users) If you have a more general question (that could be useful to several other users), please do not hesitate to post it on a dedicated forums such as Stackoverflow or Biostars. If you let us know about the question, we will answer it swiftly. Please consider giving us [feedback]( (This feedback is useful for us to justify the funding we get for developing and maintaining this package.) ## License SIAMCAT is distributed under the [GPL-3]( license. ## Citation If you use `SIAMCAT`, please cite us by using ```R citation("SIAMCAT") ``` or by > Wirbel J, Zych K, Essex M, Karcher N, Kartal E, Salazar G, Bork P, Sunagawa S, Zeller G _Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox_ Genome Biol **22**, 93 (2021) In this publication, we analyzed a large set of case-control microbiome datasets. The metadata and taxonomic profiles of these studies are available through a [Zenodo repository]( [![DOI](]( ## 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. ![Association testing](man/figures/associations_plot.png) ### 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). ![Model interpretation plot](man/figures/interpretation_plot.png) ## 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](!