Package: SIAMCAT 2.17.0

Jakob Wirbel

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

Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). 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).

Authors:Konrad Zych [aut], Jakob Wirbel [aut, cre], Georg Zeller [aut], Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SIAMCAT/json (API)

# Install 'SIAMCAT' in R:
install.packages('SIAMCAT', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On BioConductor:SIAMCAT-2.17.0(bioc 3.24)SIAMCAT-2.16.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

immunooncologymetagenomicsclassificationmicrobiomesequencingpreprocessingclusteringfeatureextractiongeneticvariabilitymultiplecomparisonregression

6.79 score 173 scripts 450 downloads 6 mentions 57 exports 102 dependencies

Last updated from:3ce77e4e8a. Checks:1 WARNING, 7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING251
linux-devel-x86_64NOTE383
source / vignettesOK401
linux-release-x86_64NOTE410
macos-release-arm64NOTE194
macos-oldrel-arm64NOTE240
windows-develNOTE349
windows-releaseNOTE363
windows-oldrelNOTE289
wasm-releaseOK151

Exports:accessSlotadd.meta.predassoc_paramassociation.plotassociationsassociations<-check.associationscheck.confounderscreate.data.splitcreate.labeldata_splitdata_split<-eval_dataeval_data<-evaluate.predictionsfeature_typefeature_weightsfilt_featfilt_feat<-filt_paramsfilter.featuresfilter.labelget.filt_feat.matrixget.norm_feat.matrixget.orig_feat.matrixlabellabel<-make.predictionsmetameta<-model_listmodel_list<-model_typemodel.evaluation.plotmodel.interpretation.plotmodelsnorm_featnorm_feat<-norm_paramsnormalize.featuresorig_featorig_feat<-physeqphyseq<-pred_matrixpred_matrix<-read.labelread.lefseselect.samplessiamcatsiamcat.to.lefsesiamcat.to.maaslinsummarize.featurestrain.modelvalidate.datavolcano.plotweight_matrix

Dependencies:ade4apebackportsbbotkbeanplotBiobaseBiocGenericsbiomformatBiostringsbootcheckmatecliclustercodetoolscorrplotcpp11crayondata.tabledigestevaluatefarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegridBasegridExtragtablehmsigraphinfotheoIRangesisobanditeratorsjsonlitelabelinglatticelgrLiblineaRlifecyclelistenvlme4lmerTestmagrittrMASSMatrixmatrixStatsmgcvminqamiraimlbenchmlr3mlr3learnersmlr3measuresmlr3miscmlr3tuningmulttestnanonextnlmenloptrnumDerivpalmerpenguinsparadoxparallellypermutephyloseqpixmappkgconfigplyrprettyunitspROCprogressPRROCR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangS4VectorsS7scalesSeqinfoshapespstringistringrsurvivaluuidvctrsveganviridisLitewithrXVector

Example dataset with Confoundering
About This Vignette | Setup | Preparations | curatedMetagenomicsData | Metadata | Taxonomic Profiles | mOTUs2 Profiles | SIAMCAT Workflow (without Confounders) | The SIAMCAT Object | Filtering | Association Plot | Confounder Analysis | Machine Learning Workflow | Model Evaluation Plot | Model Interpretation Plot | Country Confounder | Association Testing | Machine Learning | Session Info
Rendered fromSIAMCAT_confounder.Rmdusingknitr::rmarkdown

Last update: 2022-09-27
Started: 2020-11-11

Holdout Testing with SIAMCAT
Introduction | Load the Data | Model Building on the French Dataset | Preprocessing | Model Training | Predictions | Application on the Holdout Dataset | Frozen Normalization | Holdout Predictions | Model Evaluation | Session Info
Rendered fromSIAMCAT_holdout.Rmdusingknitr::rmarkdown

Last update: 2022-09-27
Started: 2018-05-13

Meta-analysis using SIAMCAT
About This Vignette | Setup | Compare Associations | Compute Associations with SIAMCAT | Plot Heatmap for Interesting Genera | Study as Confounding Factor | ML Meta-analysis | Train LASSO Models | Investigate Feature Weights | Session Info
Rendered fromSIAMCAT_meta.Rmdusingknitr::rmarkdown

Last update: 2022-09-27
Started: 2020-11-06

Machine learning pitfalls
About This Vignette | Setup | Supervised Feature Selection | Load the Data | Train Model without Feature Selection | Incorrect Procedure: Train with Supervised Feature Selection | Correct Procedure: Train with Nested Feature Selection | Plot the Results | Naive Splitting of Dependent Data | Train with Naive Cross-validation | Train with Blocked Cross-validation | Apply to External Datasets | Session Info
Rendered fromSIAMCAT_ml_pitfalls.Rmdusingknitr::rmarkdown

Last update: 2022-09-27
Started: 2020-11-11

SIAMCAT: Statistical Inference of Associations between Microbial Communities And host phenoTypes
About This Vignette | Introduction | Quick Start | Association Testing | Confounder Testing | Model Building | Data Normalization | Prepare Cross-Validation | Model Training | Make Predictions | Model Evaluation and Interpretation | Evaluation Plot | Interpretation Plot | Session Info
Rendered fromSIAMCAT_vignette.Rmdusingknitr::rmarkdown

Last update: 2022-04-03
Started: 2018-01-12

SIAMCAT input files formats
Introduction | Loading your data into R | SIAMCAT input | Features | Metadata | Label | LEfSe format files | metagenomeSeq format files | BIOM format files | Creating a siamcat object of a phyloseq object | Creating a siamcat-class object | phyloseq, label and orig_feat slots | All the other slots | Accessing and assigning slots | Slots inside the slots | Session Info
Rendered fromSIAMCAT_read-in.Rmdusingknitr::rmarkdown

Last update: 2020-11-11
Started: 2018-05-16

Readme and manuals

Help Manual

Help pageTopics
SIAMCAT: Statistical Inference of Associations between Microbial Communities And host phenoTypesSIAMCAT-package SIAMCAT
Add metadata as predictorsadd.meta.pred
Retrieve the list of parameters for association testing from a SIAMCAT objectassoc_param assoc_param,siamcat-method assoc_param_param
Visualize associations between features and classesassociation.plot
Retrieve the results of association testing from a SIAMCAT objectassociations associations,siamcat-method
Calculate associations between features and labelscheck.associations
Check for potential confounders in the metadatacheck.confounders
Split a dataset into training and a test sets.create.data.split
Create a label listcreate.label
Retrieve the data split from a SIAMCAT objectdata_split data_split,siamcat-method
Retrieve the evaluation metrics from a SIAMCAT objecteval_data eval_data,siamcat-method
Evaluate prediction resultsevaluate.predictions
Example feature matrixfeat.crc.zeller
Retrieve the feature type used for model training from a SIAMCAT objectfeature_type feature_type,siamcat-method
Retrieve the matrix of feature weights from a SIAMCAT objectfeature_weights feature_weights,siamcat-method
Retrieve the list of parameters for feature filtering from a SIAMCAT objectfilt_params filt_params,siamcat-method
Perform unsupervised feature filtering.filter.features
Filter the label of a SIMACAT objectfilter.label
Retrieve the filtered features from a SIAMCAT objectget.filt_feat.matrix
Retrieve the normalized features from a SIAMCAT objectget.norm_feat.matrix
Retrieve the original features from a SIAMCAT objectget.orig_feat.matrix
Retrieve the label from a SIAMCAT objectlabel label,siamcat-method
Make predictions on a test setmake.predictions
Retrieve the metadata from a SIAMCAT objectmeta meta,sample_data-method meta,siamcat-method
Example metadata matrixmeta.crc.zeller
Retrieve the machine learning method from a SIAMCAT objectmodel_type model_type,siamcat-method
Model Evaluation Plotmodel.evaluation.plot
Model Interpretation Plotmodel.interpretation.plot
Retrieve list of trained models from a SIAMCAT objectmodels models,siamcat-method
Retrieve the list of parameters for feature normalization from a SIAMCAT objectnorm_params norm_params,siamcat-method
Perform feature normalizationnormalize.features
Retrieve the prediction matrix from a SIAMCAT objectpred_matrix pred_matrix,siamcat-method
Read label fileread.label
Select samples based on metadataselect.samples
SIAMCAT constructor functionsiamcat
SIAMCAT examplesiamcat_example
The S4 SIAMCAT classsiamcat-class
Model trainingtrain.model
Validate samples in labels, features, and metadatavalidate.data
Visualize associations between features and classes as volcano plotvolcano.plot
Retrieve the weight matrix from a SIAMCAT objectweight_matrix weight_matrix,siamcat-method