Package: SIAMCAT 2.11.0

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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SIAMCAT.pdf |SIAMCAT.html✨
SIAMCAT/json (API)
NEWS

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

Peer review:

Datasets:

On BioConductor:SIAMCAT-2.11.0(bioc 3.21)SIAMCAT-2.10.0(bioc 3.20)

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

immunooncologymetagenomicsclassificationmicrobiomesequencingpreprocessingclusteringfeatureextractiongeneticvariabilitymultiplecomparisonregression

6.90 score 110 scripts 297 downloads 6 mentions 57 exports 114 dependencies

Last updated 23 days agofrom:56df78709e. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 18 2024
R-4.5-winNOTENov 18 2024
R-4.5-linuxNOTENov 18 2024
R-4.4-winNOTENov 18 2024
R-4.4-macNOTENov 18 2024
R-4.3-winNOTENov 18 2024
R-4.3-macNOTENov 18 2024

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:ade4apeaskpassbackportsbbotkbeanplotBiobaseBiocGenericsbiomformatBiostringsbootcheckmatecliclustercodetoolscolorspacecorrplotcpp11crayoncurldata.tabledigestevaluatefansifarverforeachfuturefuture.applygenericsGenomeInfoDbGenomeInfoDbDataggplot2glmnetglobalsgluegridBasegridExtragtablehmshttrigraphinfotheoIRangesisobanditeratorsjsonlitelabelinglatticelgrLiblineaRlifecyclelistenvlme4lmerTestmagrittrMASSMatrixmatrixStatsmgcvmimeminqamlbenchmlr3mlr3learnersmlr3measuresmlr3miscmlr3tuningmulttestmunsellnlmenloptrnumDerivopensslpalmerpenguinsparadoxparallellypermutephyloseqpillarpixmappkgconfigplyrprettyunitspROCprogressPRROCR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2rhdf5rhdf5filtersRhdf5librlangS4VectorsscalesshapespstringistringrsurvivalsystibbleUCSC.utilsutf8uuidvctrsveganviridisLitewithrXVectorzlibbioc

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

Konrad Zych, Jakob Wirbel, and Georg Zeller

Rendered fromSIAMCAT_vignette.Rmdusingknitr::rmarkdownon Nov 18 2024.

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

Example dataset with Confoundering

Jakob Wirbel and Georg Zeller

Rendered fromSIAMCAT_confounder.Rmdusingknitr::rmarkdownon Nov 18 2024.

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

Holdout Testing with SIAMCAT

Jakob Wirbel, Konrad Zych, and Georg Zeller

Rendered fromSIAMCAT_holdout.Rmdusingknitr::rmarkdownon Nov 18 2024.

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

SIAMCAT input files formats

Konrad Zych, Jakob Wirbel, and Georg Zeller

Rendered fromSIAMCAT_read-in.Rmdusingknitr::rmarkdownon Nov 18 2024.

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

Meta-analysis using SIAMCAT

Jakob Wirbel, and Georg Zeller

Rendered fromSIAMCAT_meta.Rmdusingknitr::rmarkdownon Nov 18 2024.

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

Machine learning pitfalls

Jakob Wirbel and Georg Zeller

Rendered fromSIAMCAT_ml_pitfalls.Rmdusingknitr::rmarkdownon Nov 18 2024.

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

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