Package: SIAMCAT 2.9.0

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]

SIAMCAT_2.9.0.tar.gz
SIAMCAT_2.9.0.zip(r-4.5)SIAMCAT_2.9.0.zip(r-4.4)SIAMCAT_2.9.0.zip(r-4.3)
SIAMCAT_2.9.0.tgz(r-4.4-any)SIAMCAT_2.9.0.tgz(r-4.3-any)
SIAMCAT_2.9.0.tar.gz(r-4.5-noble)SIAMCAT_2.9.0.tar.gz(r-4.4-noble)
SIAMCAT_2.9.0.tgz(r-4.4-emscripten)SIAMCAT_2.9.0.tgz(r-4.3-emscripten)
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'))

Datasets:

feat.crc.zeller - Example feature matrix
meta.crc.zeller - Example metadata matrix
siamcat_example - SIAMCAT example

On BioConductor:SIAMCAT-2.9.0(bioc 3.20)SIAMCAT-2.8.0(bioc 3.19)

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

bioconductor-package

57 exports 1.16 score 114 dependencies 6 mentions

Last updated 2 months agofrom:665aeef730

Exports:accessSlot add.meta.pred assoc_param association.plot associations associations<-check.associations check.confounders create.data.split create.label data_split data_split<-eval_data eval_data<-evaluate.predictions feature_type feature_weights filt_feat filt_feat<-filt_params filter.features filter.label get.filt_feat.matrix get.norm_feat.matrix get.orig_feat.matrix label label<-make.predictions meta meta<-model_list model_list<-model_type model.evaluation.plot model.interpretation.plot models norm_feat norm_feat<-norm_params normalize.features orig_feat orig_feat<-physeq physeq<-pred_matrix pred_matrix<-read.label read.lefse select.samples siamcat siamcat.to.lefse siamcat.to.maaslin summarize.features train.model validate.data volcano.plot weight_matrix

Dependencies:ade4 ape askpass backports bbotk beanplot Biobase BiocGenerics biomformat Biostrings boot checkmate cli cluster codetools colorspace corrplot cpp11 crayon curl data.table digest evaluate fansi farver foreach future future.apply GenomeInfoDb GenomeInfoDbData ggplot2 glmnet globals glue gridBase gridExtra gtable hms httr igraph infotheo IRanges isoband iterators jsonlite labeling lattice lgr LiblineaR lifecycle listenv lme4 lmerTest magrittr MASS Matrix matrixStats mgcv mime minqa mlbench mlr3 mlr3learners mlr3measures mlr3misc mlr3tuning multtest munsell nlme nloptr numDeriv openssl palmerpenguins paradox parallelly permute phyloseq pillar pixmap pkgconfig plyr prettyunits pROC progress PRROC R6 RColorBrewer Rcpp RcppArmadillo RcppEigen reshape2 rhdf5 rhdf5filters Rhdf5lib RhpcBLASctl rlang S4Vectors scales shape sp stringi stringr survival sys tibble UCSC.utils utf8 uuid vctrs vegan viridisLite withr XVector zlibbioc

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

Konrad Zych, Jakob Wirbel, and Georg Zeller

Rendered fromSIAMCAT_vignette.Rmdusingknitr::rmarkdownon Jun 30 2024.

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

Example dataset with Confoundering

Jakob Wirbel and Georg Zeller

Rendered fromSIAMCAT_confounder.Rmdusingknitr::rmarkdownon Jun 30 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 Jun 30 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 Jun 30 2024.

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

Meta-analysis using SIAMCAT

Jakob Wirbel, and Georg Zeller

Rendered fromSIAMCAT_meta.Rmdusingknitr::rmarkdownon Jun 30 2024.

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

Machine learning pitfalls

Jakob Wirbel and Georg Zeller

Rendered fromSIAMCAT_ml_pitfalls.Rmdusingknitr::rmarkdownon Jun 30 2024.

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

Help page	Topics
SIAMCAT: Statistical Inference of Associations between Microbial Communities And host phenoTypes	SIAMCAT-package SIAMCAT
Add metadata as predictors	add.meta.pred
Retrieve the list of parameters for association testing from a SIAMCAT object	assoc_param assoc_param,siamcat-method assoc_param_param
Visualize associations between features and classes	association.plot
Retrieve the results of association testing from a SIAMCAT object	associations associations,siamcat-method
Calculate associations between features and labels	check.associations
Check for potential confounders in the metadata	check.confounders
Split a dataset into training and a test sets.	create.data.split
Create a label list	create.label
Retrieve the data split from a SIAMCAT object	data_split data_split,siamcat-method
Retrieve the evaluation metrics from a SIAMCAT object	eval_data eval_data,siamcat-method
Evaluate prediction results	evaluate.predictions
Example feature matrix	feat.crc.zeller
Retrieve the feature type used for model training from a SIAMCAT object	feature_type feature_type,siamcat-method
Retrieve the matrix of feature weights from a SIAMCAT object	feature_weights feature_weights,siamcat-method
Retrieve the list of parameters for feature filtering from a SIAMCAT object	filt_params filt_params,siamcat-method
Perform unsupervised feature filtering.	filter.features
Filter the label of a SIMACAT object	filter.label
Retrieve the filtered features from a SIAMCAT object	get.filt_feat.matrix
Retrieve the normalized features from a SIAMCAT object	get.norm_feat.matrix
Retrieve the original features from a SIAMCAT object	get.orig_feat.matrix
Retrieve the label from a SIAMCAT object	label label,siamcat-method
Make predictions on a test set	make.predictions
Retrieve the metadata from a SIAMCAT object	meta meta,sample_data-method meta,siamcat-method
Example metadata matrix	meta.crc.zeller
Retrieve the machine learning method from a SIAMCAT object	model_type model_type,siamcat-method
Model Evaluation Plot	model.evaluation.plot
Model Interpretation Plot	model.interpretation.plot
Retrieve list of trained models from a SIAMCAT object	models models,siamcat-method
Retrieve the list of parameters for feature normalization from a SIAMCAT object	norm_params norm_params,siamcat-method
Perform feature normalization	normalize.features
Retrieve the prediction matrix from a SIAMCAT object	pred_matrix pred_matrix,siamcat-method
Read label file	read.label
Select samples based on metadata	select.samples
SIAMCAT constructor function	siamcat
SIAMCAT example	siamcat_example
The S4 SIAMCAT class	siamcat-class
Model training	train.model
Validate samples in labels, features, and metadata	validate.data
Visualize associations between features and classes as volcano plot	volcano.plot
Retrieve the weight matrix from a SIAMCAT object	weight_matrix weight_matrix,siamcat-method

Package: SIAMCAT 2.9.0

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

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

Example dataset with Confoundering

Holdout Testing with SIAMCAT

SIAMCAT input files formats

Meta-analysis using SIAMCAT

Machine learning pitfalls

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)