Package: CrcBiomeScreen 1.1.6

Chengxin Li

CrcBiomeScreen: An R package for colorectal cancer screening and microbiome analysis

A developed and benchmarked reproducible machine learning framework for microbiome-based colorectal cancer (CRC) screening. By systematically evaluating normalization strategies, taxonomic resolutions, and class imbalance handling. This R package allows users to apply the full pipeline or selectively run specific components depending on their analytical needs. It establishes a scalable foundation for developing interpretable microbiome-based screening tools to support early CRC detection. This approach could be easily implemented in a national screening programme, to improve early detection rates for this disease.

Authors:Chengxin Li [cre, aut], Rishabh Bezbaruah [aut], Henry Wood [aut], Arief Gusnanto [aut]

CrcBiomeScreen_1.1.6.tar.gz
CrcBiomeScreen_1.1.6.zip(r-4.7-any)CrcBiomeScreen_1.1.6.zip(r-4.6-any)CrcBiomeScreen_1.1.6.zip(r-4.5-any)
CrcBiomeScreen_1.1.6.tgz(r-4.6-any)CrcBiomeScreen_1.1.6.tgz(r-4.5-any)
CrcBiomeScreen_1.1.6.tar.gz(r-4.7-any)CrcBiomeScreen_1.1.6.tar.gz(r-4.6-any)
CrcBiomeScreen_1.1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CrcBiomeScreen/json (API)

# Install 'CrcBiomeScreen' in R:

install.packages('CrcBiomeScreen', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/omicsforestry/crcbiomescreen/issues

Datasets:

NHSBCSP_screeningData - NHSBCSP screening dataset
Thomas_2018_RelativeAbundance - Thomas 2018 relative abundance dataset
ZellerG_2014_RelativeAbundance - Zeller 2014 relative abundance dataset

On BioConductor:CrcBiomeScreen-1.1.6(bioc 3.24)CrcBiomeScreen-1.0.0(bioc 3.23)

software microbiome metagenomics classification normalization visualization

5.23 score 6 scripts 354 downloads 34 exports 132 dependencies

Last updated from:e98e95f74a. Checks:1 WARNING, 9 OK. Indexed: yes.

Target	Result	Time
bioc-checks	WARNING	255
linux-devel	OK	435
source / vignettes	OK	466
linux-release	OK	503
macos-release	OK	227
macos-oldrel	OK	396
windows-devel	OK	1036
windows-release	OK	1023
windows-oldrel	OK	1018
wasm-release	OK	166

Exports:checkClassBalance CreateCrcBiomeScreenObject CreateCrcBiomeScreenObjectFromTSE EvaluateCrcBiomeScreen EvaluateModel EvaluateRF EvaluateXGBoost FilterDataSet getAbsoluteAbundance getModelData getModelResult getNormalizedData getOutlierSamples getPredictResult getRelativeAbundance getSampleData getTaxaData getTaxaLevelData KeepTaxonomicLevel LoadTaxaTable ModelingRF ModelingRF_noweights ModelingXGBoost ModelingXGBoost_noweights NormalizeData PredictCrcBiomeScreen qcByCmdscale RunScreening setNormalizedData<-setTaxaData<-SplitDataSet SplitTaxas TrainModels ValidateModelOnData

Dependencies:abind ape BH Biobase BiocGenerics BiocParallel Biostrings caret class cli clock clue cluster codetools cpp11 crayon data.table DelayedArray diagram digest dirmult doFuture doParallel dplyr e1071 farver fBasics foreach formatR fs futile.logger futile.options future future.apply generics GenomicRanges ggplot2 ggrepel globals glue gower gss gtable GUniFrac hardhat hms inline ipred IRanges isoband iterators jsonlite KernSmooth labeling lambda.r lattice lava lazyeval lifecycle listenv lubridate magrittr MASS Matrix MatrixGenerics matrixStats mgcv modeest ModelMetrics nlme nnet numDeriv parallelly permute pillar pkgconfig plyr prettyunits pROC prodlim progress progressr proxy purrr R6 ranger rappdirs RColorBrewer Rcpp RcppEigen recipes reshape2 rlang rmutil rpart S4Arrays S4Vectors S7 scales Seqinfo shape SingleCellExperiment snow SparseArray sparsevctrs spatial SQUAREM stable stabledist statip statmod stringi stringr SummarizedExperiment survival tibble tidyr tidyselect tidytree timechange timeDate timeSeries treeio TreeSummarizedExperiment tzdb utf8 vctrs vegan viridisLite withr XVector yulab.utils

CrcBiomeScreen: Colorectal Cancer Microbiome Screening and Analysis

Rendered fromCrcBiomeScreen.Rmdusingknitr::rmarkdown

Last update: 2026-07-16
Started: 2025-09-03

Help page	Topics
Check the sample distribution of the dataset and give the suggestion if need the class weight or not	checkClassBalance
CrcBiomeScreen Class	CrcBiomeScreen-class
Create a CrcBiomeScreen S4 object for microbiome-based CRC analysis	CreateCrcBiomeScreenObject
Create a CrcBiomeScreen object from TreeSummarizedExperiment	CreateCrcBiomeScreenObjectFromTSE
Evaluate the performance of model predictions	EvaluateCrcBiomeScreen
Evaluate the model to select the optimal model	EvaluateModel
Evaluate the Random Forest model	EvaluateRF
Evaluate the XGBoost model	EvaluateXGBoost
Filter the CrcBiomeScreenObject dataset based on a specific label	FilterDataSet
Accessor for AbsoluteAbundance slot of CrcBiomeScreen object	getAbsoluteAbundance getAbsoluteAbundance,CrcBiomeScreen-method
Accessor for ModelData slot of CrcBiomeScreen object	getModelData
Accessor for ModelResult slot of CrcBiomeScreen object	getModelResult getModelResult,CrcBiomeScreen-method
Accessor for NormalizedData slot of CrcBiomeScreen object	getNormalizedData getNormalizedData,CrcBiomeScreen-method
Accessor for OutlierSamples	getOutlierSamples getOutlierSamples,CrcBiomeScreen-method
Accessor for PredictResult slot of CrcBiomeScreen object	getPredictResult getPredictResult,CrcBiomeScreen-method
Accessor for RelativeAbundance slot of CrcBiomeScreen object	getRelativeAbundance getRelativeAbundance,CrcBiomeScreen-method
Accessor for SampleData slot of CrcBiomeScreen object	getModelData,CrcBiomeScreen-method getSampleData getSampleData,CrcBiomeScreen-method
Accessor for TaxaData slot of CrcBiomeScreen object	getTaxaData getTaxaData,CrcBiomeScreen-method
Accessor for TaxaLevelData slot	getTaxaLevelData getTaxaLevelData,CrcBiomeScreen-method
Summarize abundance data at a given taxonomic level	KeepTaxonomicLevel
Load a custom taxa table for ASV/OTU data	LoadTaxaTable
The packaging function for Random Forest modeling	ModelingRF
The function for modeling random forest without using class weights	ModelingRF_noweights
The packaging function for XGBoost modeling	ModelingXGBoost
The packaging function for XGBoost modeling without using class weights	ModelingXGBoost_noweights
NHSBCSP screening dataset	NHSBCSP_screeningData
Normalise the absolute data to relative data by using Total Sum Scaling and Geometric Mean of Pairwise Ratios (GMPR)	NormalizeData
Predict the class and probabilities for new data	PredictCrcBiomeScreen
Quality control using classical MDS and outlier detection	qcByCmdscale
Run the screening process for the microbiome data	RunScreening
setNormalizedData<-: Setter for NormalizedData slot of CrcBiomeScreen object	setNormalizedData-setter setNormalizedData<- setNormalizedData<-,CrcBiomeScreen-method
setTaxaData<-: Setter for TaxaData slot of CrcBiomeScreen object	setTaxaData-setter setTaxaData<- setTaxaData<-,CrcBiomeScreen-method
Split the dataset into training and test sets	SplitDataSet
Split and clean taxonomy strings	SplitTaxas
Thomas 2018 relative abundance dataset	Thomas_2018_RelativeAbundance
Train the different models	TrainModels
Predict the validation data by using the trained model in CrcBiomeScreenObject	ValidateModelOnData
Zeller 2014 relative abundance dataset	ZellerG_2014_RelativeAbundance

Package: CrcBiomeScreen 1.1.6

CrcBiomeScreen: An R package for colorectal cancer screening and microbiome analysis

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