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