HyBioM2 {BioM2}

BioM2 Hyperparametric Combination

Description

BioM2 Hyperparametric Combination

Usage

HyBioM2(
  TrainData = NULL,
  pathlistDB = NULL,
  FeatureAnno = NULL,
  resampling = NULL,
  nfolds = 5,
  classifier = "liblinear",
  predMode = "probability",
  PathwaySizeUp = 200,
  PathwaySizeDown = 20,
  MinfeatureNum_pathways = 10,
  Add_UnMapped = TRUE,
  Add_FeartureSelection_Method = "wilcox.test",
  Unmapped_num = 300,
  Inner_CV = TRUE,
  inner_folds = 10,
  Stage1_FeartureSelection_Method = "cor",
  stage1_cutoff = 0.3,
  Stage2_FeartureSelection_Method = "RemoveHighcor",
  stage2_cutoff = 0.8,
  classifier2 = NULL,
  cores = 1,
  verbose = TRUE
)

Arguments

TrainData

The input training dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.

pathlistDB

A list of pathways with pathway IDs and their corresponding genes ('entrezID' is used). For details, please refer to ( data("GO2ALLEGS_BP") )

FeatureAnno

The annotation data stored in a data.frame for probe mapping. It must have at least two columns named 'ID' and 'entrezID'. (For details, please refer to data( data("MethylAnno") )

resampling

Resampling in mlr3verse.

nfolds

k-fold cross validation ( Only supported when TestData = NULL )

classifier

Learners in mlr3

predMode

The prediction mode. Available options are c('probability', 'classification').

PathwaySizeUp

The upper-bound of the number of genes in each biological pathways.

PathwaySizeDown

The lower-bound of the number of genes in each biological pathways.

MinfeatureNum_pathways

The minimal defined pathway size after mapping your own data to pathlistDB(KEGG database/GO database).

Add_UnMapped

Whether to add unmapped probes for prediction

Add_FeartureSelection_Method

Feature selection methods.

Unmapped_num

The number of unmapped probes

Inner_CV

Whether to perform a k-fold verification on the training set.

inner_folds

k-fold verification on the training set.

Stage1_FeartureSelection_Method

Feature selection methods.

stage1_cutoff

The cutoff used for feature selection threshold. It can be any value between 0 and 1.

Stage2_FeartureSelection_Method

Feature selection methods.

stage2_cutoff

The cutoff used for feature selection threshold. It can be any value between 0 and 1.

classifier2

Learner for stage 2 prediction(if classifier2==NULL,then it is the same as the learner in stage 1.)

cores

The number of cores used for computation.

verbose

Whether to print running process information to the console

Value

A data frame contains hyperparameter results