broadClass_train: Broad Class Training

In pcahan1/cancerCellNet: CellNet, for cancer

Broad Class Training

Description

Tranining broad class classifier

Usage

broadClass_train(
  stTrain,
  expTrain,
  colName_cat,
  colName_samp = "row.names",
  nTopGenes = 20,
  nTopGenePairs = 50,
  nRand = 40,
  nTrees = 1000,
  stratify = FALSE,
  sampsize = 40,
  weightedDown_total = 5e+05,
  weightedDown_dThresh = 0.25,
  transprop_xFact = 1e+05,
  quickPairs = FALSE,
  coreProportion = 0
)

Arguments

stTrain	a dataframe that matches the samples with category
expTrain	the expression matrix
colName_cat	the name of the column that contains categories
colName_samp	the name of the column that contains sample names
nTopGenes	the number of classification genes per category
nTopGenePairs	the number of top gene pairs per category
nRand	number of random profiles generate for training
nTrees	number of trees for random forest classifier
stratify	TRUE if stratified sampling
sampsize	sample size for stratified sampling
weightedDown_dThresh	the threshold at which anything lower than that is 0 for weighted_down function
transprop_xFact	scaling factor for transprop
quickPairs	TRUE if using quick version of genepair transform
coreProportion	the proportion of logical cores for finding classification genes and top scoring gene pairs. If you want to disable parallel processing, then enter 0
weightDown_total	numeric post transformation sum of read counts for weighted_down function

Value

a list containing normalized expression data, classification gene list, cnPRoc

pcahan1/cancerCellNet documentation built on July 16, 2022, 12:12 a.m.