scn_train: Training

In pcahan1/singleCellNet: cell identity from single cell RNA-Seq data

Description

Tranining broad class classifier

Usage

scn_train(stTrain, expTrain, dLevel, colName_samp = "row.names",
  nTopGenes = 10, nTopGenePairs = 25, nRand = 70, nTrees = 1000,
  stratify = FALSE, weightedDown_total = 1500,
  weightedDown_dThresh = 0.25, transprop_xFact = 10000)

Arguments

stTrain	a dataframe that matches the samples with category
expTrain	the expression matrix
dLevel	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
weightedDown_dThresh	the threshold at which anything lower than that is 0 for weighted_down function
transprop_xFact	scaling factor for transprop
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/singleCellNet documentation built on April 9, 2021, 8:49 a.m.