train: Main training function.

In BioHPC/iCAT: immune Cell Analysis Tool

Description

Main training function.

Usage

train(negatives, positives, prelist, postlist, field, count, copyrange,
  pcut, minpublic, updateProgress = NULL)

Arguments

negatives	Dataframe of sequence frequencies in negative samples.
positives	Datafram of sequence frequencies in positive samples.
prelist	Vector of negative training samples.
postlist	Vector of positive training samples.
field	String containing the column or columns (space-delimited) of interest.
count	String containing the column name for colontype counts.
copyrange	Integer Vector of the min and max copy of a sequence, within a sample, to be considered.
pcut	P-value threshold for fisher-exact test.
minpublic	Sequence frequency threshold to be considered.
updateProgress	Function for updating a progress bar in a Shiny interface.

Value

List containing both negtive (n) and positive (v) clonotype percentages.

Examples

FIELD <- "vGeneName aminoAcid jGeneName"
COUNT <- "copy"
P_CUTOFF <- 0.1
MIN_PUBLIC <- 2
COPY_RANGE <- "1 99"

listPos <- tsvDir(system.file("extdata", "Post", package="iCAT"))
listNeg <- tsvDir(system.file("extdata", "Pre", package="iCAT"))

naive <- readTrn(listNeg, FIELD, COUNT, COPY_RANGE, "naive")
vaccs <- readTrn(listPos, FIELD, COUNT, COPY_RANGE, "vacc")  

mod <- train(naive, vaccs, listNeg, listPos, FIELD, COUNT, COPY_RANGE, P_CUTOFF, MIN_PUBLIC, NULL)

BioHPC/iCAT documentation built on Oct. 30, 2021, 3:12 p.m.