exponNormalize: Normalize by adjusting exponent

In wrMisc: Analyze Experimental High-Throughput (Omics) Data

Normalize by adjusting exponent

Description

This function normalizes 'dat' by optimizing exponent function (ie dat ^exp) to fit best to 'ref' (default: average of each line of 'dat').

Usage

exponNormalize(
  dat,
  useExpon,
  dynExp = TRUE,
  nStep = 20,
  startExp = 1,
  simMeas = "cor",
  refDat = NULL,
  refGrp = NULL,
  refLines = NULL,
  rSquare = FALSE,
  silent = FALSE,
  callFrom = NULL
)

Arguments

dat	matrix or data.frame of numeric data to be normalized
useExpon	(numeric vector or matrix) exponent values to be tested
dynExp	(logical) require 'useExpon' as 2 values (matrix), will gradually increase exponent from 1st to 2nd; may be matrix or data.frame for dynamic, in this case 1st line for exp for lowest data, 2nd line for highest
nStep	(integer) number of exponent variations (steps) when testing range from-to
startExp	(numeric)
simMeas	(character) similarity metric to be used (so far only "cor"), if rSquare=TRRUE, the r-squared will be returned
refDat	(matrix or data.frame) if null average of each line from 'dat' will be used as reference in similarity measure
refGrp	(factor) designing which col of 'ref' should be used with which col of 'dat' (length equal to number of cols in 'dat'). Note: 'refGrp' not yet coded optimally to extract numeric part of character vector, protential problems when all lines or cols of dat are NA
refLines	(NULL or integer) optional subset of lines to be considered (only) when determining normalization factors
rSquare	(logical) if TRUE, add r-squared
silent	(logical) suppress messages
callFrom	(character) allow easier tracking of messages produced

Value

This functuion returns a matrix of normalized data

See Also

more eveolved than normalizeThis with arugment set to 'exponent'

Examples

set.seed(2016); dat1 <- matrix(c(runif(200)+rep(1:10,20)),nc=10)
head(rowGrpCV(dat1,gr=gl(4,3,labels=LETTERS[1:4])[2:11]))
set.seed(2016); dat1 <- c(0.1,0.2,0.3,0.5)*rep(c(1,10),each=4)
dat1 <- matrix(round(c(sqrt(dat1),dat1^1.5,3*dat1+runif(length(dat1))),2),nc=3)
dat2a <- exponNormalize(dat1[,1],useExpon=2,nSte=1,refD=dat1[,3])
layout(matrix(1:2,nc=2))
plot(dat1[,1],dat1[,3],type="b",main="init",ylab="ref") 
plot(dat2a$datNor[,1],dat1[,3],type="b",main="norm",ylab="ref")
dat2b <- exponNormalize(dat1[,1],useExpon=c(1.7,2.3),nSte=5,refD=dat1[,3])
plot(dat1[,1],dat1[,3],type="b",main="init",ylab="ref")
plot(dat2b$datNor[,1],dat1[,3],type="b",main="norm",ylab="ref")

dat2c <- exponNormalize(dat1[,-3],useExpon=matrix(c(1.7,2.3,0.6,0.8),nc=2),nSte=5,refD=dat1[,3]);
plot(dat1[,1],dat1[,3],type="b",main="init",ylab="ref ")
plot(dat2c$datNor[,1],dat1[,3],type="b",main="norm 1",ylab="ref")
plot(dat1[,2],dat1[,3],type="b",main="init",ylab="ref")
plot(dat2c$datNor[,2],dat1[,3],type="b",main="norm 2",ylab="ref"); 

wrMisc documentation built on Nov. 17, 2023, 5:09 p.m.