InterpretMSSpectrum (version 1.4.5)

mScore: mScore.

Description

mScore will calculate a mass defect weighted score for an mz/int values measure for an isotopic cluster in comparison to the theoretically expected pattern.

Usage

mScore(
  obs = NULL,
  the = NULL,
  dabs = 5e-04,
  dppm = 2,
  int_prec = 0.02,
  limit = 0,
  rnd_prec = 0
)

Value

Scalar mScore giving the quality of the observed data if theoretical data are true.

Arguments

obs

Observed (measured) values, a matrix with two rows (mz/int).

the

Theoretical (estimated from sum formula) values, a matrix with two rows (mz/int).

dabs

Absolute allowed mass deviation (the expected mass precision will influence mScore -- see Details).

dppm

Relative allowed mass deviation (the expected mass precision will influence mScore -- see Details).

int_prec

The expected intensity precision will influence mScore (see Details).

limit

minimal value of mScore. Should be left on zero.

rnd_prec

Rounding precision of mScore.

Details

The maximum expected average mass error should be specified in ppm. A observed pattern deviating that much from the theoretical pattern would still receive a reasonable (average) mScore while observations deviating stronger or less strong will reach lower or higher mScores respectively. Likewise the intensity precision should specify the average quality of your device to maintain stable isotopic ratios.

Examples

# get theoretical isotopic pattern of Glucose
glc <- c(180.063388, 0.920845, 181.066845, 0.065214, 182.068041, 0.013043)
glc <- matrix(glc, nrow=2)
mScore(obs=glc, the=glc)
# modify pattern by maximum allowable error (2ppm mass error, 2% int error)
glc_theoretic <- glc
glc[1,] <- glc[1,]+2*glc[1,]/10^6
glc[2,1:2] <- c(-0.02,0.02)+glc[2,1:2]
mScore(obs=glc, the=glc_theoretic)

# simulate mass and int defects
ef <- function(x, e) {runif(1,x-x*e,x+x*e)}
glc_obs <- glc
glc_obs[1,] <- sapply(glc[1,], ef, e=2*10^-6)
glc_obs[2,] <- sapply(glc[2,], ef, e=0.02)
mScore(obs=glc_obs, the=glc)
# simulate mass and int defects systematically
ef <- function(x, e) {runif(1,x-x*e,x+x*e)}
n <- 11
mz_err <- round(seq(0,5,length.out=n),3)
int_err <- round(seq(0,0.1,length.out=n),3)
mat <- matrix(NA, ncol=n, nrow=n, dimnames=list(mz_err, 100*int_err))
glc_obs <- glc
for (i in 1:n) {
 glc_obs[1,] <- sapply(glc[1,], ef, e=mz_err[i]*10^-6)
 for (j in 1:n) {
   glc_obs[2,] <- sapply(glc[2,], ef, e=int_err[j])
   mat[i,j] <- mScore(obs=glc_obs, the=glc)
 }
}
plot(x=1:n, y=1:n, type="n",axes=FALSE, xlab="mass error [ppm]", ylab="isoratio error [%]")
axis(3,at=1:n,rownames(mat),las=2); axis(4,at=1:n,colnames(mat),las=2); box()
cols <- grDevices::colorRampPalette(colors=c(2,6,3))(diff(range(mat))+1)
cols <- cols[mat-min(mat)+1]
text(x=rep(1:n,each=n), y=rep(1:n,times=n), labels=as.vector(mat), col=cols)