# mScore. `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. ```r mScore( obs = NULL, the = NULL, dabs = 5e-04, dppm = 2, int_prec = 0.02, limit = 0, rnd_prec = 0 ) ``` ## 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. ## Returns Scalar mScore giving the quality of the observed data if theoretical data are true. ## 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 ```r # 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) ```

mScore function