ticstats: Compute and Display Total Ion Count (TIC) statistics

Description Usage Arguments Details Value Author(s) See Also Examples


By definition the total ion count of a spectrum is the sum of all the m/z signal intensities (sum over the columns). A robust regression can be built to model the effect of the injection order on the TIC of each sample. The fitting residuals are used to evaluate the median of the absolute deviations (MAD) from the linear equation. Sample TIC and linear model are then plotted according to the injection order to identify potential outliers and or structure in the data.





A numeric data frame or matrix to be processed.


A numeric vector corresponding to the injection order of each sample.


A numeric value of threshold for detecting outlier.


As an easy diagnostic measure, the TIC can provide an estimation of factors that may affect the overall intensity of the run such as gradual instrument drift (e.g. resulting from loss of sensitivity of the ion source), or step changes in instrument characteristics after maintenance. Also, an examination of the TIC can reveal suspicious samples where unusually low or especially high signal intensities in some runs may be due to contamination or poorly extracted samples. A regression can be built to model the effect of the injection order on the TIC of each sample. As a conservative rule, any sample that deviates more than 2/3 (argument thres) times from the MAD must be examined manually to identify the origin of the different intensity behaviour and then removed before further statistical analysis if corrective measures do not improve the individual fingerprint. Further assessment of outlying samples is discussed later. In any case where a linear relationship (i.e. gradually changing TIC in sample set) is observed between the injection order and sample TIC, this dependency will be removed by TIC normalisation. If other structure related to the order of injection is noticed, for example an analytical batch effect (i.e. a step change in TIC at beginning or in middle of an injection series), the user must identify its potential origin (e.g. changes in machine calibration or mobile phase) and possibly create a new experimental factor (batch) where each step change in level corresponds to the start of a new batch.


A list containing the following components:


Sample residuals.


Robust linear model.


List of outlying samples as defined by the thres argument.


David Enot and Wanchang Lin dle,[email protected]

See Also



  dat <- abr1$pos
  res <- ticstats(dat,injorder=NULL)

wilsontom/FIEmspro documentation built on Feb. 19, 2018, 9:03 a.m.