alignmsbatch {LipidMS}

Align samples from an msbatch

Description

Align samples from an msbatch to correct time drifts during acquisition queues.

Usage

alignmsbatch(
  msbatch,
  dmz = 5,
  drt = 30,
  minsamples,
  minsamplesfrac = 0.75,
  span = 0.4,
  parallel = FALSE,
  ncores,
  verbose = TRUE
)

Arguments

msbatch

msbatch obtained from the setmsbatch function.

dmz

mass tolerance between peak groups in ppm.

drt

maximum rt distance between peaks for alignment in seconds.

minsamples

minimum number of samples represented in each cluster used for the alignment.

minsamplesfrac

minimum samples fraction represented in each cluster used for the alignment. Used to calculate minsamples in case it is missing.

span

span parameter for loess rt deviation smoothing.

parallel

logical. If TRUE, parallel processing will be performed.

ncores

number of cores to be used in case parallel is TRUE.

verbose

print information messages.

Details

First, peak partitions are created based on the enviPick algorithm to speed up the following clustering algorithm. Briefly, peaks are ordered increasingly by mz and RT and grouped based on user-defined tolerances (dmz and drt). Each peak is initialized as a partition and then, they are evaluated to decide whether or not they can be joined to the previous partition. If mz and RT of a peak matches tolerance of any of the peaks in the previous partition, it is reassigned. Then, clustering algorithm is executed to group peaks based on their RT following the next steps for each partition:

1. Each peak in the partition is initialized as a new cluster. For each cluster we will keep the minimum, maximum and mean value of the RT, which at this point have the same values. 2. Calculate a distance matrix between all clusters. This distance will be the greatest difference between minimum and maximum values of each cluster. Distances between clusters which share peaks from the same samples will be set to NA. 3. While any distance is different to NA, search the minimum distance between two clusters. 4. If distance is below the maximum distance allowed, join clusters and update minimum, maximum and mean values, else, set distance to NA and go back to point 3.

Then, clusters with a sample representation over minsamples or minsamplesfrac, will be used for alignment. To this end, an RT matrix is built containing the RT of the peaks for each sample from the selected clusters. Then, median RT is calculated for each cluster and an RT deviation matrix is obtained. Finally, time drifts for each sample are corrected using loess regression by constructing a function based on RT deviation and median.

Value

aligned msbatch

Author(s)

M Isabel Alcoriza-Balaguer <[e-mail]>

References

Partitioning algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html

Examples

## Not run: 
msbatch <- alignmsbatch(msbatch)

## End(Not run)