batchCombine: Stepwise Multi-batch LC-MS Alignment

In hhabra/metabCombiner: Method for Combining LC-MS Metabolomics Feature Measurements

Stepwise Multi-batch LC-MS Alignment

Description

This is a method for aligning multiple batches of a single metabolomics experiment in a stepwise manner using the metabCombiner workflow. The input is a list of metabData objects corresponding to the batch data frames arranged in sequential order (i.e. batch 1,2,...,N), and parameter lists for each step; the output is an aligned feature table and a metabCombiner object composed from the input batches.

Usage

batchCombine(
  batches,
  binGap = 0.005,
  fitMethod = "gam",
  means = list(mz = TRUE, rt = TRUE, Q = TRUE),
  union = FALSE,
  anchorParam = selectAnchorsParam(),
  fitParam = fitgamParam(),
  scoreParam = calcScoresParam(B = 30),
  reduceParam = reduceTableParam()
)

Arguments

batches	list of metabData objects corresponding to each LC-MS batch
binGap	numeric parameter used for grouping features by m/z. See ?mzGroup for more details.
fitMethod	RT spline-fitting method, either "gam" or "loess"
means	logical. Option to take average m/z, rt, and/or Q from metabCombiner. May be a 3-length vector, single value (TRUE/FALSE), or a list with names "mz", "rt", "Q" as names.
union	logical. If FALSE, only feature present in all batches will be in the final result. If TRUE, features missing in at least one batch are included. The mean m/z, RT, and Q values imputed for matching in each step.
anchorParam	list of parameter values for selectAnchors() function
fitParam	list of parameter values for fit_gam() or fit_loess()
scoreParam	list of parameter values for calcScores()
reduceParam	list of parameter values for reduceTable()

Details

Retention time drifting is commonly observed in large-scale LC-MS experiments in which samples are analyzed in multiple batches. Conventional LC-MS pre-processing approaches may effectively align features detected in samples from within a single batch, but fail in many cases to account for inter-batch drifting, leading to misaligned features.

batchCombine assumes that each batch has been previously processed separately using conventional LC-MS preprocessing approaches (e.g. XCMS), and can be represented as a data frame. Each batch data feature table must be filtered and formatted as a metabData object and the batches must be arranged as a list in sequential order of acquisition.

batchCombine applies the metabCombine wrapper function to successive pairs of metabolomics batches in a stepwise manner. Each iteration consists of the key steps in the package workflow (feature m/z grouping, anchor selection, retention time spline fitting, pairwise scoring, & table reduction). The first two batches are aligned together, then the combined results are aligned with the third batch, and so forth. Parameters for each sub-method are arranged in list format, with their respective defaults (e.g. fitgamParam() lists the default values for the fit_gam function).

Following each iteration, m/z, rt, and Q values from the combined dataset may be averaged to use for comparison with the next batch's feature quantitative descriptors, if the means argument is set to TRUE; if set to FALSE, feature information is drawn from the latter of the previously combined batches, identical to the manner in which id & adduct descriptors are drawn.

Value

object	metabCombiner object of the final alignment; x is set to the penultimate batch and y is set to the final batch
table	combined feature table consisting of feature descriptor values followed by per-sample abundances and extra columns

Note

batchCombine is designed for aligning multi-batch datasets, i.e. where each batch is acquired in a roughly identical manner. It is not for disparately acquired LC-MS datasets (e.g. from different instruments, chromatographic systems, laboratories, etc.).

See Also

metabCombine

Examples

#identically formatted batches in list form
data(metabBatches)

#obtain list of metabData objects
batchdata <- lapply(metabBatches, metabData, samples = "POOL",
                    extra = "SAMP", zero = TRUE)

#recommended: give each batch dataset a unique name
names(batchdata) <- paste("exb", seq_along(batchdata), sep = "")

#customize main workflow parameter lists
saparam <- selectAnchorsParam(tolmz = 0.002, tolQ = 0.2, tolrtq = 0.1)
fgparam <- fitgamParam(k = 20, iterFilter = 1)
csparam <- calcScoresParam(A = 70, B = 35, C = 0.3)
rtparam <- reduceTableParam(minScore = 0.5, maxRTerr = 0.33)

#run batchCombine program
combinedRes <- batchCombine(batches = batchdata, binGap = 0.0075,
               means = list('mz' = TRUE, 'rt' = FALSE, 'Q' = FALSE),
               anchorParam = saparam, fitParam = fgparam,
               scoreParam = csparam, reduceParam = rtparam)

#aligned table results & metabCombiner object results
cTable <- combinedRes$table
object <- combinedRes$object

#if names were set earlier, the names should be returned by this
datasets(object)

hhabra/metabCombiner documentation built on June 5, 2024, 5:46 a.m.