labelRows: Annotate and Remove Report Rows
In metabCombiner: Method for Combining LC-MS Metabolomics Feature Measurements

Description Usage Arguments Details Value Examples

Method for annotation of identity-matched, removable, & conflicting feature pair alignments (FPAs) in combinedTable. FPAs that fall within some small measure (in score or mz/rt) of the top-ranked FPA may require further inspection are organized into subgroups. .

labelRows(
  object,
  maxRankX = 3,
  maxRankY = 3,
  minScore = 0.3,
  conflict,
  method = c("score", "mzrt"),
  balanced = TRUE,
  remove = FALSE,
  brackets_ignore = c("(", "[", "{")
)

`object`	Either a `metabCombiner` object or `combinedTable`.
`maxRankX`	Integer. Maximum allowable rank for X dataset features.
`maxRankY`	Integer. Maximum allowable rank for Y dataset features.
`minScore`	Numeric. Minimum allowable score (between 0 & 1) for metabolomics FPAs.
`conflict`	numeric used to determine subgroups. If method = "score", a constant (between 0 & 1) score difference between a pair of conflicting FPAs. If method = "mzrt", a length 4 numeric: (m/z, rt, m/z, rt) tolerances, the first pair for X dataset features and the second pair for Y dataset features.
`method`	Conflict detection method. If equal to "score" (default), assigns a conflict subgroup if score of lower-ranking FPA is within some tolerance of higher-ranking FPA. If set to "mzrt", assigns a conflicting subgroup if within a small m/z & rt distance of the top-ranked FPA.
`balanced`	Logical. Optional processing of "balanced" groups, defined as groups with an equal number of features from input datasets where all features have a 1-1 match.
`remove`	Logical. Option to keep or discard rows deemed removable.
`brackets_ignore`	character. Bracketed identity strings of the types in this argument will be ignored

metabCombiner initially reports all possible FPAs in the rows of the combinedTable report. Most of these are misalignments that require removal. This function is used to automate most of the reduction process by labeling rows as removable or conflicting, based on certain conditions, and is performed after computing similarity scores.

A label may take on one of four values:

a) "": No determination made b) "IDENTITY": an alignment with matching identity "idx & idy" strings c) "REMOVE": a row determined to be a misalignment d) "CONFLICT": competing alignments for one or multiple shared features

The labeling rules are as follows: 1) Rows with matching idx & idy strings are labeled "IDENTITY". These rows are not labeled "REMOVE", irrespective of subsequent criteria. 2) Groups determined to be 'balanced': label rows with rankX > 1 & rankY > 1 "REMOVE" irrespective of conflict criteria 3) Rows with a score < minScore: label "REMOVE" 4) Rows with rankX > maxRankX and/or rankY > maxRankY: label "REMOVE" 5) Conflicting subgroup assignment as determined by method & conflict arguments. Conflicting alignments following outside conflict thresholds: labeled "REMOVE". Otherwise,

updated combinedTable or metabCombiner object. The table will have three new columns:

`labels`	characterization of feature alignments as described
`subgroup`	conflicting subgroup number of feature alignments
`alt`	alternate subgroup for rows in multiple feature pair conflicts

data(plasma30)
data(plasma20)

p30 <- metabData(plasma30, samples = "CHEAR")
p20 <- metabData(plasma20, samples = "Red", rtmax = 17.25)
p.comb = metabCombiner(xdata = p30, ydata = p20, binGap = 0.0075)
p.comb = selectAnchors(p.comb, tolmz = 0.003, tolQ = 0.3, windy = 0.02)
p.comb = fit_gam(p.comb, k = 20, iterFilter = 1)
p.comb = calcScores(p.comb, A = 90, B = 14, C = 0.5)
cTable = combinedTable(p.comb)

##example using score-based conflict detection method
lTable = labelRows(cTable, maxRankX = 3, maxRankY = 2, minScore = 0.5,
    method = "score", conflict = 0.2)

##example using mzrt-based conflict detection method
lTable = labelRows(cTable, method = "mzrt", maxRankX = 3, maxRankY = 2,
                     conflict = c(0.005, 1, 0.005,0.5))