labelRows {metabCombiner} | R Documentation |
This is a method for annotating identity-matched, removable, &
conflicting feature pair alignment (FPA) rows in the combinedTable
report. Simple thresholds for score, rank, retention time error and delta
score can computationally reduce the set of possible FPAs to the most likely
compound matches. FPAs falling within some small measure (in score or mz/rt)
of the top-ranked row are organized into subgroups to facilitate inspection;
setting delta to 0 automatically reduces to 1-1 matches.
reduceTable
behaves identically to labelRows, but with delta set to 0
& remove set to TRUE, automatically limiting to 1 - 1 feature matches
constrained by rank and score threshold parameters. Rank threshold defaults
are also stricter with reduceTable.
labelRows( object, minScore = 0.5, maxRankX = 3, maxRankY = 3, method = c("score", "mzrt"), delta = 0.1, maxRTerr = 10, remove = FALSE, balanced = TRUE, brackets_ignore = c("(", "[", "{") ) reduceTable( object, maxRankX = 2, maxRankY = 2, minScore = 0.5, maxRTerr = 10, brackets_ignore = c("(", "[", "{") )
object |
Either a |
minScore |
numeric minimum allowable score (between 0 & 1) for metabolomics feature pair alignments |
maxRankX |
integer maximum allowable rank for X dataset features. |
maxRankY |
integer maximum allowable rank 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. |
delta |
numeric score or mz/rt distances used to define subgroups. If method = "score", a value (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. |
maxRTerr |
numeric maximum allowable error between model-projected retention time (rtProj) and observed retention time (rty) |
remove |
Logical. Option to keep or discard rows deemed removable. |
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. |
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 delta
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
&
delta
arguments. Conflicting alignments following outside
delta
thresholds: labeled "REMOVE". Otherwise, they are assigned
a "CONFLICT" label and subgroup number.
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) ###merge combinedTable and featdata cTable = cbind.data.frame(combinedTable(p.comb), featdata(p.comb)) ##example using score-based conflict detection method lTable = labelRows(cTable, maxRankX = 3, maxRankY = 2, minScore = 0.5, method = "score", maxRTerr = 0.5, delta = 0.2) ##example using mzrt-based conflict detection method lTable = labelRows(cTable, method = "mzrt", maxRankX = 3, maxRankY = 2, delta = c(0.005, 0.5, 0.005,0.3), maxRTerr = 0.5)