calcScores | R Documentation |

Calculates a pairwise similarity (between 0 & 1) between all
grouped features in `metabCombiner`

object. The similarity score
calculation is described in `scorePairs`

.

calcScores( object, A = 75, B = 10, C = 0.25, fit = c("gam", "loess"), groups = NULL, useAdduct = FALSE, adduct = 1.25, usePPM = FALSE, brackets_ignore = c("(", "[", "{") )

`object` |
metabCombiner object. |

`A` |
Numeric weight for penalizing m/z differences. |

`B` |
Numeric weight for penalizing differences between fitted & observed retention times |

`C` |
Numeric weight for differences in Q (abundance quantiles). |

`fit` |
Character. Choice of fitted rt model, "gam" or "loess." |

`groups` |
integer. Vector of feature groups to score. If set to NULL (default), will compute scores for all feature groups. |

`useAdduct` |
logical. Option to penalize mismatches in (non-empty, non-bracketed) adduct column annotations. |

`adduct` |
numeric. If useAdduct is TRUE, divides score of mismatched, non-empty and non-bracked adduct column labels by this value. |

`usePPM` |
logical. Option to use relative (as opposed to absolute) m/z differences in score computations. |

`brackets_ignore` |
If useAdduct = TRUE, bracketed adduct character strings of these types will be ignored according to this argument |

This function updates the `rtProj`

, `score`

, `rankX`

, and
`rankY`

columns in the `combinedTable`

report. First, using the
RT mapping model computed in the previous step(s), `rtx`

values are
projected onto `rty`

. Then similarity scores are calculated based on
m/z, rt (fitted vs observed), and Q differences, with multiplicative weight
penalties `A`

, `B`

, and `C`

.

If the datasets contain representative set of shared identities (idx = idy),
`evaluateParams`

provides some guidance on appropriate `A`

,
`B`

, and `C`

values to use. In testing, the best values for
`A`

should lie between 50 and 120, according to mass accuracy;
`B`

should lie between 5 and 15 depending on fitting accuracy (higher
if datasets processed under roughly identical conditions) ; `C`

should
vary between 0 and 1, depending on sample similarity. See examples below.

If using ppm (`usePPM`

= TRUE), do not use the above guidelines for
`A`

values. The suggested range is between 0.01 and 0.05, though this
hasn't been thoroughly tested yet. Also, if using adduct information
(`useAdduct`

= TRUE), the score is divided by the numeric `adduct`

argument if non-empty and non-bracketed adduct values do not match. Be sure
that adduct annotations are accurate before using this functionality.

`metabCombiner`

object with updated `combinedTable`

.
rtProj column will contain fitted retention times determined from previously
computed model; score will contain computed pairwise similarity scores of
feature pairs; rankX & rankY are the integer ranks of scores for x & y
features in descending order.

`evaluateParams`

, `scorePairs`

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, family = "gaussian") #example: moderate m/z deviation, accurate rt fit, high sample similarity p.comb <- calcScores(p.comb, A = 90, B = 14, C = 0.8, useAdduct = FALSE, groups = NULL, fit = "gam", usePPM = FALSE) cTable = combinedTable(p.comb) #to view results #example 2: high m/z deviation, moderate rt fit, low sample similarity p.comb <- calcScores(p.comb, A = 50, B = 8, C = 0.2) #example 3: low m/z deviation, poor rt fit, moderate sample similarity p.comb <- calcScores(p.comb, A = 120, B = 5, C = 0.5) #example 4: using ppm for mass deviation; note different A value p.comb <- calcScores(p.comb, A = 0.05, B = 14, C = 0.5, usePPM = TRUE) #example 5: limiting to specific m/z groups 1-1000 p.comb <- calcScores(p.comb, A = 90, B = 14, C = 0.5, groups = seq(1,1000)) #example 6: using adduct information p.comb <- calcScores(p.comb, A = 90, B = 14, C = 0.5, useAdduct = TRUE, adduct = 1.25)

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