Matching of numeric values

The matchValues method matches elements from query with those in target using different matching approaches depending on parameter param. Generally, query is expected to contain MS experimental values (m/z and possibly retention time) while target reference values. query and target can be numeric, a two dimensional array (such as a data.frame, matrix or DataFrame), a SummarizedExperiment or a QFeatures, target can in addition be a Spectra::Spectra() object. For SummarizedExperiment, the information for the matching is expected to be in the object's rowData. For QFeatures matching is performed for values present in the rowData of one of the object's assays (which needs to be specified with the assayQuery parameter - if a QFeatures is used as target the name of the assay needs to be specified with parameter assayTarget). If target is a Spectra matching is performed against spectra variables of this object and the respective variable names need to be specified e.g. with mzColname and/or rtColname. matchMz is an alias for matchValues to allow backward compatibility.

Available param objects and corresponding matching approaches are:

• ValueParam: generic matching between values in query and target given acceptable differences expressed in ppm and tolerance. If query or target are not numeric, parameter valueColname has to be used to specify the name of the column that contains the values to be matched. The function returns a Matched() object.

• MzParam: match query m/z values against reference compounds for which also m/z are known. Matching is performed similarly to the ValueParam above. If query or target are not numeric, the column name containing the values to be compared must be defined with matchValues' parameter mzColname, which defaults to "mz". MzParam parameters tolerance and ppm allow to define the maximal acceptable (constant or m/z relative) difference between query and target m/z values.

• MzRtParam: match m/z and retention time values between query and target. Parameters mzColname and rtColname of the matchValues function allow to define the columns in query and target containing these values (defaulting to c("mz", "mz") and c("rt", "rt"), respectively). MzRtParam parameters tolerance and ppm have the same meaning as in MzParam; MzRtParam parameter toleranceRt allows to specify the maximal acceptable difference between query and target retention time values.

• Mass2MzParam: match m/z values against reference compounds for which only the (exact) mass is known. Before matching, m/z values are calculated from the compounds masses in the target table using the adducts specified via Mass2MzParam adducts parameter (defaults to adducts = "[M+H]+"). After conversion of adduct masses to m/z values, matching is performed similarly to MzParam (i.e. the same parameters ppm and tolerance can be used). If query is not numeric, parameter mzColname of matchValues can be used to specify the column containing the query's m/z values (defaults to "mz"). If target is a is not numeric, parameter massColname can be used to define the column containing the reference compound's masses (defaults to "exactmass").

• Mass2MzRtParam: match m/z and retention time values against reference compounds for which the (exact) mass and retention time are known. Before matching, exact masses in target are converted to m/z values as for Mass2MzParam. Matching is then performed similarly to MzRtParam, i.e. m/z and retention times of entities are compared. With matchValues' parameters mzColname, rtColname and massColname the columns containing m/z values (in query), retention time values (in query and target) and exact masses (in target) can be specified.

• Mz2MassParam: input values for query and target are expected to be m/z values but matching is performed on exact masses calculated from these (based on the provided adduct definitions). In detail, m/z values in query are first converted to masses with the MetaboCoreUtils::mz2mass() function based on the adducts defined with queryAdducts (defaults to "[M+H]+"). The same is done for m/z values in target (adducts can be defined with targetAdducts which defaults to "[M-H-]"). Matching is then performed on these converted values similarly to ValueParam. If queryortargetare not numeric, the column containing the m/z values can be specified withmatchValues' parameter mzColname(defaults to"mz"`).

• Mz2MassRtParam: same as Mz2MassParam but with additional comparison of retention times between query and target. Parameters rtColname and mzColname of matchValues allow to specify which columns contain the retention times and m/z values, respectively.

Usage

ValueParam(tolerance = 0, ppm = 5)

MzParam(tolerance = 0, ppm = 5)

Mass2MzParam(adducts = c("[M+H]+"), tolerance = 0, ppm = 5)

Mass2MzRtParam(adducts = c("[M+H]+"), tolerance = 0, ppm = 5, toleranceRt = 0)

MzRtParam(tolerance = 0, ppm = 0, toleranceRt = 0)

Mz2MassParam(
  queryAdducts = c("[M+H]+"),
  targetAdducts = c("[M-H]-"),
  tolerance = 0,
  ppm = 5
)

Mz2MassRtParam(
  queryAdducts = c("[M+H]+"),
  targetAdducts = c("[M+H]+"),
  tolerance = 0,
  ppm = 5,
  toleranceRt = 0
)

matchValues(query, target, param, ...)

# S4 method for class 'numeric,numeric,ValueParam'
matchValues(query, target, param)

# S4 method for class 'numeric,data.frameOrSimilar,ValueParam'
matchValues(
  query,
  target,
  param,
  valueColname = character(),
  targetAssay = character()
)

# S4 method for class 'data.frameOrSimilar,numeric,ValueParam'
matchValues(
  query,
  target,
  param,
  valueColname = character(),
  queryAssay = character()
)

# S4 method for class 'data.frameOrSimilar,data.frameOrSimilar,ValueParam'
matchValues(
  query,
  target,
  param,
  valueColname = character(),
  queryAssay = character(),
  targetAssay = character()
)

# S4 method for class 'numeric,numeric,Mass2MzParam'
matchValues(query, target, param)

# S4 method for class 'numeric,data.frameOrSimilar,Mass2MzParam'
matchValues(
  query,
  target,
  param,
  massColname = "exactmass",
  targetAssay = character()
)

# S4 method for class 'data.frameOrSimilar,numeric,Mass2MzParam'
matchValues(query, target, param, mzColname = "mz", queryAssay = character())

# S4 method for class 'data.frameOrSimilar,data.frameOrSimilar,Mass2MzParam'
matchValues(
  query,
  target,
  param,
  mzColname = "mz",
  massColname = "exactmass",
  queryAssay = character(0),
  targetAssay = character(0)
)

# S4 method for class 'numeric,data.frameOrSimilar,MzParam'
matchValues(query, target, param, mzColname = "mz", targetAssay = character())

# S4 method for class 'numeric,Spectra,MzParam'
matchValues(query, target, param, mzColname = "mz", targetAssay = character())

# S4 method for class 'data.frameOrSimilar,numeric,MzParam'
matchValues(query, target, param, mzColname = "mz", queryAssay = character())

# S4 method for class 'data.frameOrSimilar,data.frameOrSimilar,MzParam'
matchValues(
  query,
  target,
  param,
  mzColname = c("mz", "mz"),
  queryAssay = character(),
  targetAssay = character()
)

# S4 method for class 'data.frameOrSimilar,Spectra,MzParam'
matchValues(
  query,
  target,
  param,
  mzColname = c("mz", "mz"),
  queryAssay = character(),
  targetAssay = character()
)

# S4 method for class 'data.frameOrSimilar,data.frameOrSimilar,Mass2MzRtParam'
matchValues(
  query,
  target,
  param,
  massColname = "exactmass",
  mzColname = "mz",
  rtColname = c("rt", "rt"),
  queryAssay = character(),
  targetAssay = character()
)

# S4 method for class 'data.frameOrSimilar,data.frameOrSimilar,MzRtParam'
matchValues(
  query,
  target,
  param,
  mzColname = c("mz", "mz"),
  rtColname = c("rt", "rt"),
  queryAssay = character(),
  targetAssay = character()
)

# S4 method for class 'data.frameOrSimilar,Spectra,MzRtParam'
matchValues(
  query,
  target,
  param,
  mzColname = c("mz", "mz"),
  rtColname = c("rt", "rt"),
  queryAssay = character(),
  targetAssay = character()
)

# S4 method for class 'numeric,numeric,Mz2MassParam'
matchValues(query, target, param)

# S4 method for class 'numeric,data.frameOrSimilar,Mz2MassParam'
matchValues(query, target, param, mzColname = "mz", targetAssay = character())

# S4 method for class 'data.frameOrSimilar,numeric,Mz2MassParam'
matchValues(query, target, param, mzColname = "mz", queryAssay = character())

# S4 method for class 'data.frameOrSimilar,data.frameOrSimilar,Mz2MassParam'
matchValues(
  query,
  target,
  param,
  mzColname = c("mz", "mz"),
  queryAssay = character(),
  targetAssay = character()
)

# S4 method for class 'data.frameOrSimilar,data.frameOrSimilar,Mz2MassRtParam'
matchValues(
  query,
  target,
  param,
  mzColname = c("mz", "mz"),
  rtColname = c("rt", "rt"),
  queryAssay = character(),
  targetAssay = character()
)

Arguments

tolerance

for any param object: numeric(1) defining the maximal acceptable absolute difference in m/z (or in mass for Mz2MassParam) to consider them matching.

ppm

for any param object: numeric(1) defining the maximal acceptable m/z-dependent (or mass-dependent for Mz2MassParam) difference (in parts-per-million) in m/z values to consider them to be matching.

adducts

for Mass2MzParam or Mass2MzRtParam: either character with adduct names from MetaboCoreUtils::adducts() or data.frame with a custom adduct definition. This parameter is used to calculate m/z from target compounds' masses. Custom adduct definitions can be passed to the adduct parameter in form of a data.frame. This data.frame is expected to have columns "mass_add" and "mass_multi" defining the additive and multiplicative part of the calculation. See MetaboCoreUtils::adducts() for the expected format or use MetaboCoreUtils::adductNames("positive") and MetaboCoreUtils::adductNames("negative") for valid adduct names.

toleranceRt

for Mass2MzRtParam or MzRtParam: numeric(1) defining the maximal acceptable absolute difference in retention time values to consider them them matching.

queryAdducts

for Mz2MassParam. Adducts used to derive mass values from query m/z values. The expected format is the same as that for parameter adducts.

targetAdducts

for Mz2MassParam. Adducts used to derive mass values from target m/z values. The expected format is the same as that for parameter adducts.

query

feature table containing information on MS1 features. Can be a numeric, data.frame, DataFrame, matrix, SummarizedExperiment or QFeatures. It is expected to contain m/z values and can contain also other variables. Matchings based on both m/z and retention time can be performed when a column with retention times is present in both query and target.

target

compound table with metabolites to compare against. The expected types are the same as those for query.

param

parameter object defining the matching approach and containing the settings for that approach. See description above for details.

...

currently ignored.

valueColname

character specifying the name of the column in query or/and the one in targetwith the desired values for the matching. This parameter should only be used when param is valueParam and in this case it must be provided (unless both query and target are numeric). It can be character(1) or character(2) in a similar way to mzColname.

targetAssay

character(1) specifying the name of the assay of the provided QFeatures that should be used for the matching (values from this assay's rowData will be used for matching). Only used if target is an instance of a QFeatures object.

queryAssay

character(1) specifying the name of the assay of the provided QFeatures that should be used for the matching (values from this assay's rowData will be used for matching). Only used if query is an instance of a QFeatures object.

massColname

character(1) with the name of the column in target containing the mass of compounds. To be used when param is Mass2MzParam or Mass2MzRtParam (and target is not already numeric with the masses). Defaults to massColname = "exactmass".

mzColname

character specifying the name(s) of the column(s) in query or/and targetwith the m/z values. If one among query and target is numeric (and therefore there is no need to specify the column name) or query is not numeric and param is Mass2MzParam or Mass2MzRtParam (and therefore the name of the column with m/z needs only to be specified for query) then mzColname is expected to be character(1). If both query and target are not numeric mzColname is expected to be character(2) (or character(1) and in this last case the two column names are assumed to be the same). If not specified the assumed default name for columns with m/z values is "mz". If target is a Spectra::Spectra() object, the name of the spectra variable that should be used for the matching needs to be specified with mzColname.

rtColname

character(2) with the name of the column containing the compounds retention times in query and the name for the one in target. It can also be character(1) if the two names are the same. To be used when param is MzRtParam or Mass2MzRtParam. Defaults to rtColname = c("rt", "rt"). If target is a Spectra::Spectra() object, the name of the spectra variable that should be used for the matching needs to be specified with mzColname.

Value

Matched object representing the result.

Depending on the param object different scores representing the quality of the match are provided. This comprises absolute as well as relative differences (column/variables "score" and "ppm_error" respectively). If param is a Mz2MassParam, "score" and "ppm_error" represent differences of the compared masses (calculated from the provided m/z values). If param an MzParam, MzRtParam, Mass2MzParam or Mass2MzRtParam, "score" and "ppm_error" represent absolute and relative differences of m/z values. Additionally, if param is either an MzRtParam or Mass2MzRtParam differences between query and target retention times for each matched element is available in the column/variable "score_rt" in the returned Matched object. Negative values of "score" (or "score_rt") indicate that the m/z or mass (or retention time) of the query element is smaller than that of the target element.

See also

matchSpectra or CompareSpectraParam() for spectra data matching

Author

Andrea Vicini, Michael Witting

Examples

library(MetaboCoreUtils)
#> 
#> Attaching package: ‘MetaboCoreUtils’
#> The following object is masked from ‘package:CompoundDb’:
#> 
#>     mass2mz
## Create a simple "target/reference" compound table
target_df <- data.frame(
   name = c("Tryptophan", "Leucine", "Isoleucine"),
   formula = c("C11H12N2O2", "C6H13NO2", "C6H13NO2"),
   exactmass = c(204.089878, 131.094629, 131.094629)
)

## Create a "feature" table with m/z of features. We calculate m/z for
## certain adducts of some of the compounds in the reference table.
fts <- data.frame(
    feature_id = c("FT001", "FT002", "FT003"),
    mz = c(mass2mz(204.089878, "[M+H]+"),
           mass2mz(131.094629, "[M+H]+"),
           mass2mz(204.089878, "[M+Na]+") + 1e-6))

## Define the parameters for the matching
parm <- Mass2MzParam(
    adducts = c("[M+H]+", "[M+Na]+"),
    tolerance = 0,
    ppm = 20)
res <- matchValues(fts, target_df, parm)
res
#> Object of class Matched 
#> Total number of matches: 4 
#> Number of query objects: 3 (3 matched)
#> Number of target objects: 3 (3 matched)

## List the available variables/columns
colnames(res)
#> [1] "feature_id"       "mz"               "target_name"      "target_formula"  
#> [5] "target_exactmass" "adduct"           "score"            "ppm_error"       

## feature_id and mz are from the query data frame, while target_name,
## target_formula and target_exactmass are from the query object (columns
## from the target object have a prefix *target_* added to the original
## column name. Columns adduct, score and ppm_error represent the results
## of the matching: adduct the adduct/ion of the original compound for which
## the m/z matches, score the absolute difference of the query and target
## m/z and ppm_error the relative difference in m/z values.

## Get the full matching result:
matchedData(res)
#> DataFrame with 4 rows and 8 columns
#>      feature_id        mz target_name target_formula target_exactmass
#>     <character> <numeric> <character>    <character>        <numeric>
#> 1         FT001   205.097  Tryptophan     C11H12N2O2          204.090
#> 2         FT002   132.102     Leucine       C6H13NO2          131.095
#> 2.1       FT002   132.102  Isoleucine       C6H13NO2          131.095
#> 3         FT003   227.079  Tryptophan     C11H12N2O2          204.090
#>          adduct     score  ppm_error
#>     <character> <numeric>  <numeric>
#> 1        [M+H]+     0e+00 0.00000000
#> 2        [M+H]+     0e+00 0.00000000
#> 2.1      [M+H]+     0e+00 0.00000000
#> 3       [M+Na]+     1e-06 0.00440375

## We have thus matches of FT002 to two different compounds (but with the
## same mass).

## Individual columns can also be accessed with the $ operator:
res$feature_id
#> [1] "FT001" "FT002" "FT002" "FT003"
res$target_name
#> [1] "Tryptophan" "Leucine"    "Isoleucine" "Tryptophan"
res$ppm_error
#> [1] 0.000000000 0.000000000 0.000000000 0.004403752


## We repeat the matching requiring an exact match
parm <- Mass2MzParam(
    adducts = c("[M+H]+", "[M+Na]+"),
    tolerance = 0,
    ppm = 0)
res <- matchValues(fts, target_df, parm)
res
#> Object of class Matched 
#> Total number of matches: 3 
#> Number of query objects: 3 (2 matched)
#> Number of target objects: 3 (3 matched)

matchedData(res)
#> DataFrame with 4 rows and 8 columns
#>      feature_id        mz target_name target_formula target_exactmass
#>     <character> <numeric> <character>    <character>        <numeric>
#> 1         FT001   205.097  Tryptophan     C11H12N2O2          204.090
#> 2         FT002   132.102     Leucine       C6H13NO2          131.095
#> 2.1       FT002   132.102  Isoleucine       C6H13NO2          131.095
#> 3         FT003   227.079          NA             NA               NA
#>          adduct     score ppm_error
#>     <character> <numeric> <numeric>
#> 1        [M+H]+         0         0
#> 2        [M+H]+         0         0
#> 2.1      [M+H]+         0         0
#> 3            NA        NA        NA

## The last feature could thus not be matched to any compound.

## At last we use also different adduct definitions.
parm <- Mass2MzParam(
    adducts = c("[M+K]+", "[M+Li]+"),
    tolerance = 0,
    ppm = 20)
res <- matchValues(fts, target_df, parm)
res
#> Object of class Matched 
#> Total number of matches: 0 
#> Number of query objects: 3 (0 matched)
#> Number of target objects: 3 (0 matched)

matchedData(res)
#> DataFrame with 3 rows and 8 columns
#>    feature_id        mz target_name target_formula target_exactmass      adduct
#>   <character> <numeric> <character>    <character>        <numeric> <character>
#> 1       FT001   205.097          NA             NA               NA          NA
#> 2       FT002   132.102          NA             NA               NA          NA
#> 3       FT003   227.079          NA             NA               NA          NA
#>       score ppm_error
#>   <numeric> <numeric>
#> 1        NA        NA
#> 2        NA        NA
#> 3        NA        NA

## No matches were found.

## We can also match a "feature" table with a target data.frame taking into
## account both m/z and retention time values.
target_df <- data.frame(
  name = c("Tryptophan", "Leucine", "Isoleucine"),
  formula = c("C11H12N2O2", "C6H13NO2", "C6H13NO2"),
  exactmass = c(204.089878, 131.094629, 131.094629),
  rt = c(150, 140, 140)
)

fts <- data.frame(
  feature_id = c("FT001", "FT002", "FT003"),
  mz = c(mass2mz(204.089878, "[M+H]+"),
         mass2mz(131.094629, "[M+H]+"),
         mass2mz(204.089878, "[M+Na]+") + 1e-6),
  rt = c(150, 140, 150.1)
)

## Define the parameters for the matching
parm <- Mass2MzRtParam(
  adducts = c("[M+H]+", "[M+Na]+"),
  tolerance = 0,
  ppm = 20,
  toleranceRt = 0)

res <- matchValues(fts, target_df, parm)
res
#> Object of class Matched 
#> Total number of matches: 3 
#> Number of query objects: 3 (2 matched)
#> Number of target objects: 3 (3 matched)

## Get the full matching result:
matchedData(res)
#> DataFrame with 4 rows and 11 columns
#>      feature_id        mz        rt target_name target_formula target_exactmass
#>     <character> <numeric> <numeric> <character>    <character>        <numeric>
#> 1         FT001   205.097     150.0  Tryptophan     C11H12N2O2          204.090
#> 2         FT002   132.102     140.0     Leucine       C6H13NO2          131.095
#> 2.1       FT002   132.102     140.0  Isoleucine       C6H13NO2          131.095
#> 3         FT003   227.079     150.1          NA             NA               NA
#>     target_rt      adduct     score ppm_error  score_rt
#>     <numeric> <character> <numeric> <numeric> <numeric>
#> 1         150      [M+H]+         0         0         0
#> 2         140      [M+H]+         0         0         0
#> 2.1       140      [M+H]+         0         0         0
#> 3          NA          NA        NA        NA        NA

## FT003 could not be matched to any compound, FT002 was matched to two
## different compounds (but with the same mass).

## We repeat the matching allowing a positive tolerance for the matches
## between rt values

## Define the parameters for the matching
parm <- Mass2MzRtParam(
  adducts = c("[M+H]+", "[M+Na]+"),
  tolerance = 0,
  ppm = 20,
  toleranceRt = 0.1)

res <- matchValues(fts, target_df, parm)
res
#> Object of class Matched 
#> Total number of matches: 4 
#> Number of query objects: 3 (3 matched)
#> Number of target objects: 3 (3 matched)

## Get the full matching result:
matchedData(res)
#> DataFrame with 4 rows and 11 columns
#>      feature_id        mz        rt target_name target_formula target_exactmass
#>     <character> <numeric> <numeric> <character>    <character>        <numeric>
#> 1         FT001   205.097     150.0  Tryptophan     C11H12N2O2          204.090
#> 2         FT002   132.102     140.0     Leucine       C6H13NO2          131.095
#> 2.1       FT002   132.102     140.0  Isoleucine       C6H13NO2          131.095
#> 3         FT003   227.079     150.1  Tryptophan     C11H12N2O2          204.090
#>     target_rt      adduct     score  ppm_error  score_rt
#>     <numeric> <character> <numeric>  <numeric> <numeric>
#> 1         150      [M+H]+     0e+00 0.00000000       0.0
#> 2         140      [M+H]+     0e+00 0.00000000       0.0
#> 2.1       140      [M+H]+     0e+00 0.00000000       0.0
#> 3         150     [M+Na]+     1e-06 0.00440375       0.1

## Also FT003 was matched in this case

## It is also possible to match directly m/z values
mz1 <- c(12, 343, 23, 231)
mz2 <- mz1 + rnorm(4, sd = 0.001)

res <- matchValues(mz1, mz2, MzParam(tolerance = 0.001))

matchedData(res)
#> DataFrame with 4 rows and 4 columns
#>     query   target        score ppm_error
#>    <AsIs>   <AsIs>    <numeric> <numeric>
#> 1      12  12.0002 -0.000183083  15.25665
#> 2     343 343.0013 -0.001280555   3.73338
#> NA     23       NA           NA        NA
#> 3     231 231.0017 -0.001690184   7.31676

## Matching with a SummarizedExperiment or a QFeatures work analogously,
## only that the matching is performed on the object's `rowData`.

## Below we create a simple SummarizedExperiment with some random assay data.
## Note that results from a data preprocessing with the `xcms` package could
## be extracted as a `SummarizedExperiment` with the `quantify` method from
## the `xcms` package.
library(SummarizedExperiment)
se <- SummarizedExperiment(
    assays = matrix(rnorm(12), nrow = 3, ncol = 4,
    dimnames = list(NULL, c("A", "B", "C", "D"))),
    rowData = fts)

## We can now perform the matching of this SummarizedExperiment against the
## target_df as before.
res <- matchValues(se, target_df,
    param = Mass2MzParam(adducts = c("[M+H]+", "[M+Na]+"),
        tolerance = 0, ppm = 20))
res
#> Object of class Matched 
#> Total number of matches: 4 
#> Number of query objects: 3 (3 matched)
#> Number of target objects: 3 (3 matched)

## Getting the available columns
colnames(res)
#>  [1] "feature_id"       "mz"               "rt"               "target_name"     
#>  [5] "target_formula"   "target_exactmass" "target_rt"        "adduct"          
#>  [9] "score"            "ppm_error"       

## The query columns represent the columns of the object's `rowData`
rowData(se)
#> DataFrame with 3 rows and 3 columns
#>    feature_id        mz        rt
#>   <character> <numeric> <numeric>
#> 1       FT001   205.097     150.0
#> 2       FT002   132.102     140.0
#> 3       FT003   227.079     150.1

## matchedData also returns the query object's rowData along with the
## matching entries in the target object.
matchedData(res)
#> DataFrame with 4 rows and 10 columns
#>    feature_id        mz        rt target_name target_formula target_exactmass
#>   <character> <numeric> <numeric> <character>    <character>        <numeric>
#> 1       FT001   205.097     150.0  Tryptophan     C11H12N2O2          204.090
#> 2       FT002   132.102     140.0     Leucine       C6H13NO2          131.095
#> 3       FT002   132.102     140.0  Isoleucine       C6H13NO2          131.095
#> 4       FT003   227.079     150.1  Tryptophan     C11H12N2O2          204.090
#>   target_rt      adduct     score  ppm_error
#>   <numeric> <character> <numeric>  <numeric>
#> 1       150      [M+H]+     0e+00 0.00000000
#> 2       140      [M+H]+     0e+00 0.00000000
#> 3       140      [M+H]+     0e+00 0.00000000
#> 4       150     [M+Na]+     1e-06 0.00440375

## While `query` will return the full SummarizedExperiment.
query(res)
#> class: SummarizedExperiment 
#> dim: 3 4 
#> metadata(0):
#> assays(1): ''
#> rownames: NULL
#> rowData names(3): feature_id mz rt
#> colnames(4): A B C D
#> colData names(0):

## To illustrate use with a QFeatures object we first create a simple
## QFeatures object with two assays, `"ions"` representing the full feature
## data.frame and `"compounds"` a subset of it.
library(QFeatures)
#> Loading required package: MultiAssayExperiment
#> 
#> Attaching package: ‘QFeatures’
#> The following object is masked from ‘package:base’:
#> 
#>     sweep
qf <- QFeatures(list(ions = se, compounds = se[2,]))

## We can perform the same matching as before, but need to specify which of
## the assays in the QFeatures should be used for the matching. Below we
## perform the matching using the "ions" assay.
res <- matchValues(qf, target_df, queryAssay = "ions",
    param = Mass2MzParam(adducts = c("[M+H]+", "[M+Na]+"),
        tolerance = 0, ppm = 20))
res
#> Object of class Matched 
#> Total number of matches: 4 
#> Number of query objects: 3 (3 matched)
#> Number of target objects: 3 (3 matched)

## colnames returns now the colnames of the `rowData` of the `"ions"` assay.
colnames(res)
#>  [1] "feature_id"       "mz"               "rt"               "target_name"     
#>  [5] "target_formula"   "target_exactmass" "target_rt"        "adduct"          
#>  [9] "score"            "ppm_error"       

matchedData(res)
#> DataFrame with 4 rows and 10 columns
#>    feature_id        mz        rt target_name target_formula target_exactmass
#>   <character> <numeric> <numeric> <character>    <character>        <numeric>
#> 1       FT001   205.097     150.0  Tryptophan     C11H12N2O2          204.090
#> 2       FT002   132.102     140.0     Leucine       C6H13NO2          131.095
#> 3       FT002   132.102     140.0  Isoleucine       C6H13NO2          131.095
#> 4       FT003   227.079     150.1  Tryptophan     C11H12N2O2          204.090
#>   target_rt      adduct     score  ppm_error
#>   <numeric> <character> <numeric>  <numeric>
#> 1       150      [M+H]+     0e+00 0.00000000
#> 2       140      [M+H]+     0e+00 0.00000000
#> 3       140      [M+H]+     0e+00 0.00000000
#> 4       150     [M+Na]+     1e-06 0.00440375