Feature to chromatographic peak mapping — featureChromPeaks • xcms

During the correspondence step in the preprocessing, chromatographic peaks get assigned (grouped) to features. The abundances of these resulting LC-MS features are supposed to represent signal from the same ion across all analyzed samples. Depending on the correspondence analysis method used, multiple chromatographic peaks (also eventually from the same sample) are assigned to a feature. This mapping between features and chromatographic peaks is (for XcmsExperiment and XCMSnExp object) stored in the "peakidx" column of the featureDefinitions() data frame. Alternatively, the mapping can be extracted from an xcms result object using the functions:

featureChromPeaks(): returns a two-column data.frame with the IDs of the features and the IDs of the associated chromatographic peaks. Each row in this data.frame represents the mapping of one chromatographic peak with one feature. The order of the features in the data.frame matches the order of the features in featureDefinitions().
featurePeakidx(): returns a named list of integer indices of the rows in the chromPeaks() matrix that are assigned to a feature. The names of the list are the feature IDs. The length and order of the list matches the number of rows and order of features in featureDefinitions().

Usage

featureChromPeaks(object, ...)

featurePeakidx(object, ...)

# S4 method for class 'XcmsResult'
featurePeakidx(object, msLevel = integer())

# S4 method for class 'XcmsResult'
featureChromPeaks(object, msLevel = integer())

# S4 method for class 'XcmsExperimentHdf5'
featureChromPeaks(object, msLevel = integer())

# S4 method for class 'XcmsExperimentHdf5'
featurePeakidx(object, msLevel = integer())

Arguments

object: An xcms result object with correspondence analysis results being present.
...: Optional parameters. Currently ignored.
msLevel: Optional integer to restrict to features from a certain MS level.

Value

See description above.

Examples


## Load preprocessing results
library(MsExperiment)
xmse <- loadXcmsData()

## Get the mapping between features and chromatographic peaks
map <- featureChromPeaks(xmse)

head(map)
#>   feature_id chrom_peak_id
#> 1      FT001        CP0511
#> 2      FT001        CP1261
#> 3      FT001        CP2957
#> 4      FT001        CP3129
#> 5      FT001        CP3447
#> 6      FT001        CP3536

## Column `"feature_id"` contains the IDs for the features defined in
## `featureDefinitions()`
featureDefinitions(xmse) |> head()
#>       mzmed mzmin mzmax    rtmed    rtmin    rtmax npeaks KO WT      peakidx
#> FT001 200.1 200.1 200.1 2902.634 2882.603 2922.664      2  2  0 458, 116....
#> FT002 205.0 205.0 205.0 2789.901 2782.955 2796.531      8  4  4 44, 443,....
#> FT003 206.0 206.0 206.0 2789.405 2781.389 2794.219      7  3  4 29, 430,....
#> FT004 207.1 207.1 207.1 2718.560 2714.047 2727.347      7  4  3 16, 420,....
#> FT005 233.0 233.0 233.1 3023.579 3015.145 3043.959      7  3  4 69, 959,....
#> FT006 241.1 241.1 241.2 3683.299 3661.586 3695.886      8  3  4 276, 284....
#>       ms_level
#> FT001        1
#> FT002        1
#> FT003        1
#> FT004        1
#> FT005        1
#> FT006        1

## Column `"chrom_peak_id"` contains the IDs of the chromatographic peaks
chromPeaks(xmse) |> head()
#>           mz mzmin mzmax       rt    rtmin    rtmax     into     intb  maxo sn
#> CP0001 594.0 594.0 594.0 2607.809 2587.465 2643.803 161042.2 146073.3  7850 11
#> CP0002 577.0 577.0 577.0 2610.939 2587.465 2632.848 136105.2 128067.9  6215 11
#> CP0003 307.0 307.0 307.0 2625.024 2598.419 2651.628 284782.4 264907.0 16872 20
#> CP0004 302.0 302.0 302.0 2623.459 2601.549 2646.933 687146.6 669778.1 30552 43
#> CP0005 370.1 370.1 370.1 2679.797 2650.063 2706.592 449284.6 417225.3 25672 17
#> CP0006 427.0 427.0 427.0 2681.362 2650.063 2690.804 283334.7 263943.2 11025 13
#>        sample
#> CP0001      1
#> CP0002      1
#> CP0003      1
#> CP0004      1
#> CP0005      1
#> CP0006      1

## Alternatively, get the mapping as a `list` of `integer` indices
featurePeakidx(xmse) |> head()
#> $FT001
#> [1]  458 1161 2677 2849 3167 3256 3369 3530
#> 
#> $FT002
#> [1]   44  443  947 1155 1404 1798 2157 2389
#> 
#> $FT003
#> [1]   29  430 1145 1388 1786 2145 2376 2850
#> 
#> $FT004
#> [1]   16  420  930 1135 1376 1775 2361 3370
#> 
#> $FT005
#> [1]   69  959 1168 1451 1814 2183 2417 2763
#> 
#> $FT006
#> [1]  276  284 1067 1313 1654 1993 2308 2611 2764
#>