This function combines the features in an "MSnSet" instance applying a summarisation function (see fun argument) to sets of features as defined by a factor (see fcol argument). Note that the feature names are automatically updated based on the groupBy parameter.

The coefficient of variations are automatically computed and collated to the featureData slot. See cv and cv.norm arguments for details.

If NA values are present, a message will be shown. Details on how missing value impact on the data aggregation are provided below.

Arguments

object

An instance of class "MSnSet" whose features will be summerised.

groupBy

A factor, character, numeric or a list of the above defining how to summerise the features. The list must be of length nrow(object). Each element of the list is a vector describing the feature mapping. If the list can be named, its names must match fetureNames(object). See redundancy.handler for details about the latter.

fun

Deprecated; use method instead.

method

The summerising function. Currently, mean, median, weighted mean, sum, median polish, robust summarisation (using MASS::rlm, implemented in MsCoreUtils::robustSummary()), iPQF (see iPQF for details) and NTR (see NTR for details) are implemented, but user-defined functions can also be supplied. Note that the robust menthods assumes that the data are already log-transformed.

fcol

Feature meta-data label (fData column name) defining how to summerise the features. It must be present in fvarLabels(object) and, if present, will be used to defined groupBy as fData(object)[, fcol]. Note that fcol is ignored if groupBy is present.

redundancy.handler

If groupBy is a list, one of "unique" (default) or "multiple" (ignored otherwise) defining how to handle peptides that can be associated to multiple higher-level features (proteins) upon combination. Using "unique" will only consider uniquely matching features (features matching multiple proteins will be discarded). "multiple" will allow matching to multiple proteins and each feature will be repeatedly tallied for each possible matching protein.

cv

A logical defining if feature coefficients of variation should be computed and stored as feature meta-data. Default is TRUE.

cv.norm

A character defining how to normalise the feature intensitites prior to CV calculation. Default is sum. Use none to keep intensities as is. See featureCV for more details.

verbose

A logical indicating whether verbose output is to be printed out.

...

Additional arguments for the fun function.

Value

A new "MSnSet" instance is returned with ncol (i.e. number of samples) is unchanged, but nrow (i.e. the number od features) is now equals to the number of levels in groupBy. The feature metadata (featureData slot) is updated accordingly and only the first occurrence of a feature in the original feature meta-data is kept.

Details

Missing values have different effect based on the aggregation method employed, as detailed below. See also examples below.

  1. When using either "sum", "mean", "weighted.mean" or "median", any missing value will be propagated at the higher level. If na.rm = TRUE is used, then the missing value will be ignored.

  2. Missing values will result in an error when using "medpolish", unless na.rm = TRUE is used.

  3. When using robust summarisation ("robust"), individual missing values are excluded prior to fitting the linear model by robust regression. To remove all values in the feature containing the missing values, use filterNA.

  4. The "iPQF" method will fail with an error if missing value are present, which will have to be handled explicitly. See below.

More generally, missing values often need dedicated handling such as filtering (see filterNA) or imputation (see impute).

Author

Laurent Gatto <lg390@cam.ac.uk> with contributions from Martina Fischer for iPQF and Ludger Goeminne, Adriaan Sticker and Lieven Clement for robust.

References

iPQF: a new peptide-to-protein summarization method using peptide spectra characteristics to improve protein quantification. Fischer M, Renard BY. Bioinformatics. 2016 Apr 1;32(7):1040-7. doi:10.1093/bioinformatics/btv675. Epub 2015 Nov 20. PubMed PMID:26589272.

See also

featureCV to calculate coefficient of variation, nFeatures to document the number of features per group in the feature data, and the aggvar to explore variability within protein groups.

iPQF for iPQF summarisation.

NTR for normalisation to reference summarisation.

Examples

data(msnset)
msnset <- msnset[11:15, ]
exprs(msnset)
#>     iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> X19  32838.044  37066.058  41429.627  39700.475
#> X2    3715.089   4254.323   4748.462   5249.904
#> X20  34509.686  34928.747  41911.032  42843.839
#> X21  21262.148  23168.729  25407.068  25949.954
#> X22   8635.316  10036.529   9254.432   7769.749

## arbitrary grouping into two groups
grp <- as.factor(c(1, 1, 2, 2, 2))
msnset.comb <- combineFeatures(msnset, groupBy = grp, method = "sum")
dim(msnset.comb)
#> [1] 2 4
exprs(msnset.comb)
#>   iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> 1   36553.13   41320.38   46178.09   44950.38
#> 2   64407.15   68134.01   76572.53   76563.54
fvarLabels(msnset.comb)
#>  [1] "spectrum"            "ProteinAccession"    "ProteinDescription" 
#>  [4] "PeptideSequence"     "file"                "retention.time"     
#>  [7] "precursor.mz"        "precursor.intensity" "charge"             
#> [10] "peaks.count"         "tic"                 "ionCount"           
#> [13] "ms.level"            "acquisition.number"  "collision.energy"   
#> [16] "CV.iTRAQ4.114"       "CV.iTRAQ4.115"       "CV.iTRAQ4.116"      
#> [19] "CV.iTRAQ4.117"      

## grouping with a list
grpl <- list(c("A", "B"), "A", "A", "C", c("C", "B"))
## optional naming
names(grpl) <- featureNames(msnset)
exprs(combineFeatures(msnset, groupBy = grpl, method = "sum", redundancy.handler = "unique"))
#>   iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> A   38224.78   39183.07   46659.49   48093.74
#> C   21262.15   23168.73   25407.07   25949.95
exprs(combineFeatures(msnset, groupBy = grpl, method = "sum", redundancy.handler = "multiple"))
#>   iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> A   71062.82   76249.13   88089.12   87794.22
#> B   41473.36   47102.59   50684.06   47470.22
#> C   29897.46   33205.26   34661.50   33719.70

## missing data
exprs(msnset)[4, 4] <-
    exprs(msnset)[2, 2] <- NA
exprs(msnset)
#>     iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> X19  32838.044   37066.06  41429.627  39700.475
#> X2    3715.089         NA   4748.462   5249.904
#> X20  34509.686   34928.75  41911.032  42843.839
#> X21  21262.148   23168.73  25407.068         NA
#> X22   8635.316   10036.53   9254.432   7769.749
## NAs propagate in the 115 and 117 channels
exprs(combineFeatures(msnset, grp, "sum"))
#> Your data contains missing values. Please read the relevant section in
#> the combineFeatures manual page for details on the effects of missing
#> values on data aggregation.
#>   iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> 1   36553.13         NA   46178.09   44950.38
#> 2   64407.15   68134.01   76572.53         NA
## NAs are removed before summing
exprs(combineFeatures(msnset, grp, "sum", na.rm = TRUE))
#> Your data contains missing values. Please read the relevant section in
#> the combineFeatures manual page for details on the effects of missing
#> values on data aggregation.
#>   iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> 1   36553.13   37066.06   46178.09   44950.38
#> 2   64407.15   68134.01   76572.53   50613.59

## using iPQF
data(msnset2)
anyNA(msnset2)
#> [1] FALSE
res <- combineFeatures(msnset2,
           groupBy = fData(msnset2)$accession,
           redundancy.handler = "unique",
           method = "iPQF",
           low.support.filter = FALSE,
           ratio.calc = "sum",
           method.combine = FALSE)
#> The following 1 proteins are only supported by 1 or 2 peptide spectra,
#> hence, protein quantification is not reliable and can only be calculated
#> by the 'mean' in these cases, corresponding protein accessions are:
#>   O95678
head(exprs(res))
#>        X114.ions X115.ions X116.ions X117.ions
#> O95678 0.2404726 0.2682764 0.2584247 0.2328263
#> P01766 0.2610278 0.2467206 0.2544715 0.2377801
#> P01776 0.2678859 0.2591250 0.2423396 0.2306495
#> P02749 0.2640340 0.2523566 0.2510357 0.2325736
#> P02763 0.2503318 0.2524583 0.2501628 0.2470472
#> P07225 0.2533961 0.2506013 0.2504353 0.2455673

## using robust summarisation
data(msnset) ## reset data
msnset <- log(msnset, 2) ## log2 transform

## Feature X46, in the ENO protein has one missig value
which(is.na(msnset), arr.ind = TRUE)
#>     row col
#> X2    2   2
#> X21   4   4
exprs(msnset["X46", ])
#> Error in h(simpleError(msg, call)) error in evaluating the argument 'object' in selecting a method for function 'exprs': subscript out of bounds
## Only the missing value in X46 and iTRAQ4.116 will be ignored
res <- combineFeatures(msnset,
           fcol = "ProteinAccession",
           method = "robust")
#> Your data contains missing values. Please read the relevant section in
#> the combineFeatures manual page for details on the effects of missing
#> values on data aggregation.
tail(exprs(res))
#>         iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> ECA1032   15.00308   15.17781   15.33838   15.27687
#> ECA1104   14.37600   14.49989   14.63294         NA
#> ECA1294   11.85918         NA   12.21324   12.35808
#> ECA3356   13.07603   13.29297   13.17593   12.92365
#> ECA4514   15.07471   15.09213   15.35504   15.38680

msnset2 <- filterNA(msnset) ## remove features with missing value(s)
res2 <- combineFeatures(msnset2,
      fcol = "ProteinAccession",
      method = "robust")
## Here, the values for ENO are different because the whole feature
## X46 that contained the missing value was removed prior to fitting.
tail(exprs(res2))
#>         iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117
#> ECA1032   15.00308   15.17781   15.33838   15.27687
#> ECA3356   13.07603   13.29297   13.17593   12.92365
#> ECA4514   15.07471   15.09213   15.35504   15.38680