combineFeatures {MSnbase} | R Documentation

## Combines features in an `MSnSet` object

### Description

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.

### 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`).

### 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.

### Author(s)

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)

    ## 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)
    exprs(msnset.comb)
    fvarLabels(msnset.comb)

    ## 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"))
    exprs(combineFeatures(msnset, groupBy = grpl, method = "sum", redundancy.handler = "multiple"))

    ## missing data
    exprs(msnset)[4, 4] <-
        exprs(msnset)[2, 2] <- NA
    exprs(msnset)
    ## NAs propagate in the 115 and 117 channels
    exprs(combineFeatures(msnset, grp, "sum"))
    ## NAs are removed before summing
    exprs(combineFeatures(msnset, grp, "sum", na.rm = TRUE))

    ## using iPQF
    data(msnset2)
    anyNA(msnset2)
    res <- combineFeatures(msnset2,
    		       groupBy = fData(msnset2)$accession,
    		       redundancy.handler = "unique",
    		       method = "iPQF",
    		       low.support.filter = FALSE,
    		       ratio.calc = "sum",
    		       method.combine = FALSE)
    head(exprs(res))

    ## 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)
    exprs(msnset["X46", ])
    ## Only the missing value in X46 and iTRAQ4.116 will be ignored
    res <- combineFeatures(msnset,
    		       fcol = "ProteinAccession",
    		       method = "robust")
    tail(exprs(res))

    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))

[Package MSnbase version 2.20.1 Index]