Count the number of identifications per scan

The function takes a Spectra object containing identification results as input. It then counts the number of identifications each scan (or their descendants) has lead to - this is either 0 or 1 for MS2 scans, or, for MS1 scans, the number of MS2 scans originating from any MS1 peak that lead to an identification.

This function can be used to generate id-annotated total ion chromatograms, as can illustrated here.

Usage

countIdentifications(
  object,
  identification = "sequence",
  f = dataStorage(object),
  BPPARAM = bpparam()
)

Arguments

object

An instance of class Spectra that contains identification data, as defined by the sequence argument.

identification

character(1) with the name of the spectra variable that defines whether a scan lead to an identification (typically containing the idenfified peptides sequence in proteomics). The absence of identification is encode by an NA. Default is "sequence".

f

A factor defining how to split object for parallelized processing. Default is dataOrigin(x), i.e. each raw data files is processed in parallel.

BPPARAM

Parallel setup configuration. See BiocParallel::bpparam() for details.

Value

An updated Spectra() object that now contains an integer spectra variable countIdentifications with the number of identification for each scan.

Details

The computed number of identifications is stored in a new spectra variables named "countIdentifications". If it already exists, the function throws a message and returns the object unchanged. To force the recomputation of the "countIdentifications" variable, users should either delete or rename it.

See also

addProcessing() for other data analysis functions.

Author

Laurent Gatto

Examples

spdf <- new("DFrame", rownames = NULL, nrows = 86L,
   listData = list(
       msLevel = c(1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
                   2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
                   2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
                   2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
                   2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
                   2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L,
                   2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
                   2L, 2L),
       acquisitionNum = 8975:9060,
       precScanNum = c(NA, 8956L, 8956L, 8956L, 8956L, 8956L, 8956L,
                       8956L, 8956L, 8956L, 8956L, 8956L, 8956L,
                       8956L, 8956L, 8956L, 8956L, 8956L, 8956L, NA,
                       8975L, 8975L, 8975L, 8975L, 8975L, 8975L,
                       8975L, 8975L, 8975L, 8975L, 8975L, 8975L,
                       8975L, 8975L, 8975L, 8975L, 8975L, NA, 8994L,
                       8994L, 8994L, 8994L, 8994L, 8994L, 8994L,
                       8994L, 8994L, 8994L, 8994L, 8994L, 8994L, NA,
                       9012L, 9012L, 9012L, 9012L, 9012L, 9012L,
                       9012L, 9012L, 9012L, 9012L, 9012L, 9012L,
                       9012L, 9012L, 9012L, 9012L, 9012L, 9012L, NA,
                       9026L, 9026L, 9026L, 9026L, 9026L, 9026L,
                       9026L, 9026L, 9026L, 9026L, 9026L, 9026L,
                       9026L, 9026L, 9026L),
       sequence = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
                    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
                    "LSEHATAPTR", NA, NA, NA, NA, NA, NA, NA,
                    "EGSDATGDGTK", NA, NA, "NEDEDSPNK", NA, NA, NA,
                    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
                    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
                    NA, NA, NA, NA, NA, NA, NA, NA, NA, "GLTLAQGGVK",
                    NA, NA, NA, NA, "STLPDADRER", NA, NA, NA, NA, NA,
                    NA, NA, NA)),
   elementType = "ANY", elementMetadata = NULL, metadata = list())

sp <- Spectra(spdf)

## We have in this data 5 MS1 and 81 MS2 scans
table(msLevel(sp))
#> 
#>  1  2 
#>  5 81 

## The acquisition number of the MS1 scans
acquisitionNum(filterMsLevel(sp, 1))
#> [1] 8975 8994 9012 9026 9045

## And the number of MS2 scans with precursor ions selected
## from MS1 scans (those in the data and others)
table(precScanNum(sp))
#> 
#> 8956 8975 8994 9012 9026 
#>   18   17   13   18   15 

## Count number of sequences/identifications per scan
sp <- countIdentifications(sp)

## MS2 scans either lead to an identification (5 instances) or none
## (76). Among the five MS1 scans in the experiment, 3 lead to MS2
## scans being matched to no peptides and two MS1 scans produced two
## and three PSMs respectively.
table(sp$countIdentifications, sp$msLevel)
#>    
#>      1  2
#>   0  3 76
#>   1  0  5
#>   2  1  0
#>   3  1  0