Preprocess outputs from MS signal processing tools for analysis with MSstats

Preprocess outputs from MS signal processing tools for analysis with MSstats

MSstatsPreprocess(
  input,
  annotation,
  feature_columns,
  remove_shared_peptides = TRUE,
  remove_single_feature_proteins = TRUE,
  feature_cleaning = list(remove_features_with_few_measurements = TRUE,
    summarize_multiple_psms = max),
  score_filtering = list(),
  exact_filtering = list(),
  pattern_filtering = list(),
  columns_to_fill = list(),
  aggregate_isotopic = FALSE,
  ...
)

Arguments

input

data.table processed by the MSstatsClean function.
annotation

annotation file generated by a signal processing tool.
feature_columns

character vector of names of columns that define spectral features.
remove_shared_peptides

logical, if TRUE shared peptides will be removed.
remove_single_feature_proteins

logical, if TRUE, proteins that only have one feature will be removed.
feature_cleaning

named list with maximum two (for MSstats converters) or three (for MSstatsTMT converter) elements. If handle_few_measurements is set to "remove", feature with less than three measurements will be removed (otherwise it should be equal to "keep"). summarize_multiple_psms is a function that will be used to aggregate multiple feature measurements in a run. It should return a scalar and accept an na.rm parameter. For MSstatsTMT converters, setting remove_psms_with_any_missing will remove features which have missing values in a run from that run.
score_filtering

a list of named lists that specify filtering options. Details are provided in the vignette.
exact_filtering

a list of named lists that specify filtering options. Details are provided in the vignette.
pattern_filtering

a list of named lists that specify filtering options. Details are provided in the vignette.
columns_to_fill

a named list of scalars. If provided, columns with names defined by the names of this list and values corresponding to its elements will be added to the output data.frame.
aggregate_isotopic

logical. If TRUE, isotopic peaks will by summed.
...

additional parameters to data.table::fread.

Value

data.table

Examples

evidence_path = system.file("tinytest/raw_data/MaxQuant/mq_ev.csv",
                            package = "MSstatsConvert")
pg_path = system.file("tinytest/raw_data/MaxQuant/mq_pg.csv",
                      package = "MSstatsConvert")
evidence = read.csv(evidence_path)
pg = read.csv(pg_path)
imported = MSstatsImport(list(evidence = evidence, protein_groups = pg),
                         "MSstats", "MaxQuant")
#> INFO  [2021-05-10 23:03:42] ** Raw data from MaxQuant imported successfully.
cleaned_data = MSstatsClean(imported, protein_id_col = "Proteins")
#> INFO  [2021-05-10 23:03:42] ** Rows with values of Potentialcontaminant equal to + are removed 
#> INFO  [2021-05-10 23:03:42] ** Rows with values of Reverse equal to + are removed 
#> INFO  [2021-05-10 23:03:42] ** Rows with values of Potentialcontaminant equal to + are removed 
#> INFO  [2021-05-10 23:03:42] ** Rows with values of Reverse equal to + are removed 
#> INFO  [2021-05-10 23:03:42] ** + Contaminant, + Reverse, + Potential.contaminant proteins are removed.
#> INFO  [2021-05-10 23:03:42] ** Raw data from MaxQuant cleaned successfully.
annot_path = system.file("tinytest/raw_data/MaxQuant/annotation.csv",
                         package = "MSstatsConvert")
mq_annot = MSstatsMakeAnnotation(cleaned_data, read.csv(annot_path),
                                 Run = "Rawfile")
#> INFO  [2021-05-10 23:03:42] ** Using provided annotation.
#> INFO  [2021-05-10 23:03:42] ** Run labels were standardized to remove symbols such as '.' or '%'.

# To filter M-peptides and oxidatin peptides 
m_filter = list(col_name = "PeptideSequence", pattern = "M",
                filter = TRUE, drop_column = FALSE)
oxidation_filter = list(col_name = "Modifications", pattern = "Oxidation",
                        filter = TRUE, drop_column = TRUE)
msstats_format = MSstatsPreprocess(
cleaned_data, mq_annot,
feature_columns = c("PeptideSequence", "PrecursorCharge"),
columns_to_fill = list(FragmentIon = NA, ProductCharge = NA),
pattern_filtering = list(oxidation = oxidation_filter, m = m_filter)
)
#> INFO  [2021-05-10 23:03:42] ** The following options are used:
#>   - Features will be defined by the columns: PeptideSequence, PrecursorCharge
#>   - Shared peptides will be removed.
#>   - Proteins with a single feature will be removed.
#>   - Features with less than 3 measurements across runs will be removed.
#> INFO  [2021-05-10 23:03:42] ** Sequences containing Oxidation are removed.
#> INFO  [2021-05-10 23:03:42] ** Sequences containing M are removed.
#> INFO  [2021-05-10 23:03:42] ** Features with all missing measurements across runs are removed.
#> INFO  [2021-05-10 23:03:42] ** Shared peptides are removed.
#> INFO  [2021-05-10 23:03:42] ** Multiple measurements in a feature and a run are summarized by summaryforMultipleRows: max
#> INFO  [2021-05-10 23:03:42] ** Features with one or two measurements across runs are removed.
#> INFO  [2021-05-10 23:03:42] Proteins with a single feature are removed.
#> INFO  [2021-05-10 23:03:42] ** Run annotation merged with quantification data.
# Output in the standard MSstats format
head(msstats_format)
#>                                            Run PeptideSequence PrecursorCharge
#> 1: 121219_S_CCES_01_01_LysC_Try_1to10_Mixt_1_1     AEAPAAAPAAK               2
#> 2: 121219_S_CCES_01_02_LysC_Try_1to10_Mixt_1_2     AEAPAAAPAAK               2
#> 3: 121219_S_CCES_01_03_LysC_Try_1to10_Mixt_1_3     AEAPAAAPAAK               2
#> 4: 121219_S_CCES_01_05_LysC_Try_1to10_Mixt_2_2     AEAPAAAPAAK               2
#> 5: 121219_S_CCES_01_06_LysC_Try_1to10_Mixt_2_3     AEAPAAAPAAK               2
#> 6: 121219_S_CCES_01_08_LysC_Try_1to10_Mixt_3_2     AEAPAAAPAAK               2
#>    Intensity ProteinName Condition BioReplicate Experiment IsotopeLabelType
#> 1:   4023100      P06959         1            1        1_1                L
#> 2:   5132500      P06959         1            1        1_2                L
#> 3:   2761600      P06959         1            1        1_3                L
#> 4:   4091800      P06959         2            2        2_2                L
#> 5:   4727000      P06959         2            2        2_3                L
#> 6:   2258400      P06959         3            3        3_2                L
#>    FragmentIon ProductCharge
#> 1:          NA            NA
#> 2:          NA            NA
#> 3:          NA            NA
#> 4:          NA            NA
#> 5:          NA            NA
#> 6:          NA            NA