MSstatsPreprocess {MSstatsConvert} | R Documentation |
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, ... )
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 |
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 |
aggregate_isotopic |
logical. If |
... |
additional parameters to |
data.table
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") cleaned_data = MSstatsClean(imported, protein_id_col = "Proteins") annot_path = system.file("tinytest/raw_data/MaxQuant/annotation.csv", package = "MSstatsConvert") mq_annot = MSstatsMakeAnnotation(cleaned_data, read.csv(annot_path), Run = "Rawfile") # 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) ) # Output in the standard MSstats format head(msstats_format)