process_long_format: Long format to a wide format table using the MaxLFQ algorithm

In iq: Protein Quantification in Mass Spectrometry-Based Proteomics

Long format to a wide format table using the MaxLFQ algorithm

Description

A convenient function combining multiple steps to process a long format table using the MaxLFQ algorithm.

Usage

process_long_format(input_data,
                    output_filename,
                    sample_id = "File.Name",
                    primary_id = "Protein.Group",
                    secondary_id = "Precursor.Id",
                    intensity_col = "Fragment.Quant.Corrected",
                    annotation_col = NULL,
                    filter_string_equal = NULL,
                    filter_string_not_equal = NULL,
                    filter_double_less = c("Q.Value" = "0.01", "PG.Q.Value" = "0.01"),
                    filter_double_greater = NULL,
                    intensity_col_sep = ";",
                    intensity_col_id = NULL,
                    na_string = "0",
                    normalization = "median",
                    log2_intensity_cutoff = 0,
                    pdf_out = "qc-plots.pdf",
                    pdf_width = 12,
                    pdf_height = 8,
                    show_boxplot = TRUE,
                    peptide_extractor = NULL,
                    rfasta = NULL)

Arguments

input_data	A data frame or a filename. See filename in fast_read.
output_filename	Output filename.
sample_id	See sample_id in fast_read.
primary_id	See primary_id in fast_read.
secondary_id	See secondary_id in fast_read.
intensity_col	See intensity_col in fast_read.
annotation_col	See annotation_col in fast_read.
filter_string_equal	See filter_string_equal in fast_read.
filter_string_not_equal	See filter_string_not_equal in fast_read.
filter_double_less	See filter_double_less in fast_read.
filter_double_greater	See filter_double_greater in fast_read.
intensity_col_sep	See intensity_col_sep in fast_read.
intensity_col_id	See intensity_col_id in fast_read.
na_string	See intensity_col_id in fast_read.
normalization	Normalization type. Possible values are median and none. The default value median is for median normalization in fast_preprocess.
log2_intensity_cutoff	See log2_intensity_cutoff in fast_preprocess.
pdf_out	See pdf_out in fast_preprocess.
pdf_width	See pdf_width in fast_preprocess.
pdf_height	See pdf_height in fast_preprocess.
show_boxplot	See show_boxplot in fast_preprocess.
peptide_extractor	A function to parse peptides
rfasta	If available, calculates the sequence coverage.

Value

Either an input data frame is processed with fast_MaxLFQ or an input file is processed with fast_read, fast_preprocess, and fast_MaxLFQ. Subsequently, the result is written to output_filename. The quantification values are in log2 space. A NULL value is returned. If peptide_extractor is not NULL, fragment statistics for each protein will be calculated based on the result of the extractor function. Counting the number of peptides contributing to a protein is possible using an appropriate extractor function. An example value for peptide_extractor is function(x) gsub("[0-9].*$", "", x), which removes the charge state and fragment descriptors in an ion descriptor to obtain unique peptide sequences. One can examine the ion component returned by the fast_read function to derive a regular expression to be used in the gsub function above. Another example is function(x) gsub("\(UniMod:\d+\)|\d|_", "", x) which removes annotations for modifications on the peptides.

Author(s)

Thang V. Pham

References

Pham TV, Tran CTM, Henneman AA, Pham LHC, Le DG, Can AH, Bui PHL, Piersma SR, Jimenez CR. Boosting the speed and accuracy of protein quantification algorithms in mass spectrometry-based proteomics. Journal of Proteome Research 2026 Feb 6; 25(2):1198–1203.

Pham TV, Henneman AA, Jimenez CR. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 2020 Apr 15;36(8):2611-2613.

See Also

fast_read, fast_preprocess, fast_MaxLFQ

iq documentation built on Oct. 28, 2026, 1:07 a.m.