fast_MaxLFQ: The MaxLFQ algorithm

In iq: Protein Quantification in Mass Spectrometry-Based Proteomics

The MaxLFQ algorithm

Description

A fast implementation of the MaxLFQ algorithm.

Usage

fast_MaxLFQ(norm_data, row_names = NULL, col_names = NULL)

Arguments

norm_data	A list of four vectors with equal length protein_list, sample_list, id and quant as prepared by the fast_preprocess function or the quant_table component returned by the fast_read function. Note that quant should contain log2 intensities.
row_names	A vector of character strings for row names. If NULL, unique values in the protein_list component of norm_data will be used. Otherwise, it should be the first column of the protein component returned by the fast_read.
col_names	A vector of character strings for column names. If NULL, unique values in the sample_list component of norm_data will be used. Otherwise, it should be the sample component returned by the fast_read.

Value

A list is returned with two components

estimate	A quantification result table in log2 space.
annotation	A vector of strings indicating membership in case of multiple connected components for each row of estimate.

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

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