This package is considered a duplicate. The official version of this package is found at:https://tvpham.r-universe.dev/iq

Package: iq 2.0.1

Thang Pham

iq: Protein Quantification in Mass Spectrometry-Based Proteomics

An implementation of the MaxLFQ algorithm by Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive pipeline for processing proteomics data in data-independent acquisition mode (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>; Pham et al. 2026 <doi:10.1021/acs.jproteome.5c01038>). It offers additional options for protein quantification using the N most intense fragment ions, using all fragment ions, the median polish algorithm by Tukey (1977, ISBN:0201076160), and a robust linear model. In general, the tool can be used to integrate multiple proportional observations into a single quantitative value.

Authors:Thang Pham [aut, cre, cph, ctb], Alex Henneman [ctb]

iq_2.0.1.tar.gz
iq_2.0.1.tar.gz(r-4.7-arm64)iq_2.0.1.tar.gz(r-4.7-x86_64)iq_2.0.1.tar.gz(r-4.6-arm64)iq_2.0.1.tar.gz(r-4.6-x86_64)
iq_2.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
iq/json (API)

# Install 'iq' in R:

install.packages('iq', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tvpham/iq/issues

Uses libs:

c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

spikeins - An example dataset of 12 spike-in proteins

On CRAN:

cpp openmp

3.27 score 47 scripts 790 downloads 19 exports 2 dependencies

Last updated from:f144aa58c7. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	119
linux-devel-x86_64	OK	129
source / vignettes	OK	187
linux-release-arm64	OK	114
linux-release-x86_64	OK	119
wasm-release	OK	98

Exports:connected_component create_protein_list create_protein_table extract_annotation fast_MaxLFQ fast_preprocess fast_read long_format_to_iq_format maxLFQ meanInt median_polish plot_protein preprocess process_iq_format process_long_format process_matrix process_wide_format rescale topN

Dependencies:Rcpp RcppEigen

Fast MaxLFQ

Thang V Pham

Rendered fromiq-fast.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-10-06
Started: 2020-05-18

Relative protein quantification for DIA-MS-based proteomics

Thang V Pham

Rendered fromiq.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-10-06
Started: 2019-11-15

Help page	Topics
Group connected samples	connected_component
Creating a list of matrices of fragment ion intensities for all proteins	create_protein_list
Protein quantification for a list of proteins	create_protein_table
Protein annotation extraction	extract_annotation
The MaxLFQ algorithm	fast_MaxLFQ
Data filtering and normalization	fast_preprocess
Reading data from an input file	fast_read
Long format input data to iq format	long_format_to_iq_format
The MaxLFQ algorithm for protein quantification	maxLFQ
The meanInt algorithm for protein quantification	meanInt
A wrapper for the R implementation of the median polish algorithm	median_polish
Plotting the underlying quantitative data for a protein	plot_protein
Data preprocessing for protein quantification	preprocess
Process data in the iq format	process_iq_format
Long format to a wide format table using the MaxLFQ algorithm	process_long_format
Process individual matrix	process_matrix
Merging rows with identical values in a particular column in a table	process_wide_format
Rescale relative quantification	rescale
An example dataset of 12 spike-in proteins	spikeins
The topN algorithm for protein quantification	topN

Package: iq 2.0.1

iq: Protein Quantification in Mass Spectrometry-Based Proteomics

Fast MaxLFQ

Relative protein quantification for DIA-MS-based proteomics

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)