Quality Control for Label-Free Proteomics Expression Data
Overview
Label-free bottom-up proteomics expression data is often affected by data heterogeneity and missing values. Normalization and missing value imputation are commonly used techniques to address these issues and make the dataset suitable for further downstream analysis. This package provides an optimal combination of normalization and imputation methods for the dataset. The package utilizes three normalization methods and three imputation methods.The statistical evaluation measures named pooled co-efficient of variance, pooled estimate of variance and pooled median absolute deviation are used for selecting the best combination of normalization and imputation method for the given dataset. The user can also visualize the results by using various plots available in this package. The user can also perform the differential expression analysis between two sample groups with the function included in this package. The chosen three normalization methods, three imputation methods and three evaluation measures were chosen for this study based on the research papers published by Välikangas et al. (2016) doi:10.1093/bib/bbw095, Jin et al. (2021) doi:10.1038/s41598-021-81279-4 and Srivastava et al. (2023) doi:10.2174/1574893618666230223150253.This work has published by Sakthivel et al. (2025) doi:10.1021/acs.jproteome.4c00552.
Install
Health
- OK13 OK2026-09-18
- ERROR12 OK, 1 ERROR2026-09-17
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
Show 2 earlier changesHide 2 earlier changes
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 15 (2 internal)includes a package overview page
- Examples that run
- 78%7 run in a plain check · 2 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 90%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 7%
- user_guide HTML
Downloads
Repository
- PRs merged (all time)
- not measured
- Median days to close, 50 most recently updated closed issues
- 1
- Median days to close, 50 most recently updated closed PRs
- not measured
Stars over time
Issues over time
2 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/kabilansbio/lfproqc on 2026-10-18.
Dependencies
Installing lfproQC adds 89 packages (52 with compiled code, 87 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB. 3 more not on CRAN are not measured.
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2024-05-23
- Total releases
- 8 / 2 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 4.2
- Bundled data
- 104 KB / 4 files
- Download size
- 1.5 MB
Cite
Cite this package
Run in R for the authors' entry:
citation("lfproQC")The package provides a citation file, so citation() returns the authors' own entry.
It points to 10.1021/acs.jproteome.4c00552.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-20, which the citation names so these numbers can be found later. More on citing and the projects behind them.