Introduction to prolfqua
4 November 2026
Package
prolfqua 0.99.8
Contents
1 Documentation
The vignettes of this package, in reading order:
- Introduction to prolfqua (this vignette)
- Importing your data: reading your own data into an
AnalysisConfiguration - Comparing two groups: QC, normalization and contrasts with
lm_impute - All modelling methods at a glance: every
build_contrast_analysis()method on one small dataset - Two-factor designs
- Contrasts from factors
- Filling the gaps: model-based imputation for heatmaps and PCA: completing the data matrix with
impute_from_model() - QC and sample size estimation
The package website https://fgcz.github.io/prolfqua/ has three more articles:
- Comparing modelling methods on one dataset: all
build_contrast_analysis()methods side by side, with plots - Simulating peptide-level data
- Under the hood: strategy, model and contrasts: the classes behind
build_contrast_analysis()
2 Introduction
prolfqua covers the steps of a label-free quantification (LFQ) proteomics analysis: quality control, normalization, aggregation of peptides to proteins, modelling, hypothesis testing and sample size estimation (Wolski et al. 2023).
The centre of the package is a factory, build_contrast_analysis(). It takes the data, a model formula, the contrasts and a method, and returns a ready analysis: one model per protein and the requested contrasts.
Figure 1: build_contrast_analysis() is a factory: the method key selects the model, and every model rolls out with the same interface
The method argument selects the model: linear models (lm, rlm, rfit), limma and limma-voom, DEqMS variance moderation, limpa’s detection probability model, Firth logistic and binomial models of detection, mixed models and ROPECA on peptide-level data, a hurdle model combining intensity and detection, and variants that impute groups without observations at the limit of detection.
Every method returns an object with the same interface: get_contrasts(), get_Plotter(), get_missing() and to_wide(). Results of different methods therefore have the same columns and can be compared or combined directly.
build_contrast_analysis() takes its data as an LFQData object. The package works with data in long format, one row per measured feature (protein, peptide, precursor) and sample:
AnalysisConfigurationmaps the columns of the table to their roles: the feature hierarchy (protein, peptide, …), the explanatory factors, the sample and the intensity column.LFQDataholds the data together with its configuration. Its methods return helpers for transformation (get_Transformer()), aggregation (get_Aggregator()), summary statistics (get_Stats()), missing-value summaries (get_Summariser()) and plots (get_Plotter()).
3 Installation
Install prolfqua from Bioconductor with:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("prolfqua")The development version is available on GitHub:
BiocManager::install("fgcz/prolfqua")4 Quick start
We simulate peptide-level intensities for 100 proteins measured in three groups (A, B and Ctrl). sim_lfq_data_peptide_config() returns the data together with a matching AnalysisConfiguration.
library(prolfqua)
istar <- sim_lfq_data_peptide_config(Nprot = 100, seed = 1234)
lfqdata <- LFQData$new(istar$data, istar$config)
lfqdata$factors() |> head()
#> # A tibble: 6 × 3
#> sample sampleName group_
#> <chr> <chr> <chr>
#> 1 A_V1 A_V1 A
#> 2 A_V2 A_V2 A
#> 3 A_V3 A_V3 A
#> 4 A_V4 A_V4 A
#> 5 B_V1 B_V1 B
#> 6 B_V2 B_V2 BWe \(\log_2\) transform the intensities, scale them robustly, and aggregate the peptides to proteins with Tukey’s median polish.
lfqdata <- lfqdata$get_Transformer()$log2()$robscale()$lfq
lfqprot <- lfqdata$get_Aggregator()$aggregate()lfqprot$get_Plotter()$intensity_distribution_density()
Figure 2: Distribution of the normalized protein intensities per sample
We compare groups A and B with the control group using lm_impute: a linear model per protein with moderated variances. Proteins whose fit fails because a group has no observations are refitted with the missing values imputed at the limit of detection, borrowing the variance from the other proteins.
contrasts <- c(
"A_vs_Ctrl" = "group_A - group_Ctrl",
"B_vs_Ctrl" = "group_B - group_Ctrl"
)
fa <- build_contrast_analysis(lfqprot, "~ group_", contrasts, method = "lm_impute")
res <- fa$get_contrasts()
head(res[, c("protein_Id", "contrast", "diff", "p.value", "FDR")])
#> # A tibble: 6 × 5
#> protein_Id contrast diff p.value FDR
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 0EfVhX~3967 A_vs_Ctrl 0.199 0.000123 0.000397
#> 2 0YSKpy~2865 A_vs_Ctrl -0.175 0.00277 0.00554
#> 3 0m5WN4~6025 A_vs_Ctrl 0.178 0.0000160 0.0000726
#> 4 3QLHfm~8938 A_vs_Ctrl 0.214 0.00456 0.00807
#> 5 3QYop0~7543 A_vs_Ctrl -0.221 0.00000298 0.0000179
#> 6 76k03k~7094 A_vs_Ctrl -0.0822 0.00980 0.0156fa$get_Plotter()$volcano()$FDR
Figure 3: Volcano plot of the two contrasts
All available methods are listed by list_facades(); the vignette All modelling methods at a glance runs each of them on one small dataset.
names(list_facades())
#> [1] "binomial_firth_nested" "binomial_nested" "deqms"
#> [4] "deqms_voom" "firth" "firth_nested"
#> [7] "hurdle_nested" "limma" "limma_impute"
#> [10] "limma_voom" "limma_voom_impute" "limpa"
#> [13] "limpa_nested" "lm" "lm_impute"
#> [16] "lmer_nested" "rfit" "rfit_impute"
#> [19] "rlm" "ropeca_nested"5 Session info
sessionInfo()
#> R version 4.6.1 (2026-06-24)
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