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()

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.

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:

AnalysisConfiguration maps the columns of the table to their roles: the feature hierarchy (protein, peptide, …), the explanatory factors, the sample and the intensity column.
LFQData holds 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()).

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")

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       B

We \(\log_2\) 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()

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 0m5WN4~6025 A_vs_Ctrl  0.178  0.0000160  0.0000726
#> 3 0YSKpy~2865 A_vs_Ctrl -0.175  0.00277    0.00554  
#> 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    0.0156

fa$get_Plotter()$volcano()$FDR

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"

Session info

sessionInfo()
#> R version 4.6.2 (2026-10-29)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] ggplot2_4.0.3    prolfqua_0.99.8  dplyr_1.2.1      BiocStyle_2.41.0
#> 
#> loaded via a namespace (and not attached):
#>   [1] Rdpack_2.6.6           gridExtra_2.3.1        rlang_1.3.0           
#>   [4] magrittr_2.0.5         clue_0.3-68            GetoptLong_1.1.1      
#>   [7] otel_0.3.0             matrixStats_1.5.0      compiler_4.6.2        
#>  [10] mgcv_1.9-5             png_0.1-9              vctrs_0.7.3           
#>  [13] pkgconfig_2.0.3        shape_1.4.6.1          crayon_1.5.3          
#>  [16] fastmap_1.2.0          backports_1.5.1        labeling_0.4.3        
#>  [19] utf8_1.2.6             promises_1.5.0         rmarkdown_2.32        
#>  [22] nloptr_2.2.1           UpSetR_1.4.1           purrr_1.2.2           
#>  [25] xfun_0.62              glmnet_5.2             jomo_2.7-6            
#>  [28] logistf_1.26.1         cachem_1.1.0           jsonlite_2.0.0        
#>  [31] progress_1.2.3         later_1.4.8            pan_2.0               
#>  [34] Rfit_0.27.0            prettyunits_1.2.0      broom_1.0.13          
#>  [37] parallel_4.6.2         cluster_2.1.8.3        R6_2.6.1              
#>  [40] bslib_0.12.0           stringi_1.8.9          RColorBrewer_1.1-3    
#>  [43] limma_3.99.4           boot_1.3-32            rpart_4.1.27          
#>  [46] numDeriv_2016.8-1.1    jquerylib_0.1.4        Rcpp_1.1.2            
#>  [49] iterators_1.0.14       knitr_1.52             IRanges_2.47.5        
#>  [52] httpuv_1.6.17          Matrix_1.7-6           splines_4.6.2         
#>  [55] nnet_7.3-21            tidyselect_1.2.1       yaml_2.3.12           
#>  [58] doParallel_1.0.17      codetools_0.2-20       lmerTest_3.2-1        
#>  [61] lattice_0.23-1         tibble_3.3.1           plyr_1.8.9            
#>  [64] shiny_1.14.0           withr_3.0.3            S7_0.2.2              
#>  [67] evaluate_1.0.5         survival_3.8-12        circlize_0.4.18       
#>  [70] pillar_1.11.1          BiocManager_1.30.27    mice_3.19.0           
#>  [73] KernSmooth_2.23-27     foreach_1.5.2          stats4_4.6.2          
#>  [76] reformulas_0.4.4       plotly_4.12.1          generics_0.1.4        
#>  [79] hms_1.1.4              S4Vectors_0.51.10      scales_1.4.0          
#>  [82] minqa_1.2.8            xtable_1.8-8           glue_1.8.1            
#>  [85] maketools_1.3.2        tools_4.6.2            sys_3.4.3             
#>  [88] data.table_1.18.6.1    lme4_2.1-0             forcats_1.0.1         
#>  [91] buildtools_1.0.0       grid_4.6.2             limpa_1.5.2           
#>  [94] tidyr_1.3.2            rbibutils_2.4.1        crosstalk_1.2.2       
#>  [97] colorspace_2.1-4       nlme_3.1-172           formula.tools_1.7.1   
#> [100] cli_3.6.6              viridisLite_0.4.3      ComplexHeatmap_2.29.0 
#> [103] gtable_0.3.6           sass_0.4.10            digest_0.6.39         
#> [106] operator.tools_1.6.3.1 BiocGenerics_0.59.13   ggrepel_0.9.8         
#> [109] rjson_0.2.23           htmlwidgets_1.6.4      farver_2.1.2          
#> [112] htmltools_0.5.9        lifecycle_1.0.5        httr_1.4.9            
#> [115] mime_0.13              GlobalOptions_0.1.4    mitml_0.4-5           
#> [118] statmod_1.5.2          MASS_7.3-66

References

Wolski, Witold E., Paolo Nanni, Jonas Grossmann, Maria d’Errico, Ralph Schlapbach, and Christian Panse. 2023. “prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis.” Journal of Proteome Research 22 (4): 1092–104. https://doi.org/10.1021/acs.jproteome.2c00441.