Protein Signal Intensities within Groups

Author

The CompMS team

Published

July 29, 2026

Samples12

Groups3

Proteins100

Visual abstract: quantified protein signals are ranked by abundance, with dominant proteins and per-sample protein counts summarized across groups.

This report profiles how quantified protein signal is distributed across sample groups. It identifies dominant proteins and potential contaminants, then compares the number of quantified proteins per sample.

Input: quantified protein abundances from 12 samples. The detailed source-data reference is recorded in Session Info.

Figure 1: Per-protein iBAQ signal contribution versus abundance rank, one panel per group. Each point is one protein; the x-axis is the protein’s abundance-rank percentile (proteins ordered from lowest to highest mean iBAQ abundance) and the y-axis (log10 scale) is the percentage of the total iBAQ (intensity-based absolute quantification) signal contributed by that protein. Contaminant and decoy proteins are highlighted in red.

Table columns

The columns of the protein abundance table contain:

protein_Id - protein identifier
nrPeptides - number of peptides assigned to the protein
description - protein description from the FASTA header
nrMeasured_<GroupName> - number of samples in which the protein was quantified, by group and overall
meanAbundance_<GroupName> - mean protein abundance per group
signal_percent_<GroupName> - percentage of the total protein signal attributed to the protein

What is the iBAQ signal?

The iBAQ signal per protein is calculated by summing the Precursor.Quantity values across all precursors assigned to the protein and dividing by the number of theoretically observable peptides. A large share of the recorded signal can be concentrated in a small set of highly abundant proteins. If the dominant proteins are the cleavage enzyme, common contaminants such as human keratins, or the bait protein, this can indicate shortcomings in sample preparation or cleanup.

Figure 2: Number of proteins quantified per sample.

Table 1: Compact report provenance, including the source input-data reference.

Field	Value
Workunit ID	n/a
Order ID	n/a
Project ID	n/a
Project name	n/a
Creator	runner
Created at	2026-07-29 11:16:25 UTC
Input data	n/a
Quantification software	n/a
Model	n/a
prolfquapp version	2.6.1

R version 4.6.1 (2026-06-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.4 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-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_1.2.1   ggplot2_4.0.3

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          jsonlite_2.0.0             
  [3] shape_1.4.6.1               magrittr_2.0.5             
  [5] jomo_2.7-6                  farver_2.1.2               
  [7] logistf_1.26.1              nloptr_2.2.1               
  [9] rmarkdown_2.31              GlobalOptions_0.1.4        
 [11] vctrs_0.7.3                 minqa_1.2.8                
 [13] progress_1.2.3              htmltools_0.5.9            
 [15] S4Arrays_1.12.0             forcats_1.0.1              
 [17] broom_1.0.13                cellranger_1.1.0           
 [19] SparseArray_1.12.2          mitml_0.4-5                
 [21] sass_0.4.10                 bslib_0.11.0               
 [23] htmlwidgets_1.6.4           plyr_1.8.9                 
 [25] cachem_1.1.0                plotly_4.12.1              
 [27] mime_0.13                   lifecycle_1.0.5            
 [29] iterators_1.0.14            pkgconfig_2.0.3            
 [31] Matrix_1.7-5                R6_2.6.1                   
 [33] fastmap_1.2.0               shiny_1.14.0               
 [35] rbibutils_2.4.1             MatrixGenerics_1.24.0      
 [37] clue_0.3-68                 digest_0.6.39              
 [39] dtplyr_1.3.3                colorspace_2.1-3           
 [41] lobstr_1.2.1                S4Vectors_0.50.1           
 [43] crosstalk_1.2.2             GenomicRanges_1.64.0       
 [45] labeling_0.4.3              httr_1.4.8                 
 [47] abind_1.4-8                 mgcv_1.9-4                 
 [49] compiler_4.6.1              bit64_4.8.2                
 [51] withr_3.0.3                 doParallel_1.0.17          
 [53] pander_0.6.6                S7_0.2.2                   
 [55] backports_1.5.1             UpSetR_1.4.1               
 [57] prolfquasaint_0.1.5         pan_2.0                    
 [59] MASS_7.3-65                 DelayedArray_0.38.2        
 [61] rjson_0.2.23                optparse_1.8.2             
 [63] tools_4.6.1                 otel_0.2.0                 
 [65] httpuv_1.6.17               nnet_7.3-20                
 [67] glue_1.8.1                  promises_1.5.0             
 [69] nlme_3.1-169                grid_4.6.1                 
 [71] cluster_2.1.8.2             generics_0.1.4             
 [73] operator.tools_1.6.3.1      gtable_0.3.6               
 [75] tzdb_0.5.0                  formula.tools_1.7.1        
 [77] preprocessCore_1.74.0       tidyr_1.3.2                
 [79] data.table_1.18.4           hms_1.1.4                  
 [81] XVector_0.52.0              BiocGenerics_0.58.1        
 [83] ggrepel_0.9.8               foreach_1.5.2              
 [85] pillar_1.11.1               stringr_1.6.0              
 [87] limma_3.68.4                later_1.4.8                
 [89] circlize_0.4.18             splines_4.6.1              
 [91] lattice_0.22-9              survival_3.8-6             
 [93] bit_4.6.0                   tidyselect_1.2.1           
 [95] ComplexHeatmap_2.28.0       knitr_1.51                 
 [97] reformulas_0.4.4            gridExtra_2.3.1            
 [99] prolfquapp_2.6.1            bookdown_0.47              
[101] IRanges_2.46.0              Seqinfo_1.2.0              
[103] SummarizedExperiment_1.42.0 stats4_4.6.1               
[105] xfun_0.60                   prolfqua_1.7.0             
[107] Biobase_2.72.0              statmod_1.5.2              
[109] matrixStats_1.5.0           DT_0.34.0                  
[111] stringi_1.8.7               lazyeval_0.2.3             
[113] yaml_2.3.12                 boot_1.3-32                
[115] evaluate_1.0.5              codetools_0.2-20           
[117] tibble_3.3.1                BiocManager_1.30.27        
[119] cli_3.6.6                   affyio_1.82.0              
[121] rpart_4.1.27                xtable_1.8-8               
[123] arrow_25.0.0                Rdpack_2.6.6               
[125] jquerylib_0.1.4             Rcpp_1.1.2                 
[127] readxl_1.5.0                png_0.1-9                  
[129] parallel_4.6.1              readr_2.2.0                
[131] assertthat_0.2.1            prettyunits_1.2.0          
[133] lme4_2.0-6                  glmnet_5.0                 
[135] viridisLite_0.4.3           scales_1.4.0               
[137] affy_1.90.0                 purrr_1.2.2                
[139] crayon_1.5.3                writexl_1.5.4              
[141] GetoptLong_1.1.1            rlang_1.3.0                
[143] vsn_3.80.0                  mice_3.19.0

--- title: "Protein Signal Intensities within Groups" author: "The CompMS team" date: today format: html: grid: sidebar-width: 300px include-after-body: fgcz-plot-finder.html toc: false params: pap_file: null factors: true project_info: null vignette: > %\VignetteIndexEntry{QC Protein Abundances} %\VignetteEngine{quarto::format} %\VignetteEncoding{UTF-8} --- ```{r setup, include=FALSE} library(ggplot2) library(dplyr) knitr::opts_chunk$set( echo = FALSE, message = FALSE, warning = FALSE, fig.width = 6.5, fig.height = 6.5, out.width = "600px", fig.align = "center" ) pap_file <- params$pap_file if (is.null(pap_file) || !nzchar(pap_file)) { pap_file <- file.path(tempdir(), "prolfquapp-example-qc-generator.rds") if (!file.exists(pap_file)) { saveRDS(prolfquapp::example_qc_generator(), pap_file) } } pap <- readRDS(pap_file) lfqdataProt <- pap$lfqdata_prot tableconfig <- pap$lfqdata_prot$get_config() factors <- params$factors project_info <- params$project_info if (is.null(project_info)) { project_info <- list() } report_provenance <- prolfquapp:::.report_provenance( project_spec = project_info ) ``` ::: {.panel-tabset} # Overview ```{r overview-data-summary} #| results: asis cat(prolfquapp:::.report_overview_cards(lfqdataProt)) ``` <img src="`r knitr::image_uri(system.file("report-assets", "protein-abundances.png", package = "prolfquapp"))`" alt="Visual abstract: quantified protein signals are ranked by abundance, with dominant proteins and per-sample protein counts summarized across groups." style="width: 100%; max-height: 22rem; object-fit: contain;"> This report profiles how quantified protein signal is distributed across sample groups. It identifies dominant proteins and potential contaminants, then compares the number of quantified proteins per sample. Input: quantified protein abundances from `r nrow(lfqdataProt$factors())` samples. The detailed source-data reference is recorded in **Session Info**. # Protein abundances ```{r createPlot} protID <- tableconfig$hierarchy_keys_depth() precabund <- pap$get_protein_per_group_abundance() if (!factors) { precabund <- dplyr::filter(precabund, !!sym(tableconfig$factor_keys()[1]) == "ALL") } pp <- prolfquapp::plot_abundance_vs_percent( precabund, cfg_config = tableconfig, top_N = NULL, factors = factors, colors = c("^zz" = "red", "Y-FGCZ" = "red"), columnAb = "abundance_percent", alpha = 0.4, highlight_alpha = 0.8, logY = TRUE) pp <- plotly::ggplotly(pp) ``` ```{r createDT} precabund_table <- pap$get_protein_per_group_small_wide() datax <- crosstalk::SharedData$new( as.data.frame(precabund_table), key = as.formula(paste(" ~ ", protID)), group = "BB") table <- DT::datatable( datax, filter = "bottom", extensions = "Scroller", style = "auto", class = "compact", options = list( deferRender = TRUE, scrollY = 300, scrollX = 400, scroller = TRUE )) ``` ```{r} #| label: fig-protein-cumulative #| fig-dpi: 96 #| fig-cap: "Per-protein iBAQ signal contribution versus abundance rank, one panel per group. Each point is one protein; the x-axis is the protein's abundance-rank percentile (proteins ordered from lowest to highest mean iBAQ abundance) and the y-axis (log10 scale) is the percentage of the total iBAQ (intensity-based absolute quantification) signal contributed by that protein. Contaminant and decoy proteins are highlighted in red." pp ``` ```{r} table ``` ::: {.callout-note collapse="true"} ## Table columns The columns of the protein abundance table contain: - `protein_Id` - protein identifier - `nrPeptides` - number of peptides assigned to the protein - `description` - protein description from the FASTA header - `nrMeasured_<GroupName>` - number of samples in which the protein was quantified, by group and overall - `meanAbundance_<GroupName>` - mean protein abundance per group - `signal_percent_<GroupName>` - percentage of the total protein signal attributed to the protein ::: ::: {.callout-note collapse="true"} ## What is the iBAQ signal? The iBAQ signal per protein is calculated by summing the `Precursor.Quantity` values across all precursors assigned to the protein and dividing by the number of theoretically observable peptides. A large share of the recorded signal can be concentrated in a small set of highly abundant proteins. If the dominant proteins are the cleavage enzyme, common contaminants such as human keratins, or the bait protein, this can indicate shortcomings in sample preparation or cleanup. ::: # Proteins per sample ```{r} #| label: fig-hierarchy-counts-sample #| fig-cap: "Number of proteins quantified per sample." srs <- lfqdataProt$get_Summariser() srs$plot_hierarchy_counts_sample() ``` # Session Info ::: {.panel-tabset} ## Report provenance ```{r} #| label: tbl-report-provenance #| tbl-cap: "Compact report provenance, including the source input-data reference." knitr::kable(prolfquapp:::.report_provenance_table(report_provenance)) ``` ## R session info ```{r} #| label: session-info sessionInfo() ``` ::: :::

Protein Signal Intensities within Groups

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