Overview | Relationship with the Bioconductor ecosystem | How ProteinBatcher differs from msqrob2 | Input data and workflow parameters | Input data | Sample annotation structure | Workflow parameters | The example data set | Example 1: workflow with interaction testing (condition-by-batch) | Accessing the organised effects | Filtering and imputation summary tables | Differential effects: IL13 vs NoTreated | Main effect (all proteins) | Common effect (batch-independent proteins) | Interaction effect (batch-dependent proteins) | Deregulogram: interpreting batch-dependent effects | Example 2: workflow without interaction testing | Session info
Package: ProteinBatcher 0.99.7
Aitor Moruno-Cuenca
ProteinBatcher: An end-to-end proteomics workflow with condition-aware imputation, flexible statistical modelling and interactive visualization
ProteinBatcher provides utilities to read, validate, and harmonize quantitative proteomics result tables generated by DIA-NN from data-independent acquisition mass spectrometry experiments. The package enables batch processing of multiple DIA-NN output files, consistent handling of protein identifiers and associated metadata, and transformation of tabular data into analysis-ready formats. It facilitates downstream differential abundance analysis using linear modeling frameworks based on limma, with explicit support for multivariable experimental designs including batch effects and other confounding covariates, as well as correlation-aware designs via duplicateCorrelation.
Authors:
ProteinBatcher_0.99.7.tar.gz
ProteinBatcher_0.99.7.zip(r-4.7-any)ProteinBatcher_0.99.7.zip(r-4.6-any)ProteinBatcher_0.99.7.zip(r-4.5-any)
ProteinBatcher_0.99.7.tgz(r-4.6-any)ProteinBatcher_0.99.7.tgz(r-4.5-any)
ProteinBatcher_0.99.7.tar.gz(r-4.7-any)ProteinBatcher_0.99.7.tar.gz(r-4.6-any)
ProteinBatcher_0.99.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ProteinBatcher/json (API)
| # Install 'ProteinBatcher' in R: |
| install.packages('ProteinBatcher', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/datasciencerd-almirall/proteinbatcher/issues
On BioConductor:ProteinBatcher-0.99.7(bioc 3.24)
proteomicsmassspectrometrydataimportdatarepresentationsoftwarebatcheffect
Last updated from:1c6df35e6b. Checks:1 NOTE, 9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | NOTE | 159 | ||
| linux-devel | OK | 347 | ||
| source / vignettes | OK | 231 | ||
| linux-release | OK | 319 | ||
| macos-release | OK | 217 | ||
| macos-oldrel | OK | 201 | ||
| windows-devel | OK | 247 | ||
| windows-release | OK | 235 | ||
| windows-oldrel | OK | 243 | ||
| wasm-release | OK | 122 |
Exports:.pp_organize_effectsfilter_se_missingimpute_seplot_deregulogramplot_volcano_customizedrun_proteomics_pipelinetest_limma_customized
Dependencies:abindBiobaseBiocGenericsclicpp11data.tableDelayedArrayfarvergenericsGenomicRangesggplot2ggrepelgluegtableIRangesisobandlabelinglatticelifecyclelimmaMatrixMatrixGenericsmatrixStatsR6RColorBrewerRcpprlangS4ArraysS4VectorsS7scalesSeqinfoSparseArraystatmodSummarizedExperimentvctrsviridisLitewithrXVector
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Filter proteins by missingness within conditions (SummarizedExperiment) | filter_se_missing |
| Impute missing proteomics intensities within conditions (SummarizedExperiment) | impute_se |
| Deregulogram plot for interaction models (2-level factors) | plot_deregulogram |
| Volcano plot for differential abundance results | plot_volcano_customized |
| Run a the downstream proteomics pipeline (filter, impute, limma, reporting) | run_proteomics_pipeline |
| Differential abundance testing with limma (imputation-aware FDR) | test_limma_customized |