Package: MSstatsResponse 1.2.0

Sarah Szvetecz

MSstatsResponse: Statistical Methods for Chemoproteomics Dose-Response Analysis

Tools for detecting drug-protein interactions and estimating IC50 values from chemoproteomics data. Implements semi-parametric isotonic regression, bootstrapping, and curve fitting to evaluate compound effects on protein abundance.

Authors:Sarah Szvetecz [aut, cre], Tony Wu [aut], Devon Kohler [aut], Olga Vitek [aut]

MSstatsResponse_1.2.0.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
MSstatsResponse/json (API)
NEWS

# Install 'MSstatsResponse' in R:

install.packages('MSstatsResponse', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/vitek-lab/msstatsresponse/issues

Datasets:

DIA_MSstats_Normalized - Example pre-processed DIA-MS dataset

On BioConductor:MSstatsResponse-1.3.0(bioc 3.24)MSstatsResponse-1.2.0(bioc 3.23)

proteomics massspectrometry statisticalmethod software regression

6.03 score 1 stars 1 packages 15 scripts 224 downloads 11 exports 72 dependencies

Last updated from:dcf6958d09 (on RELEASE_3_23). Checks:4 ERROR, 4 WARNING, 2 OK. Indexed: no.

Target	Result	Time
bioc-checks	ERROR	178
linux-devel-x86_64	WARNING	245
source / vignettes	OK	313
linux-release-x86_64	WARNING	230
macos-release-arm64	WARNING	137
macos-oldrel-arm64	WARNING	125
windows-devel	ERROR	185
windows-release	ERROR	211
windows-oldrel	ERROR	199
wasm-release	OK	150

Exports:calculatePeptideWeights calculateTurnoverRatios convertGroupToNumericDose doseResponseFit futureExperimentSimulation MSstatsPrepareDoseResponseFit plot_tpr_power_curve predictIC50 predictIC50Parallel run_tpr_simulation visualizeResponseProtein

Dependencies:askpass base64enc BH BiocParallel bslib cachem cli codetools cpp11 crosstalk curl data.table digest dplyr evaluate farver fastmap fontawesome formatR fs futile.logger futile.options generics ggplot2 glue gtable highr htmltools htmlwidgets httr isoband jquerylib jsonlite knitr labeling lambda.r later lazyeval lifecycle magrittr memoise mime openssl otel pillar pkgconfig plotly promises purrr R6 rappdirs RColorBrewer Rcpp rlang rmarkdown S7 sass scales snow stringi stringr sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml

MSstatsResponse: A package for detecting drug-protein interactions in dose-response mass spectrometry-based proteomics experiments

Sarah Szvetecz ([email protected]), Devon Kohler ([email protected]), Tony Wu ([email protected]), Swaraj Chandrakant Patil ([email protected]), Olga Vitek ([email protected])

Rendered fromMSstatsResponse.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2026-04-23
Started: 2025-06-18

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Helper function to extract template profiles from user data	.extractTemplatesFromData
Bootstrap IC50 Estimates and Confidence Interval (ratio scale)	bootstrapIC50
Bootstrap IC50 Estimates and Confidence Interval (log scale)	bootstrapIC50LogScale
Bootstrap IC50 with pre-calculated ratios	bootstrapIC50Precalculated
Calculate quality-based weights for peptide measurements	calculatePeptideWeights
Calculate turnover ratios from MSstats FeatureLevelData	calculateTurnoverRatios
Convert MSstats GROUP labels to numeric dose in nM and extract drug name	convertGroupToNumericDose
Example pre-processed DIA-MS dataset	DIA_MSstats_Normalized
Drug-protein interaction detection tested by F-test (fitted curve vs average response)	doseResponseFit
Fit Isotonic Regression Model	fitIsotonicRegression
Test future experimental design using simulated data with user-defined or default templates	futureExperimentSimulation
Prepare data for dose-response fitting with isotonic regression	MSstatsPrepareDoseResponseFit
Visualize detection power across experimental designs	plot_tpr_power_curve
Plot hit rates by category	plotHitRateMSstatsResponse
Plot Isotonic Regression Model	plotIsotonic
Predict IC50 (dose where response = target) for each protein and drug	predictIC50
Parallel version of predictIC50 function	predictIC50Parallel
Simulate detection power across experimental design configurations	run_tpr_simulation
Simulate chemoproteomics data at the protein level - non-parametric approach	simulateChemoProteinLevelNonParametric
Plot isotonic regression fit with optional IC50 for a single protein and drug	visualizeResponseProtein