MSstatsResponse: A package for detecting drug-protein interactions in dose-response mass spectrometry-based proteomics experiments
Introduction
MSstatsResponse provides statistical tools for detecting drug-protein interactions and estimating IC50 values from dose-response mass spectrometry-based proteomics experiments. The package implements isotonic regression models to detect monotonic dose-response relationships and provides robust statistical inference through F-tests and bootstrap confidence intervals.
MSstatsResponse is designed to work with summarized data from MSstats and MSstatsTMT workflows, but can also process other dose-response proteomics datasets that include protein-level quantifications across drug treatments.
Statistical framework
The package uses isotonic regression, a non-parametric method that fits monotonic functions without assuming a specific functional form. This approach is particularly suitable for dose-response data where monotonicity (increasing or decreasing trend) is expected.
Analysis workflow
MSstatsResponse implements the following statistical analysis pipeline:
- Data preparation:
MSstatsPrepareDoseResponseFit()- Convert input data to standardized format - Interaction detection:
doseResponseFit()- Detect drug-protein interactions using isotonic regression and F-tests - IC50 estimation:
predictIC50()- Estimate IC50 values with bootstrap confidence intervals - Experimental design planning:
futureExperimentSimulation()andrun_tpr_simulation()- Simulate dose-response experiments to determine optimal study designs
Installation and setup
Install MSstatsResponse
Data preprocessing with MSstats
When analyzing a mass spectrometry-based proteomics dataset, data should be preprocessed using the standard MSstats workflow for cleaning, normalization, and protein summarization prior to analysis with MSstatsResponse.
When using MSstats, data can be converted with one of the available
converter functions (e.g., SpectronauttoMSstatsFormat())
and subsequently processed with dataProcess().
For MSstatsTMT, converter functions (e.g.,
MaxQtoMSstatsTMTFormat()) may be used, followed by
proteinSummarization() for data processing.
Please refer to the MSstats and MSstatsTMT Bioconductor vignettes for a comprehensive list of supported converters and detailed preprocessing recommendations.
Example MSstats workflow
# Read raw data (example with Spectronaut output)
raw_data <- read_tsv("path/to/spectronaut_report.tsv")
# Convert to MSstats format
msstats_data <- SpectronauttoMSstatsFormat(raw_data)
# Process data: normalization and protein summarization
processed_data <- dataProcess(
msstats_data,
normalization = "equalizeMedians",
summaryMethod = "TMP",
MBimpute = TRUE,
maxQuantileforCensored = 0.999
)
# Extract protein-level data for dose-response analysis
protein_level_data <- processed_data$ProteinLevelDataLoading example data
For this vignette, we use pre-processed data prepared following the steps described above. For more information on preprocessing mass spectrometry-based proteomics experiments, see the vignettes for MSstats and/or MSstatsTMT.
data("DIA_MSstats_Normalized", package = "MSstatsResponse")
dia_normalized <- DIA_MSstats_Normalized
str(dia_normalized$ProteinLevelData[1:5, ])
#> 'data.frame': 5 obs. of 11 variables:
#> $ RUN : Factor w/ 27 levels "1","2","3","4",..: 1 2 3 4 5
#> $ Protein : Factor w/ 3 levels "PROTEIN_A","PROTEIN_B",..: 1 1 1 1 1
#> $ LogIntensities : num 5.05 5.65 5.15 5.02 5.32
#> $ originalRUN : Factor w/ 27 levels "20240221_3uL_neu-01_dmso-1_dia_2x10_0p65-1p45_85min_Slot1-1_1_2571",..: 1 2 3 4 5
#> $ GROUP : Factor w/ 9 levels "Drug1_001nM",..: 9 9 9 1 1
#> $ SUBJECT : num 1 2 3 1 2
#> $ TotalGroupMeasurements: int 396 396 396 396 396
#> $ NumMeasuredFeature : int 132 132 132 132 132
#> $ MissingPercentage : num 0 0 0 0 0
#> $ more50missing : logi FALSE FALSE FALSE FALSE FALSE
#> $ NumImputedFeature : int 0 0 0 0 0Data preparation for dose-response analysis
Converting GROUP labels to numeric doses
The convertGroupToNumericDose() function parses
MSstats-style GROUP labels to extract drug names and numeric dose
values.
Format expectations:
- Control samples:
"DMSO"(assigned dose = 0) - Treatment samples:
"DrugName_ValueUnit"(e.g.,"Drug1_003uM","Drug2_300nM")
dose_info <- convertGroupToNumericDose(dia_normalized$ProteinLevelData$GROUP)
dia_normalized$ProteinLevelData <- dia_normalized$ProteinLevelData %>%
mutate(
dose_nM = dose_info$dose_nM,
drug = dose_info$drug
)
dia_normalized$ProteinLevelData %>%
select(Protein, GROUP, drug, dose_nM) %>%
head(10)
#> Protein GROUP drug dose_nM
#> 1 PROTEIN_A DMSO DMSO 0
#> 2 PROTEIN_A DMSO DMSO 0
#> 3 PROTEIN_A DMSO DMSO 0
#> 4 PROTEIN_A Drug1_001nM Drug1 1
#> 5 PROTEIN_A Drug1_001nM Drug1 1
#> 6 PROTEIN_A Drug1_001nM Drug1 1
#> 7 PROTEIN_A Drug1_001uM Drug1 1000
#> 8 PROTEIN_A Drug1_001uM Drug1 1000
#> 9 PROTEIN_A Drug1_001uM Drug1 1000
#> 10 PROTEIN_A Drug1_003nM Drug1 3Formatting data with MSstatsPrepareDoseResponseFit()
This function standardizes the data structure for downstream
analysis. The dose_column corresponds to the x-axis values
(e.g., dose, concentration, time, or temperature), the
protein_column should contain unique identifiers (e.g.,
protein, gene, or peptide sequence), and the
log_abundance_column represents the response values on the
y-axis (e.g., log intensity, expression, or growth).
dia_prepared <- MSstatsPrepareDoseResponseFit(
data = dia_normalized$ProteinLevelData,
dose_column = "dose_nM",
drug_column = "drug",
protein_column = "Protein",
log_abundance_column = "LogIntensities",
transform_nM_to_M = TRUE
)
str(dia_prepared)
#> 'data.frame': 81 obs. of 5 variables:
#> $ protein : Factor w/ 3 levels "PROTEIN_A","PROTEIN_B",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ drug : chr "DMSO" "DMSO" "DMSO" "Drug1" ...
#> $ dose : num 0e+00 0e+00 0e+00 1e-09 1e-09 1e-09 1e-06 1e-06 1e-06 3e-09 ...
#> $ response: num 5.05 5.65 5.15 5.02 5.32 ...
#> $ dose_nM : num 0 0 0 1 1 1 1000 1000 1000 3 ...
dia_prepared %>%
filter(protein %in% c("PROTEIN_A", "PROTEIN_B")) %>%
arrange(protein, drug, dose) %>%
head(20)
#> protein drug dose response dose_nM
#> 1 PROTEIN_A DMSO 0e+00 5.050662 0
#> 2 PROTEIN_A DMSO 0e+00 5.647209 0
#> 3 PROTEIN_A DMSO 0e+00 5.148809 0
#> 4 PROTEIN_A Drug1 1e-09 5.019425 1
#> 5 PROTEIN_A Drug1 1e-09 5.318420 1
#> 6 PROTEIN_A Drug1 1e-09 5.141087 1
#> 7 PROTEIN_A Drug1 3e-09 5.218344 3
#> 8 PROTEIN_A Drug1 3e-09 5.199884 3
#> 9 PROTEIN_A Drug1 3e-09 5.320023 3
#> 10 PROTEIN_A Drug1 1e-08 4.848356 10
#> 11 PROTEIN_A Drug1 1e-08 5.205914 10
#> 12 PROTEIN_A Drug1 1e-08 4.743209 10
#> 13 PROTEIN_A Drug1 3e-08 4.663491 30
#> 14 PROTEIN_A Drug1 3e-08 4.486120 30
#> 15 PROTEIN_A Drug1 3e-08 4.505798 30
#> 16 PROTEIN_A Drug1 1e-07 3.936098 100
#> 17 PROTEIN_A Drug1 1e-07 4.231085 100
#> 18 PROTEIN_A Drug1 1e-07 4.327733 100
#> 19 PROTEIN_A Drug1 3e-07 3.590255 300
#> 20 PROTEIN_A Drug1 3e-07 3.212578 300Drug-protein interaction detection
Isotonic regression
Isotonic regression fits a monotonic function to the data without assuming a parametric form. For inhibitor data, we typically expect a non-increasing response: drug binding reduces protein abundance monotonically with dose.
Running doseResponseFit()
The doseResponseFit() function:
- Fits isotonic regression for each protein-drug pair
- Performs an F-test comparing the fitted model to a flat (null) model
- Adjusts p-values for multiple testing using the Benjamini-Hochberg procedure
response_results <- doseResponseFit(
data = dia_prepared,
increasing = FALSE,
transform_dose = TRUE,
ratio_response = FALSE
)
#> | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100%
response_results %>%
select(Protein, drug, F_statistic, log2FC, adj.pvalue) %>%
arrange(adj.pvalue) %>%
head(10)
#> Protein drug F_statistic log2FC adj.pvalue
#> 1 PROTEIN_B Drug1 255.431210 -5.201664 0.000000e+00
#> 2 PROTEIN_A Drug1 76.470195 -2.515487 3.885392e-12
#> 3 PROTEIN_C Drug1 1.180529 -0.105177 3.627180e-01Interpreting results
- log2FC: Log2 fold change between the highest and lowest doses
- F_statistic: Ratio of between-dose to within-dose variance
- P_value: Raw p-value from F-test
- adj.pvalue: FDR-adjusted p-value (Benjamini-Hochberg)
Proteins with adj.pvalue < 0.05 show significant
dose-dependent responses.
n_total <- nrow(response_results)
n_significant <- sum(response_results$adj.pvalue < 0.05)
cat("Total protein-drug pairs tested:", n_total, "\n")
#> Total protein-drug pairs tested: 3
cat("Significant interactions (FDR < 0.05):", n_significant, "\n")
#> Significant interactions (FDR < 0.05): 2
cat("Percentage significant:", round(100 * n_significant / n_total, 1), "%\n")
#> Percentage significant: 66.7 %
top_hits <- response_results %>%
filter(adj.pvalue < 0.05) %>%
arrange(adj.pvalue) %>%
head(5)
print(top_hits)
#> Protein drug direction SSE_Full SSE_Null F_statistic P_value
#> 1 PROTEIN_B Drug1 decreasing 1.0502856 120.28394 255.4312 0.000000e+00
#> 2 PROTEIN_A Drug1 decreasing 0.7482337 26.17827 76.4702 2.590261e-12
#> log2FC adj.pvalue
#> 1 -5.201664 0.000000e+00
#> 2 -2.515487 3.885392e-12IC50 estimation
Understanding IC50 calculation
IC50 represents the dose at which the response is reduced to 50% of the control (DMSO) level. MSstatsResponse estimates IC50 by:
- Fitting isotonic regression on the ratio scale (response relative to DMSO)
- Finding the dose where the fitted response equals 0.5
- Computing confidence intervals via bootstrap resampling
Running predictIC50()
ic50_predictions <- predictIC50(
data = dia_prepared,
ratio_response = TRUE,
transform_dose = TRUE,
increasing = FALSE,
bootstrap = TRUE,
n_samples = 1000,
alpha = 0.10
)
ic50_predictions %>%
arrange(IC50) %>%
head(10)
#> Protein drug IC50 IC50_lower_bound IC50_upper_bound direction
#> <fctr> <char> <num> <num> <num> <char>
#> 1: PROTEIN_A Drug1 7.47197 7.887699 7.207284 decreasing
#> 2: PROTEIN_B Drug1 8.19419 8.519009 7.965246 decreasing
#> 3: PROTEIN_C Drug1 NA NA NA decreasingParallel processing for large datasets
For datasets with many proteins, use parallel processing via BiocParallel:
library(BiocParallel)
if (.Platform$OS.type == "windows") {
register(SnowParam(workers = 4, type = "SOCK"))
} else {
register(MulticoreParam(workers = 4))
}
ic50_parallel <- predictIC50(
data = dia_prepared,
ratio_response = TRUE,
transform_dose = TRUE,
bootstrap = TRUE,
n_samples = 1000,
BPPARAM = bpparam()
)Visualization
Individual protein dose-response curves
The visualizeResponseProtein() function creates
dose-response plots:
visualizeResponseProtein(
data = dia_prepared,
protein_name = "PROTEIN_A",
drug_name = "Drug1",
ratio_response = TRUE,
show_ic50 = TRUE,
add_ci = TRUE,
transform_dose = TRUE,
n_samples = 1000
)
visualizeResponseProtein(
data = dia_prepared,
protein_name = "PROTEIN_B",
drug_name = "Drug1",
ratio_response = TRUE,
show_ic50 = TRUE,
add_ci = TRUE
)
Comparing log2 vs ratio scale
p1 <- visualizeResponseProtein(
data = dia_prepared,
protein_name = "PROTEIN_A",
drug_name = "Drug1",
ratio_response = FALSE,
show_ic50 = TRUE,
add_ci = FALSE
)
p2 <- visualizeResponseProtein(
data = dia_prepared,
protein_name = "PROTEIN_A",
drug_name = "Drug1",
ratio_response = TRUE,
show_ic50 = TRUE,
add_ci = FALSE
)
gridExtra::grid.arrange(p1, p2, ncol = 2)
Predicting at other response levels
The target_response argument allows prediction at any
response level (default 0.50). This example predicts IC25 with
confidence intervals:
visualizeResponseProtein(
data = dia_prepared,
protein_name = "PROTEIN_B",
drug_name = "Drug1",
ratio_response = TRUE,
show_ic50 = TRUE,
add_ci = TRUE,
transform_dose = TRUE,
target_response = 0.25
)
Experimental design planning
Simulating future experiments
futureExperimentSimulation() evaluates detection
performance under a user-defined experimental design by simulating a
full experiment and measuring true-positive and false-positive rates.
Proteins from your own data can be used as templates for more realistic
simulations.
# pull unique doses
dose_levels = as.numeric(unique(dia_prepared$dose))
# run simulation
custom_simulation <- futureExperimentSimulation(
N_proteins = 300,
N_rep = 2,
N_Control_Rep = 2,
Concentrations = dose_levels,
data = dia_prepared,
strong_proteins = "PROTEIN_B",
weak_proteins = "PROTEIN_A",
no_interaction_proteins = "PROTEIN_C",
drug_name = "Drug1",
IC50_Prediction = FALSE
)
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print(custom_simulation$Hit_Rates_Plot)
print(custom_simulation$Template_Used)
#> $strong_interaction
#> dose Intensity LogIntensities ratio
#> 1 0e+00 42.256113 5.4010882 1.00000000
#> 2 1e-09 38.863432 5.2803414 0.91971147
#> 3 1e-06 1.266790 0.3411772 0.02997885
#> 4 3e-09 26.873610 4.7481182 0.63596977
#> 5 3e-06 1.177035 0.2351572 0.02785479
#> 6 1e-08 22.303763 4.4792152 0.52782334
#> 7 3e-08 17.327563 4.1149968 0.41006050
#> 8 1e-07 10.777063 3.4298922 0.25504152
#> 9 3e-07 1.186329 0.2465041 0.02807473
#>
#> $weak_interaction
#> dose Intensity LogIntensities ratio
#> 1 0e+00 38.914246 5.282227 1.0000000
#> 2 1e-09 35.744368 5.159644 0.9185420
#> 3 1e-06 7.395559 2.886659 0.1900476
#> 4 3e-09 37.951462 5.246084 0.9752588
#> 5 3e-06 6.878926 2.782183 0.1767714
#> 6 1e-08 30.537135 4.932493 0.7847289
#> 7 3e-08 23.454666 4.551803 0.6027270
#> 8 1e-07 17.938304 4.164972 0.4609701
#> 9 3e-07 9.453305 3.240819 0.2429266
#>
#> $no_interaction
#> dose Intensity LogIntensities ratio
#> 1 0e+00 59.16019 5.886555 1.0000000
#> 2 1e-09 60.11579 5.909672 1.0161526
#> 3 1e-06 56.94126 5.831402 0.9624927
#> 4 3e-09 56.23066 5.813285 0.9504813
#> 5 3e-06 55.61656 5.797443 0.9401010
#> 6 1e-08 57.99096 5.857756 0.9802362
#> 7 3e-08 57.62351 5.848586 0.9740250
#> 8 1e-07 57.80438 5.853107 0.9770823
#> 9 3e-07 54.28170 5.762394 0.9175376Sweeping experimental design parameters
run_tpr_simulation() sweeps over combinations of
replicate counts and number of concentration levels, measuring the true
positive rate (TPR) for each design configuration. Control (0) and the
highest dose are always included; intermediate doses are selected by
greedily choosing the concentration farthest from the log-median of the
already-selected set.
Use plot_tpr_power_curve() to visualize the resulting
power curves.
power_results <- run_tpr_simulation(
rep_range = c(2,4),
concentrations = dose_levels,
dose_range = c(2, 9),
data = dia_prepared,
protein = "PROTEIN_A",
n_proteins = 300
)
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plot_tpr_power_curve(power_results)Session information
sessionInfo()
#> R version 4.5.3 (2026-03-11)
#> 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=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] data.table_1.18.2.1 ggplot2_4.0.3 dplyr_1.2.1
#> [4] MSstatsResponse_1.1.2 BiocStyle_2.39.0
#>
#> loaded via a namespace (and not attached):
#> [1] plotly_4.12.0 sass_0.4.10 generics_0.1.4
#> [4] tidyr_1.3.2 stringi_1.8.7 digest_0.6.39
#> [7] magrittr_2.0.5 evaluate_1.0.5 grid_4.5.3
#> [10] RColorBrewer_1.1-3 fastmap_1.2.0 jsonlite_2.0.0
#> [13] gridExtra_2.3 BiocManager_1.30.27 httr_1.4.8
#> [16] purrr_1.2.2 crosstalk_1.2.2 viridisLite_0.4.3
#> [19] scales_1.4.0 codetools_0.2-20 lazyeval_0.2.3
#> [22] jquerylib_0.1.4 cli_3.6.6 rlang_1.2.0
#> [25] withr_3.0.2 cachem_1.1.0 yaml_2.3.12
#> [28] otel_0.2.0 tools_4.5.3 parallel_4.5.3
#> [31] BiocParallel_1.45.0 buildtools_1.0.0 vctrs_0.7.3
#> [34] R6_2.6.1 lifecycle_1.0.5 stringr_1.6.0
#> [37] htmlwidgets_1.6.4 pkgconfig_2.0.3 pillar_1.11.1
#> [40] bslib_0.10.0 gtable_0.3.6 glue_1.8.1
#> [43] xfun_0.57 tibble_3.3.1 tidyselect_1.2.1
#> [46] sys_3.4.3 knitr_1.51 farver_2.1.2
#> [49] htmltools_0.5.9 labeling_0.4.3 rmarkdown_2.31
#> [52] maketools_1.3.2 compiler_4.5.3 S7_0.2.2References
Szvetecz, S., et al. (2026). MSstatsResponse: Semi-parametric statistical model enhances drug-protein interaction detection and IC50 estimation in chemoproteomics experiments. Manuscript in preparation.
Choi, M., et al. (2014). MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics, 30(17), 2524-2526.