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Merge TPR simulation function into Development #6

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Merged
swaraj-neu merged 11 commits into from
Apr 7, 2026
Merged

Merge TPR simulation function into Development #6

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5a26bdc
Transfer the TPR simulation function from MSstatsShiny and export it …
swaraj-neu Mar 20, 2026
8b7eaf9
Resolve TPR function nitpicks
swaraj-neu Mar 20, 2026
5d87188
Rollback the documentation files from dev branch
swaraj-neu Mar 26, 2026
b1ed032
Sample size calculation task:
swaraj-neu Mar 30, 2026
cfabb47
Resolve nitpicks in TPR power curve function
swaraj-neu Mar 30, 2026
38a10d5
Resolved a documentation nitpick in TPR simulation function
swaraj-neu Mar 30, 2026
bf82171
TPR Simulation task:
swaraj-neu Apr 3, 2026
6236e60
Update the example in the docs for TPR simulation function
swaraj-neu Apr 3, 2026
73527a2
Update the TPR simulation plot color palettes for replicate levels
swaraj-neu Apr 4, 2026
71a46e9
- Throw an exception that prevents a user from simulating with more t…
swaraj-neu Apr 6, 2026
308e2d8
- Enhance the test cases for ensuring the TPR simulation returns corr…
swaraj-neu Apr 7, 2026
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3 changes: 2 additions & 1 deletion DESCRIPTION
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Expand Up @@ -31,7 +31,8 @@ Imports:
dplyr,
stats,
parallel,
data.table
data.table,
plotly
Suggests:
MSstats,
MSstatsTMT,
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6 changes: 6 additions & 0 deletions NAMESPACE
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Expand Up @@ -4,8 +4,10 @@ export(MSstatsPrepareDoseResponseFit)
export(convertGroupToNumericDose)
export(doseResponseFit)
export(futureExperimentSimulation)
export(plot_tpr_power_curve)
export(predictIC50)
export(predictIC50Parallel)
export(run_tpr_simulation)
export(visualizeResponseProtein)
import(dplyr)
importFrom(BiocParallel,bplapply)
Expand All @@ -15,6 +17,7 @@ importFrom(dplyr,arrange)
importFrom(dplyr,distinct)
importFrom(dplyr,filter)
importFrom(dplyr,group_by)
importFrom(dplyr,if_else)
importFrom(dplyr,mutate)
importFrom(dplyr,select)
importFrom(dplyr,summarise)
Expand All @@ -35,13 +38,16 @@ importFrom(ggplot2,scale_fill_manual)
importFrom(ggplot2,scale_x_continuous)
importFrom(ggplot2,scale_y_continuous)
importFrom(ggplot2,theme)
importFrom(ggplot2,theme_bw)
importFrom(ggplot2,theme_classic)
importFrom(ggplot2,theme_minimal)
importFrom(ggplot2,ylim)
importFrom(parallel,clusterExport)
importFrom(parallel,makeCluster)
importFrom(parallel,parLapply)
importFrom(parallel,stopCluster)
importFrom(plotly,ggplotly)
importFrom(plotly,layout)
importFrom(stats,approx)
importFrom(stats,p.adjust)
importFrom(stats,pf)
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25 changes: 16 additions & 9 deletions R/ExperimentalDesignSimulation.R
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Expand Up @@ -222,23 +222,30 @@ futureExperimentSimulation = function(N_proteins = 300,
))
}

# Get mean profile across proteins
# Get mean profile across proteins using string-based grouping to avoid floating point precision issues
profile = drug_data %>%
filter(protein %in% protein_ids) %>%
mutate(dose_nM = round(dose * 1e9)) %>% # Convert M to nM and round to handle precision
group_by(dose_nM) %>%
mutate(dose_str = as.character(dose)) %>%
group_by(dose_str) %>%
summarise(
LogIntensities = mean(response, na.rm = TRUE),
dose_numeric = dose[1],
.groups = 'drop'
) %>%
rename(dose = dose_nM)
)

# Create complete profile with all concentrations
# Match profile doses to target concentrations using nearest-neighbor on log scale.
# This handles unit mismatches without assuming a fixed conversion factor.
complete_profile = data.frame(dose = concentrations)

# Merge with existing data
if (nrow(profile) > 0 && !any(profile$dose_numeric != 0)) {
stop("No non-zero dose measurements found for the selected protein.")
}

# Match by string representation to avoid floating point issues
complete_profile = complete_profile %>%
left_join(profile, by = "dose")
mutate(dose_str = as.character(dose)) %>%
left_join(profile %>% select(dose_str, LogIntensities), by = "dose_str") %>%
select(-dose_str)

# Fill missing values with interpolation or nearest neighbor
for (i in which(is.na(complete_profile$LogIntensities))) {
Expand Down Expand Up @@ -306,7 +313,7 @@ futureExperimentSimulation = function(N_proteins = 300,
return(complete_profile)
}

# Extract templates for each category
# Extract templates: use user data for specified proteins, baseline for others
template = list(
strong_interaction = .getMeanProfile(strong_proteins, drug_data, concentrations),
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tonywu1999 marked this conversation as resolved.
weak_interaction = .getMeanProfile(weak_proteins, drug_data, concentrations),
Expand Down
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