| Title: | Designing Population PDX Studies |
|---|---|
| Description: | Run simulations to assess the impact of various designs features and the underlying biological behaviour on the outcome of a Patient Derived Xenograft (PDX) population study. This project can either be deployed to a server as a 'shiny' app or installed locally as a package and run the app using the command 'populationPDXdesignApp()'. |
| Authors: | Maria Luisa Guerriero [aut, cre], Natasha Karp [aut] |
| Maintainer: | Maria Luisa Guerriero <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.3 |
| Built: | 2024-11-28 06:35:39 UTC |
| Source: | CRAN |
This is an internal function. Please use cautiously if calling directly.
Samples some animals and classify as responders or non-responders based on number of models studied (PDXn), number of mice measured per model (PDXr), the classification accuracy (C_Acc) and the underlying biological response rate (Biol_RR).
Example usage: callsInSingleExperiment(PDXn=8, PDXr=3, C_Acc=0.95, Biol_RR=30)
callsInSingleExperiment(PDXn, PDXr, C_Acc, Biol_RR)callsInSingleExperiment(PDXn, PDXr, C_Acc, Biol_RR)
PDXn |
number of PDX models studied |
PDXr |
number of mice measured per PDX model |
C_Acc |
classification accuracy |
Biol_RR |
underlying biological response rate for this treatment |
dataframe with three columns:
- PDXModel is a string that indicates the model id
- PDXclassification is a numeric value that indicates the true biological classification of that PDX - 0 equal non-responder and 1 equal responder
- StudyResult is a numeric value that indicates the classification of the PDX model after sampling - 0 equal non-responder and 1 equal responder
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly. Returns the mode from numeric vector. Example usage: getMode(c(0,1,1))
getMode(v)getMode(v)
v |
vector of numeric values |
a numeric value
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly. Returns the number of false calls from a simulation study exploring the impact of varying PDXn and PDXr for an underlying Biol_RR for a particularly go-no go threshold.
A false call can only arise in the situation where the underlying Biol_RR is below the go-no go threshold.
Example usage: noFalseCalls(ImpactVarying_PDXn_PDXr_BRR, GoNoGoThreshold=30)
noFalseCalls(dataset, GoNoGoThreshold)noFalseCalls(dataset, GoNoGoThreshold)
dataset |
dataset obtained as output from the 'varying_PDXn_PDXr' function |
GoNoGoThreshold |
go-no go threshold |
vector with three elements:
- numeric value indicating the number of experiments simulated
- numeric value indicating the number of experiments which were above the go-no go threshold
- numeric value indicating the FPR
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly.
Returns the number of missed calls from a simulation study exploring the impact of varying PDXn and PDXr for an underlying Biol_RR for a particularly go-no go threshold.
A missed call can only arise in the situation where the underlying Biol_RR exceeds the go-no go threshold. Example usage: noMissedCalls(ImpactVarying_PDXn_PDXr_BRR, GoNoGoThreshold=30)
noMissedCalls(dataset, GoNoGoThreshold)noMissedCalls(dataset, GoNoGoThreshold)
dataset |
dataset obtained as output from the 'varying_PDXn_PDXr' function |
GoNoGoThreshold |
go-no go threshold |
vector with three elements:
- numeric value indicating the number of experiments simulated
- numeric value indicating the number of experiments which were below the go-no go threshold
- numeric value indicating the percent of missed calls
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly. From a simulation of a single experiment, the estimated response rate is determined and captured with the meta data (e.g. PDXn, PDXr) for that experiment.
Example usage: outcomeInSingleExperiment(df=outcomeInSingleExperiment_1, PDXn=8, PDXr=3, C_Acc=0.95, Biol_RR=30)
outcomeInSingleExperiment(df, PDXn, PDXr, C_Acc, Biol_RR)outcomeInSingleExperiment(df, PDXn, PDXr, C_Acc, Biol_RR)
df |
data frame from callsInSingleExperiment |
PDXn |
PDXn |
PDXr |
PDXr |
C_Acc |
the classification accuracy (numeric value between 0 and 1) |
Biol_RR |
Biol_RR |
a vector with 8 values that captures the input design and the estimated response rate for that design from a single simulation
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly. Simulations are used to mimic population PDX studies for specified values of PDXn, PDXr, Biol_RR and C_Acc.
Example usage: outcomeMultipleExperiments(PDXn=8, PDXr=3, C_Acc=0.95, Biol_RR=30, iterations=500)
outcomeMultipleExperiments(PDXn, PDXr, C_Acc, Biol_RR, iterations)outcomeMultipleExperiments(PDXn, PDXr, C_Acc, Biol_RR, iterations)
PDXn |
PDXn |
PDXr |
PDXr |
C_Acc |
the classification accuracy (numeric value between 0 and 1) |
Biol_RR |
Biol_RR |
iterations |
no of experiments to simulated |
a dataframe where each row represents the results from a simulation mimicking an individual experiment for a particular design with meta data returned to describe the experimental design
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly. A visualisation of the false positive rate behaviour from the simulations
plotFalsepositive(data)plotFalsepositive(data)
data |
data frame with four columns which indicate the PDXn, PDXr, Biol_RR and the FPR for a specified go-no go threshold |
a graphic visualisation
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly. A visualisation of the sensitivity from the simulations
plotSensitivity(data)plotSensitivity(data)
data |
data frame with four columns which indicate the PDXn, PDXr, Biol_RR and the MissedCalls for a specified go-no go threshold |
a graphic visualisation
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
populationPDXdesignApp
Runs the 'shiny' app.
populationPDXdesignApp()populationPDXdesignApp()
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
if (interactive()) { populationPDXdesignApp() }if (interactive()) { populationPDXdesignApp() }
This is an internal function. Please use cautiously if calling directly
server(input, output, session)server(input, output, session)
input |
input |
output |
output |
session |
session |
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly.
ui()ui()
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]
This is an internal function. Please use cautiously if calling directly.
Simulations are used to mimic population PDX studies by inputing a variety of experimental factors (PDXn and PDXr) and biological factors (Biol_RR and C_Acc).
Example usage: varyingPDXnPDXrBiolRR(PDXn_range=c(8,10,12), PDXr_range=c(1,3,5), Biol_RR_range=c(30,40,50), C_Acc=0.95, iterations=500)
varyingPDXnPDXrBiolRR(PDXn_range, PDXr_range, Biol_RR_range, C_Acc, iterations)varyingPDXnPDXrBiolRR(PDXn_range, PDXr_range, Biol_RR_range, C_Acc, iterations)
PDXn_range |
a vector of PDXn values to study |
PDXr_range |
a vector of PDXr values to study |
Biol_RR_range |
a vector of values between 0 and 100 to indicate the Biol_RR to study |
C_Acc |
the classification accuracy (numeric value between 0 and 1) |
iterations |
iterations |
a dataframe where each row represents the results from a simulation mimicking an individual experiment for a particular design with meta data returned to describe the experimental design
Maria Luisa Guerriero, [email protected]
Natasha A. Karp, [email protected]