populationPDXdesign

Help for package populationPDXdesign const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {populationPDXdesign} Contents callsInSingleExperiment getMode noFalseCalls noMissedCalls outcomeInSingleExperiment outcomeMultipleExperiments plotFalsepositive plotSensitivity populationPDXdesignApp server ui varyingPDXnPDXrBiolRR Type: Package Title: Designing Population PDX Studies Version: 1.0.3 Date: 2018-08-08 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()'. License: GPL (≥ 3) Depends: R (≥ 3.0.0) Imports: devtools, ggplot2, plyr, roxygen2, shiny, shinycssloaders Suggests: testthat RoxygenNote: 6.0.1 NeedsCompilation: no Packaged: 2018-08-08 12:19:41 UTC; kpkr710 Author: Maria Luisa Guerriero [aut, cre], Natasha Karp [aut] Maintainer: Maria Luisa Guerriero <maria.guerriero@astrazeneca.com> Repository: CRAN Date/Publication: 2018-08-08 14:40:07 UTC Simulation of a single population PDX experiment Description 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) Usage callsInSingleExperiment(PDXn, PDXr, C_Acc, Biol_RR) Arguments 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 Value 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 Author(s) Maria Luisa Guerriero, maria.guerriero@astrazeneca.com Natasha A. Karp, natasha.karp@astrazeneca.com Function to return the mode of a vector of values Description This is an internal function. Please use cautiously if calling directly. Returns the mode from numeric vector. Example usage: getMode(c(0,1,1)) Usage getMode(v) Arguments v vector of numeric values Value a numeric value Author(s) Maria Luisa Guerriero, maria.guerriero@astrazeneca.com Natasha A. Karp, natasha.karp@astrazeneca.com Function to calculate the number of false calls for a design for a go-no go threshold Description 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) Usage noFalseCalls(dataset, GoNoGoThreshold) Arguments dataset dataset obtained as output from the 'varying_PDXn_PDXr' function GoNoGoThreshold go-no go threshold Value 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 Author(s) Maria Luisa Guerriero, maria.guerriero@astrazeneca.com Natasha A. Karp, natasha.karp@astrazeneca.com Function to calculate the number of missed calls for a design for a go-no go threshold Description 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) Usage noMissedCalls(dataset, GoNoGoThreshold) Arguments dataset dataset obtained as output from the 'varying_PDXn_PDXr' function GoNoGoThreshold go-no go threshold Value 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 Author(s) Maria Luisa Guerriero, maria.guerriero@astrazeneca.com Natasha A. Karp, natasha.karp@astrazeneca.com Function to summarise the results for a single simulation representing one experiment Description 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) Usage outcomeInSingleExperiment(df, PDXn, PDXr, C_Acc, Biol_RR) Arguments df data frame from callsInSingleExperiment PDXn PDXn PDXr PDXr C_Acc the classification accuracy (numeric value between 0 and 1) Biol_RR Biol_RR Value a vector with 8 values that captures the input design and the estimated response rate for that design from a single simulation Author(s) Maria Luisa Guerriero, maria.guerriero@astrazeneca.com Natasha A. Karp, natasha.karp@astrazeneca.com Function to run simulations to mimic population PDX studies for a defined scenario Description 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) Usage outcomeMultipleExperiments(PDXn, PDXr, C_Acc, Biol_RR, iterations) Arguments 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 Value 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 Author(s) Maria Luisa Guerriero, maria.guerriero@astrazeneca.com Natasha A. Karp, natasha.karp@astrazeneca.com A function to visualise the false postive rate as a function of PDXn and PDXr Description This is an internal function. Please use cautiously if calling directly. A visualisation of the false positive rate behaviour from the simulations Usage plotFalsepositive(data) Arguments data data frame with four columns which indicate the PDXn, PDXr, Biol_RR and the FPR for a specified go-no go threshold Value a graphic visualisation Author(s) Maria Luisa Guerriero, maria.guerriero@astrazeneca.com Natasha A. Karp, natasha.karp@astrazeneca.com A function to visualise the sensitivity as a function of PDXn and PDXr Description This is an internal function. Please use cautiously if calling directly. A visualisation of the sensitivity from the simulations Usage plotSensitivity(data) Arguments data data frame with four columns which indicate the PDXn, PDXr, Biol_RR and the MissedCalls for a specified go-no go threshold Value a graphic visualisation Author(s) Maria Luisa Guerriero, maria.guerriero@astrazeneca.com Natasha A. Karp, natasha.karp@astrazeneca.com Function populationPDXdesignApp Description Runs the 'shiny' app. Usage populationPDXdesignApp() Author(s) Maria Luisa Gue