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proteomicdesign (version 2.0)

Optimization of a multi-stage proteomic study

Description

This package provides functions to identify the optimal solution that maximizes numbers of detectable differentiated proteins from a multi-stage clinical proteomic study.

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Version

Install

install.packages('proteomicdesign')

Monthly Downloads

4

Version

2.0

License

GPL-3

Maintainer

Irene Zeng

Last Published

January 4th, 2013

Functions in proteomicdesign (2.0)

power.t

The power function used in the optim.two.stage.single function
do.one.experiment

Simulation function for calculate the expected number of detectable proteins from the three stage proteomic study using group information
power.appr

The power function used in the optim.two.stage.appr function
ots.env

assign the current working environment
genseq.appr

A sub function to generate the sub solution space
power.group.cost

Derive the averaged estimated costs of stage II and III and the stage III sample size from the 1000 Monte Carlo simulated functions of a three-stage proteomis study, given a solution of the design parameters
Ftest.Ttest

A function to perform the paired t test for each protein, and the Hotelling T test for the group that the protein is assigned.
genseq.single

A sub function to generate the sub solution space
optim.two.stage.single

Optimization of the number of discoveries from a multistage clinical proteomic study
do.one.experiment.t

Simulation function for calculating the expected number of detectable proteins from the three stage proteomic study
calculate.cost

Function to calculate the total cost of the multi-stage design
optim.two.stage.appr

Optimize numbers of discoveries by using an approximated analytical objective function in a multi-stage clinical proteomic study that utilizes biological group information
genseq.group

A sub function to generate the sub solution space
power.single.cost

Derive the averaged estimated costs of stage II and III and the stage III sample size from the 1000 Monte Carlo simulated functions of a three-stage proteomic study, given a solution of the design parameters
proteomicdesign-package

Optimization of a multi stage proteomic study
calculate.n3

A function to calculate the stage III sample size based on the current design parameters
Ttest

The function to calculate the p values for each protein in the optim.two.stage.single function
optim.two.stage.group

Optimization of the design parameters in the discovery , verification and validation stage from a multi-stage clinical proteomic study using biological grouping information
power

The power function used in the optim.two.stage.single function