simresults: A function to summarize the results of a simulation

In jlakkis/mnnsim: Perform Simulations to Compare Various Batch Correction Methods

Description

This function condenses the output of the simstudy function and into simple and interpretable tables from which conclusions can be made about the efficacy of various batch correction methods.

Usage

simresults(finishedstudy, sds = TRUE)

Arguments

finishedstudy	The output of the simstudy function.
sds	Whether to generate standard deviations for the various summary quantities in the outputted tables.

Value

A list of five tables

All Cells Breakdown	This table summarizes information from all replicates using all cells. For every parameter combination, this table provides the number of simulation replicates, the mean of mean silhouette coefficients across all replicates for uncorrected data, MNN corrected data, Limma corrected data, and ComBat data, as well as (optionally) the standard deviation of mean silhouette coefficients for each of these batch correction methods.
Cell Type 1 Breakdown	This table summarizes information from all replicates using cells of type 1. For every parameter combination, this table provides the number of simulation replicates, the mean of mean silhouette coefficients across all replicates for uncorrected data, MNN corrected data, Limma corrected data, and ComBat data, as well as (optionally) the standard deviation of mean silhouette coefficients for each of these batch correction methods.
Cell Type 2 Breakdown	This table summarizes information from all replicates using cells of type 2. For every parameter combination, this table provides the number of simulation replicates, the mean of mean silhouette coefficients across all replicates for uncorrected data, MNN corrected data, Limma corrected data, and ComBat data, as well as (optionally) the standard deviation of mean silhouette coefficients for each of these batch correction methods.
Cell Type 3 Breakdown	This table summarizes information from all replicates using cells of type 3. For every parameter combination, this table provides the number of simulation replicates, the mean of mean silhouette coefficients across all replicates for uncorrected data, MNN corrected data, Limma corrected data, and ComBat data, as well as (optionally) the standard deviation of mean silhouette coefficients for each of these batch correction methods.
Replicate Runtime Analysis	This table summarizes runtime information from all replicates. For every parameter combination, this table provides the mean and standard deviation of runtime per replicate.

Examples

## Not run: 
parameternames=list('Original', 'Smaller Differences', 'More Genes')
nsims=list(50,50,50)
seed=0
cellcounts=list(500,500,500)
genecounts=list(100,100,5000)
xmeans=list(c(0,5,5),c(0,2,2),c(0,5,5))
xsdss=list(c(1,0.1,1),c(1,0.1,1),c(1,0.1,1))
ymeans=list(c(5,5,0),c(2,2,0),c(5,5,0))
ysdss=list(c(1,0.1,1),c(1,0.1,1),c(1,0.1,1))
propsbatch1=list(c(0.3,0.5,0.2),c(0.3,0.5,0.2),c(0.3,0.5,0.2))
propsbatch2=list(c(0.65,0.3,0.05),c(0.65,0.3,0.05),c(0.65,0.3,0.05))

simstudy(
parameternames=parameternames,
nsims=nsims,seed=seed,
cellcounts=cellcounts,
genecounts=genecounts,
xmeans=xmeans,xsdss=xsdss,ymeans=ymeans,
ysdss=ysdss,propsbatch1=propsbatch1,
propsbatch2=propsbatch2,mycore=1
)

simresults(mystudy)


## End(Not run)

jlakkis/mnnsim documentation built on Jan. 21, 2020, 1:21 a.m.