estimateAufcWeights: Estimate AUFC weights

In pmoulos/metaseqR2: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms

Estimate AUFC weights

Description

This function automatically estimates weights for the "weight" and "dperm_weight" options of metaseqR2 for combining p-values from multiple statistical tests. It creates simulated dataset based on real data and then performs statistical analysis with metaseqR2 several times in order to derive False Discovery Curves. Then, the average areas under the false discovery curves are used to construct weights for each algorithm, according to its performance when using simulated data.

Usage

    estimateAufcWeights(counts, normalization,
        statistics, nsim = 10, N = 10000, 
        samples = c(3, 3), ndeg = c(500, 500),
        top = 500, modelOrg = "mm9", fcBasis = 1.5,
        drawFpc = FALSE, rc = NULL,
        ...)

Arguments

counts	the real raw counts table from which the simulation parameters will be estimated. It must not be normalized and must contain only integer counts, without any other annotation elements and unique gene identifiers as the rownames attribute.
normalization	same as normalization in metaseqr2.
statistics	same as statistics in metaseqr2.
nsim	the number of simulations to perform to estimate the weights. It default to 10.
N	the number of genes to produce. See makeSimDataSd.
samples	a vector with 2 integers, which are the number of samples for each condition (two conditions currently supported).
ndeg	a vector with 2 integers, which are the number of differentially expressed genes to be produced. The first element is the number of up-regulated genes while the second is the number of down-regulated genes.
fcBasis	the minimum fold-change for deregulation.
top	the top top best ranked (according to p-value) to use, to calculate area under the false discovery curve.
modelOrg	the organism from which the data are derived. It must be one of metaseqr2 supported organisms.
drawFpc	draw the averaged false discovery curves? Default to FALSE.
rc	the fraction of the available cores to use in a multicore system.
...	Further arguments to be passed to estimateSimParams.

Details

The weight estimation process involves a lot of random sampling. For guaranteed reproducibility, be sure to use set.seed prior to any calculations. By default, when the metaseqR2 package is loaded, the seed is set to 42.

Value

A vector of weights to be used in metaseqr2 with the weights option.

Author(s)

Panagiotis Moulos

Examples

require(zoo)
data("mm9GeneData",package="metaseqR2")
weights <- estimateAufcWeights(
    counts=as.matrix(mm9GeneCounts[sample(nrow(mm9GeneCounts),1000),9:12]),
    normalization="edaseq",
    statistics=c("edger","limma"),
    nsim=1,N=100,ndeg=c(10,10),top=10,modelOrg=NULL,
    rc=0.01,libsizeGt=1e+5
)

pmoulos/metaseqR2 documentation built on May 20, 2024, 5:48 a.m.