estimateAufcWeights {metaseqR2} | R Documentation

## 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=0.1,libsizeGt=1e+5
    )

[Package metaseqR2 version 1.2.0 Index]