meta.perm {metaseqR} | R Documentation

## Permutation tests for meta-analysis

### Description

This function performs permutation tests in order to derive a meta p-value by combining several of the statistical algorithms of metaseqr. This is probably the most accurate way of combining multiple statistical algorithms for RNA-Seq data, as this issue is different from the classic interpretation of the term "meta-analysis" which implies the application of the same statistical test on different datasets treating the same subject/experiment. For other methods, see also the main `metaseqr` help page. You should keep in mind that the permutation procedure can take a long time, even when executed in parallel.

### Usage

        meta.perm(contrast, counts, sample.list, statistics,
            stat.args, libsize.list, nperm = 10000,
            weight = rep(1/ncol(counts), ncol(counts)),
            select = c("min", "max", "weight"), replace = "auto",
            reprod=TRUE, multic = FALSE)

### Arguments

`contrast` | the contrasts to be tested by each statistical algorithm. See the main `metaseqr` help page.
`counts` | a normalized read counts table, one row for each gene, one column for each sample.
`sample.list` | the list containing condition names and the samples under each condition. See the main `metaseqr` help page.
`statistics` | the statistical algorithms used in metaseqr. See the main `metaseqr` help page.
`stat.args` | the parameters for each statistical algorithm. See the main `metaseqr` help page.
`libsize.list` | a list with library sizes. See the main `metaseqr` and the `stat.*` help pages.
`nperm` | the number of permutations (Monte Carlo simulations) to perform.
`weight` | a numeric vector of weights for each statistical algorithm.
`select` | how to select the initial vector of p-values. It can be `"min"` to select the minimum p-value for each gene (more conservative), `"max"` to select the maximum p-value for each gene (less conservative), `"weight"` to apply the weights to the p-value vector for each gene and derive a weighted p-value.
`replace` | same as the `replace` argument in the `sample` function. Implies bootstraping or simple resampling without replacement. It can also be `"auto"`, to determine bootstraping or not with the following rule: if `ncol(counts)<=6` `replace=FALSE else` `replace=TRUE`. This protects from the case of having zero variability across resampled conditions. In such cases, most statistical tests would crash.
`reprod` | create reproducible permutations. Ideally one would want to create the same set of indices for a given dataset so as to create reproducible p-values. If `reprod=TRUE`, a fixed seed is used by `meta.perm` for all the datasets analyzed with `metaseqr`. If `reprod=FALSE`, then the p-values will not be reproducible, although statistical significance is not expected to change for a large number of resambling. Finally, `reprod` can be a numeric vector of seeds with the same length as `nperm` so that the user can supply his/her own seeds.
`multic` | use multiple cores to execute the premutations. This is an external parameter and implies the existence of multicore package in the execution environment. See the main `metaseqr` help page.

### Value

A vector of meta p-values

### Author(s)

Panagiotis Moulos

### Examples

    # Not yet available

[Package metaseqR version 1.20.0 Index]