baySeq.createRmd {compcodeR} | R Documentation

## Generate a `.Rmd` file containing code to perform differential expression analysis with baySeq

### Description

A function to generate code that can be run to perform differential expression analysis of RNAseq data (comparing two conditions) using the `baySeq` package. The code is written to a `.Rmd` file. This function is generally not called by the user, the main interface for performing differential expression analysis is the `runDiffExp` function.

### Usage

    baySeq.createRmd(data.path, result.path, codefile, norm.method, equaldisp,
      sample.size = 5000, estimation = "QL", pET = "BIC")

### Arguments

`data.path` | The path to a .rds file containing the `compData` object that will be used for the differential expression analysis.
`result.path` | The path to the file where the result object will be saved.
`codefile` | The path to the file where the code will be written.
`norm.method` | The between-sample normalization method used to compensate for varying library sizes and composition in the differential expression analysis. Possible values are `"quantile"`, `"total"` and `"edgeR"`.
`equaldisp` | Logical parameter indicating whether or not equal dispersion should be assumed across all conditions.
`sample.size` | The size of the sample used to estimate the priors (default 5000).
`estimation` | The approach used to estimate the priors. Possible values are `"QL"` (default), `"ML"` and `"edgeR"`.
`pET` | The method used to re-estimate the priors. Possible values are `"BIC"` (default), `"none"` and `"iteratively"`.

### Details

For more information about the methods and the interpretation of the parameters, see the `baySeq` package and the corresponding publications.

### Value

The function generates a `.Rmd` file containing the code for performing the differential expression analysis. This file can be executed using e.g. the `knitr` package.

### Author(s)

Charlotte Soneson

### References

Hardcastle TJ (2012): baySeq: Empirical Bayesian analysis of patterns of differential expression in count data. R package

Hardcastle TJ and Kelly KA (2010): baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 11:422

### Examples

    try(
    if (require(baySeq)) {
    tmpdir <- normalizePath(tempdir(), winslash = "/")
    mydata.obj <- generateSyntheticData(dataset = "mydata", n.vars = 1000,
                                        samples.per.cond = 5, n.diffexp = 100,
                                        output.file = file.path(tmpdir, "mydata.rds"))
    ## Note! In the interest of speed, we set sample.size=10 in this example.
    ## In a real analysis, much larger sample sizes are recommended (the default is 5000).
    runDiffExp(data.file = file.path(tmpdir, "mydata.rds"), result.extent = "baySeq",
               Rmdfunction = "baySeq.createRmd",
               output.directory = tmpdir, norm.method = "edgeR",
               equaldisp = TRUE, sample.size = 10)
    })

[Package compcodeR version 1.20.1 Index]