Description Usage Arguments Details Value Author(s) References Examples

View source: R/generateRmdCodeDiffExp.R

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.

1 2 3 4 5 6 7 8 9 10 | ```
baySeq.createRmd(
data.path,
result.path,
codefile,
norm.method,
equaldisp,
sample.size = 5000,
estimation = "QL",
pET = "BIC"
)
``` |

`data.path` |
The path to a .rds file containing the |

`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 |

`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 |

`pET` |
The method used to re-estimate the priors. Possible values are |

For more information about the methods and the interpretation of the parameters, see the `baySeq`

package and the corresponding publications.

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.

Charlotte Soneson

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

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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)
})
``` |

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