Generate a .Rmd file containing code to perform differential expression analysis with EBSeq

A function to generate code that can be run to perform differential expression analysis of RNAseq data (comparing two conditions) using the EBSeq 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.

EBSeq.createRmd(data.path, result.path, codefile, norm.method)

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 "median" and "quantile".

Value

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

Details

For more information about the methods and the meaning of the parameters, see the EBSeq package and the corresponding publications.

References

Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BMG, Haag JD, Gould MN, Stewart RM and Kendziorski C (2013): EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics

Author

Charlotte Soneson

Examples

try(
if (require(EBSeq)) {
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"))
runDiffExp(data.file = file.path(tmpdir, "mydata.rds"), result.extent = "EBSeq",
           Rmdfunction = "EBSeq.createRmd",
           output.directory = tmpdir, norm.method = "median")
}
)
#> Loading required package: EBSeq
#> Loading required package: blockmodeling
#> To cite package 'blockmodeling' in publications please use package
#> citation and (at least) one of the articles:
#> 
#>   Žiberna, Aleš (2007). Generalized blockmodeling of valued networks.
#>   Social Networks 29(1), 105-126.
#> 
#>   Žiberna, Aleš (2008). Direct and indirect approaches to blockmodeling
#>   of valued networks in terms of regular equivalence. Journal of
#>   Mathematical Sociology 32(1), 57–84.
#> 
#>   Žiberna, Aleš (2014). Blockmodeling of multilevel networks. Social
#>   Networks 39, 46–61. https://doi.org/10.1016/j.socnet.2014.04.002.
#> 
#>   Žiberna, Aleš (2023).  Generalized and Classical Blockmodeling of
#>   Valued Networks, R package version 1.1.5.
#>   Cugmas, Marjan (2023).  Generalized and Classical Blockmodeling of
#>   Valued Networks, R package version 1.1.5.
#> 
#> To see these entries in BibTeX format, use 'format(<citation>,
#> bibtex=TRUE)', or 'toBibtex(.)'.
#> Loading required package: gplots
#> 
#> Attaching package: ‘gplots’
#> The following object is masked from ‘package:IRanges’:
#> 
#>     space
#> The following object is masked from ‘package:S4Vectors’:
#> 
#>     space
#> The following object is masked from ‘package:stats’:
#> 
#>     lowess
#> Loading required package: testthat
#> 
#> 
#> processing file: /private/var/folders/xz/lz5thm6s3vb1vdkk77vmkgbr0000gn/T/RtmpDwlx4a/tempcode21d05963d48.Rmd
#> 1/2                  
#> 2/2 [unnamed-chunk-1]
#> output file: /private/var/folders/xz/lz5thm6s3vb1vdkk77vmkgbr0000gn/T/RtmpDwlx4a/tempcode21d05963d48.md
#> [1] TRUE