Abstract
Short vignette pointing to RNAseqNet User’s Guide.The R package RNAseqNet
can be used to infer networks from RNA-seq expression data. The count data are given as a \(n \times p\) matrix in which \(n\) is the number of individuals and \(p\) the number of genes. This matrix is denoted by \(\mathbf{X}\) in the sequel.
Eventually, the RNA-seq dataset is complemented with an \(n' \times d\) matrix, \(\mathbf{Y}\) which can be used to impute missing individuals in \(\mathbf{X}\) as described in [Imbert et al., 2018].
The RNAseqNet User’s Guide can be opened with:
RNAseqNetUsersGuide()
The location of the source file is given by:
RNAseqNetUsersGuide(html = FALSE)
Imbert, A., Valsesia, A., Le Gall, C., Armenise, C., Lefebvre, G., Gourraud, P.A., Viguerie, N. and Villa-Vialaneix, N. (2018) Multiple hot-deck imputation for network inference from RNA sequencing data. Bioinformatics. DOI: http://dx.doi.org/10.1093/bioinformatics/btx819.
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
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## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## loaded via a namespace (and not attached):
## [1] compiler_3.6.3 magrittr_1.5 htmltools_0.3.6 tools_3.6.3
## [5] yaml_2.2.0 Rcpp_1.0.4.6 stringi_1.4.3 rmarkdown_1.14
## [9] knitr_1.23 stringr_1.4.0 digest_0.6.20 xfun_0.13
## [13] evaluate_0.14