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bjsws icon sdkcbfcheckwinsearch bioconductor-biobase 1.48.0 dbioutils_1.54.0 hg19codegen_3.0.0 szfile_1.2-1 biocTools_3.14.3 masonlite_0.10.2 limma_3.32.7 luma_3.21.5 magick_2.0-10 benchmarking_1.4.1 brushwork_2.3.1 e1071_1.6-4 ggplot2_2.2.1 grid_3.3.3 lecme_0.26-3 plotfor_1.0.2 seurat_2.3.0 biojava_1.5.1 BioJava_1.53.0 biomisc_1.3.3 R_2.13.0 data.table_1.10.4 GenomicRanges_1.22.0 data.table_1.10.4 rtracklayer_1.44.1 Descriptive and graphical analyses using R A quick introduction to the R language and its packages was given in a previous post. Although R is a powerful language for data analysis and statistics, its main strength lies in its ease of use and the availability of a wide range of packages for specific tasks and problems. The package base is so strong, that there are plenty of online communities supporting R users. In these communities people often suggest how to perform specific statistical tasks. In the data analysis component of microarray experiments, I found the following short introduction on inferential statistics in R particularly helpful: Limma For the main part of this post I will focus on the statistical methods for differential gene expression analyses. Limma is a package which makes use of the lmFit() function, which is part of the nlme library. To use lmFit() in your own package, just add it to your library package (or leave it commented). The following is an example from the limma documentation: lmFit

 

 

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