g

e limma package

ma (LInear Models for MicroArrays) package [Smyth, 2004] is

e best ones so far for employing the modified t test for a gene

n data generated using the microarray technology. In limma, a

tical approach called the shrinkage approach was used to estimate

ackage was developed by Gordon Smyth and his colleagues and

included in the Bioconductor environment. The package has been

sed to discover DEGs for the microarray gene expression data

et al., 2019; Nilson, et al., 2020]. It has also been used to analyse

gene expression data although this kind of data has a different

ibution [Mou, et al., 2020].

mma, the p values are calculated using an approach called the

Bayes (eBayes) [Smyth, 2004; McCarthy and Smyth, 2009]. By

e shrinkage approach, the standard deviations of genes are

d by the package making the extreme values move toward centre

ribution. From this, significantly differentially expressed genes

greater fold change values.

e the limma package, especially the eBayes function of the

to discover DEGs based on the microarray gene expression data,

pression matrix is required to be generated to have genes in rows

les in columns.

irst thing limma does is to generate a linear model. To do so, a

atrix is required, which defines the experimental conditions, i.e.,

cates distribute in two experimental conditions. A prostate cancer

data set (GSE3325) [Varambally, et al., 2005] was used for the

ation. The data was composed of six primary cancer samples as

x metastasis cancer samples. The code shown below was used to

a design matrix which is shown in Table 6.1.

D=data.frame(Primary=rep(1,12),

Metastasis=c(rep(0,6),rep(1,6)))