Simulated data with 1000 genes measured under two different
experimental conditions 1 and 2. 100 genes among the 1000 were generated as
differentially expressed (DE) genes. The expression levels of all no DE genes
were generated by \(N(0,1)\) distribution in both conditions 1 and 2.
The DE genes were generated using the \(N(0,1)\) and \(N(\mu_g,1)\) distributions
for conditions 1 and 2, respectively, with \(|\mu_g|=\Delta\).
Parameter \(\Delta\) sets the importance of gene g, where the bigger \(\Delta\) is,
the more important gene g is. We considered \(\Delta\) in \(\{1.5, 2, 3\}\).
Each row \(g\) in simexpr corresponds to a simulated gene.
simexprA dataframe with 1000 rows and 62 variables:
It indicates whether gene g is DE or not.
It contains \(\Delta\) values.
These columns have the expression levels under experimental condition 1.
These columns have the expression levels under experimental condition 2.