  Identification of essential genes is one of the ultimate goals of drug
designs. Here we introduce an {\it in silico} method to select essential genes
through the microarray assay. We construct a graph of genes, called the gene
transcription network, based on the Pearson correlation coefficient of the
microarray expression level. Links are connected between genes following the
order of the pair-wise correlation coefficients. We find that there exist two
meaningful fractions of links connected, $p_m$ and $p_s$, where the number of
clusters becomes maximum and the connectivity distribution follows a power law,
respectively. Interestingly, one of clusters at $p_m$ contains a high density
of essential genes having almost the same functionality. Thus the deletion of
all genes belonging to that cluster can lead to lethal inviable mutant
efficiently. Such an essential cluster can be identified in a self-organized
way. Once we measure the connectivity of each gene at $p_s$. Then using the
property that the essential genes are likely to have more connectivity, we can
identify the essential cluster by finding the one having the largest mean
connectivity per gene at $p_m$.
