I want to construct Bayesian network for a 800 genes(genes are my node/variables). I have only 30 cancer samples and 30 normal sample. So I want to create network for cancer samples and for the normal samples whether my data is reasonable to learn Bayesian network?
I strongly recommend you read up on k-dependent bayesian networks. You need O(2^K) examples where K is the maximum number of nodes on the bayesian network that can be connected to another node. So in other words, the more dependencies you consider between your features, very very rapidly you need more examples. So a full bayesian network for 800 genes means you need 2^800 examples - astronomical.
Nevertheless you could consider only connecting considerably less genes. The way you would do this is by using information theoretic clustering algorithms.
If you only have 30 examples, then I'd suggest only considering pairs, or triples of genes being dependent.
Furthermore if you have such few examples, and (as most people do) your going to use Maximum Likelihood + some kind of Laplacian Smoothing for probability estimation, then your probabilities are going to be way out and quite meaningless. Maximum likelihood only starts making sense when you have 100s of examples.