How to calculate similarity in gene expression for each gene in two conditions and rank them?

I have expression values (log2) for 200 genes in two conditions treated and untreated and for each condition I have 20 replicates. The dataset looks like this:

Gene      UT1             UT2             T1              T2
DDR1      8.111795978   7.7606511867    7.9362235824    7.5974674936
RFC2      10.2418824097 9.7752152714    10.0085488406   9.5723427524
HSPA6     6.5850239731  6.7916563534    6.6883401632    7.3659252344
PAX8      9.2965160827  9.2031177653    9.249816924     8.667772504
GUCA1A    5.4828021059  5.3797749957    5.4312885508    5.1297319374


I have shown only two replicates for each sample in the sample data.

How can I calculate similarity in gene expression for each gene in two conditions and rank them from highest to lowest (in terms of similarity)?

I am looking for a solution in R or python. The cor function in R does not give me what i want.

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What do you mean when you say that you want to test for correlation? Do you mean that for each of the 200 genes you want to do a separate test that the correlation between the treated and untreated groups is different from 0? That would mean doing 200 tests and would have the usual problem of multiplicity (which is a serious problem when doing so many tests). On the other hand you might want to test for a single correaltion between groups ignoring what the specific gene is. – Michael Chernick Jun 15 '12 at 11:35
This would then involve only 1 test but would require the assumption that the gene doesn't matter(1.e. correlation is the same for each gene). – Michael Chernick Jun 15 '12 at 11:35
Hi Micheal, I have edited the question. I think t-test will do the trick ???????? – Angelo Jun 15 '12 at 11:45