Testing for similarity on count data Is there a statistical test tat can test for similarity between two data sets on a count data? 
The only test I can think of doing on a count data is chi squared test and fisher exact test and both test for differences between the two data sets. 
I don't think I can use the P value for similarity as 1 - (p value of difference), can I ?
My actual data is as follows:
 
Alterrnatively the data can be arranged in the table below:

 A: The question you are asking is about agreement.  You may want to check out John Uebersax's website on agreement.  You need to think in terms of the format of your second contingency table, because your data are matched in the sense that the observations are of the same genes.  The test you want is Cohen's kappa.  In R, it could be done like this:  
library(irr)                         # you need to use this package
dat = matrix(c(rep(c("u", "d"), 4),  # here I input your data
               rep(c("d", "u"), 2),
               rep(c("d", "d"), 9) ), ncol=2, byrow=T)
dat
#       [,1] [,2]
#  [1,] "u"  "d" 
#  [2,] "u"  "d" 
#  [3,] "u"  "d" 
#  [4,] "u"  "d" 
#  [5,] "d"  "u" 
#  [6,] "d"  "u" 
#  [7,] "d"  "d" 
#  [8,] "d"  "d" 
#  [9,] "d"  "d" 
# [10,] "d"  "d" 
# [11,] "d"  "d" 
# [12,] "d"  "d" 
# [13,] "d"  "d" 
# [14,] "d"  "d" 
# [15,] "d"  "d" 
kappa2(dat)  # this is the test, you have (non-significant) disagreement
#  Cohen's Kappa for 2 Raters (Weights: unweighted)
# 
#  Subjects = 15 
#    Raters = 2 
#     Kappa = -0.216 
#         z = -0.916 
#   p-value = 0.36 

