Comparing datasets from 2 biological replicates I have two datasets containing experiment data based on two biological replicates. I wonder what the best statistical methods are to find out and test how similar these two datasets are, and also how do I sensibly graphically show the results since each of the the datasets contain thousands of rows. I will like to do this analysis in R.
Thanks
Edit:
Data sample:
        Rep1    Rep2
Row1    1.0426  1.1337
Row2    -2.21   -2.1997
Row3    0.5002  0.6933
Row4    -4.2332 -3.9332
Row5    0.9000  0.8700

 A: You can start by plotting those 2 variables on a scatterplot, and also plot a y=x line. If your data is a dataframe object called DATA, you can run 
plot(DATA$Rep1~DATA$Rep2)
abline(0,1)

If the dots line up in a line very close to each other, the two variables are highly correlated. If those dots are close to the y=x line, then the numerical values are close to each other. 
From the first 5 rows of your data, I suspect the correlation is high and positive, and they will fall very close to the line. 
A: In addition to graphic display advised by Hotaka, following test can also be run:


*

*Pearson's correlation: to see the degree of correlation

*Difference of each pair of values can be calculated. The descriptive statistics of the differences will be informative regarding the mean and SD of difference, the range, the maximum and mean difference. 

*If there is some order in which the rows have been listed, a plot of row number on x-axis and difference on y-axis will show in which area the difference in more.


If you post in you question what exactly do the numbers mean, one may be able to suggest better methods, for example, possible role of residual plots from regression analysis. In addition, many packages are available for genetic analysis which may be useful here. 
Edit: If you are using R and your data.frame is mydf, then these command will give you the differences and their summary: 
mydf$diffs = with(mydf, Rep2-Rep1)
summary(mydf$diffs)

You may consider viewing the differences in raw values rather than these log values. You can plot these differences and their density in order of rows by following simple commands 
plot(mydf$diffs)
plot(density(mydf$diffs))

For more questions regarding R commands you should post at http://stackoverflow.com
There are many genetics package listed on http://cran.r-project.org/web/views/Genetics.html . Bioconductor (http://www.bioconductor.org/) may be especially useful. 
