I have a matrix of
N gene-based observations (between 0 and 1) from 2 experiments like the following (The actual matrix is much bigger).
GeneName & Exp1 & Exp2 \\
Gene1 & 0.721& 0.733 \\
Gene2 & 0.072& 0.325 \\
Gene3& 0.66& 0.16 \\
Gene4& 0.244& 0.279 \\
Gene5& 0.613& 0.9 \\
Gene6& 0.76& 0.753 \\
Gene7& 0.101& 0.863 \\
The experiments are independent and the goal is to look for variation in gene-based observations between the two experiments using non-parameteric bootstrapping.
I believe that, according to the bootstrapping theory, one will repeatedly take
N samples (7 in case of above) with replacement from input matrix of
N gene-based observations. Since, in my case, the experiments are independent, I was thinking to take 2 independent samples (of size
N each) each time, merge them by gene name and compute the difference in corresponding gene-based observations. However, while merging by gene name, I observed that the resulting matrix size (in terms of rows) may be greater than the original matrix size.
Will the above sampling algorithm be incorrect according to bootstrapping strategy? In case it is incorrect, how can I take bootstrap samples in a problem like above?