|
Abstract:
|
A typical microarray study might involve two groups of subjects: controls and treatments. Each subject provides material for his or her individual microarray, reporting some large number of genetic expressions at the same time, yielding an m by n data matrix "X," with m genes and n subjects. We expect the measurements down any one column to be correlated, since genes act in concert, making the rows of X correlated. However the columns, that is the microarrays, are usually assumed to be independent, an essential assumption for t-tests, cross-validation, permutation tests, etc. This talk concerns testing a matrix X for column-wise independence when the rows may be highly correlated. The effect of row-wise correlation is to reduce effective sample size---from 20426 to 17 in my main example.
|
