Your explanation regarding partial correlation makes sense.
Related questions:
Suppose you have two cell lines (normal vs. tumor), and you treat each cell line with varying concentrations of a compound. Your gene count file looks like the following:
Gene control11 control12 treat11 treat12 control21 control22 treat21 treat22
Gene1 ...
Gene2 ...
...
GeneN ...
The first column is the gene name, each other column is a sample. Each row is the count (expression level) for a gene.
When doing DEG analysis, we can use complicated models such as expression(GeneI) = cell_line + compound_concentration + cell_line * compound_concentration. There is no problem with it, and this is powerful.
Now when we use parcor to derive partial correlation coefficients, we simply use the expression matrix above, and ignoring cell_line and compound_concentration. Is there a problem here? -- Don't we lose information of the samples?
Even more complicated: how if we want to find differentially regulated gene-gene networks? i.e. if we are looking for gene-gene sub-networks that only occurs in the tumor samples, how can we find them? A naive way is to divide the samples into two groups: a normal group and a tumor group and derive networks in each group for comparison. But this does not seem to be a decent way.
Your help would be highly appreciated!
