I have RNA-seq data with 6 replicates over 12 time points ... but the data is just two different 'experimental conditions' (two different sides of a plant in sunlight over 24 hours) with no control -- which I might have suggested be the plant in the absence of normal stimuli on each side, or some unrelated stem tissue from the plant.
I plan on using EdgeR to do a two factor analysis and testing for differential expression between the samples by using the first time point as a normalizing factor. So I will test:
(TimePoint_X_Condition1 - TimePoint_1_Condition1) - (TimePoint_X_Condition2 - TimePoint_1_Condition2)
for each time point X (2 - 12), where Condition and TimePoint are the two factors for the count based experiment.
Does this test make sense (sound, valid)? I reason that since I can't really treat either treatment as the control for the other in normal differential expression testing, it might make sense to test the differences in expression compared to baseline for each treatment. This assumes that TimePoint_1 is indeed a baseline for the treatments, which it really isn't... so I may end up testing each difference in expression between two points in the time series for one condition against each other such difference in the other condition:
(TimePoint_X_Condition_1 - TimePoint_Y_Condition_1) - (TimePoint_X_Condition_2 - TimePoint_Y_Conditon_2)
for each time point such that X ≠ Y. I guess I would then combine the differentially expressed genes for each unique test of a combination of time points and then count how many times the genes appear in the resulting combined list ... and thus how many of these tests showed differential expression.... Might then break them into sublists of "differentially expressed in greater than X" tests, where X is some arbitrary number of tests...
It just all seems wrong, but I cannot think of anything better to do without a control for the 'experimental conditions'.
What can I do to analyze two experimental conditions for differential expression without a true control?