I'm comparing the growth of two different cell lines across a fixed time period - what sort of test do I do to show that they are statistically significant (or not)? Would I do a 2 way ANOVA as the two variables are the 1. the cell line 2. time? I've plotted this as time vs cell count and want to see if the cell line growth is statistically significant. How do I show this on the graph as well?
I think a One-way Repeated Measure is enough for this study.
In this Model, cell lines is used as a factor (Independent Variable)
Two ANOVA is so good, although you still can use a chi square or a t- test if you have several readings.
I wonder why you plotted time vs. count and not vice versa. It would mean that you plot the time values on the ordinate (Y) and the counts on the abcissa (X).
If you counted the cells at several (perhaps many) different points in time, you should get a growth curve. Unlimited growth is exponential, in which case you would get a straight line if you plot log(count) against time. If you have several such experiments (=lines) for both groups, you can calculate the slopes and compare them using a t-test. If you have to sacrifice the cells for counting so that you have a single count value per experiment, you would put all these data together and end up with two lines (one per condition). You can model this as an ANCOVA model and test the difference in slopes of the two lines.
If the growth is not unlimited, it is likely logistic, so instead of a line (to log(count) vs. time) you woule fit logistic models (count vs. time, or count vs. log(time), depending on the symmetry). Here you can ask for different spects, like the initial growth rate, or the time needed to get half of the maximum count value, or the maximum count values that is obtained. The principle of analyis is the same as for the linear regression.
If you have the cell counts only at the beginning and after a given time span t, you can use a simple t-test using the log(count) values. It may be a paired test if you have the counts determined in each well at both points in time.
Note: the mere number of cells is not a direct measure of cell growth. It may be that you lose cells (loss of adherence, apoptosis, whatever), so the actual growth might be larger than what you think if you look at the counts. If you see a stronger increase of counts in condition A than in B, it can also just be the case that the growth is actually the same, but in condition you lose more cells for some reason. If you really want to look at growth, you should measure this more directly, e.g. by an MTT assay or by measuring DNA synthesis (3H-Thymidine or BrdU incorporation). You also need to check that you get similar amounts of dead cells in both conditions.
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