Hi guys,
I have an experiemnt where three groups of fish were used, two of them were treated with two doses of virus, while the thrid is a control group. I want to compare the means of gene expression levels between the three groups at different time points after challenge and I am going to use ANOVA..
I am dealing with 10 time points, where 9 fish were sampled/ each time point (3 fish /group).
My question is: can I test the normality and equality of variance on the whole data set (values from all time points)at the sam time?or shall I do it on each time point independently from the other time points?
Does the small number (3fish /group)obligate me to use non-parametric tests?
Thanks in advance
Why don't you model the time-course of gene expression using a regression model?? Should be logistic, peaking, or a combination thereof. This would allow you to test meaningful hypotheses. IMO, comparing expression levels between groups separately for each time point is nonsense. I strongly suggest you involve a local statistician, because the non-linear regression models you might need are not always simple to fit, and testing interesting hypotheses can be challenging.
[it can be hard, possibly even impossible to get answers to the right questions -- but that should never be an excuse to ask the wrong questions, just because these could be answered more easily!]
You can use factorial analysis with tow factors doses of virus factor , and time is factor by this you can compare between doses and time via interaction , then use regression
I think you can calculate mean for different time points and apply kruskal wallis test. Sample size is too small for ANOVA.
Ms ceren, I think the kruskal is one way anova and he have to factor , if he use kruskal he can't compare sample with time . Two why anova is the ideal solution .
Hi,
I would recommend using repeated measures ANOVA, as you have related samples (different time points). repeated measures ANOVA will give you between group effect (treated and untreated) and also within group effect (effect of time).
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