I have an issue in analysing qRT-PCR datasets. For my gene of interest, treatment A's mRNA fold change values are 10, 40, and 200, over the control group. For treatment B, the corresponding values are 0.5, 10, and 5. Therefore, between the two treatments, I know that A's is always higher than B's and that too hugely (20, 4, and 40 fold difference). However, if I perform routine statistical tests like a t-test, there is no significant difference because of the huge standard deviations.
Can you suggest a way to represent this data and also make proper sense statistically? Thanks in advance.
Hello there
This problem occurred also to me while calculating the DNA numbers.
I think you can solve this problem by clicking the "save standardized values as variable" box on in the descriptive tab in the analyse-descriptive section in spss. This option will open a new variable and take the logarithm of the DNA.
However I am not quite sure about this solution.
This is a great question, and I will wait for responses from experts on this question. For now, I'm going to take a wild guess that instead of using the conventional arithmetic means (and SDs) to subject the data to statistical analysis, perhaps using the geometric or logarithmic mean (and SDs) counterparts instead might be useful here. As an alternative to fold change, I will also check if utilizing the actual Cq values can provide insights as well.
Use the logarithms (log fold-changes).
In your example, the p-value of a paired t-test on the log fold-changes is 0.059. n = 3 is a tiny sample size, so you should not expect too much.
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