I have 53 genes of interest. I have two conditions Tumour and Normal samples at 24, 48 and 72 hours from three mice (i.e. 3xTumour and 3x Normal for each timepoint). The Tumours and Normal skin are sampled repeatedly (i.e. each mouse has Tumor and Normal samples taken at each time point). I have measured the expression of all my genes of interest (53) in each sample using qRT-PCR.
If these data were RNA-seq or microarray I would turn to something like DEseq2 to perform a differential expression analysis. If I only had up to 10 genes of interest I would have no concern to use a repeated measures ANOVA or linear models to investigate differential expression between my two conditions.
However I feel like I am somewhere in between each method, as correcting for multiple testing for 53 ANOVAs is diminishing my Pvalues.
I am aware that N=3 is a very small sample, but this is the nature of biological sciences (especially during a PhD). With this in mind humour me in your answer to my questions.
After how many measurements does one decide that they are performing enough tests to warrant a software package such as DEseq2 (i.e. a microarray/RNA-seq approach) to investigate differential expression and how many tests are not enough?
Are there tests or packages that better deal with low to moderate numbers of statistical tests?
In the case that I am not raising a valid concern (quite possible), what would be the most powerful approach to discerning any difference in gene expression that exists between tumour and normal tissue, at any point in time, in my study?
Thanks in advance