How to choose statistical test method appropriate to my experiment design?
I have following RNAseq samples. (I know 2 replicates is not enough...)
To identify DEG(differential expression gene), I prepare these hypothesis.
・Null hypothesis : there is no expression change when manipulating gene X expression.
・Alternative hypothesis : there is some expression change when manipulating gene X expression.
I think ANOVA-like test in edgeR is appropriate method so far. In using this, multiple test correction is unnecessary and comparison between samples more than 3 is available. (In edgeR user's guide, ANOVA-like test is 3.2.6.)
However, I have not ever seen these experimental RNAseq design, so cannot choose the most appropriate statistical method. So I'm wondering if anyone knows some advice about this problem...
I have following RNAseq samples. (I know 2 replicates is not enough...)
- WT : wild type (replicate1, replicate2)
- mm : gene X mutant (replicate1, replicate2)
- OE : gene X overexpression (replicate1, replicate2)
To identify DEG(differential expression gene), I prepare these hypothesis.
・Null hypothesis : there is no expression change when manipulating gene X expression.
・Alternative hypothesis : there is some expression change when manipulating gene X expression.
I think ANOVA-like test in edgeR is appropriate method so far. In using this, multiple test correction is unnecessary and comparison between samples more than 3 is available. (In edgeR user's guide, ANOVA-like test is 3.2.6.)
However, I have not ever seen these experimental RNAseq design, so cannot choose the most appropriate statistical method. So I'm wondering if anyone knows some advice about this problem...