Question: How to interpret result from glmQLFTest in edgeR ?
0
Björn40 wrote:

I am sharing a screenshot after running a command in edgeR

``````qlf<-glmQLFTest(fit, coef = 2:3)
topTags(qlf, n=5)
``````

![anova like test]: https://ibb.co/jsDff7 The comparision is between coef 2 and 3 while there is intercept (negative control) as well. How to interpret the pvalue and FDR ? Are these DE genes ? NOTE: This analysis is from edgeRUsersGuide.pdf, section 3.2.6

edger de genes edgerusersguide • 1.4k views
modified 20 months ago • written 20 months ago by Björn40
1

Are you sure that's exactly as in the edgeR user's guide. I think you've used `design <- model.matrix(~ -1 + group)` instead of `design <- model.matrix(~ group)` [strictly, the comparison you've described isn't testing _between_ coefficient 2 and coefficient 3, it's a test to see if either coefficient2 or coefficient3, or some combination thereof accounts for an appreciable amount of the variation for a given probe]

ya, I am sure. edgeRUsersGuide.pdf, section 3.2.6 page 30-31 The design matrix I used is

``````design<-model.matrix(~0+diagroup)
colnames(design)<-levels(diagroup)
design
``````

The "diagroup" has 5 groups, the first one is negative control (Intercept)

can you have a look at the contents of all of the following design matrices `model.matrix(~ -1 + diagroup)`, `model.matrix(~ 0 + diagroup)`, `model.matrix(~ diagroup)`.

Within section 3.2.6, `design` is `model.matrix(~ group)`, as defined on p30. There really is a big difference between the design you've just given and the design used in section 3.2.6. Your design fits a separate coefficient for each level of the group; the design `model.matrix(~ group)` fits a coefficient for the first level and two other coefficients - one for the difference between levels 2&1 and one for levels 3&1

I am not aware of first one you mentioned, however, between

``````model.matrix(~ 0 + diagroup)
model.matrix(~ diagroup)
``````

, the first model don't keep first group as intercept unlike the second model matrix

then use the second one.

how you would then compare other groups with negative control (intercept) ?

1

You find genes that are significant for some-combination-of-coefficients using glmQLFTest using all the non-intercept coefficients (using the same code as in your original post `glmQLFTest(fit, coef = 2:5)`). Then for those that are significant, you run individual A vs B comparisons using `glmQLFTest(fit, coef = 2)`, `glmQLFTest(fit, coef = 3)`, `glmQLFTest(fit, coef = 4)`, `glmQLFTest(fit, coef = 5)`