Question: How to interpret result from glmQLFTest in edgeR ?

0

Björn •

**40**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][1][1]: 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

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]4.7kya, I am sure. edgeRUsersGuide.pdf, section 3.2.6 page 30-31 The design matrix I used is

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

40can 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&14.7kI am not aware of first one you mentioned, however, between

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

40then use the second one.

4.7khow you would then compare other groups with negative control (intercept) ?

40You 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)`

4.7k