Question: Interaction Design and continous variable In EdgeR
0
gravatar for biostar.anon
4 months ago by
biostar.anon0 wrote:

Hi,

I have a question about the GLM options of edgeR, I have a set-up where individuals were subjected to different condition (Continuous) and the samples are of two type. I aim to look at the interaction of those two term. However the design from the manual seem odd to me (however I am relatively new to these statistical analysis) :

(Intercept) Condition Type1 Condition:Type1

1 1 1 1 1

...

6 1 1 1 1

7 1 2 1 2

...

12 1 2 1 2

13 1 3 1 3

..

18 1 3 1 3

19 1 1 0 0

...

24 1 1 0 0

25 1 2 0 0

...

30 1 2 0 0

31 1 3 0 0

..

36 1 3 0 0

Type<- factor(c(rep("Type1",12),rep("Type2",12)))
Condition<- (c(rep(1,4),rep(2,4),rep(3,4),rep(1,4),rep(2,4),rep(3,4)))
design <- model.matrix(~ Condition*Type, data=count)
lrt <- glmQLFTest(fit, coef = 4)

The last column strikes me as odd since the type one does not have a factor indicating there condition. Can someone help me interpret this model and what that last column test ?

My initial thinking was an interaction was that the type would influence how the condition affect the transcriptomic data (in my case). However that last column make me think the last one might test the reaction for one type but not the other...

rna-seq next-gen R • 128 views
ADD COMMENTlink modified 4 months ago by Devon Ryan95k • written 4 months ago by biostar.anon0
0
gravatar for Devon Ryan
4 months ago by
Devon Ryan95k
Freiburg, Germany
Devon Ryan95k wrote:

Type1 doesn't need a non-zero value in the last column because it serves as the reference level. In other words, the interaction effect is relative to it.

ADD COMMENTlink written 4 months ago by Devon Ryan95k
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