Entering edit mode

3.6 years ago

poecile.pal
▴
50

Good evening,

I'd like to perform a DRIMSeq analysis with more than 2 categories in the group. Later I considered two categories (control and sick):

```
n.small<-min(table(DRIMSeq::samples(d)$condition)) #How many samples are in the smallest group
n.big<-max(table(DRIMSeq::samples(d)$condition)) #How many samples are in the largest group
n <- n.small+n.big #Since we have 2 groups of diagnoses, the total number of samples is equal to the sum of n.small and n.big
d <- dmFilter(d,
min_samps_feature_expr=n.small, min_feature_expr=10,
min_samps_gene_expr=n, min_gene_expr=10)
design_full <- model.matrix(~condition, data=DRIMSeq::samples(d))
```

Now I am interested in examining the various stages of the disease, so, the categories will be like this: control, sick-stage1, sick-stage2, etc. Do I understand correctly that in this case my code will be like this?

```
n.small<-min(table(DRIMSeq::samples(d)$condition)) #How many samples are in the smallest group
n <- ... #I'll take this number from the metadata table, it will be the sum of samples belonging to the group "control" + "sick-stage1" + "sick-stage2". Actually, this is the same n that was in the code above.
d <- dmFilter(d,
min_samps_feature_expr=n.small, min_feature_expr=10,
min_samps_gene_expr=n, min_gene_expr=10)
design_full <- model.matrix(~condition, data=DRIMSeq::samples(d))
```

Thanks in advance!

Best regards, Poecile

Hi @poecile.pal

Were you able to figure this out? I also have a similar situation. I have data from 5 different layers of a plant (each having 3 replicates) collected at different days of flowering 4, 8, 10, 16.

Good afternoon @rohitsatyam102,

Sorry for the very late answer - unfortunately, I could not find a solution.

Did you find a solution?

Good afternoon ponganta,

Sorry for the very late answer - unfortunately, I could not find a solution.