DESeq2: model design in rna seq - person variability

[acebollada]

I have 8 samples that correspond to 4 persons measured in two times, 0h and 20h.
names_chip person time Dample
1 IonCode_0109 A1 0 Donor 1- Day 0
2 IonCode_0110 A1 20 Donor 1- Day 20
3 IonCode_0111 A2 0 Donor 2- Day 0
4 IonCode_0112 A2 20 Donor 2- Day 20
5 IonCode_0113 A3 0 Donor 3- Day 0
6 IonCode_0114 A3 20 Donor 3- Day 20
7 IonCode_0115 A4 0 Donor 4- Day 0
8 IonCode_0116 A4 20 Donor 4- Day 20

The researchers would to see what genes are DE between the two timepoints. They hope there are many changes.
The service of genomic send me the rowdata counts with 20812 genes. I follow the pipelines of deseq2 library.

dds <- DESeqDataSetFromMatrix(countData = counts,
colData = annotation,
design = ~ time+person)

I have made pca plots and clustering of normalizated counts and i can see that the samples of the same person are closely to each other, but between persons are very separated.I could hope this. I attach pca plot.

At the moment i don't filter by number of counts. I do

dds.parametric.wald<-DESeq(dds)
contrast_oe <- c("time","0","20")
res.parametric.wald <- results(dds.parametric.wald,contrast=contrast_oe,independentFiltering = T)
summary(res.parametric.wald)

and the follow result

out of 17633 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 6, 0.034%
LFC < 0 (down) : 14, 0.079%
outliers [1] : 0, 0%
low counts [2] : 2706, 15%
(mean count < 1)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results

Oh! Only 20 DEG!!
I attach the polot of dispersions. I think that it's ok! Any suggestion???

If I study the contrasts between persons (e.g)

res.parametric.wald.a1.a2 <- results(dds.parametric.wald,contrast=c("subject","A1","A2"),independentFiltering = T)
summary(res.parametric.wald.a1.a2)

I get
out of 17633 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4194, 24%
LFC < 0 (down) : 3317, 19%
outliers [1] : 0, 0%
low counts [2] : 4064, 23%
(mean count < 2)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results


Is it possible when i contrast between timepoints there are some background noise by the variability of the persons, and thus got reduce the number of DEG?
With the goal of increase the number of genes DE between timepoints, would it be correct select the genes that are not DE between persons, and only with this genes compare between timepoints??
Methodologically and statistically is correct?? Any suggestion/way/design to increase the number of DEG between timepoints?