DESeq1 design, result and inference

Question

[rammohanshukla]

Hi I am new user of DESeq2 and have few questions regarding the design formula, results and there inferences:

I am having four celltypes (A,B,C,D) from old and young subject detailed as below:

table(pdata$Age)
  Old Young
   47    35
table(pdata$Age, pdata$CellType)
            A    B   C   D
  Old   11  12  12  12
  Young  8   9   9   9

I used the following designs with the respective objectives in mind

1) To know the age specific effect over celltype

dds2 <- DESeqDataSetFromMatrix(countData = count2,colData = traitdata2,design = ~Age + CellType + Age:CellType)

2) To know the differential expression for age and various cell type without interaction

dds2 <- DESeqDataSetFromMatrix(countData = count2,colData = traitdata2,design = ~Age + CellType)

3) To know how individual cell type behave in old and young subjects

dds2 <- DESeqDataSetFromMatrix(countData = count2,colData = traitdata2,design = ~Age + CellType)
dds2$group <- factor(paste0(dds2$Age, dds2$CellType))
design(dds2) <- ~group

4) To get one pvalue for the differential expression across all the cell types (similar to ANOVA, without interaction with age)

dds2 <- DESeqDataSetFromMatrix(countData = count2,colData = traitdata2,design = ~CellType )
dds2LTR_ACTI <- DESeq(dds2,test="LRT", reduced=~1)

5) To get one pvalue for the differential expression across all the Age (similar to ANOVA, without interaction with cell type)

dds2 <- DESeqDataSetFromMatrix(countData = count2,colData = traitdata2,design = ~Age )
dds2LTR_ACTI <- DESeq(dds2,test="LRT", reduced=~1)

6) To get one pvalue for the differential expression across all the Age and CellType(similar to ANOVA, with Interaction)

dds2 <- DESeqDataSetFromMatrix(countData = count2,colData = traitdata2,design = ~Age + CellType + Age:CellType)
dds2LTR_ACTI <- DESeq(dds2,test="LRT", reduced=~Age + CellType)

Kindly answer these questions A) Am I using the correct design? B) With Contrast “results(dds2,contrast=c("Age","Old","Young"))” I am getting different results. with design 1 (up: 348 & down: 139) and design 2 (up:5 & down9). That is expected, (I guess??) but with design 3 (where in I am trying to know how the individual cells are behaving in young and old conditions), I am not getting any significant difference. Also with design 5 I am not getting any significant difference with respect to age. Any explanation? C) After using design 4 I am getting around 3 times more differentially expressed genes as compared to design 1 and 2. For example between cell A and B I am getting (up:516 & down:360) with design 1 with design 2 I am getting (up: 689 & down : 844) but when I use design 4 I am getting (up: 1617 and down:2025). Any explanation?

I understand I might not be able to explain my problem completely. Please let me know if you need any other information.

Thanks

Ram