Question: RUV integration in DESeq2 design
0
gravatar for jonathan.limwc
9 weeks ago by
jonathan.limwc0 wrote:

Hi all,

First off, I'm a wet lab scientist learning to analyse my own data. I've design my experiment as such

> coldata
    sample        condition litter
1 KO1_sort.bam        KO      A
2 KO2_sort.bam        KO      B
3 KO3_sort.bam        KO      A
4 KO4_sort.bam        KO      B
5 WT1_sort.bam        WT      A
6 WT2_sort.bam        WT      B
7 WT3_sort.bam        WT      A
8 WT4_sort.bam        WT      B

Before normalization, WT3 and KO1 shows higher variability from the RLE plot, and also cluster together based on the first principal component on my PCA plot. RLE plot before normalization PCA plot before normalization

RUVg with k=2 is able to reduce the variation seen in the RLE plot and results in WT and KO samples clustering separately on PCA plot. RLE plot after normalization PCA plot after normalization

Empirical genes for RUVg were obtained using a cutoff of pvalue > 0.5 and design = ~ litter + condition in DESeq2. My question is whether I should still account for the 'litter' factor in my DESeq2 design after taking into account the variation modelled using RUVg, or not? Option 1:

design(ddsruv) <- ~W1 + W2 + litter + condition

Option 2:

design(ddsruv) <- ~W1 + W2 + condition

Thank you!

normalization deseq2 • 105 views
ADD COMMENTlink modified 9 weeks ago by Michael Love22k • written 9 weeks ago by jonathan.limwc0
Answer: RUV integration in DESeq2 design
0
gravatar for Michael Love
9 weeks ago by
Michael Love22k
United States
Michael Love22k wrote:

I would opt for design 1 here. There's not much harm to adding a simple coefficient that mark litter, and I think given how you ran RUVg W1 and W2 should be somewhat orthogonal to litter.

ADD COMMENTlink written 9 weeks ago by Michael Love22k
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