Question: DESeq2 biological replicates and RIN values
gravatar for pmoranlosada
13 months ago by
pmoranlosada0 wrote:

Hi all,

I'm sure you have already talked about how to treat biological replicates in RNA-Seq differential expression but I can't find information about how  to incorporate RIN values into the DE analysis.

I have iPSCs where some genes have been duplicated (Group - DUP), deleted (Group- DEL)  and I have the control group - CTL. The iPSCs are coming from 9 different subjects (3 in each Del, DUP and CTL groups). Each subject has 2 clones (clone X and clone Y) taken for the RNAseq, and each clone was grown in parallel (in 2 different wells) to create relicas (a, b in last column) . Here is my study design:

Sample RIN Group Patient Clone Replicate
101_c1a 8.6 CTL Subjet1 Clone1 a
101_c1b 9 CTL Subjet1 Clone1 b
101_c3a 9.5 CTL Subjet1 Clone2 a
101_c3b 9.7 CTL Subjet1 Clone2 b
11_c1a 6 CTL Subjet2 Clone3 a
11_c1b 8 CTL Subjet2 Clone3 b
11_c6a 7.7 CTL Subjet2 Clone4 a
11_c6b 8 CTL Subjet2 Clone4 b
61_c3a 8.7 CTL Subject3 Clone5 a
61_c3b 8.9 CTL Subject3 Clone5 b
61_c5a 9.8 CTL Subject3 Clone6 a
61_c5b 9.7 CTL Subject3 Clone6 b
16A_c11a 7.5 DEL Subject4 Clone7 a
16A_c11b 7.1 DEL Subject4 Clone7 b
16A_c12a 8.9 DEL Subject4 Clone8 a
16A_c12b 9.2 DEL Subject4 Clone8 b
16B_c10a 9.4 DEL Subject5 Clone9 a
16B_c10b 9.4 DEL Subject5 Clone9 b
16B_c4a 10 DEL Subject5 Clone10 a
16B_c4b 9.4 DEL Subject5 Clone10 b
16C_c2a 6.7 DEL Subject6 Clone11 a
16C_c2b 7.2 DEL Subject6 Clone11 b
16C_c4a 6 DEL Subject6 Clone12 a
16C_c4b 5.5 DEL Subject6 Clone12 b
16X_c1a 5 DUP Subject7 Clone13 a
16X_c1b 7.3 DUP Subject7 Clone13 b
16X_c3a 5.9 DUP Subject7 Clone14 a
16X_c3b 6 DUP Subject7 Clone14 b
16Y_c2a 5.9 DUP Subject8 Clone15 a
16Y_c2b 6.2 DUP Subject8 Clone15 b
16Y_c4a 5.7 DUP Subject8 Clone16 a
16Y_c4b 5.1 DUP Subject8 Clone16 b
16Z_c1a 5.7 DUP Subject9 Clone17 a
16Z_c1b 7.7 DUP Subject9 Clone17 b
16Z_c8a 5.7 DUP Subject9 Clone18 a
16Z_c8b 5.5 DUP Subject9 Clone18 b

I'm interested on the differential gene expression at the Group level (controlvsdup, control vs del and dup vs del). But, should I consider all samples from the same group as biological replicates?  The design matrix should be like :

1.  ddseq <- DESeqDataSetFromMatrix(countData = counts, colData = metadata, design = ~ Group  )

Or should I consider possible differences between patients and clones?

2. ddseq <- DESeqDataSetFromMatrix(countData = counts, colData = metadata, design = ~ Group + Patient)

3. ddseq <- DESeqDataSetFromMatrix(countData = counts, colData = metadata, design = ~ Group + Patient+Clone

And how would you incorporate the RIN values into design matrix?

ddseq <- DESeqDataSetFromMatrix(countData = counts, colData = metadata, design = ~ Group + RIN ) ?


Thanks a lot for your help. Any hint will be more than appreciated : )  



ADD COMMENTlink modified 13 months ago by Michael Love19k • written 13 months ago by pmoranlosada0
gravatar for Michael Love
13 months ago by
Michael Love19k
United States
Michael Love19k wrote:

You can't compare directly across group and control for the subject and clone differences using fixed effects (you can compare fold changes across group, if you had pre/post, and this is an example in the vignette, but that's a different case). You would have to use duplicateCorrelation in limma to do direct comparisons and control for subject and clone correlations.

I don't recommend people add RIN to the design off the bat. For one thing, if it is confounded with your biological groups, you have bigger problems and just adding to the design doesn't fix them. What is the correlation with RIN among CTRL vs DEL and RIN among CTRL vs DUP, it seems like it would be high? If you have any very bad samples, I would recommend detecting them using QC measures as shown in the vignette. If they are not obvious outliers in e.g. a PCA plot, you can include them in DESeq2, and they will just appear as additional within-group variation for some genes. If they were to show up as outliers for some genes, DESeq2 will automatically remove these problematic counts, increasing sensitivity for those genes. Another option is to use limma with sample weights.

ADD COMMENTlink written 13 months ago by Michael Love19k
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