Hi all, hoping someone has some insight on this because I am completely stumped... ![Confused](https://www.seqanswers.com/core/images/smilies/confused.png)
I'm using DESeq2 for differential analysis between 2 different experiment/sequencing types (wholly different methods, not like SE vs PE) and with multiple treatment conditions vs. one control condition. I do have a batch effect so I have to include that term in my design as well.
![](http://seqanswers.com/forums/data:image/png;base64,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)
With reference levels for Condition=ctrl & SeqType=X, this gives me resultsNames of:
It's my understanding that using results(dds, name="SeqTypeY.ConditionA") should give me the genes for which changes (ie. A vs. ctrl) in sequencing type Y are independent of changes in sequencing type X.
This analysis works and I get reasonable results for all of my conditions when I do not subset the data (i.e. conditions A, B, C, and ctrl are all in the same counts file input to the DESeqDataSet object, then results are called separately for each). However, when I subset by condition (i.e. input condition A & ctrl only), all the genes have padj>0.95 in the interaction term results.
Does anyone know why this is happening or if it matters? I'm concerned it means I'm doing something wrong in the multiple conditions analysis?
A related question - does the padj value correct for multiple comparisons between all genes within a specified results table (i.e. for all genes in the "SeqTypeY.ConditionA" results) or does it correct for multiple comparisons between all genes across all possible results tables (i.e. SeqTypeY.ConditionA vs. SeqTypeY.ConditionB vs. SeqTypeY.ConditionC)? I did the subset analysis because I was trying to answer this question for myself and I'm afraid I've just made my confusion worse....![Confused](https://www.seqanswers.com/core/images/smilies/confused.png)
![Confused](https://www.seqanswers.com/core/images/smilies/confused.png)
I'm using DESeq2 for differential analysis between 2 different experiment/sequencing types (wholly different methods, not like SE vs PE) and with multiple treatment conditions vs. one control condition. I do have a batch effect so I have to include that term in my design as well.
ID Batch Condition SeqType
1 1 ctrl X
2 1 A X
3 1 B X
4 1 C X
5 2 ctrl X
6 2 A X
7 2 B X
8 2 C X
9 1 ctrl Y
10 1 A Y
11 1 B Y
12 1 C Y
13 2 ctrl Y
14 2 A Y
15 2 B Y
16 2 C Y
![](http://seqanswers.com/forums/data:image/png;base64,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)
So the design ends up being " ~ Batch + SeqType + Condition + Condition:SeqType "1 1 ctrl X
2 1 A X
3 1 B X
4 1 C X
5 2 ctrl X
6 2 A X
7 2 B X
8 2 C X
9 1 ctrl Y
10 1 A Y
11 1 B Y
12 1 C Y
13 2 ctrl Y
14 2 A Y
15 2 B Y
16 2 C Y
With reference levels for Condition=ctrl & SeqType=X, this gives me resultsNames of:
- Intercept
- Batch_2_vs_1
- SeqType_Y_vs_X
- Condition_A_vs_ctrl
- Condition_B_vs_ctrl
- Condition_C_vs_ctrl
- SeqTypeY.ConditionA
- SeqTypeY.ConditionB
- SeqTypeY.ConditionC
It's my understanding that using results(dds, name="SeqTypeY.ConditionA") should give me the genes for which changes (ie. A vs. ctrl) in sequencing type Y are independent of changes in sequencing type X.
This analysis works and I get reasonable results for all of my conditions when I do not subset the data (i.e. conditions A, B, C, and ctrl are all in the same counts file input to the DESeqDataSet object, then results are called separately for each). However, when I subset by condition (i.e. input condition A & ctrl only), all the genes have padj>0.95 in the interaction term results.
Does anyone know why this is happening or if it matters? I'm concerned it means I'm doing something wrong in the multiple conditions analysis?
A related question - does the padj value correct for multiple comparisons between all genes within a specified results table (i.e. for all genes in the "SeqTypeY.ConditionA" results) or does it correct for multiple comparisons between all genes across all possible results tables (i.e. SeqTypeY.ConditionA vs. SeqTypeY.ConditionB vs. SeqTypeY.ConditionC)? I did the subset analysis because I was trying to answer this question for myself and I'm afraid I've just made my confusion worse....
![Confused](https://www.seqanswers.com/core/images/smilies/confused.png)
![Confused](https://www.seqanswers.com/core/images/smilies/confused.png)