Hi,
I would like to use the R-package DESeq for the identification of differentially expressed genes between 2 conditions. I have got 4 different conditions and 3 technical replicates per conditions (that is 12 samples of count data). I have read the vignette, and have understood that technical replicates should be merged into one column. When running DESeq this way, i get very few genes called as differentially expressed.
I have several questions:
1.
This question is about the merging part. Suppose we have two technical replicates from a given condition. For a certain gene we get 150 counts for replicate 1 and 75 counts for replicate 2. If this is simply do to the fact that the sequencing depth of replicate 1 is twice the one of replicate 2, when we add counts from these replicates together without normalization, aren't we committing a mistake?
2.
When using DESeq to identify differentially expressed genes with technical replicates merged, I get very few hits. Is this what we expect?
3.
Should all of the 12 samples be considered in the initial count data object in order to estimate dispersion and normalize the data, or should there be a new count data object for each pair of compared conditions? (that is only 2 conditions per count data object)
I would like to use the R-package DESeq for the identification of differentially expressed genes between 2 conditions. I have got 4 different conditions and 3 technical replicates per conditions (that is 12 samples of count data). I have read the vignette, and have understood that technical replicates should be merged into one column. When running DESeq this way, i get very few genes called as differentially expressed.
I have several questions:
1.
This question is about the merging part. Suppose we have two technical replicates from a given condition. For a certain gene we get 150 counts for replicate 1 and 75 counts for replicate 2. If this is simply do to the fact that the sequencing depth of replicate 1 is twice the one of replicate 2, when we add counts from these replicates together without normalization, aren't we committing a mistake?
2.
When using DESeq to identify differentially expressed genes with technical replicates merged, I get very few hits. Is this what we expect?
3.
Should all of the 12 samples be considered in the initial count data object in order to estimate dispersion and normalize the data, or should there be a new count data object for each pair of compared conditions? (that is only 2 conditions per count data object)
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