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Hello,
I have the following RNA-seq experimental design:
- Control: 3 biological replicates from ther first sequencing + resequencing from the 3 same samples
- Treatment1: 3 biological replicates from ther first sequencing + resequencing from the 3 same samples
- Treatment2: 3 biological replicates from ther first sequencing + resequencing from the 3 same samples
- The resequencing was made from the same libraries but with more sequencing depth (much more depth)
So, technically the 3 resequencing data are technical replicates with a batch effect and the depth effect.
To solve the batch effect i used the function collapseReplicates and then I used the function DESeq from normalization data; which If I'm correct, consider library size. So, the effect of different sequencig depth is considered? or I have to do another kind of normalization?
I have to take another consideration to do the proper analysis? I think a need another consideration but I don't know what
Thank you ,
If you have additional metadata on your samples (e.g., sequencing run information, processing dates, etc.), it may be useful to perform a PCA or hierarchical clustering analysis to see how your samples group. For this though, you'll want to make sure to use either the rlog or vst transformations in the DESeq2 package on your count data. If your samples are clustering by sequencing run, processing date, or some other technical variable, you may be able to use COMBAT, RUV-seq, or SVA to account for unwanted variation depending on how the processing was performed. I think at least one of the methods I mentioned allows you to utilize technical replicates to account for batch effects.
I haven't personally used the batch correction methods, but hopefully this gives you a starting point.
Yes, I have additional metadata that includes the sequencing run information. So, I´m going to try with a PCA
Thanks