Hello
I'd like to know the potential (biostatistics) issues on the differential expression analysis that could come from combining replicates from low and deeper sequencing depth.
The experiment will compare conditions A and B. We plan to have 6 biological replicates of each max (and 4 replicates min).
The goal is to extract differentially expressed genes, but we do not know the optimal depth for this experiment and further. We already did a first RNA-seq with a suboptimal depth and estimated we could x4 the depth before reaching the plateau of an extrapolation curve (Preseq).
The proposition is the following:
- Prepare the 2x6 replicates.
- For each condition, sequence 3 replicates at the suboptimal depth, and sequence the 3 others with a x4 depth.
- Use the 2x3 deep-seq replicates to estimate the optimal depth in term of number of DEGs for the future experiments.
- For our current experiment, compute the DEGs by combining the low and deep-seq replicates.
A batch effect could be added to the GLM model to distinguish between the low and deep-seq replicates.
Questions:
- does it make sense?
- is combining 3 low to 3 deep-seq replicates better than 6 low-seq replicates or 3 deep-seq replicates?
- or conversely, are the low-seq replicates useless?
Thanks in advance for your time
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michaelp •
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In experimental terms, if you are sequencing the same library to different levels there should be no batch effect in terms of content. This would essentially be a technical replicate of sequencing.
What do you mean by optimal depth? Are you going to be looking for extremely rare transcripts https://www.ecseq.com/support/ngs/what-is-a-good-sequencing-depth-for-bulk-rna-seq discusses the number of reads generally recommended per sample.
thanks for your answer. We are working with biological replicates to avoid technical replicates as we have two goals at once: differential expression + optimization for future experiments.
The samples have different depths so they should be sequenced in different flowcells, which is introducing a batch effect between the 6 biological replicates with X million reads and 6 others with Y million reads.
Yet the design is blocked here so conditions A and B are comparable. But there may be other considerations to account for when "pooling" samples with different depths.
NB: Optimal depth refers to the depth where we identify the differential expression we are expecting, which is specific to our particular experimental setup.
That is great since technical replication is not necessary. See note about biological reps below.
If the starting materials are different amounts of RNA (is that what you are referring as "depths"?) then that is something you may need to control for when making the libraries and pooling them (so all samples have comparable reads). A particular library sequenced on multiple flowcells to different depths (as long as FC/sequencer are the same kind) will not introduce a batch effect.
Rather than depth it may need to be number of biological replicates, in case the difference you are looking for is subtle. Have you seen a study done in yeast (mostly impractical for common uses) on number of replicates needed https://pmc.ncbi.nlm.nih.gov/articles/PMC4878611/ when using RNAseq.