Hi, I'm pretty new to the scRNA-seq world and while working on my own sets of data, I'm starting to wonder when should the batch correction algorithm be used appropriately.

Let's say we have a Day0, a Day1, a Day2 and a Day3 scRNA-seq sample.

To elaborate, starting from Day0, assume we treated certain chemical, and sampled it on a daily basis during the course of experiment.

**Would it be ok or reasonable to apply batch correction algorithm (e.g. CCA) to this aggregation of samples?
I mean, is CCA algorithm designed for this kind of experiment design?**

From the experiment from [Kang et al., 2017][1] which is comprised of PBMC, splitted into a control group and a stimulated group treated with interferon beta, [they state that][2] "the repsonse to interferon caused cell type specific gene expression changes that makes a joint analysis of all the data difficult with cells clustering both by stimulation condition and by cell type". But is it reasonable?

My understanding is that if you are to use batch correction you should have biological or technical batches from the "same condition". So if you have replicate samples with the same condition and when somehow they are separated from each other for technical reason, it's appropriate to use batch correction. 

Going back to the supposed experiment I stated above, I think (maybe i'm wrong and i am most of time) it's not reasonable to apply batch correction to this Day0-4 experiment.

Can someone give me some clear explanation to the use of batch correction?

Thank you.
Ryan

  [1]: https://www.nature.com/articles/nbt.4042
  [2]: https://satijalab.org/seurat/v3.0/immune_alignment.html
> if you are to use batch correction you should have biological or technical batches from the "same condition"

From the [integration vignette][1]: "These methods aim to identify shared cell states that are present across different datasets, even if they were collected from different individuals, **experimental conditions**, technologies, or even species"

Thus, the "official" answer is that different conditions are fine.

Really, it depends on the questions you want to ask and on the data that you have. For example, if all your time points segregate and form distinct clusters, it's going to be hard to present any kind of coherent analysis.


  [1]: https://satijalab.org/seurat/v3.0/integration.html