Normalize before differential accessibility between conditions

[hhvu0102]

Hello,

I hope you can help explain how you normalize between 2 conditions before doing differential accessibility analysis.
I understand we need to integrate data if there's a batch effect, then follow the tutorial: https://satijalab.org/seurat/v3.2/immune_alignment.html. Also, according to issue #222 I should use 'peaks' assay. However, when building 'peaks' assay (as in https://satijalab.org/signac/articles/mouse_brain_vignette.html), I presume there's no normalization step. Am I understanding this wrong? And if not, how should be normalize before differential accessibility analysis?
Thanks a lot!

[timoast]

The differential accessibility test uses the TF-IDF values, which incorporates a per-cell depth normalization step. If you are comparing between multiple batches, you can also include batch as a latent variable in the test.

[hhvu0102]

Hi Tim,

Thank you very much for your reply. I only have 2 batches (i.e. 2 conditions) right now, but can you elaborate how to put batch as a latent variable? Does this mean we would build a meta information column in our @meta.data?

[timoast]

To incorporate batch as a latent variable in the model, you can pass the latent.vars argument in Seurat::FindMarkers(). This should be the name of a metadata column in the object that identifies the different batches. See the docs for FindMarkers for more information.

[hhvu0102]

Thank you!