Hi,

So I have two datasets from two different yet related cell lines: pre and post relapse cancer cell line from same patient.

I have performed single cell sequencing for both of them with the hypothesis that I will be able to find rarely expressed cells in either that are key to becoming relapse.

To do that I am following two protocols:

1) I am following the Seurat tutorial of integrating simulated and normal pbmc

https://satijalab.org/seurat/v3.1/immune_alignment.html

2) I am following this second tutorial where there is no integration steps involved.

https://davetang.org/muse/2018/01/24/merging-two-10x-single-cell-datasets/

However both these approaches give two different results! Using 1) I am getting more cells from two different lines to be similar with very few cells from each to be different. Using 2) I am getting opposite, yet expected, result that most of the cells are different with only few common cells.

Which one is the correct way of analyzing? Are two approaches giving me two different results become they are doing different things? 1) is finding common genes and 2) finding distinct genes?
These are very different approaches. The first tries to account for technical variation between the two sets, mapping similar cells between the two to each other. The second literally just merges the columns and rows of the two sets together into one object - it's not doing any special normalization. It's just a straight merging of data.

It's tough for us to say which is more appropriate - the first may help you to identify cell populations that truly differentiate the two, but it could be blowing away real differences due to how Seurat's integration works. It will force populations that aren't similar together if there aren't many overlapping cell types between the two samples. The second may be revealing significant technical variation or batch effects, or it could just be that your cell lines are quite different from each other. You are in the best position to determine if this is the case or not - we know nothing about your samples.

This is where the true difficulty of RNA-seq analysis lies - nobody is really going to be able to tell which is truly correct.

You might try other integration methods if you feel you have batch effects or technical variation that needs to be addressed. I've found the `SeuratWrapper` around [fastMNN](https://htmlpreview.github.io/?https://github.com/satijalab/seurat.wrappers/blob/master/docs/fast_mnn.html) to be quite good, personally, as it handles cases where samples don't have much overlap in terms of cell types much more appropriately.