Condition specific marker detection and problem with the distribution of cells from different samples
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Entering edit mode
6 months ago
Sara ▴ 260

I have 6 scRNAseq samples and made a umap using all cells from all 6 samples and in the UMAP every sample has a different color (in total 6 colors) and the goal was to see how much overlap these samples have in the UMAP. and do the follow up analysis and compare different samples. samples 1, 2 and 3 are from the condition A and samples 4, 5 and 6 are from the condition B. at the end I would compare conditions A vs B. here is the UMAP I mentioned above: ![enter image description here][1]

this UMAP shows 5 samples (which are from both conditions) have a lot of overlap but sample 2 has different pattern.
to compare 2 conditions, I would like to combine all cells from all 6 samples as one and cluster all cells and see how the cells are clustered and detect the markers of each clusters like the following UMAP:

in this UMAP we have 3 clusters and it shows 5 samples (from both conditions) are in one cluster and sample 2 is in all 3 clusters. if I detect the markers, they will not be specific to any condition due to the pattern that sample 2 has. if I remove the sample2 and make UMAP using the other 5 samples, 4 samples would have a lot of overlap and sample 5 would have a quite different pattern (which shows different pattern are not due to condition). if I remove the sample 5 , I would encounter the same issue. if I want to do the procedure, I would have to remove all samples. therefore to be able to use all 6 samples and to detect condition specific markers, what would be the solution?

scRNAseq • 668 views
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Entering edit mode
6 months ago
CTLong ▴ 110

It seems that sample 2 here contains some batch effect. One possible solution for this is that you correct for this technical variation using some sort of integration or batch correction algorithm. Canonical correlation analysis (CCA) is one of the more common integration method used on scRNA-seq data and is available within the Seurat package https://satijalab.org/seurat/articles/integration_introduction.html.

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