Using seurat FindClusters on the tSNE embeddings

Question

[themangoscholar]

My objective is using Leiden algorithm on the 2D tSNE embeddings of the pbmc.

What I am unable to understand is that if FindClusters is working on the reduced dimensions or on the whole dataset, since the size of clust_obj is same as sce . Also, the number of clusters are way more than scanpy provides using the 2D tSNE projection on the same data.

Please help me understand if I am doing this correctly. If I have made any mistakes, kindly help me correct it.

[ATpoint]

A reduced dimension is the whole dataset in terms of all cells have values for the redDims. Typically these redDims though are based on a selection of genes (the highly variable ones) and the reducedDim, (usually PCA) is then used for graph-based clustering.

[themangoscholar]

So, what I have done will find clusters on the reduced dims itself, and not use the whole assay?

[ATpoint]

Yes, and I strongly suggest you exactly follow the Seurat clustering and/or integration vignette.

Answer 1

[igor]

the size of clust_obj is same as sce

The input and output of all those functions is a Seurat object. Most of the size will be due to the expression data which would not change.

the number of clusters are way more than scanpy provides

Is it using the same resolution?

seurat documentations shows clustering on the whole assay

FindNeighbors uses PCA by default (the PCA is based on variable genes by default). Then FindClusters will use that SNN graph.

You should check the documentation for all the functions you are using which provide all of this information.

[themangoscholar]

Yes, it's using the same resolution as scanpy.