Some questions related to dimension choosing for seurat v3

JinmiaoChenLab / Batch-effect-removal-benchmarking

A benchmark of batch-effect correction methods for single-cell RNA sequencing data

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Some questions related to dimension choosing for seurat v3 #12

Open HelloWorldLTY opened 2 years ago

HelloWorldLTY commented 2 years ago

call_seurat3 <- function(batch_list, batch_label, celltype_label, npcs = 20, plotout_dir = "", saveout_dir = "", outfilename_prefix = "", visualize = T, save_obj = T)

Sorry to disturb you, but I wonder why I need to set npcs as 20 in this step, to run Seurat v3, because the default value of this step is 30. Thanks.

nhuhoa commented 2 years ago

Hi,

20 PCs capture the most variances within used datasets, that's why we used this number of dimensions. For your dataset, I suggest you to run JackStraw function, JackStrawPlot and ElbowPlot as an example in this link to determine the number of significant PCs in your data, and extract these PCs vectors for your downstream analysis.

https://satijalab.org/seurat/archive/v3.0/pbmc3k_tutorial.html

Best, Hoa Tran

HelloWorldLTY commented 2 years ago

Ok, thanks a lot!