What is the best practice for analysing single-cell data with low sequencing depth?

2

Hi everyone,

I have a quick question about my recent single-cell datasets that I hope you could help with. I am about to start analyzing two datasets (Control vs. Treat; libs were created by 10X 3' expression platform, and sequenced by HiSeqX-PE150). Based on the CellRanger metrics, in both samples, NGS detected an average of 20,000 cells, 6000 reads per cell, 600 genes per cell. In this case, what kind of pipeline or strategy would you recommend me to properly analyze the data? For different datasets with a higher number of reads and genes per cell, I used to follow a Seurat-based pipeline including merging datasets, integration, SCTransform, and finding DEGs etc. Do you think I should follow the same way to extract variable genes between the control and treat samples? Or, is it a good idea to rerun the samples in an NGS with a deeper sequencing capacity like NovaSeqS4-PE150?

Thank you
Volkan


10X


depth


scRNA-seq


Sequencing


Seurat


data

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