What is the difference between single-cell RNA-Seq and typical RNA-Seq in terms of data analysis procedure

Question

[wangdp123]

Hi there,

I am working on some single-cell RNA-Seq samples and wondering what the difference between a typical RNA-Seq analysis and single-cell RNA-Seq analysis is? Can we use standard Subread and DESeq2 pipeline to work with the data?

Thank you very much,

Best wishes,

Tom

Answer 1

[Devon Ryan]

The main difference between scRNA-seq and standard RNA-seq is (A) a bit more complicated analysis workflow and (B) often different goals.

The additions to the normal analysis in scRNA-seq include handling cell and UMI barcodes throughout the process so you can reach a reliable matrix of values per-cell. This assumes you're using an scRNA-seq method that uses UMIs, of course. Aside from that you can mostly use the same tools. salmon and a lot of other tools now provide options to better handle quantification of scRNA-seq data (i.e., they now make it easier), so take advantage of these options if you can.

There's often a difference in goal between the two sets of methods. Bulk RNA-seq is typically used to assess changes due to experimental conditions, whereas scRNA-seq is more frequently used to assess differences between cell types (or to find cell types). You can assess changes due to an experimental condition in scRNA-seq too, of course, though it's a bit more involved since you need to identify cell types for comparison across samples.

[wangdp123]

Hi, thanks for this.

I am just handling some SMART-Seq2 single-cell RNA-Seq datasets and it seems that it doesn't use UMI barcodes and its analysis pipeline is quite similar to that of bulk RNA-Seq. Is it true? If not, any differences?

Thanks,

[Devon Ryan]

Yes, SMART-Seq2 and other low-throughput methods are essentially the same as regular RNAseq.