Thread by @rmassonix

Do you want to get into scRNA-seq but are lost in the sea of never-ending papers?

No worries, I got you covered.

Here's a list of the best review that have help me understand the core concepts of the technique, analysis and interpretation:🧵

https://twitter.com/lutherfabian_/status/1577384251716210703

First, you need to master the experimental protocol. My top pick here is this tutorial by @Ati_lz & @Moutinho_C. I highly suggest reading the glossary to get familiar with the jargon.

nature.com/articles/s4159…

I would also suggest this classic benchmark by @chris_zie and @B_Vieth, which provides a clear comparison of the different steps in the different scRNA-seq protocols.

sciencedirect.com/science/articl…

Once you know how to get your data, you need to analyze it. The following are the best reviews on computational analysis of scRNA-seq data in chronological order.

Let's start by the classic review by @OliverStegle @teichlab and John Marioni:

nature.com/articles/nrg38…

This one on the best practices in scRNA-seq by @MDLuecken and @fabian_theis is a landmark review that I've cited in every single methods section:

embopress.org/doi/full/10.15…

As you have already experienced, best practices become obsolete relatively fast, so my current go-to resource is "Orchestrating Single-Cell Analysis with Bioconductor"
bioconductor.org/books/release/…

3 bonus tracks on the analysis side:

1. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data, by @m_hemberg lab

nature.com/articles/s4159…

2. The triumphs and limitations of computational methods for scRNA-seq, by Peter Karchenko

nature.com/articles/s4159…

3. This is one of my favourites to understand clustering, the most important method in scRNA-seq: "Challenges in unsupervised clustering of single-cell RNA-seq data", again by @m_hemberg lab.

nature.com/articles/s4157…

Great, now you have your clusters. How do you annotate them?

"Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods", by Gary Bader lab
nature.com/articles/s4159…

Let's get philosophical. What even is a cell type?

"What is a cell type and how to define it?"

cell.com/cell/fulltext/…

Can we obtain a periodic table of human cells? @ItaiYanai argues so.

journals.biologists.com/dev/article/14…

But wait, is the transcriptome enough to characterize cell identity? Regev and co. introduce the concept of "the basis vectors of cell identity": all the facets that are need to characterize a cell: RNA, spatial location, cell-cell comm, epigenome...

nature.com/articles/nbt.3…

You guessed it: we need to go full multiomics. In the context of cancer, @landau_lab and co. revise the best single-cell multiomics platforms to map cancer evolution:
nature.com/articles/s4157…

Another great one by @timoast and @satijalab: "Integrative single-cell analysis".

nature.com/articles/s4157…

If you have multimodal data, you need to integrate it. @RArgelaguet @AnnaSECuomo and co. coin the concepts of "vertical" and "horizontal" integration, and revise the most appropriate methods for each of them:

nature.com/articles/s4158…

What are your favourite resources to learn scRNA-seq?

Feel free to comment below!