Single-cell reading list

A curated selection of blog posts on single-cell data analysis. This list is a (constant) work in progress.

The creation of this list was inspired by Gopher Reading List.

Start here

• Analysis of single-cell RNA-seq data course

• Introduction to single-cell RNA-seq technologies with slides here

• Orchestrating single-cell analysis with bioconductor

• Current best practices in scRNA-seq data analysis paper (Mol Syst Biol 2019)

• Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data paper (Nature Protocols 2020)

• Next-generation computational tools for interrogating cancer immunity paper (Nature Genetics Reviews 2019)

Videos

• scRNA-seq analyses: challanges, opportunities, and best practices (2020) primer by Stephen Fleming (Broad Institute)

Beginner

• On stabilizing mean-variance relationship in scRNA-seq data

Benchmarks

• Batch correction methods (2020), also see the GitHub repository

• Data integration methods (2020), also see the GitHub repository

• Systematic comparison of single-cell and single-nucleus RNA-sequencing methods (2020), also see the Bitbucket repository

• Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape (2021)

Intermediate

• Unique Molecular Identifiers – the problem, the solution and the proof

• Comment on Villani2017 paper in Science about new types of dendritic cells and monocytes in human blood

• Common pitfalls when clustering biological data paper

• Droplet scRNA-seq is not zero-inflated (2020) and UMI or not UMI, that is the question for scRNA-seq zero-inflation (2021)

• Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis (2021), which highlights the importance of disentangling expression and measurement models, and states that a Poisson measurement model is usually the most appropriate one to use

• Equivalence classes in RNA-seq analyses

• Normalization and variance stabilization and analytic Pearson residuals for scRNA-seq data

• Transformation and preprocessing of scRNA-seq data.

Advanced

• Linking scRNA-seq to scDNA-seq data by Kieran Campbell

• Low Rank Approximation of Kernels — intuition behind FIt-tSNE. Also see this paper about revealing fine-grained structure by adjusting degree of freedom

• RNA velocity of single cells (2018) and dynamic modelling with scVelo (2020)

• Optimizing expression quantitative trait locus mapping workflows for single-cell studies (Genome Biology 2021)

Autoencoders

• Count based autoencoders and the future for scRNA-seq analysis by Valentine Svensson

• Why variational autoencoders pursue PCA directions

Multi-omics

• Single-cell multiomics: technologies and data analysis methods review (2020)

Other lists

• awesome-single-cell

• awesome-multi-omics