Chapter 7 Appendix
7.1 Useful resources
Here, we include a list of useful websites and publications related to the computational analysis of single-cell data. This mainly comprises general reviews / tutorials as well as benchmarking studies on certain aspects of single-cell analysis. If you think that there is a useful paper that can be included here, please contact me at john.f.ouyang@gmail.com
7.1.1 Useful websites
- https://www.scrna-tools.org/: Database recording details of computational tools designed for analyzing scRNA-seq data, maintained by the Oshlack lab.
7.1.2 General features of single-cell data
- (Svensson 2020) “Droplet scRNA-seq is not zero-inflated”
7.1.3 General reviews / tutorials
(Lun, McCarthy, and Marioni 2016) “A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor”: One of the pioneering scRNA-seq analysis tutorials covering quality control, normalization, feature selection and dimension reduction
(Yuan et al. 2017) “Challenges and emerging directions in single-cell analysis”: Challenges in data preprocessing and potential applications and extensions of scRNA-seq
(Hwang, Lee, and Bang 2018) “Single-cell RNA sequencing technologies and bioinformatics pipelines”: Computational challenges (e.g. data preprocessing) and unsolved problems (cell type identification, inferring regulatory networks, cell hierarchy reconstruction)
(Chen, Ning, and Shi 2019) “Single-Cell RNA-Seq Technologies and Related Computational Data Analysis”: Summarizes the various steps in single cell analysis (e.g. read mapping, quality control, batch correction, normalization, imputation, dimension reduction, cell population identification, differential expression, pseudotime) and comprehensively list tools that are used in these analysis steps
(Luecken and Theis 2019) “Current best practices in single‐cell RNA‐seq analysis: a tutorial”: Summarizes the various steps in single cell analysis (e.g. quality control, normalization, data integration, dimension reduction, cluster analysis, trajectory analysis, cell-vs-gene-level analysis) with special attention to “pitfalls and reccomendations”
(Lahnemann et al. 2020) “Eleven grand challenges in single-cell data science”
7.1.4 Dimension reduction / trajectory inferrence
(Saelens et al. 2019) “A comparison of single-cell trajectory inference methods”
(Sun et al. 2019) “Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis”
(Tsuyuzaki et al. 2020) “Benchmarking principal component analysis for large-scale single-cell RNA-sequencing”
7.1.5 Clustering / differential expression / cell identification
(Soneson and Robinson 2018) “Bias, robustness and scalability in single-cell differential expression analysis”
(Abdelaal et al. 2019) “A comparison of automatic cell identification methods for single-cell RNA sequencing data”
(Kiselev, Andrews, and Hemberg 2019) “Challenges in unsupervised clustering of single-cell {RNA}-seq data”
7.1.6 Network / pathway-based algorithms
(Holland et al. 2020) “Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data”
(Pratapa et al. 2020) “Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data”
7.1.7 Others
(Tian et al. 2019) “Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments”
(Tran et al. 2020) “A benchmark of batch-effect correction methods for single-cell RNA sequencing data”
(Wagner and Klein 2020) “Lineage tracing meets single-cell omics: opportunities and challenges”
(Xi and Li 2021) “Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data”
7.1.8 Experimental protocols
(Ding et al. 2020) “Systematic comparison of single-cell and single-nucleus RNA-sequencing methods”
(Mereu et al. 2020) “Benchmarking single-cell RNA-sequencing protocols for cell atlas projects”
7.2 Installing packages: some tips
Most analysis performed in this guide uses the R programming language.
Furthermore, it is useful to install RStudio, which is the preferred IDE (in
layman’s term, a graphic user interface) for R. There are two ways to
install R and RStudio. The first installation method (reccomended) is
to install it via the Anaconda platform here.
The second installation method is to install it via the Rstudio website here.
Next, we need to install the required packages: