In this roadmap it is described the path from a beginner in bioinformatics/biology to single-cell expert.
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Here is a link to draw.io: https://viewer.diagrams.net/?highlight=0000ff&edit=_blank&layers=1&nav=1#G1Pt7qlrlkqKgcTHseUnjHDvku7OcioKLA
To start with you have to understand basic biological principles, know how cell works, which tissues are composed of which cells and what fundctions do they have. Basic course of school biology and some auxiliary maerials will be enought to have a general overview. After that you can better understand the importance of research in single-cell resolution.
Also, you have to understand how sequencing works and what are differences between main sequencing platforms.
Andrews, Tallulah S., et al. “Tutorial: Guidelines for the Computational Analysis of Single-Cell RNA Sequencing Data.” Nature Protocols, Dec. 2020, doi:10.1038/s41596-020-00409-w. https://www.nature.com/articles/s41596-020-00409-w
Stuart, T. and Satija, R. (2019). Integrative single-cell analysis. Nature Reviews Genetics, [online] 20(5), pp.257–272. Available at: https://www.nature.com/articles/s41576-019-0093-7
Luecken, M.D. and Theis, F.J. (2019). Current best practices in single‐cell RNA‐seq analysis: a tutorial. Molecular Systems Biology, 15(6). https://www.embopress.org/doi/full/10.15252/msb.20188746
Chen, G., Ning, B. and Shi, T. (2019). Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Frontiers in Genetics, 10. Available at: https://www.frontiersin.org/articles/10.3389/fgene.2019.00317/full
Tang, X., Huang, Y., Lei, J., Luo, H. and Zhu, X. (2019). The single-cell sequencing: new developments and medical applications. Cell & Bioscience, 9(1). Available at: https://cellandbioscience.biomedcentral.com/articles/10.1186/s13578-019-0314-y
Dal Molin, Alessandra, and Barbara Di Camillo. “How to Design a Single-Cell RNA-Sequencing Experiment: Pitfalls, Challenges and Perspectives.” Briefings in Bioinformatics, vol. 20, no. 4, Jan. 2018, pp. 1384–94, doi:10.1093/bib/bby007.
https://rna-seqblog.com/an-introduction-to-single-cell-rna-sequencing-and-clinical-applications/
“Vignettes.” Satijalab.org, 2019, https://satijalab.org/seurat/vignettes.html.
Theislab. “Theislab/Single-Cell-Tutorial.” GitHub, 6 Oct. 2020, https://github.com/theislab/single-cell-tutorial.
After receiving general overview you have to deeper your knowledge and understand main differences in single-cell sequencing protocols, pipelines of data processing and other tools.
A beautiful and very explanatory tutorial of single-cell analysis: Bioconductor.org. (2020). Orchestrating Single-Cell Analysis with Bioconductor. [online] Available at: http://bioconductor.org/books/release/OSCA/
Westoby, J. (2019). Analysis of single cell RNA-seq data. [online] Sanger.ac.uk. Available at: https://scrnaseq-course.cog.sanger.ac.uk/website/index.html
McCarthy, Davis J. “Analysis of Single Cell RNA-Seq Data.” Svi.Edu.Au, 3 Oct. 2019, http://biocellgen-public.svi.edu.au/mig_2019_scrnaseq-workshop/public/index.html.
Ashton, John M., et al. Comparative Analysis of Single-Cell RNA Sequencing Platforms and Methods. July 2020, doi:10.1101/2020.07.20.212100. https://www.biorxiv.org/content/10.1101/2020.07.20.212100v1
Gao, Mingxuan, et al. Comparison of High-Throughput Single-Cell RNA Sequencing Data Processing Pipelines. Feb. 2020, doi:10.1101/2020.02.09.940221. https://www.biorxiv.org/content/10.1101/2020.02.09.940221v1
Vieth, Beate, et al. “A Systematic Evaluation of Single Cell RNA-Seq Analysis Pipelines.” Nature Communications, vol. 10, no. 1, Oct. 2019, doi:10.1038/s41467-019-12266-7. https://www.nature.com/articles/s41467-019-12266-7
Feng, Chao, et al. “Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.” International Journal of Molecular Sciences, vol. 21, no. 6, Mar. 2020, p. 2181, doi:10.3390/ijms21062181. https://www.mdpi.com/1422-0067/21/6/2181
Peng, Lihong, et al. “Single-Cell RNA-Seq Clustering: Datasets, Models, and Algorithms.” RNA Biology, vol. 17, no. 6, Mar. 2020, pp. 765–83, doi:10.1080/15476286.2020.1728961. https://www.tandfonline.com/doi/abs/10.1080/15476286.2020.1728961?journalCode=krnb20
Petegrosso, Raphael, et al. “Machine Learning and Statistical Methods for Clustering Single-Cell RNA-Sequencing Data.” Briefings in Bioinformatics, vol. 21, no. 4, June 2019, pp. 1209–23, doi:10.1093/bib/bbz063. https://academic.oup.com/bib/article-abstract/21/4/1209/5519426
Zhang, Lihua, and Shihua Zhang. “Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data.” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, pp. 1–1, doi:10.1109/tcbb.2018.2848633. https://pubmed.ncbi.nlm.nih.gov/29994128/
Chen, Wenan, et al. “A Comparison of Methods Accounting for Batch Effects in Differential Expression Analysis of UMI Count Based Single Cell RNA Sequencing.” Computational and Structural Biotechnology Journal, vol. 18, 2020, pp. 861–73, doi:10.1016/j.csbj.2020.03.026. https://www.sciencedirect.com/science/article/pii/S200103701930409X
Qiu, Peng. “Embracing the Dropouts in Single-Cell RNA-Seq Analysis.” Nature Communications, vol. 11, no. 1, Mar. 2020, doi:10.1038/s41467-020-14976-9. https://www.nature.com/articles/s41467-020-14976-9
Lan, Tian, et al. “Sequencing Dropout-and-Batch Effect Normalization for Single-Cell MRNA Profiles: A Survey and Comparative Analysis.” Briefings in Bioinformatics, Oct. 2020, doi:10.1093/bib/bbaa248. https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbaa248/5929825
Ma, Ying, et al. “Integrative Differential Expression and Gene Set Enrichment Analysis Using Summary Statistics for ScRNA-Seq Studies.” Nature Communications, vol. 11, no. 1, Mar. 2020, doi:10.1038/s41467-020-15298-6. https://www.nature.com/articles/s41467-020-15298-6
Saelens, Wouter, et al. “A Comparison of Single-Cell Trajectory Inference Methods.” Nature Biotechnology, vol. 37, no. 5, Apr. 2019, pp. 547–54, doi:10.1038/s41587-019-0071-9. https://www.nature.com/articles/s41587-019-0071-9
https://github.com/agitter/single-cell-pseudotime
https://broadinstitute.github.io/2019_scWorkshop/pseudotime-cell-trajectories.html
Hsiao, Chiaowen Joyce, et al. “Characterizing and Inferring Quantitative Cell Cycle Phase in Single-Cell RNA-Seq Data Analysis.” Genome Research, vol. 30, no. 4, Apr. 2020, pp. 611–21, doi:10.1101/gr.247759.118. https://genome.cshlp.org/content/early/2020/04/20/gr.247759.118.abstract
https://satijalab.org/seurat/v3.2/cell_cycle_vignette.html
Chen, Shuonan, and Jessica C. Mar. “Evaluating Methods of Inferring Gene Regulatory Networks Highlights Their Lack of Performance for Single Cell Gene Expression Data.” BMC Bioinformatics, vol. 19, no. 1, June 2018, doi:10.1186/s12859-018-2217-z. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2217-z
Iacono, Giovanni, et al. “Single-Cell Transcriptomics Unveils Gene Regulatory Network Plasticity.” Genome Biology, vol. 20, no. 1, June 2019, doi:10.1186/s13059-019-1713-4. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1713-4
Nguyen, Hung, et al. “A Comprehensive Survey of Regulatory Network Inference Methods Using Single Cell RNA Sequencing Data.” Briefings in Bioinformatics, Sept. 2020, doi:10.1093/bib/bbaa190. https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbaa190/5904505
Luecken, MD, et al. Benchmarking Atlas-Level Data Integration in Single-Cell Genomics. May 2020, doi:10.1101/2020.05.22.111161. https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2
Cakir, Batuhan, et al. “Comparison of Visualization Tools for Single-Cell RNAseq Data.” NAR Genomics and Bioinformatics, vol. 2, no. 3, July 2020, doi:10.1093/nargab/lqaa052. https://academic.oup.com/nargab/article/2/3/lqaa052/5877814
Shao, Xin, et al. “New Avenues for Systematically Inferring Cell-Cell Communication: Through Single-Cell Transcriptomics Data.” Protein & Cell, May 2020, doi:10.1007/s13238-020-00727-5. https://link.springer.com/article/10.1007/s13238-020-00727-5
Armingol, Erick, et al. “Deciphering Cell–Cell Interactions and Communication from Gene Expression.” Nature Reviews Genetics, Nov. 2020, doi:10.1038/s41576-020-00292-x. https://www.nature.com/articles/s41576-020-00292-x
Example: Zhou, Zilu, et al. “Surface Protein Imputation from Single Cell Transcriptomes by Deep Neural Networks.” Nature Communications, vol. 11, no. 1, Jan. 2020, doi:10.1038/s41467-020-14391-0. https://www.nature.com/articles/s41467-020-14391-0
22 classification methods benchmarking https://github.com/tabdelaal/scRNAseq_Benchmark Paper: Abdelaal, Tamim, et al. “A Comparison of Automatic Cell Identification Methods for Single-Cell RNA Sequencing Data.” Genome Biology, vol. 20, no. 1, Sept. 2019, doi:10.1186/s13059-019-1795-z. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1795-z
https://www.scrna-tools.org/tools The scRNA-tools database records details of software tools designed for analysing scRNA-seq data. Each tool is categorised according to the analysis tasks it can be used for.
https://bioinformaticshome.com/tools/rna-seq/scRNA-seq.html 230 Free Single-cell RNA-seq (scRNA-seq) Tools - Software and Resources