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Readings

Access to materials listed here is available to current students

Files listed for download below can only be accessed after authenticating via Hopkin's SSO

Chapter 1: Course introduction and single cell omics

  1. New York Times article: A potential cure for type one diabetes pdf

  2. Macosko EZ, Basu A, Satija R, et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015;161(5):1202-1214. doi:10.1016/j.cell.2015.05.002 pmid:26000488/ pdf

  3. Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019 Jun 19;15(6):e8746. doi: 10.15252/msb.20188746. PMID: 31217225; PMCID: PMC6582955. pdf

  4. The cell cycle From The Science of Stem Cells by Jonathan Slack pdf

  5. Ringnér M. What is principal component analysis? Nat Biotechnol. 2008 Mar;26(3):303-4. doi: 10.1038/nbt0308-303. PMID: 18327243 pdf

  6. D'haeseleer P. How does gene expression clustering work? Nat Biotechnol. 2005 Dec;23(12):1499-501. doi: 10.1038/nbt1205-1499. PMID: 16333293 pdf

Chapter 2: Cell Identity

  1. What is a cell type? pdf

  2. scMap pdf

  3. BBKNN pdf

  4. singleCellNet pdf

Chapter 3: Trajectory inference (TI) and cell fate potential

  1. Monocle Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014 Apr;32(4):381-386. doi: 10.1038/nbt.2859. Epub 2014 Mar 23. PMID: 24658644

  2. TI Benchmarking Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019 May;37(5):547-554. doi: 10.1038/s41587-019-0071-9. Epub 2019 Apr 1. PMID: 30936559

  3. scFates Faure L, Soldatov R, Kharchenko PV, Adameyko I. scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data. Bioinformatics. 2023 Jan 1;39(1):btac746. doi: 10.1093/bioinformatics/btac746. PMID: 36394263

  4. Some notes on scFates pdf

  5. Chapter on embryo development pdf

  6. Stemness -- Cytotrace pdf

  7. Stemness -- StemID pdf Grün D, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J, Jansen E, Clevers H, de Koning EJP, van Oudenaarden A. De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data. Cell Stem Cell. 2016 Aug 4;19(2):266-277. doi: 10.1016/j.stem.2016.05.010. Epub 2016 Jun 23. PMID: 27345837; PMCID: PMC4985539.

  8. Optimal transport Schiebinger G, Shu J, Tabaka M, Cleary B, Subramanian V, Solomon A, Gould J, Liu S, Lin S, Berube P, Lee L, Chen J, Brumbaugh J, Rigollet P, Hochedlinger K, Jaenisch R, Regev A, Lander ES. Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming. Cell. 2019 Feb 7;176(4):928-943.e22. doi: 10.1016/j.cell.2019.01.006. Epub 2019 Jan 31. Erratum in: Cell. 2019 Mar 7;176(6):1517. doi: 10.1016/j.cell.2019.02.026. PMID: 30712874

  9. RNA velocity La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V, Lidschreiber K, Kastriti ME, Lönnerberg P, Furlan A, Fan J, Borm LE, Liu Z, van Bruggen D, Guo J, He X, Barker R, Sundström E, Castelo-Branco G, Cramer P, Adameyko I, Linnarsson S, Kharchenko PV. RNA velocity of single cells. Nature. 2018 Aug;560(7719):494-498. doi: 10.1038/s41586-018-0414-6. Epub 2018 Aug 8. PMID: 30089906

Chapter 4: Gene regulatory networks and cell fate engineering

  1. Context likelihood of relatedness Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007 Jan;5(1):e8. doi: 10.1371/journal.pbio.0050008. PMID: 17214507

  2. SCENIC Plus Bravo González-Blas C, De Winter S, Hulselmans G, Hecker N, Matetovici I, Christiaens V, Poovathingal S, Wouters J, Aibar S, Aerts S. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat Methods. 2023 Sep;20(9):1355-1367. doi: 10.1038/s41592-023-01938-4. Epub 2023 Jul 13. PMID: 37443338

  3. Beeline Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali TM. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat Methods. 2020 Feb;17(2):147-154. doi: 10.1038/s41592-019-0690-6. Epub 2020 Jan 6. PMID: 31907445

  4. Open questions in CSCB Cahan P, Cacchiarelli D, Dunn SJ, Hemberg M, de Sousa Lopes SMC, Morris SA, Rackham OJL, Del Sol A, Wells CA. Computational Stem Cell Biology: Open Questions and Guiding Principles. Cell Stem Cell. 2021 Jan 7;28(1):20-32. doi: 10.1016/j.stem.2020.12.012. PMID: 33417869