References#
Ehsan Amid and Manfred K. Warmuth. Trimap: large-scale dimensionality reduction using triplets. 2019. URL: https://arxiv.org/abs/1910.00204, doi:10.48550/ARXIV.1910.00204.
El-ad David Amir, Kara L Davis, Michelle D Tadmor, Erin F Simonds, Jacob H Levine, Sean C Bendall, Daniel K Shenfeld, Smita Krishnaswamy, Garry P Nolan, and Dana Pe’er. Visne enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nature Biotechnology, 31(6):545–552, may 2013. URL: https://doi.org/10.1038/nbt.2594, doi:10.1038/nbt.2594.
Philipp Angerer, Laleh Haghverdi, Maren Büttner, Fabian J. Theis, Carsten Marr, and Florian Buettner. Destiny: diffusion maps for large-scale single-cell data in r. Bioinformatics, 32(8):1241–1243, dec 2015. URL: https://doi.org/10.1093/bioinformatics/btv715, doi:10.1093/bioinformatics/btv715.
Maayan Baron, Adrian Veres, Samuel L. Wolock, Aubrey L. Faust, Renaud Gaujoux, Amedeo Vetere, Jennifer Hyoje Ryu, Bridget K. Wagner, Shai S. Shen-Orr, Allon M. Klein, Douglas A. Melton, and Itai Yanai. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Systems, 3(4):346–360.e4, oct 2016. URL: https://doi.org/10.1016/j.cels.2016.08.011, doi:10.1016/j.cels.2016.08.011.
Etienne Becht, Leland McInnes, John Healy, Charles-Antoine Dutertre, Immanuel W H Kwok, Lai Guan Ng, Florent Ginhoux, and Evan W Newell. Dimensionality reduction for visualizing single-cell data using umap. Nature Biotechnology, 37(1):38–44, dec 2018. URL: https://doi.org/10.1038/nbt.4314, doi:10.1038/nbt.4314.
Austin R. Benson, David F. Gleich, and James Demmel. Direct qr factorizations for tall-and-skinny matrices in mapreduce architectures. In 2013 IEEE International Conference on Big Data, 264–272. IEEE, oct 2013. URL: https://doi.org/10.1109/BigData.2013.6691583, doi:10.1109/bigdata.2013.6691583.
Nicholas J. Bernstein, Nicole L. Fong, Irene Lam, Margaret A. Roy, David G. Hendrickson, and David R. Kelley. Solo: doublet identification in single-cell rna-seq via semi-supervised deep learning. Cell Systems, 11(1):95–101.e5, jul 2020. URL: https://doi.org/10.1016/j.cels.2020.05.010, doi:10.1016/j.cels.2020.05.010.
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, oct 2008. URL: https://doi.org/10.1088/1742-5468/2008/10/P10008, doi:10.1088/1742-5468/2008/10/p10008.
Andrew Butler, Paul Hoffman, Peter Smibert, Efthymia Papalexi, and Rahul Satija. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5):411–420, apr 2018. URL: https://doi.org/10.1038/nbt.4096, doi:10.1038/nbt.4096.
Bhargav Chippada. Forceatlas2 for python. bhargavchippada/forceatlas2, 2018. URL: bhargavchippada/forceatlas2.
Neil R Clark, Kevin S Hu, Axel S Feldmann, Yan Kou, Edward Y Chen, Qiaonan Duan, and Avi Ma’ayan. The characteristic direction: a geometrical approach to identify differentially expressed genes. BMC Bioinformatics, mar 2014. URL: https://doi.org/10.1186/1471-2105-15-79, doi:10.1186/1471-2105-15-79.
R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, and S. W. Zucker. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proceedings of the National Academy of Sciences, 102(21):7426–7431, may 2005. URL: https://doi.org/10.1073/pnas.0500334102, doi:10.1073/pnas.0500334102.
G. Csárdi and T. Nepusz. The igraph software package for complex network research. InterJournal Complex Systems, pages 1695, 2006. URL: https://www.bibsonomy.org/bibtex/252e3e774bac8424cc9a3845a9c597344/lantiq.
Gökcen Eraslan, Lukas M. Simon, Maria Mircea, Nikola S. Mueller, and Fabian J. Theis. Single-cell rna-seq denoising using a deep count autoencoder. Nature Communications, jan 2019. URL: https://doi.org/10.1038/s41467-018-07931-2, doi:10.1038/s41467-018-07931-2.
Ron Fechtner. Pypairs - a python scrna-seq classifier. rfechtner/pypairs, 2018. URL: rfechtner/pypairs.
Thomas M. J. Fruchterman and Edward M. Reingold. Graph drawing by force‐directed placement. Software: Practice and Experience, 21(11):1129–1164, nov 1991. URL: https://doi.org/10.1002/spe.4380211102, doi:10.1002/spe.4380211102.
Timothy S. Gardner, Charles R. Cantor, and James J. Collins. Construction of a genetic toggle switch in escherichia coli. Nature, 403(6767):339–342, jan 2000. URL: https://doi.org/10.1038/35002131, doi:10.1038/35002131.
Christoph Hafemeister and Rahul Satija. Normalization and variance stabilization of single-cell rna-seq data using regularized negative binomial regression. Genome Biology, dec 2019. URL: https://doi.org/10.1186/s13059-019-1874-1, doi:10.1186/s13059-019-1874-1.
Laleh Haghverdi, Florian Buettner, and Fabian J. Theis. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics, 31(18):2989–2998, may 2015. URL: https://doi.org/10.1093/bioinformatics/btv325, doi:10.1093/bioinformatics/btv325.
Laleh Haghverdi, Maren Büttner, F Alexander Wolf, Florian Buettner, and Fabian J Theis. Diffusion pseudotime robustly reconstructs lineage branching. Nature Methods, 13(10):845–848, aug 2016. URL: https://doi.org/10.1038/nmeth.3971, doi:10.1038/nmeth.3971.
Laleh Haghverdi, Aaron T L Lun, Michael D Morgan, and John C Marioni. Batch effects in single-cell rna-sequencing data are corrected by matching mutual nearest neighbors. Nature Biotechnology, 36(5):421–427, apr 2018. URL: https://doi.org/10.1038/nbt.4091, doi:10.1038/nbt.4091.
Nathan Halko, Per-Gunnar Martinsson, and Joel A. Tropp. Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. 2009. URL: https://arxiv.org/abs/0909.4061, doi:10.48550/ARXIV.0909.4061.
Brian Hie, Bryan Bryson, and Bonnie Berger. Efficient integration of heterogeneous single-cell transcriptomes using scanorama. Nature Biotechnology, 37(6):685–691, may 2019. URL: https://doi.org/10.1038/s41587-019-0113-3, doi:10.1038/s41587-019-0113-3.
Tomislav Ilicic, Jong Kyoung Kim, Aleksandra A. Kolodziejczyk, Frederik Otzen Bagger, Davis James McCarthy, John C. Marioni, and Sarah A. Teichmann. Classification of low quality cells from single-cell rna-seq data. Genome Biology, feb 2016. URL: https://doi.org/10.1186/s13059-016-0888-1, doi:10.1186/s13059-016-0888-1.
Saiful Islam, Una Kjällquist, Annalena Moliner, Pawel Zajac, Jian-Bing Fan, Peter Lönnerberg, and Sten Linnarsson. Characterization of the single-cell transcriptional landscape by highly multiplex rna-seq. Genome Research, 21(7):1160–1167, may 2011. URL: https://doi.org/10.1101/gr.110882.110, doi:10.1101/gr.110882.110.
Saiful Islam, Amit Zeisel, Simon Joost, Gioele La Manno, Pawel Zajac, Maria Kasper, Peter Lönnerberg, and Sten Linnarsson. Quantitative single-cell rna-seq with unique molecular identifiers. Nature Methods, 11(2):163–166, dec 2013. URL: https://doi.org/10.1038/nmeth.2772, doi:10.1038/nmeth.2772.
Mathieu Jacomy, Tommaso Venturini, Sebastien Heymann, and Mathieu Bastian. Forceatlas2, a continuous graph layout algorithm for handy network visualization designed for the gephi software. PLoS ONE, 9(6):e98679, jun 2014. URL: https://doi.org/10.1371/journal.pone.0098679, doi:10.1371/journal.pone.0098679.
W. Evan Johnson, Cheng Li, and Ariel Rabinovic. Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics, 8(1):118–127, apr 2006. URL: https://doi.org/10.1093/biostatistics/kxj037, doi:10.1093/biostatistics/kxj037.
Chris Kang. Mnnpy - mnn-correct in python. chriscainx/mnnpy, 2018. URL: chriscainx/mnnpy.
Ilya Korsunsky, Nghia Millard, Jean Fan, Kamil Slowikowski, Fan Zhang, Kevin Wei, Yuriy Baglaenko, Michael Brenner, Po-ru Loh, and Soumya Raychaudhuri. Fast, sensitive and accurate integration of single-cell data with harmony. Nature Methods, 16(12):1289–1296, nov 2019. URL: https://doi.org/10.1038/s41592-019-0619-0, doi:10.1038/s41592-019-0619-0.
Jan Krumsiek, Carsten Marr, Timm Schroeder, and Fabian J. Theis. Hierarchical differentiation of myeloid progenitors is encoded in the transcription factor network. PLoS ONE, 6(8):e22649, aug 2011. URL: https://doi.org/10.1371/journal.pone.0022649, doi:10.1371/journal.pone.0022649.
Gioele La Manno, Ruslan Soldatov, Amit Zeisel, Emelie Braun, Hannah Hochgerner, Viktor Petukhov, Katja Lidschreiber, Maria E. Kastriti, Peter Lönnerberg, Alessandro Furlan, Jean Fan, Lars E. Borm, Zehua Liu, David van Bruggen, Jimin Guo, Xiaoling He, Roger Barker, Erik Sundström, Gonçalo Castelo-Branco, Patrick Cramer, Igor Adameyko, Sten Linnarsson, and Peter V. Kharchenko. Rna velocity of single cells. Nature, 560(7719):494–498, aug 2018. URL: https://doi.org/10.1038/s41586-018-0414-6, doi:10.1038/s41586-018-0414-6.
Renaud Lambiotte, Jean-Charles Delvenne, and Mauricio Barahona. Random walks, markov processes and the multiscale modular organization of complex networks. IEEE Transactions on Network Science and Engineering, 1(2):76–90, jul 2014. URL: https://doi.org/10.1109/TNSE.2015.2391998, doi:10.1109/tnse.2015.2391998.
Jan Lause, Philipp Berens, and Dmitry Kobak. Analytic pearson residuals for normalization of single-cell rna-seq umi data. Genome Biology, sep 2021. URL: https://doi.org/10.1186/s13059-021-02451-7, doi:10.1186/s13059-021-02451-7.
Jeffrey T. Leek, W. Evan Johnson, Hilary S. Parker, Elana J.Fertig, Andrew E. Jaffe, John D. Storey, Yuqing Zhang, and Leonardo Collado Torres. Sva. 2017. URL: https://bioconductor.org/packages/sva, doi:10.18129/B9.BIOC.SVA.
Jacob H. Levine, Erin F. Simonds, Sean C. Bendall, Kara L. Davis, El-ad D. Amir, Michelle D. Tadmor, Oren Litvin, Harris G. Fienberg, Astraea Jager, Eli R. Zunder, Rachel Finck, Amanda L. Gedman, Ina Radtke, James R. Downing, Dana Pe’er, and Garry P. Nolan. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell, 162(1):184–197, jul 2015. URL: https://doi.org/10.1016/j.cell.2015.05.047, doi:10.1016/j.cell.2015.05.047.
Mohammad Lotfollahi, F. Alexander Wolf, and Fabian J. Theis. Scgen predicts single-cell perturbation responses. Nature Methods, 16(8):715–721, jul 2019. URL: https://doi.org/10.1038/s41592-019-0494-8, doi:10.1038/s41592-019-0494-8.
Malte Luecken, Daniel Burkhardt, Robrecht Cannoodt, Christopher Lance, Aditi Agrawal, Hananeh Aliee, Ann Chen, Louise Deconinck, Angela Detweiler, Alejandro Granados, Shelly Huynh, Laura Isacco, Yang Kim, Dominik Klein, Bony De Kumar, Sunil Kuppasani, Heiko Lickert, Aaron McGeever, Joaquin Melgarejo, Honey Mekonen, Maurizio Morri, Michaela Müller, Norma Neff, Sheryl Paul, Bastian Rieck, Kaylie Schneider, Scott Steelman, Michael Sterr, Daniel Treacy, Alexander Tong, Alexandra-Chloe Villani, Guilin Wang, Jia Yan, Ce Zhang, Angela Pisco, Smita Krishnaswamy, Fabian Theis, and Jonathan M Bloom. A sandbox for prediction and integration of dna, rna, and proteins in single cells. In J. Vanschoren and S. Yeung, editors, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1. Curran, 2021. URL: https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/158f3069a435b314a80bdcb024f8e422-Paper-round2.pdf.
Aaron T.L. Lun, Davis J. McCarthy, and John C. Marioni. A step-by-step workflow for low-level analysis of single-cell rna-seq data with bioconductor. F1000Research, 5:2122, oct 2016. URL: https://doi.org/10.12688/f1000research.9501.2, doi:10.12688/f1000research.9501.2.
Davis J McCarthy, Kieran R Campbell, Aaron T L Lun, and Quin F Wills. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in r. Bioinformatics, 33(8):1179–1186, jan 2017. URL: https://doi.org/10.1093/bioinformatics/btw777, doi:10.1093/bioinformatics/btw777.
Leland McInnes, John Healy, and James Melville. Umap: uniform manifold approximation and projection for dimension reduction. 2018. URL: https://arxiv.org/abs/1802.03426, doi:10.48550/ARXIV.1802.03426.
Victoria Moignard, Steven Woodhouse, Laleh Haghverdi, Andrew J Lilly, Yosuke Tanaka, Adam C Wilkinson, Florian Buettner, Iain C Macaulay, Wajid Jawaid, Evangelia Diamanti, Shin-Ichi Nishikawa, Nir Piterman, Valerie Kouskoff, Fabian J Theis, Jasmin Fisher, and Berthold Göttgens. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nature Biotechnology, 33(3):269–276, feb 2015. URL: https://doi.org/10.1038/nbt.3154, doi:10.1038/nbt.3154.
Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel B. Burkhardt, William S. Chen, Kristina Yim, Antonia van den Elzen, Matthew J. Hirn, Ronald R. Coifman, Natalia B. Ivanova, Guy Wolf, and Smita Krishnaswamy. Visualizing structure and transitions in high-dimensional biological data. Nature Biotechnology, 37(12):1482–1492, dec 2019. URL: https://doi.org/10.1038/s41587-019-0336-3, doi:10.1038/s41587-019-0336-3.
Mauro J. Muraro, Gitanjali Dharmadhikari, Dominic Grün, Nathalie Groen, Tim Dielen, Erik Jansen, Leon van Gurp, Marten A. Engelse, Francoise Carlotti, Eelco J.P. de Koning, and Alexander van Oudenaarden. A single-cell transcriptome atlas of the human pancreas. Cell Systems, 3(4):385–394.e3, oct 2016. URL: https://doi.org/10.1016/j.cels.2016.09.002, doi:10.1016/j.cels.2016.09.002.
Sonja Nowotschin, Manu Setty, Ying-Yi Kuo, Vincent Liu, Vidur Garg, Roshan Sharma, Claire S. Simon, Nestor Saiz, Rui Gardner, Stéphane C. Boutet, Deanna M. Church, Pamela A. Hoodless, Anna-Katerina Hadjantonakis, and Dana Pe’er. The emergent landscape of the mouse gut endoderm at single-cell resolution. Nature, 569(7756):361–367, apr 2019. URL: https://doi.org/10.1038/s41586-019-1127-1, doi:10.1038/s41586-019-1127-1.
Vasilis Ntranos, Lynn Yi, Páll Melsted, and Lior Pachter. A discriminative learning approach to differential expression analysis for single-cell rna-seq. Nature Methods, 16(2):163–166, jan 2019. URL: https://doi.org/10.1038/s41592-018-0303-9, doi:10.1038/s41592-018-0303-9.
Franziska Paul, Ya’ara Arkin, Amir Giladi, Diego Adhemar Jaitin, Ephraim Kenigsberg, Hadas Keren-Shaul, Deborah Winter, David Lara-Astiaso, Meital Gury, Assaf Weiner, Eyal David, Nadav Cohen, Felicia Kathrine Bratt Lauridsen, Simon Haas, Andreas Schlitzer, Alexander Mildner, Florent Ginhoux, Steffen Jung, Andreas Trumpp, Bo Torben Porse, Amos Tanay, and Ido Amit. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell, 163(7):1663–1677, dec 2015. URL: https://doi.org/10.1016/j.cell.2015.11.013, doi:10.1016/j.cell.2015.11.013.
Brent Pedersen. Combat.py. brentp/combat.py, 2012. URL: brentp/combat.py.
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. Scikit-learn: machine learning in python. Journal of Machine Learning Research, 12(null):2825–2830, nov 2011.
Mireya Plass, Jordi Solana, F. Alexander Wolf, Salah Ayoub, Aristotelis Misios, Petar Glažar, Benedikt Obermayer, Fabian J. Theis, Christine Kocks, and Nikolaus Rajewsky. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science, may 2018. URL: https://doi.org/10.1126/science.aaq1723, doi:10.1126/science.aaq1723.
Krzysztof Polański, Matthew D Young, Zhichao Miao, Kerstin B Meyer, Sarah A Teichmann, and Jong-Eun Park. Bbknn: fast batch alignment of single cell transcriptomes. Bioinformatics, 36(3):964–965, aug 2019. URL: https://doi.org/10.1093/bioinformatics/btz625, doi:10.1093/bioinformatics/btz625.
Rahul Satija, Jeffrey A Farrell, David Gennert, Alexander F Schier, and Aviv Regev. Spatial reconstruction of single-cell gene expression data. Nature Biotechnology, 33(5):495–502, apr 2015. URL: https://doi.org/10.1038/nbt.3192, doi:10.1038/nbt.3192.
Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Joshua Gould, Siyan Liu, Stacie Lin, Peter Berube, Lia Lee, Jenny Chen, Justin Brumbaugh, Philippe Rigollet, Konrad Hochedlinger, Rudolf Jaenisch, Aviv Regev, and Eric S. Lander. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell, 176(4):928–943.e22, feb 2019. URL: https://doi.org/10.1016/j.cell.2019.01.006, doi:10.1016/j.cell.2019.01.006.
Antonio Scialdone, Kedar N. Natarajan, Luis R. Saraiva, Valentina Proserpio, Sarah A. Teichmann, Oliver Stegle, John C. Marioni, and Florian Buettner. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods, 85:54–61, sep 2015. URL: https://doi.org/10.1016/j.ymeth.2015.06.021, doi:10.1016/j.ymeth.2015.06.021.
Åsa Segerstolpe, Athanasia Palasantza, Pernilla Eliasson, Eva-Marie Andersson, Anne-Christine Andréasson, Xiaoyan Sun, Simone Picelli, Alan Sabirsh, Maryam Clausen, Magnus K. Bjursell, David M. Smith, Maria Kasper, Carina Ämmälä, and Rickard Sandberg. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metabolism, 24(4):593–607, oct 2016. URL: https://doi.org/10.1016/j.cmet.2016.08.020, doi:10.1016/j.cmet.2016.08.020.
Manu Setty, Vaidotas Kiseliovas, Jacob Levine, Adam Gayoso, Linas Mazutis, and Dana Pe’er. Characterization of cell fate probabilities in single-cell data with palantir. Nature Biotechnology, 37(4):451–460, mar 2019. URL: https://doi.org/10.1038/s41587-019-0068-4, doi:10.1038/s41587-019-0068-4.
Manu Setty, Michelle D Tadmor, Shlomit Reich-Zeliger, Omer Angel, Tomer Meir Salame, Pooja Kathail, Kristy Choi, Sean Bendall, Nir Friedman, and Dana Pe’er. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nature Biotechnology, 34(6):637–645, may 2016. URL: https://doi.org/10.1038/nbt.3569, doi:10.1038/nbt.3569.
Charlotte Soneson and Mark D Robinson. Bias, robustness and scalability in single-cell differential expression analysis. Nature Methods, 15(4):255–261, feb 2018. URL: https://doi.org/10.1038/nmeth.4612, doi:10.1038/nmeth.4612.
Tim Stuart, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M. Mauck, Yuhan Hao, Marlon Stoeckius, Peter Smibert, and Rahul Satija. Comprehensive integration of single-cell data. Cell, 177(7):1888–1902.e21, jun 2019. URL: https://doi.org/10.1016/j.cell.2019.05.031, doi:10.1016/j.cell.2019.05.031.
Marcin Tabaka, Joshua Gould, and Aviv Regev. Scsva: an interactive tool for big data visualization and exploration in single-cell omics. bioRxiv, 2019. URL: https://www.biorxiv.org/content/early/2019/01/06/512582, arXiv:https://www.biorxiv.org/content/early/2019/01/06/512582.full.pdf, doi:10.1101/512582.
Alexander J Tarashansky, Yuan Xue, Pengyang Li, Stephen R Quake, and Bo Wang. Self-assembling manifolds in single-cell rna sequencing data. eLife, sep 2019. URL: https://doi.org/10.7554/eLife.48994, doi:10.7554/elife.48994.
V. A. Traag, L. Waltman, and N. J. van Eck. From louvain to leiden: guaranteeing well-connected communities. Scientific Reports, mar 2019. URL: https://doi.org/10.1038/s41598-019-41695-z, doi:10.1038/s41598-019-41695-z.
Vincent Traag. Louvain-igraph: v0.5.3. 2015. URL: https://zenodo.org/record/35117, doi:10.5281/ZENODO.35117.
Dmitry Ulyanov. Multicore-tsne. DmitryUlyanov/Multicore-TSNE, 2016. URL: DmitryUlyanov/Multicore-TSNE.
Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(86):2579–2605, 2008. URL: http://jmlr.org/papers/v9/vandermaaten08a.html.
David van Dijk, Roshan Sharma, Juozas Nainys, Kristina Yim, Pooja Kathail, Ambrose J. Carr, Cassandra Burdziak, Kevin R. Moon, Christine L. Chaffer, Diwakar Pattabiraman, Brian Bierie, Linas Mazutis, Guy Wolf, Smita Krishnaswamy, and Dana Pe’er. Recovering gene interactions from single-cell data using data diffusion. Cell, 174(3):716–729.e27, jul 2018. URL: https://doi.org/10.1016/j.cell.2018.05.061, doi:10.1016/j.cell.2018.05.061.
Florian Wagner. Straightforward clustering of single-cell rna-seq data with t-sne and dbscan. bioRxiv, 2020. URL: https://www.biorxiv.org/content/early/2020/04/09/770388, arXiv:https://www.biorxiv.org/content/early/2020/04/09/770388.full.pdf, doi:10.1101/770388.
Yue J. Wang, Maria L. Golson, Jonathan Schug, Daniel Traum, Chengyang Liu, Kumar Vivek, Craig Dorrell, Ali Naji, Alvin C. Powers, Kyong-Mi Chang, Markus Grompe, and Klaus H. Kaestner. Single-cell mass cytometry analysis of the human endocrine pancreas. Cell Metabolism, 24(4):616–626, oct 2016. URL: https://doi.org/10.1016/j.cmet.2016.09.007, doi:10.1016/j.cmet.2016.09.007.
Michael Waskom, Olga Botvinnik, Drewokane, Paul Hobson, , David, Yaroslav Halchenko, Saulius Lukauskas, John B. Cole, Jordi Warmenhoven, Julian De Ruiter, Stephan Hoyer, Jake Vanderplas, Santi Villalba, Gero Kunter, Eric Quintero, Marcel Martin, Alistair Miles, Kyle Meyer, Tom Augspurger, Tal Yarkoni, Pete Bachant, Mike Williams, Constantine Evans, Clark Fitzgerald, , Brian, Daniel Wehner, Gregory Hitz, Erik Ziegler, Adel Qalieh, and Antony Lee. Seaborn: v0.7.1 (june 2016). 2016. URL: https://zenodo.org/record/54844, doi:10.5281/ZENODO.54844.
Caleb Weinreb, Alejo Rodriguez-Fraticelli, Fernando D. Camargo, and Allon M. Klein. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science, feb 2020. URL: https://doi.org/10.1126/science.aaw3381, doi:10.1126/science.aaw3381.
Caleb Weinreb, Samuel Wolock, and Allon M Klein. Spring: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics, 34(7):1246–1248, dec 2017. URL: https://doi.org/10.1093/bioinformatics/btx792, doi:10.1093/bioinformatics/btx792.
Dominik M Wittmann, Jan Krumsiek, Julio Saez-Rodriguez, Douglas A Lauffenburger, Steffen Klamt, and Fabian J Theis. Transforming boolean models to continuous models: methodology and application to t-cell receptor signaling. BMC Systems Biology, sep 2009. URL: https://doi.org/10.1186/1752-0509-3-98, doi:10.1186/1752-0509-3-98.
F. Alexander Wolf, Philipp Angerer, and Fabian J. Theis. Scanpy: large-scale single-cell gene expression data analysis. Genome Biology, 19(1):15, feb 2018. URL: https://doi.org/10.1186/s13059-017-1382-0, doi:10.1186/s13059-017-1382-0.
F. Alexander Wolf, Fiona K. Hamey, Mireya Plass, Jordi Solana, Joakim S. Dahlin, Berthold Göttgens, Nikolaus Rajewsky, Lukas Simon, and Fabian J. Theis. Paga: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biology, mar 2019. URL: https://doi.org/10.1186/s13059-019-1663-x, doi:10.1186/s13059-019-1663-x.
Samuel L. Wolock, Romain Lopez, and Allon M. Klein. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Systems, 8(4):281–291.e9, apr 2019. URL: https://doi.org/10.1016/j.cels.2018.11.005, doi:10.1016/j.cels.2018.11.005.
Grace X. Y. Zheng, Jessica M. Terry, Phillip Belgrader, Paul Ryvkin, Zachary W. Bent, Ryan Wilson, Solongo B. Ziraldo, Tobias D. Wheeler, Geoff P. McDermott, Junjie Zhu, Mark T. Gregory, Joe Shuga, Luz Montesclaros, Jason G. Underwood, Donald A. Masquelier, Stefanie Y. Nishimura, Michael Schnall-Levin, Paul W. Wyatt, Christopher M. Hindson, Rajiv Bharadwaj, Alexander Wong, Kevin D. Ness, Lan W. Beppu, H. Joachim Deeg, Christopher McFarland, Keith R. Loeb, William J. Valente, Nolan G. Ericson, Emily A. Stevens, Jerald P. Radich, Tarjei S. Mikkelsen, Benjamin J. Hindson, and Jason H. Bielas. Massively parallel digital transcriptional profiling of single cells. Nature Communications, jan 2017. URL: https://doi.org/10.1038/ncomms14049, doi:10.1038/ncomms14049.
Eli R. Zunder, Ernesto Lujan, Yury Goltsev, Marius Wernig, and Garry P. Nolan. A continuous molecular roadmap to ipsc reprogramming through progression analysis of single-cell mass cytometry. Cell Stem Cell, 16(3):323–337, mar 2015. URL: https://doi.org/10.1016/j.stem.2015.01.015, doi:10.1016/j.stem.2015.01.015.