Pub 2021-2022

2022-07-20

  1. Zhiyi Qin, Peter Stoilov, Xuegong Zhang, and Yi Xing. Seastar: systematic evaluation of alternative transcription start sites in rna. Nucleic Acids Research, 46(8):e45–e45, 2018. URL: https://academic.oup.com/nar/article/46/8/e45/4931254#116446609.

  2. Yunlong Ma, Fei Qiu, Chunyu Deng, Jingjing Li, Yukuan Huang, Zeyi Wu, Yijun Zhou, Yaru Zhang, Yichun Xiong, Yinghao Yao, and others. Integrating single-cell sequencing data with gwas summary statistics reveals cd16+ monocytes and memory cd8+ t cells involved in severe covid-19. Genome medicine, 14(1):1–21, 2022. URL: https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-022-01021-1#Sec17.

  3. Haoyu Chen, Marta Pelizzola, and Andreas Futschik. Haplotype based testing for a better understanding of the selective architecture. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/07/18/2022.07.18.500395, arXiv:https://www.biorxiv.org/content/early/2022/07/18/2022.07.18.500395.full.pdf, doi:10.1101/2022.07.18.500395.

  4. Hong Seo Lim and Peng Qiu. Quantifying cell-type-specific differences of single-cell datasets using umap and shap. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/07/18/2022.07.15.500285, arXiv:https://www.biorxiv.org/content/early/2022/07/18/2022.07.15.500285.full.pdf, doi:10.1101/2022.07.15.500285.

  5. Xiaojie Qiu, Yan Zhang, Jorge D Martin-Rufino, Chen Weng, Shayan Hosseinzadeh, Dian Yang, Angela N Pogson, Marco Y Hein, Kyung Hoi Joseph Min, Li Wang, and others. Mapping transcriptomic vector fields of single cells. Cell, 185(4):690–711, 2022. URL: https://www.sciencedirect.com/science/article/pii/S0092867421015774.

  6. Cynthia A Kalita and Alexander Gusev. Decaf: a novel method to identify cell-type specific regulatory variants and their role in cancer risk. Genome Biology, 23(1):1–22, 2022. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02708-9.

2022-06-22

  1. Atul Deshpande, Melanie Loth, Dimitrios N. Sidiropoulos, Shuming Zhang, Long Yuan, Alexander Bell, Qingfeng Zhu, Won Jin Ho, Cesar Santa-Maria, Daniele Gilkes, Stephen R. Williams, Cedric R. Uytingco, Jennifer Chew, Andrej Hartnett, Zachary W. Bent, Alexander V. Favorov, Aleksander S. Popel, Mark Yarchoan, Lei Zheng, Elizabeth M. Jaffee, Robert Anders, Ludmila Danilova, Genevieve Stein-O\textquoteright Brien, Luciane T. Kagohara, and Elana J. Fertig. Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/06/02/2022.06.02.490672, arXiv:https://www.biorxiv.org/content/early/2022/06/02/2022.06.02.490672.full.pdf, doi:10.1101/2022.06.02.490672.

  2. Shaolong Cao, Jennifer R Wang, Shuangxi Ji, Peng Yang, Yaoyi Dai, Shuai Guo, Matthew D Montierth, John Paul Shen, Xiao Zhao, Jingxiao Chen, and others. Estimation of tumor cell total mrna expression in 15 cancer types predicts disease progression. Nature Biotechnology, pages 1–10, 2022. URL: https://www.nature.com/articles/s41587-022-01342-x.

  3. Maxwell Sherman, others, and Bonnie Berger. Genome-wide mapping of somatic mutation rates uncovers drivers of cancer. Nature Biotechnology, pages 1–10, 2022. URL: https://www.nature.com/articles/s41587-022-01353-8.

  4. Fulong Yu, Liam D Cato, Chen Weng, L Alexander Liggett, Soyoung Jeon, Keren Xu, Charleston WK Chiang, Joseph L Wiemels, Jonathan S Weissman, Adam J de Smith, and others. Variant to function mapping at single-cell resolution through network propagation. Nature Biotechnology, pages 1–10, 2022. URL: https://www.nature.com/articles/s41587-022-01341-y.

  5. Pascal Notin, José Miguel Hernández-Lobato, and Yarin Gal. Improving black-box optimization in vae latent space using decoder uncertainty. Advances in Neural Information Processing Systems, 34:802–814, 2021.

  6. Christopher D Steele, Ammal Abbasi, SM Islam, Amy L Bowes, Azhar Khandekar, Kerstin Haase, Shadi Hames-Fathi, Dolapo Ajayi, Annelien Verfaillie, Pawan Dhami, and others. Signatures of copy number alterations in human cancer. Nature, pages 1–8, 2022. URL: https://www.nature.com/articles/s41586-022-04738-6.

  7. Taro Leo Saito, Shin-ichi Hashimoto, Sam Guoping Gu, J Jason Morton, Michael Stadler, Thomas Blumenthal, Andrew Fire, and Shinichi Morishita. The transcription start site landscape of c. elegans. Genome research, 23(8):1348–1361, 2013. URL: https://genome.cshlp.org/content/23/8/1348.short.

2022-06-08

  1. Brendan F Miller, Feiyang Huang, Lyla Atta, Arpan Sahoo, and Jean Fan. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nature communications, 13(1):1–13, 2022.

  2. Peiyao Zhao, Jiaqiang Zhu, Ying Ma, and Xiang Zhou. Modeling zero inflation is not necessary for spatial transcriptomics. Genome Biology, 23(1):1–19, 2022.

  3. Dian Yang, Matthew G Jones, Santiago Naranjo, William M Rideout III, Kyung Hoi Joseph Min, Raymond Ho, Wei Wu, Joseph M Replogle, Jennifer L Page, Jeffrey J Quinn, and others. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell, 185(11):1905–1923, 2022.

  4. Anja Nusser, Jeremy B Swann, Brigitte Krauth, Dagmar Diekhoff, Lesly Calderon, Christiane Happe, Dominic Grün, Thomas Boehm, and others. Developmental dynamics of two bipotent thymic epithelial progenitor types. Nature, pages 1–7, 2022.

2022-05-18

  1. Kasper Karlsson, Moritz Przybilla, Hang Xu, Eran Kotler, Kremena Karagyozova, Alexandra Sockell, Katherine Liu, Amanda Mah, Yuan-Hung Lo, Bingxin Lu, Kathleen E. Houlahan, Aziz Khan, Zhicheng Ma, Carlos J. Suarez, Christopher P. Barnes, Calvin J. Kuo, and Christina Curtis. Experimental evolution in tp53 deficient gastric organoids recapitulates tumorigenesis. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/04/10/2022.04.09.487529, doi:10.1101/2022.04.09.487529.

  2. Rebecca Elyanow, Ron Zeira, Max Land, and Benjamin J Raphael. Starch: copy number and clone inference from spatial transcriptomics data. Physical Biology, 18(3):035001, 2021.

  3. Andrew Erickson, Emelie Berglund, Mengxiao He, Maja Marklund, Reza Mirzazadeh, Niklas Schultz, Ludvig Bergenstråhle, Linda Kvastad, Alma Andersson, Joseph Bergenstråhle, and others. The spatial landscape of clonal somatic mutations in benign and malignant tissue. bioRxiv, 2021.

  4. Eli N Weinstein, Alan N Amin, Will S Grathwohl, Daniel Kassler, Jean Disset, and Debora Marks. Optimal design of stochastic dna synthesis protocols based on generative sequence models. In International Conference on Artificial Intelligence and Statistics, 7450–7482. PMLR, 2022. URL: https://www.biorxiv.org/content/10.1101/2021.10.28.466307v1.

  5. Aparna Nathan, Samira Asgari, Kazuyoshi Ishigaki, Cristian Valencia, Tiffany Amariuta, Yang Luo, Jessica I Beynor, Yuriy Baglaenko, Sara Suliman, Alkes L Price, and others. Single-cell eqtl models reveal dynamic t cell state dependence of disease loci. Nature, 606(7912):120–128, 2022. URL: https://www.nature.com/articles/s41586-022-04713-1.

  6. Zhi-Jie Cao and Ge Gao. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nature Biotechnology, pages 1–9, 2022. URL: https://www.nature.com/articles/s41587-022-01284-4.

2022-05-04

  1. Carlos F Buen Abad Najar, Prakruthi Burra, Nir Yosef, and Liana F Lareau. Identifying cell-state associated alternative splicing events and their co-regulation. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.07.23.453605v2.

  2. Mislav Balunovic, Anian Ruoss, and Martin Vechev. Fair normalizing flows. In International Conference on Learning Representations. 2022. URL: https://openreview.net/forum?id=BrFIKuxrZE.

2022-04-13

  1. Tianyu Zhu, Jacklyn Liu, Stephan Beck, Sun Pan, David Capper, Matt Lechner, Chrissie Thirlwell, Charles E Breeze, and Andrew E Teschendorff. A pan-tissue dna methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution. Nature methods, 19(3):296–306, 2022. URL: https://www.nature.com/articles/s41592-022-01412-7.

  2. Andrew E Teschendorff, Tianyu Zhu, Charles E Breeze, and Stephan Beck. Episcore: cell type deconvolution of bulk tissue dna methylomes from single-cell rna-seq data. Genome biology, 21(1):1–33, 2020. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02126-9.

  3. Longxing Cao, Brian Coventry, Inna Goreshnik, Buwei Huang, Joon Sung Park, Kevin M Jude, Iva Marković, Rameshwar U Kadam, Koen HG Verschueren, Kenneth Verstraete, and others. Design of protein binding proteins from target structure alone. Nature, pages 1–1, 2022. URL: https://www.nature.com/articles/s41586-022-04654-9.

  4. Sergey Nurk, Sergey Koren, Arang Rhie, Mikko Rautiainen, Andrey V Bzikadze, Alla Mikheenko, Mitchell R Vollger, Nicolas Altemose, Lev Uralsky, Ariel Gershman, and others. The complete sequence of a human genome. Science, 376(6588):44–53, 2022. URL: https://www.science.org/doi/10.1126/science.abj6987.

  5. Seyhan Yazar, Jose Alquicira-Hernandez, Kristof Wing, Anne Senabouth, M Grace Gordon, Stacey Andersen, Qinyi Lu, Antonia Rowson, Thomas RP Taylor, Linda Clarke, and others. Single-cell eQTL mapping identifies cell type–specific genetic control of autoimmune disease. Science, 376(6589):eabf3041, 2022. URL: https://www.science.org/doi/10.1126/science.abf3041.

  6. Kasper Karlsson, Moritz Przybilla, Hang Xu, Eran Kotler, Kremena Karagyozova, Alexandra Sockell, Katherine Liu, Amanda Mah, Yuan-Hung Lo, Bingxin Lu, Kathleen E. Houlahan, Aziz Khan, Zhicheng Ma, Carlos J. Suarez, Christopher P. Barnes, Calvin J. Kuo, and Christina Curtis. Experimental evolution in tp53 deficient gastric organoids recapitulates tumorigenesis. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/04/10/2022.04.09.487529, doi:10.1101/2022.04.09.487529.

  7. Joannella Morales, Shashikant Pujar, Jane E Loveland, Alex Astashyn, Ruth Bennett, Andrew Berry, Eric Cox, Claire Davidson, Olga Ermolaeva, Catherine M Farrell, and others. A joint ncbi and embl-ebi transcript set for clinical genomics and research. Nature, pages 1–6, 2022. URL: https://www.nature.com/articles/s41586-022-04558-8.

  8. Zhong Wang, Alexandra G Chivu, Lauren A Choate, Edward J Rice, Donald C Miller, Tinyi Chu, Shao-Pei Chou, Nicole B Kingsley, Jessica L Petersen, Carrie J Finno, and others. Prediction of histone post-translational modification patterns based on nascent transcription data. Nature Genetics, pages 1–11, 2022.

2022-03-30

  1. Yang Xu and Rachel Patton McCord. Costa: unsupervised convolutional neural network learning for spatial transcriptomics analysis. BMC bioinformatics, 22(1):1–26, 2021.

  2. Caibin Sheng, Rui Lopes, Gang Li, Sven Schuierer, Annick Waldt, Rachel Cuttat, Slavica Dimitrieva, Audrey Kauffmann, Eric Durand, Giorgio G. Galli, Guglielmo Roma, and Antoine de Weck. Probabilistic machine learning ensures accurate ambient denoising in droplet-based single-cell omics. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/03/24/2022.01.14.476312, arXiv:https://www.biorxiv.org/content/early/2022/03/24/2022.01.14.476312.full.pdf, doi:10.1101/2022.01.14.476312.

  3. Sayan Ghosal, Qiang Chen, Giulio Pergola, Aaron L Goldman, William Ulrich, Daniel R Weinberger, and Archana Venkataraman. A biologically interpretable graph convolutional network to link genetic risk pathways and imaging phenotypes of disease. In International Conference on Learning Representations. 2022. URL: https://openreview.net/forum?id=Lwr8We4MIxn.

  4. Deniz Demircioğlu, Engin Cukuroglu, Martin Kindermans, Tannistha Nandi, Claudia Calabrese, Nuno A Fonseca, André Kahles, Kjong-Van Lehmann, Oliver Stegle, Alvis Brazma, and others. A pan-cancer transcriptome analysis reveals pervasive regulation through alternative promoters. Cell, 178(6):1465–1477, 2019.

  5. Lu Pan, Huy Q Dinh, Yudi Pawitan, and Trung Nghia Vu. Isoform-level quantification for single-cell rna sequencing. Bioinformatics, 38(5):1287–1294, 2022.

  6. Zexian Zeng, Yawei Li, Yiming Li, and Yuan Luo. Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biology, 23(1):1–23, 2022. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02653-7#Sec7.

  7. Felipe A. Simão, Robert M Waterhouse, Panagiotis Ioannidis, Evgenia V Kriventseva, and Evgeny M Zdobnov. BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics, 31(19):3210–3212, 2015. arXiv:arXiv:1410.3554v1, doi:10.1093/bioinformatics/btv351.

2022-03-09

  1. Teng Gao, Ruslan Soldatov, Hirak Sarkar, Adam Kurkiewicz, Evan Biederstedt, Po-Ru Loh, and Peter Kharchenko. Haplotype-enhanced inference of somatic copy number profiles from single-cell transcriptomes. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/02/10/2022.02.07.479314, arXiv:https://www.biorxiv.org/content/early/2022/02/10/2022.02.07.479314.full.pdf, doi:10.1101/2022.02.07.479314.

  2. Marc J Williams, Tyler Funnell, Ciara H O\textquoteright Flanagan, Andrew McPherson, Sohrab Salehi, Ignacio Vázquez-García, Farhia Kabeer, Hakwoo Lee, Tehmina Masud, Peter Eirew, Damian Yap, Beixi Wang, Jazmine Brimhall, Justina Biele, Jerome Ting, Sean Beatty, Daniel Lai, Jenifer Pham, Diljot Grewal, Douglas Abrams, Eliyahu Havasov, Samantha Leung, Viktoria Bojilova, Adam C Weiner, Nicole Rusk, Florian Uhlitz, Nicholas Ceglia, IMAXT consortium, Samuel Aparicio, and Sohrab P. Shah. Evolutionary tracking of cancer haplotypes at single-cell resolution. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/06/06/2021.06.04.447031, arXiv:https://www.biorxiv.org/content/early/2021/06/06/2021.06.04.447031.full.pdf, doi:10.1101/2021.06.04.447031.

  3. Biswajyoti Sahu, Tuomo Hartonen, Päivi Pihlajamaa, Bei Wei, Kashyap Dave, Fangjie Zhu, Eevi Kaasinen, Katja Lidschreiber, Michael Lidschreiber, Carsten O Daub, and others. Sequence determinants of human gene regulatory elements. Nature Genetics, pages 1–12, 2022. URL: https://www.nature.com/articles/s41588-021-01009-4.

  4. Julia Eve Olivieri, Roozbeh Dehghannasiri, and Julia Salzman. The spliz generalizes ‘percent spliced in’to reveal regulated splicing at single-cell resolution. Nature Methods, pages 1–4, 2022. URL: https://www.nature.com/articles/s41592-022-01400-x.

  5. Gonzalo Benegas, Jonathan Fischer, and Yun S Song. Robust and annotation-free analysis of alternative splicing across diverse cell types in mice. eLife, 11:e73520, 2022. URL: https://elifesciences.org/articles/73520.

  6. Tyler E Miller, Caleb A Lareau, Julia A Verga, Erica AK DePasquale, Vincent Liu, Daniel Ssozi, Katalin Sandor, Yajie Yin, Leif S Ludwig, Chadi A El Farran, and others. Mitochondrial variant enrichment from high-throughput single-cell rna sequencing resolves clonal populations. Nature Biotechnology, pages 1–5, 2022. URL: https://www.nature.com/articles/s41587-022-01210-8.

2022-02-23

  1. Shou-Wen Wang, Michael J Herriges, Kilian Hurley, Darrell N Kotton, and Allon M Klein. Cospar identifies early cell fate biases from single-cell transcriptomic and lineage information. Nature Biotechnology, pages 1–9, 2022. URL: https://www.nature.com/articles/s41587-022-01209-1.

  2. Ran Su, YiXuan Huang, De-gan Zhang, Guobao Xiao, and Leyi Wei. Srdfm: siamese response deep factorization machine to improve anti-cancer drug recommendation. Briefings in Bioinformatics, 01 2022. bbab534. URL: https://doi.org/10.1093/bib/bbab534, arXiv:https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab534/42236165/bbab534.pdf, doi:10.1093/bib/bbab534.

2022-02-09

  1. Morten Rasmussen, Mitsu Reddy, Rory Nolan, Joan Camunas-Soler, Arkady Khodursky, Nikolai M Scheller, David E Cantonwine, Line Engelbrechtsen, Jia Dai Mi, Arup Dutta, and others. Rna profiles reveal signatures of future health and disease in pregnancy. Nature, pages 1–6, 2022. URL: https://www.nature.com/articles/s41586-021-04249-w.

  2. Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli. Data2vec: a general framework for self-supervised learning in speech, vision and language. Meta AI, 2022. URL: https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language.

  3. Jingsi Ming, Jia Zhao, and Can Yang. Scpi: a scalable framework for probabilistic inference in single-cell rna-sequencing data analysis. Statistics in Biosciences, pages 1–24, 2022. URL: https://link.springer.com/article/10.1007/s12561-022-09335-9.

  4. Michael Eisenstein. Seven technologies to watch in 2022. Nature, 601(7894):658–661, 2022. URL: https://www.nature.com/articles/d41586-022-00163-x.

  5. Sevahn K Vorperian, Mira N Moufarrej, Stephen R Quake, Tabula Sapiens Consortium, and others. Cell types of origin in the cell free transcriptome in human health and disease. bioRxiv, 2021. URL: https://www.nature.com/articles/s41587-021-01188-9.

  6. Logan G Wright, Tatsuhiro Onodera, Martin M Stein, Tianyu Wang, Darren T Schachter, Zoey Hu, and Peter L McMahon. Deep physical neural networks trained with backpropagation. Nature, 601(7894):549–555, 2022. URL: https://www.nature.com/articles/s41586-021-04223-6.

2022-01-19

  1. Vitalii Kleshchevnikov, Artem Shmatko, Emma Dann, Alexander Aivazidis, Hamish W. King, Tong Li, Rasa Elmentaite, Artem Lomakin, Veronika Kedlian, Adam Gayoso, Mika Sarkin Jain, Jun Sung Park, Lauma Ramona, Elizabeth Tuck, Anna Arutyunyan, Roser Vento-Tormo, Moritz Gerstung, Louisa James, Oliver Stegle, and Omer Ali Bayraktar. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology, Jan 2022. URL: https://doi.org/10.1038/s41587-021-01139-4, doi:10.1038/s41587-021-01139-4.

  2. Britta Velten, Jana M. Braunger, Ricard Argelaguet, Damien Arnol, Jakob Wirbel, Danila Bredikhin, Georg Zeller, and Oliver Stegle. Identifying temporal and spatial patterns of variation from multimodal data using mefisto. Nature Methods, Jan 2022. URL: https://doi.org/10.1038/s41592-021-01343-9, doi:10.1038/s41592-021-01343-9.

  3. Marius Lange, Volker Bergen, Michal Klein, Manu Setty, Bernhard Reuter, Mostafa Bakhti, Heiko Lickert, Meshal Ansari, Janine Schniering, Herbert B. Schiller, Dana Pe’er, and Fabian J. Theis. Cellrank for directed single-cell fate mapping. Nature Methods, Jan 2022. URL: https://doi.org/10.1038/s41592-021-01346-6, doi:10.1038/s41592-021-01346-6.

  4. Jialin Qu and Yuehua Cui. Gene set analysis with graph-embedded kernel association test. Bioinformatics, 12 2021. btab851. URL: https://doi.org/10.1093/bioinformatics/btab851, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab851/42003921/btab851.pdf, doi:10.1093/bioinformatics/btab851.

  5. Xuejian Li, Shiqiang Ma, Jin Liu, Jijun Tang, and Fei Guo. Inferring gene regulatory network via fusing gene expression image and RNA-seq data. Bioinformatics, 01 2022. btac008. URL: https://doi.org/10.1093/bioinformatics/btac008, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btac008/42091226/btac008.pdf, doi:10.1093/bioinformatics/btac008.

  6. Ilia Kats, Roser Vento-Tormo, and Oliver Stegle. Spatialde2: fast and localized variance component analysis of spatial transcriptomics. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/11/11/2021.10.27.466045, arXiv:https://www.biorxiv.org/content/early/2021/11/11/2021.10.27.466045.full.pdf, doi:10.1101/2021.10.27.466045.

  7. J Monroe, Thanvi Srikant, Pablo Carbonell-Bejerano, Claude Becker, Mariele Lensink, Moises Exposito-Alonso, Marie Klein, Julia Hildebrandt, Manuela Neumann, Daniel Kliebenstein, and others. Mutation bias reflects natural selection in arabidopsis thaliana. Nature, pages 1–5, 2022. URL: https://www.nature.com/articles/s41586-021-04269-6.

  8. Chen Li, Maria Virgilio, Kathleen Collins, and Joshua D Welch. Single-cell multi-omic velocity infers dynamic and decoupled gene regulation. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.12.13.472472v1.

  9. Wei Xu, Weilong Yang, Yunlong Zhang, Yawen Chen, Qian Zhang, Xuefei Wang, Kun Song, Wenfei Jin, and Xi Chen. Issaac-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/01/17/2022.01.16.476488, doi:10.1101/2022.01.16.476488.

  10. Gaoyang Li, Shaliu Fu, Shuguang Wang, Chenyu Zhu, Bin Duan, Chen Tang, Xiaohan Chen, Guohui Chuai, Ping Wang, and Qi Liu. A deep generative model for multi-view profiling of single-cell rna-seq and atac-seq data. Genome Biology, 23(1):1–23, 2022.

  11. Liangtao Zheng, Shishang Qin, Wen Si, Anqiang Wang, Baocai Xing, Ranran Gao, Xianwen Ren, Li Wang, Xiaojiang Wu, Ji Zhang, and others. Pan-cancer single-cell landscape of tumor-infiltrating t cells. Science, 374(6574):abe6474, 2021.

2021-12-10

  1. Antonio De Falco, Francesca P Caruso, Xiao Dong Su, Antonio Iavarone, and Michele Ceccarelli. A fast variational algorithm to detect the clonal copy number substructure of tumors from single-cell data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.11.20.469390v1.

  2. Alex Davies, Petar Veličković, Lars Buesing, Sam Blackwell, Daniel Zheng, Nenad Tomašev, Richard Tanburn, Peter Battaglia, Charles Blundell, András Juhász, and others. Advancing mathematics by guiding human intuition with ai. Nature, 600(7887):70–74, 2021.

  3. Minxing Pang, Kenong Su, and Mingyao Li. Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/11/28/2021.11.28.470212, arXiv:https://www.biorxiv.org/content/early/2021/11/28/2021.11.28.470212.full.pdf, doi:10.1101/2021.11.28.470212.

  4. Allen W. Lynch, Christina V. Theodoris, Henry Long, Myles Brown, X. Shirley Liu, and Clifford A. Meyer. MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.12.06.471401v1?rss=1, doi:10.1101/2021.12.06.471401.

  5. Ludvig Bergenstråhle, Bryan He, Joseph Bergenstråhle, Xesús Abalo, Reza Mirzazadeh, Kim Thrane, Andrew L Ji, Alma Andersson, Ludvig Larsson, Nathalie Stakenborg, and others. Super-resolved spatial transcriptomics by deep data fusion. Nature biotechnology, pages 1–4, 2021. URL: https://www.nature.com/articles/s41587-021-01075-3.

  6. Youjin Hu, Jiawei Zhong, Yuhua Xiao, Zheng Xing, Katherine Sheu, Shuxin Fan, Qin An, Yuanhui Qiu, Yingfeng Zheng, Xialin Liu, and others. Single-cell rna cap and tail sequencing (scrcat-seq) reveals subtype-specific isoforms differing in transcript demarcation. Nature communications, 11(1):1–11, 2020. URL: https://www.nature.com/articles/s41467-020-18976-7.

2021-11-26

  1. Jian Zhou and Olga G Troyanskaya. An analytical framework for interpretable and generalizable single-cell data analysis. Nature Methods, pages 1–5, 2021. URL: https://www.nature.com/articles/s41592-021-01286-1.

  2. Jing Gong, Kui Xu, Ziyuan Ma, Zhi John Lu, and Qiangfeng Cliff Zhang. A deep learning method for recovering missing signals in transcriptome-wide rna structure profiles from probing experiments. Nature Machine Intelligence, 3(11):995–1006, 2021.

  3. Claudio Lorenzi, Sylvain Barriere, Katharina Arnold, Reini F Luco, Andrew J Oldfield, and William Ritchie. Irfinder-s: a comprehensive suite to discover and explore intron retention. Genome biology, 22(1):1–13, 2021. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02515-8.

  4. Luyi Tian, Jafar S Jabbari, Rachel Thijssen, Quentin Gouil, Shanika L Amarasinghe, Oliver Voogd, Hasaru Kariyawasam, Mei RM Du, Jakob Schuster, Changqing Wang, and others. Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing. Genome biology, 22(1):1–24, 2021.

  5. Duanchen Sun, Xiangnan Guan, Amy E Moran, Ling-Yun Wu, David Z Qian, Pepper Schedin, Mu-Shui Dai, Alexey V Danilov, Joshi J Alumkal, Andrew C Adey, and others. Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data. Nature Biotechnology, pages 1–12, 2021. URL: https://www.nature.com/articles/s41587-021-01091-3.

  6. Maren Buettner, Johannes Ostner, Christian L Mueller, Fabian J Theis, and Benjamin Schubert. Sccoda is a bayesian model for compositional single-cell data analysis. Nature communications, 12(1):1–10, 2021. URL: https://www.nature.com/articles/s41467-021-27150-6.

2021-11-12

  1. Luke Zappia and Fabian J Theis. Over 1000 tools reveal trends in the single-cell rna-seq analysis landscape. bioRxiv, 2021. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02519-4.

  2. PF Ferreira, J Kuipers, and N Beerenwinkel. Mapping single-cell transcriptomes to copy number evolutionary trees. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.11.04.467244v1.full.pdf+html.

  3. Viktor Petukhov, Rosalind J Xu, Ruslan A Soldatov, Paolo Cadinu, Konstantin Khodosevich, Jeffrey R Moffitt, and Peter V Kharchenko. Cell segmentation in imaging-based spatial transcriptomics. Nature Biotechnology, pages 1–10, 2021. URL: https://www.nature.com/articles/s41587-021-01044-w.

  4. Tommaso Biancalani, Gabriele Scalia, Lorenzo Buffoni, Raghav Avasthi, Ziqing Lu, Aman Sanger, Neriman Tokcan, Charles R Vanderburg, Åsa Segerstolpe, Meng Zhang, and others. Deep learning and alignment of spatially resolved single-cell transcriptomes with tangram. Nature Methods, pages 1–11, 2021. URL: https://www-nature-com.eproxy.lib.hku.hk/articles/s41592-021-01264-7.

  5. Jonathan Moody, Tsukasa Kouno, Akari Suzuki, Youtaro Shibayama, Chikashi Terao, Jen-Chien Chang, Fernando López-Redondo, Chi Wai Yip, Jessica Severin, Hiroyuki Suetsugu, and others. Profiling of transcribed cis-regulatory elements in single cells. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.04.04.438388v2.full.

2021-10-29

  1. Ran Cheng and Peter K Jackson. Identifying cancer drivers. Science, 374(6563):38–39, 2021. URL: https://www.science.org/doi/epdf/10.1126/science.abl9080.

  2. J. Frazer, P. Notin, M. Dias, A. Gomez, J. Min, K. Brock, Y. Gal, and D. Marks. Disease variant prediction with deep generative models of evolutionary data. Nature, pages 1–10, 2021. URL: https://www.nature.com/articles/s41586-021-04043-8.

  3. Sabrina Rashid, Sohrab Shah, Ziv Bar-Joseph, and Ravi Pandya. Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data. Bioinformatics, 37(11):1535–1543, 02 2019. URL: https://doi.org/10.1093/bioinformatics/btz095, arXiv:https://academic.oup.com/bioinformatics/article-pdf/37/11/1535/38923762/btz095.pdf, doi:10.1093/bioinformatics/btz095.

  4. Jian Hu, Xiangjie Li, Kyle Coleman, Amelia Schroeder, David J Irwin, Edward B Lee, Russell T Shinohara, and Mingyao Li. Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. bioRxiv, 2020.

  5. Ruochen Jiang, Tianyi Sun, Dongyuan Song, and Jingyi Jessica Li. Statistics or biology: the zero-inflation controversy about scrna-seq data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/09/21/2020.12.28.424633, arXiv:https://www.biorxiv.org/content/early/2021/09/21/2020.12.28.424633.full.pdf, doi:10.1101/2020.12.28.424633.

  6. Xian Adiconis, Adam L Haber, Sean K Simmons, Ami Levy Moonshine, Zhe Ji, Michele A Busby, Xi Shi, Justin Jacques, Madeline A Lancaster, Jen Q Pan, and others. Comprehensive comparative analysis of 5′-end rna-sequencing methods. Nature methods, 15:505–511, 2018. URL: https://www.nature.com/articles/s41592-018-0014-2.

  7. Jingjing Xu, Hao Zhou, Chun Gan, Zaixiang Zheng, and Lei Li. Vocabulary learning via optimal transport for neural machine translation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 7361–7373. Online, August 2021. Association for Computational Linguistics. URL: https://aclanthology.org/2021.acl-long.571, doi:10.18653/v1/2021.acl-long.571.

2021-10-15

  1. Gamze Gürsoy, Nancy Lu, Sarah Wagner, and Mark Gerstein. Recovering genotypes and phenotypes using allele-specific genes. Genome biology, 22(1):1–9, 2021. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02477-x.

  2. Johan Winnubst and Silvia Arber. A census of cell types in the brain’s motor cortex. 2021. URL: https://www.nature.com/articles/d41586-021-02493-8.

  3. A Booeshaghi, Zizhen Yao, Cindy van Velthoven, Kimberly Smith, Bosiljka Tasic, Hongkui Zeng, and Lior Pachter. Isoform cell-type specificity in the mouse primary motor cortex. Nature, 598(7879):195–199, 2021.

  4. Lukas Breitwieser, Ahmad Hesam, Jean de Montigny, Vasileios Vavourakis, Alexandros Iosif, Jack Jennings, Marcus Kaiser, Marco Manca, Alberto Di Meglio, Zaid Al-Ars, Fons Rademakers, Onur Mutlu, and Roman Bauer. BioDynaMo: a modular platform for high-performance agent-based simulation. Bioinformatics, 09 2021. btab649. URL: https://doi.org/10.1093/bioinformatics/btab649, arXiv:https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab649/40395475/btab649.pdf, doi:10.1093/bioinformatics/btab649.

  5. Samuel Morabito, Emily Miyoshi, Neethu Michael, Saba Shahin, Alessandra Cadete Martini, Elizabeth Head, Justine Silva, Kelsey Leavy, Mari Perez-Rosendahl, and Vivek Swarup. Single-nucleus chromatin accessibility and transcriptomic characterization of alzheimer’s disease. Nature Genetics, 53(8):1143–1155, 2021. URL: https://www-nature-com.eproxy.lib.hku.hk/articles/s41588-021-00894-z.

  6. Michael VanInsberghe, Jeroen van den Berg, Amanda Andersson-Rolf, Hans Clevers, and Alexander van Oudenaarden. Single-cell ribo-seq reveals cell cycle-dependent translational pausing. Nature, pages 1–5, 2021. URL: https://www.nature.com/articles/s41586-021-03887-4.

2021-09-17

  1. Kamila Naxerova. Mutation fingerprints encode cellular histories. 2021. URL: https://www.nature.com/articles/d41586-021-02269-0.

  2. Natsuhiko Kumasaka, Raghd Rostom, Ni Huang, Krzysztof Polanski, Kerstin Meyer, Sharad Patel, Rachel Boyd, Celine Gomez, Sam Barnett, Nikolaos Panousis, and others. Mapping interindividual dynamics of innate immune response at single-cell resolution. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.09.01.457774v1.

  3. Anna SE Cuomo, Tobias Heinen, Danai Vagiaki, Danilo Horta, John Marioni, and Oliver Stegle. Cellregmap: a statistical framework for mapping context-specific regulatory variants using scrna-seq. bioRxiv, 2021. URL: https://www.biorxiv.org/content/10.1101/2021.09.01.458524v1.

  4. Wei Wei, Daniel J Gaffney, and Patrick F Chinnery. Cell reprogramming shapes the mitochondrial dna landscape. Nature Communications, 12(1):1–15, 2021.

  5. Akdes Serin Harmanci, Arif O Harmanci, Xiaobo Zhou, Benjamin Deneen, Ganesh Rao, Tiemo Klisch, and Akash Patel. Scregulocity: detection of local rna velocity patterns in embeddings of single cell rna-seq data. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/06/02/2021.06.01.446674, arXiv:https://www.biorxiv.org/content/early/2021/06/02/2021.06.01.446674.full.pdf, doi:10.1101/2021.06.01.446674.

  6. Ismael Lemhadri, Feng Ruan, and Rob Tibshirani. Lassonet: neural networks with feature sparsity. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, 10–18. PMLR, 13–15 Apr 2021. URL: https://proceedings.mlr.press/v130/lemhadri21a.html.

  7. Marissa Connor, Gregory Canal, and Christopher Rozell. Variational autoencoder with learned latent structure. In Arindam Banerjee and Kenji Fukumizu, editors, Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, 2359–2367. PMLR, 13–15 Apr 2021. URL: https://proceedings.mlr.press/v130/connor21a.html.

  8. Wenjing Ma, Kenong Su, and Hao Wu. Evaluation of some aspects in supervised cell type identification for single-cell rna-seq: classifier, feature selection, and reference construction. Genome Biology, 22(1):1–23, 2021. URL: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02480-2.

  9. Zachary J. DeBruine, Karsten Melcher, and Timothy J. Triche. Fast and robust non-negative matrix factorization for single-cell experiments. bioRxiv, 2021. URL: https://www.biorxiv.org/content/early/2021/09/01/2021.09.01.458620, arXiv:https://www.biorxiv.org/content/early/2021/09/01/2021.09.01.458620.full.pdf, doi:10.1101/2021.09.01.458620.

  10. Mohammad Lotfollahi, Mohsen Naghipourfar, Malte D Luecken, Matin Khajavi, Maren Büttner, Marco Wagenstetter, Žiga Avsec, Adam Gayoso, Nir Yosef, Marta Interlandi, and others. Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology, pages 1–10, 2021. URL: https://www.nature.com/articles/s41587-021-01001-7.