Pub 2022-2023

2023-07-12

2023-06-21

  1. Almut S Eisele, Marcel Tarbier, Alexia A Dormann, Vicent Pelechano, and David M Suter. Barcode-free prediction of cell lineages from scrna-seq datasets. bioRxiv, pages 2022–09, 2022. URL: https://www.biorxiv.org/content/10.1101/2022.09.20.508646v1.

2023-05-04

  1. Anna Arutyunyan, Kenny Roberts, Kevin Troulé, Frederick CK Wong, Megan A Sheridan, Ilia Kats, Luz Garcia-Alonso, Britta Velten, Regina Hoo, Elias R Ruiz-Morales, and others. Spatial multiomics map of trophoblast development in early pregnancy. Nature, 616(7955):143–151, 2023.

  2. Isaac D Lutz, Shunzhi Wang, Christoffer Norn, Alexis Courbet, Andrew J Borst, Yan Ting Zhao, Annie Dosey, Longxing Cao, Jinwei Xu, Elizabeth M Leaf, and others. Top-down design of protein architectures with reinforcement learning. Science, 380(6642):266–273, 2023.

  3. Steffen Schneider, Jin Hwa Lee, and Mackenzie Weygandt Mathis. Learnable latent embeddings for joint behavioural and neural analysis. Nature, pages 1–9, 2023. URL: https://www.nature.com/articles/s41586-023-06031-6.

2023-03-15

  1. Gryte Satas, Simone Zaccaria, Geoffrey Mon, and Benjamin J Raphael. Scarlet: single-cell tumor phylogeny inference with copy-number constrained mutation losses. Cell systems, 10(4):323–332, 2020. URL: https://pubmed.ncbi.nlm.nih.gov/31744515/.

  2. Dinithi Sumanaweera, Chenqu Suo, Daniele Muraro, Emma Dann, Krzysztof Polanski, Alexander S. Steemers, Jong-Eun Park, Bianca Dumitrascu, and Sarah A. Teichmann. Gene-level alignment of single cell trajectories informs the progression of in vitro t cell differentiation. bioRxiv, 2023. URL: https://www.biorxiv.org/content/early/2023/03/10/2023.03.08.531713, arXiv:https://www.biorxiv.org/content/early/2023/03/10/2023.03.08.531713.full.pdf, doi:10.1101/2023.03.08.531713.

  3. Ya Cui, Frederick J Arnold, Fanglue Peng, Dan Wang, Jason Sheng Li, Sebastian Michels, Eric J Wagner, Albert R La Spada, and Wei Li. Alternative polyadenylation transcriptome-wide association study identifies apa-linked susceptibility genes in brain disorders. Nature Communications, 14(1):583, 2023. URL: https://www.nature.com/articles/s41467-023-36311-8#Sec2.

  4. Anjun Ma, Xiaoying Wang, Jingxian Li, Cankun Wang, Tong Xiao, Yuntao Liu, Hao Cheng, Juexin Wang, Yang Li, Yuzhou Chang, and others. Single-cell biological network inference using a heterogeneous graph transformer. Nature Communications, 14(1):964, 2023.

2023-03-01

  1. Brian L Hie, Kevin K Yang, and Peter S Kim. Evolutionary velocity with protein language models predicts evolutionary dynamics of diverse proteins. Cell Systems, 13(4):274–285, 2022.

2023-02-15

  1. Mohammad Lotfollahi, Sergei Rybakov, Karin Hrovatin, Soroor Hediyeh-Zadeh, Carlos Talavera-López, Alexander V Misharin, and Fabian J Theis. Biologically informed deep learning to query gene programs in single-cell atlases. Nature Cell Biology, pages 1–14, 2023. URL: https://www.nature.com/articles/s41556-022-01072-x.

  2. Cheng-kai Shiau, Lina Lu, Rachel Kieser, Kazutaka Fukumura, Timothy Pan, Hsiao-Yun Lin, Jie Yang, Eric L Tong, GaHyun Lee, Yuanqing Yan, and others. Delineating genotypes and phenotypes of individual cells from long-read single cell transcriptomes. bioRxiv, pages 2023–01, 2023. URL: https://www.biorxiv.org/content/10.1101/2023.01.24.525264v2.

  3. Anna Lyubetskaya, Brian Rabe, Andrew Fisher, Anne Lewin, Isaac Neuhaus, Constance Brett, Todd Brett, Ethel Pereira, Ryan Golhar, Sami Kebede, and others. Assessment of spatial transcriptomics for oncology discovery. Cell Reports Methods, 2(11):100340, 2022.

2023-02-01

  1. Ziqi Zhang, Haoran Sun, Ragunathan Mariappan, Xi Chen, Xinyu Chen, Mika S Jain, Mirjana Efremova, Sarah A Teichmann, Vaibhav Rajan, and Xiuwei Zhang. Scmomat jointly performs single cell mosaic integration and multi-modal bio-marker detection. Nature Communications, 14(1):384, 2023. URL: https://www.nature.com/articles/s41467-023-36066-2.

  2. 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.

  3. Raquel García-Pérez, Jose Miguel Ramirez, Aida Ripoll-Cladellas, Ruben Chazarra-Gil, Winona Oliveros, Oleksandra Soldatkina, Mattia Bosio, Paul Joris Rognon, Salvador Capella-Gutierrez, Miquel Calvo, and others. The landscape of expression and alternative splicing variation across human traits. Cell Genomics, 2023. URL: https://www.sciencedirect.com/science/article/pii/S2666979X22002075.

  4. Alexander P Wu, Rohit Singh, and Bonnie Berger. Granger causal inference on dags identifies genomic loci regulating transcription. In International Conference on Learning Representations. 2022.

  5. Junjie Huang, Yanchao Xu, Yunfan Xue, Yue Huang, Xu Li, Xiaohui Chen, Yao Xu, Dongxiang Zhang, Peng Zhang, Junbo Zhao, and others. Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences. Nature Biomedical Engineering, pages 1–14, 2023.

2023-01-11

  1. Julia Joung, Sai Ma, Tristan Tay, Kathryn R. Geiger-Schuller, Paul C. Kirchgatterer, Vanessa K. Verdine, Baolin Guo, Mario A. Arias-Garcia, William E. Allen, Ankita Singh, Olena Kuksenko, Omar O. Abudayyeh, Jonathan S. Gootenberg, Zhanyan Fu, Rhiannon K. Macrae, Jason D. Buenrostro, Aviv Regev, and Feng Zhang. A transcription factor atlas of directed differentiation. Cell, 186(1):209–229.e26, jan 2023. URL: https://doi.org/10.1016%2Fj.cell.2022.11.026, doi:10.1016/j.cell.2022.11.026.

  2. Kaelan J Brennan, Melanie Weilert, Sabrina Krueger, Anusri Pampari, Hsiao-Yun Liu, Ally WH Yang, Timothy R Hughes, Christine A Rushlow, Anshul Kundaje, and Julia Zeitlinger. Chromatin accessibility is a two-tier process regulated by transcription factor pioneering and enhancer activation. bioRxiv, 2022.

  3. Dongze He, Charlotte Soneson, and Rob Patro. Understanding and evaluating ambiguity in single-cell and single-nucleus rna-sequencing. bioRxiv, 2023. URL: https://www.biorxiv.org/content/early/2023/01/04/2023.01.04.522742, arXiv:https://www.biorxiv.org/content/early/2023/01/04/2023.01.04.522742.full.pdf, doi:10.1101/2023.01.04.522742.

2022-12-07

2022-11-23

  1. Edwin Fong, Chris Holmes, and Stephen G Walker. Martingale posterior distributions. arXiv preprint arXiv:2103.15671, 2021.

  2. Dylan M Cable, Evan Murray, Vignesh Shanmugam, Simon Zhang, Luli S Zou, Michael Diao, Haiqi Chen, Evan Z Macosko, Rafael A Irizarry, and Fei Chen. Cell type-specific inference of differential expression in spatial transcriptomics. Nature methods, 19(9):1076–1087, 2022. URL: https://www.nature.com/articles/s41592-022-01575-3.

  3. Lei Xiong, Kang Tian, Yuzhe Li, and Qiangfeng Cliff Zhang. Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space. bioRxiv., 2021. URL: https://www.nature.com/articles/s41467-022-33758-z.

2022-11-09

  1. Yuxuan Hu, Jiazhen Rong, Runzhi Xie, Yafei Xu, Jacqueline Peng, Lin Gao, and Kai Tan. Learning predictive models of tissue cellular neighborhoods from cell phenotypes with graph pooling. bioRxiv, 2022. URL: https://www.biorxiv.org/content/early/2022/11/06/2022.11.06.515344, arXiv:https://www.biorxiv.org/content/early/2022/11/06/2022.11.06.515344.full.pdf, doi:10.1101/2022.11.06.515344.

  2. Qian Qin, Eli Bingham, Gioele La Manno, David M Langenau, and Luca Pinello. Pyro-velocity: probabilistic rna velocity inference from single-cell data. bioRxiv, 2022. URL: https://www.biorxiv.org/content/10.1101/2022.09.12.507691v2.

  3. Qing Zhou, Guannan Kang, Peiyong Jiang, Rong Qiao, WK Jacky Lam, Stephanie CY Yu, Mary-Jane L Ma, Lu Ji, Suk Hang Cheng, Wanxia Gai, and others. Epigenetic analysis of cell-free dna by fragmentomic profiling. Proceedings of the National Academy of Sciences, 119(44):e2209852119, 2022.

2022-10-26

  1. Gherman Novakovsky, Nick Dexter, Maxwell W Libbrecht, Wyeth W Wasserman, and Sara Mostafavi. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nature Reviews Genetics, pages 1–13, 2022.

  2. Zhaoyang Liu, Dongqing Sun, and Chenfei Wang. Evaluation of cell-cell interaction methods by integrating single-cell rna sequencing data with spatial information. Genome biology, 23(1):1–38, 2022.

  3. Alexander J Tarashansky, Yuan Xue, Pengyang Li, Stephen R Quake, and Bo Wang. Self-assembling manifolds in single-cell rna sequencing data. Elife, 8:e48994, 2019.

  4. Yuqiong Hu, Zhenhuan Jiang, Kexuan Chen, Zhangxian Zhou, Xin Zhou, Yan Wang, Jingwei Yang, Bo Zhang, Lu Wen, and Fuchou Tang. Scnanoatac-seq: a long-read single-cell atac sequencing method to detect chromatin accessibility and genetic variants simultaneously within an individual cell. Cell Research, pages 1–4, 2022. URL: https://www.nature.com/articles/s41422-022-00730-x.

2022-10-12

  1. Kun Wang, Liangzhen Hou, Zhaolian Lu, Xin Wang, Zhike Zi, Weiwei Zhai, Xionglei He, Christina Curtis, Da Zhou, and Zheng Hu. Cell division history encodes directional information of fate transitions. bioRxiv, 2022. URL: https://www.biorxiv.org/content/10.1101/2022.10.06.511094v2.

  2. Wei Wei, Katherine R Schon, Greg Elgar, Andrea Orioli, Melanie Tanguy, Adam Giess, Marc Tischkowitz, Mark J Caulfield, and Patrick F Chinnery. Nuclear-embedded mitochondrial dna sequences in 66,083 human genomes. Nature, pages 1–10, 2022.

2022-09-28

  1. Grace Hui Ting Yeo, Sachit D Saksena, and David K Gifford. Generative modeling of single-cell time series with prescient enables prediction of cell trajectories with interventions. Nature communications, 12(1):1–12, 2021. URL: https://www.nature.com/articles/s41467-021-23518-w#Sec10.

  2. Teng Gao, Ruslan Soldatov, Hirak Sarkar, Adam Kurkiewicz, Evan Biederstedt, Po-Ru Loh, and Peter V Kharchenko. Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nature Biotechnology, pages 1–10, 2022. URL: https://www.nature.com/articles/s41587-022-01468-y.

2022-09-14

  1. Qi Liu, Chih-Yuan Hsu, and Yu Shyr. Scalable and model-free detection of spatial patterns and colocalization. Genome Research, 2022. URL: https://genome.cshlp.org/content/early/2022/09/09/gr.276851.122.abstract.

  2. Chao Zhang, Renchao Chen, and Yi Zhang. Accurate inference of genome-wide spatial expression with ispatial. Science Advances, 2022. URL: https://www.science.org/doi/10.1126/sciadv.abq0990.

  3. Han Yuan and David R Kelley. Scbasset: sequence-based modeling of single-cell atac-seq using convolutional neural networks. Nature Methods, pages 1–9, 2022. URL: https://www.nature.com/articles/s41592-022-01562-8.

  4. Francisco X Galdos, Sidra Xu, William R Goodyer, Lauren Duan, Yuhsin V Huang, Soah Lee, Han Zhu, Carissa Lee, Nicholas Wei, Daniel Lee, and others. Devcellpy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data. Nature communications, 13(1):1–20, 2022. URL: https://www.nature.com/articles/s41467-022-33045-x.