{"type":"ScholarlyArticle","title":"Realistic Cell Type Annotation and Discovery for Single-cell RNA-seq Data","authors":[{"name":"Yuyao Zhai","slug":"yuyao-zhai","given":"Yuyao","family":"Zhai","affiliation":"School of Mathematical Sciences , Peking University"},{"name":"Liang Chen","slug":"liang-chen","given":"Liang","family":"Chen","affiliation":"Huawei Technologies Co., Ltd."},{"name":"Minghua Deng","slug":"minghua-deng","given":"Minghua","family":"Deng","affiliation":"School of Mathematical Sciences , Peking University; Center for Statistical Science , Peking University; Center for Quantitative Biology , Peking University"}],"abstract":"The rapid development of single-cell RNA sequencing (scRNA-seq) technologies allows us to explore tissue heterogeneity at the cellular level. Cell type annotation plays an essential role in the substantial downstream analysis of scRNA-seq data. Existing methods usually classify the novel cell types in target data as an “unassigned” group and rarely discover the fine-grained cell type structure among them. Besides, these methods carry risks, such as susceptibility to batch effect between reference and target data, thus further compromising of inherent discrimination of target data. Considering these limitations, here we propose a new and practical task called realistic cell type annotation and discovery for scRNA-seq data. In this task, cells from seen cell types are given class labels, while cells from novel cell types are given cluster labels. To tackle this problem, we propose an end-to-end algorithm framework called scPOT from the perspective of optimal transport (OT). Specifically,  we first design an OT-based prototypical representation learning paradigm to encourage both global discriminations of clusters and local consistency of cells to uncover the intrinsic structure of target data. Then we propose an unbalanced OT-based partial alignment strategy with statistical filling to detect the cells from the seen cell types across reference and target data. Notably, scPOT also introduces an easy yet effective solution to automatically estimate the overall cell type number in target data. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scPOT over various state-of-the-art clustering and annotation methods.","doi":"10.24963/ijcai.2023/552","url":"https://ijcai.org/proceedings/2023/552","pdf":"https://ijcai.org/proceedings/2023/0552.pdf","markdown":"https://ijcai.org/proceedings/2023/552.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/552","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":552,"pages":{"first":4967,"last":4974},"keywords":["Machine Learning: ML: Applications","Multidisciplinary Topics and Applications: MDA: Bioinformatics"],"videos":[],"references":[{"position":1,"raw":"[Brbić et al., 2020] Maria Brbić, Marinka Zitnik, Sheng Wang, Angela O Pisco, Russ B Altman, Spyros Darma- nis, and Jure Leskovec. Mars: discovering novel cell types across heterogeneous single-cell experiments. Na- ture methods, 17(12):1200-1206, 2020.","title":"Mars: discovering novel cell types across heterogeneous single-cell experiments","authors":"Maria Brbić, Marinka Zitnik, Sheng Wang, Angela O Pisco, Spyros Russ B Altman, Jure Darmanis, Leskovec","year":"2020","venue":"Nature methods","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Brent, 2013] Richard P Brent. Algorithms for minimization without derivatives. Courier Corporation, 2013.","title":"Algorithms for minimization without derivatives","authors":"P Richard, Brent","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2019] Zhi-Jie Cao, Lin Wei, Shen Lu, De-Chang Yang, and Ge Gao. Cell blast: searching large-scale scrna- seq databases via unbiased cell embedding. BioRxiv, page 587360, 2019.","title":"Cell blast: searching large-scale scrnaseq databases via unbiased cell embedding","authors":"Zhi-Jie Cao, Lin Wei, Shen Lu, De-Chang Yang, Ge Gao","year":"2019","venue":"BioRxiv","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Caron et al., 2020] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by con- trasting cluster assignments. Advances in Neural Informa- tion Processing Systems, 33:9912-9924, 2020.","title":"Unsupervised learning of visual features by contrasting cluster assignments","authors":"Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin","year":"2020","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2020a] Liang Chen, Weinan Wang, Yuyao Zhai, and Minghua Deng. Deep soft k-means clustering with self-training for single-cell rna sequence data. NAR ge- nomics and bioinformatics, 2(2):lqaa039, 2020.","title":"Deep soft k-means clustering with self-training for single-cell rna sequence data","authors":"Chen","year":"2020","venue":"NAR genomics and bioinformatics","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2020b] Liang Chen, Yuyao Zhai, Qiuyan He, Weinan Wang, and Minghua Deng. Integrating deep su- pervised, self-supervised and unsupervised learning for single-cell rna-seq clustering and annotation. Genes, 11(7):792, 2020.","title":"Integrating deep supervised, self-supervised and unsupervised learning for single-cell rna-seq clustering and annotation","authors":"Chen","year":"2020","venue":"Genes","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chizat et al., 2018] Lenaic Chizat, Gabriel Peyré, Bernhard Schmitzer, and Franc ¸ois-Xavier Vialard. Scaling algo- rithms for unbalanced optimal transport problems. Math- ematics of Computation, 87(314):2563-2609, 2018.","title":"Scaling algorithms for unbalanced optimal transport problems","authors":"Chizat","year":"2018","venue":"Mathematics of Computation","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cuturi, 2013] Marco Cuturi. Sinkhorn distances: Light- speed computation of optimal transport. Advances in neu- ral information processing systems, 26, 2013.","title":"Sinkhorn distances: Lightspeed computation of optimal transport","authors":"Marco Cuturi","year":"2013","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Eraslan et al., 2019] Gökcen Eraslan, Lukas M. Simon, Maria Mircea, Nikola S. Mueller, and Fabian J. Theis. Single-cell rna-seq denoising using a deep count autoen- coder. Nature communications, (1):1-14, 2019.","title":"Single-cell rna-seq denoising using a deep count autoencoder","authors":"Eraslan","year":"2019","venue":"Nature communications","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[He et al., 2020] Kaiming He, Haoqi Fan, Yuxin Wu, Sain- ing Xie, and Ross Girshick. Momentum contrast for unsu- pervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020.","title":"Momentum contrast for unsupervised visual representation learning","authors":"Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick","year":"2020","venue":"Proceedings of the IEEE/CVF conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hu et al., 2020] Jian Hu, Xiangjie Li, Gang Hu, Yafei Lyu, Katalin Susztak, and Mingyao Li. Iterative transfer learn- ing with neural network for clustering and cell type clas- sification in single-cell rna-seq analysis. Nature machine intelligence, 2(10):607-618, 2020.","title":"Iterative transfer learning with neural network for clustering and cell type classification in single-cell rna-seq analysis","authors":"Jian Hu, Xiangjie Li, Gang Hu, Yafei Lyu, Katalin Susztak, Mingyao Li","year":"2020","venue":"Nature machine intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kimmel and Kelley, 2020] Jacob C Kimmel and David R Kelley. scnym: Semi-supervised adversarial neural net- works for single cell classification. bioRxiv, 2020.","title":"scnym: Semi-supervised adversarial neural networks for single cell classification","authors":"C Jacob, David R Kimmel, Kelley","year":"2020","venue":"bioRxiv","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kiselev et al., 2019] Vladimir Yu Kiselev, Tallulah S An- drews, and Martin Hemberg. Challenges in unsupervised clustering of single-cell rna-seq data. Nature Reviews Ge- netics, 20(5):273-282, 2019.","title":"Challenges in unsupervised clustering of single-cell rna-seq data","authors":"Vladimir Yu Kiselev, Tallulah S Andrews, Martin Hemberg","year":"2019","venue":"Nature Reviews Genetics","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kuhn, 1955] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955.","title":"The hungarian method for the assignment problem","authors":"Harold W Kuhn, Kuhn","year":"1955","venue":"Naval research logistics quarterly","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Lähnemann et al., 2020] David Lähnemann, Johannes Köster, Ewa Szczurek, Davis J McCarthy, Stephanie C Hicks, Mark D Robinson, Catalina A Vallejos, Kieran R Campbell, Niko Beerenwinkel, Ahmed Mahfouz, et al. Eleven grand challenges in single-cell data science. Genome biology, 21(1):1-35, 2020.","title":"Eleven grand challenges in single-cell data science","authors":"David Lähnemann, Johannes Köster, Ewa Szczurek, Davis J Mccarthy, Stephanie C Hicks, Catalina A Mark D Robinson, Vallejos, Niko Kieran R Campbell, Ahmed Beerenwinkel, Mahfouz","year":"2020","venue":"Genome biology","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lakkis et al., 2021] Justin Lakkis, David Wang, Yuanchao Zhang, Gang Hu, Kui Wang, Huize Pan, Lyle Ungar, Muredach P Reilly, Xiangjie Li, and Mingyao Li. A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell tran- scriptomics. Genome research, 31(10):1753-1766, 2021.","title":"A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics","authors":"Justin Lakkis, David Wang, Yuanchao Zhang, Gang Hu, Kui Wang, Huize Pan, Lyle Ungar, P Muredach, Xiangjie Reilly, Mingyao Li, Li","year":"2021","venue":"Genome research","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Lotfollahi et al., 2022] Mohammad Lotfollahi, Mohsen Naghipourfar, Malte D Luecken, Matin Khajavi, Maren Büttner, Marco Wagenstetter, Žiga Avsec, Adam Gayoso, Nir Yosef, Marta Interlandi, et al. Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology, 40(1):121-130, 2022.","title":"Mapping single-cell data to reference atlases by transfer learning","authors":"Mohammad Lotfollahi, Mohsen Naghipourfar, Matin Malte D Luecken, Maren Khajavi, Marco Büttner, Žiga Wagenstetter, Adam Avsec, Nir Gayoso, Marta Yosef, Interlandi","year":"2022","venue":"Nature Biotechnology","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Luecken and Theis, 2019] Malte D Luecken and Fabian J Theis. Current best practices in single-cell rna-seq anal- ysis: a tutorial. Molecular systems biology, 15(6):e8746, 2019.","title":"Current best practices in single-cell rna-seq analysis: a tutorial","authors":"D Malte, Fabian J Luecken, Theis","year":"2019","venue":"Molecular systems biology","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Satija et al., 2015] Rahul Satija, Jeffrey A Farrell, David Gennert, Alexander F Schier, and Aviv Regev. Spatial re- construction of single-cell gene expression data. Nature biotechnology, 33(5):495-502, 2015.","title":"Spatial reconstruction of single-cell gene expression data","authors":"Rahul Satija, Jeffrey A Farrell, David Gennert, Alexander F Schier, Aviv Regev","year":"2015","venue":"Nature biotechnology","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Shao et al., 2020] Xin Shao, Jie Liao, Xiaoyan Lu, Rui Xue, Ni Ai, and Xiaohui Fan. sccatch: automatic annotation on cell types of clusters from single-cell rna sequencing data. Iscience, 23(3):100882, 2020.","title":"sccatch: automatic annotation on cell types of clusters from single-cell rna sequencing data","authors":"Xin Shao, Jie Liao, Xiaoyan Lu, Rui Xue, Ni Ai, Xiaohui Fan","year":"2020","venue":"Iscience","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Villani, 2009] Cédric Villani. Optimal transport: old and new, volume 338. Springer, 2009.","title":"Optimal transport: old and new","authors":"Villani","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wan et al., 2022] Hui Wan, Liang Chen, and Minghua Deng. scname: neighborhood contrastive clustering with ancillary mask estimation for scrna-seq data. Bioinformat- ics, 38(6):1575-1583, 2022.","title":"scname: neighborhood contrastive clustering with ancillary mask estimation for scrna-seq data","authors":"Liang Hui Wan, Minghua Chen, Deng","year":"2022","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2022] Hai-Yun Wang, Jian-Ping Zhao, Chun- Hou Zheng, and Yan-Sen Su. sccnc: a method based on capsule network for clustering scrna-seq data. Bioinfor- matics, 2022.","title":"sccnc: a method based on capsule network for clustering scrna-seq data","authors":"Hai-Yun Wang, Jian-Ping Zhao, Chun-Hou Zheng, Yan-Sen Su","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xu et al., 2021] Chenling Xu, Romain Lopez, Edouard Mehlman, Jeffrey Regier, Michael I. Jordan, and Nir. Yosef. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative mod- els. Molecular systems biology, (1):e9620, 2021.","title":"Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models","authors":"Xu","year":"2021","venue":"Molecular systems biology","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ziegenhain et al., 2017] Christoph Ziegenhain, Beate Vieth, Swati Parekh, Björn Reinius, Amy Guillaumet-Adkins, Martha Smets, Heinrich Leonhardt, Holger Heyn, Ines Hellmann, and Wolfgang Enard. Comparative analysis of single-cell rna sequencing methods. Molecular cell, 65(4):631-643, 2017.","title":"Comparative analysis of single-cell rna sequencing methods","authors":"Christoph Ziegenhain, Beate Vieth, Swati Parekh, Björn Reinius, Amy Guillaumet-Adkins, Martha Smets, Heinrich Leonhardt, Holger Heyn, Ines Hellmann, Wolfgang Enard","year":"2017","venue":"Molecular cell","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}