Motivation: Single cell RNA sequencing (scRNA-seq) data makes studying the development of cells possible at unparalleled resolution. Given that many cellular differentiation processes are hierarchical, their scRNA-seq data is expected to be approximately tree-shaped in gene expression space. Inference and representation of this tree-structure in two dimensions is highly desirable for biological interpretation and exploratory analysis.Results:Our two contributions are an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data, and a visualization method respecting the tree-structure. We extract the tree structure by means of a density based minimum spanning tree on a vector quantization of the data and show that it captures biological information well. We then introduce DTAE, a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space. We compare to other dimension reduction methods and demonstrate the success of our method both qualitatively and quantitatively on real and toy data.Availability: Our implementation relying on PyTorch and Higra is available at https://github.com/hci-unihd/DTAE.
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-seq Data Analysis
School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）；Laboratory of Mathematics and Its Applications (LMAM), Peking University（北京大学数学及其应用实验室）；Center for Quantitative Biology, Peking University（北京大学定量生物学中心）；National Engineering Laboratory for Big Data Analysis and Applications, Peking University（北京大学大数据分析与应用国家工程实验室）；AI for Science Institute, Beijing（北京科学智能研究院）
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq
Stanford University（斯坦福大学）；California Institute of Technology（加州理工学院）；University of California, San Francisco（加州大学旧金山分校）；Chan-Zuckerberg Biohub（陈·扎克翰生物中心）
Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data
DOT: A flexible multi-objective optimization framework for transferring features across single-cell and spatial omics
Heidelberg University（海德堡大学）；Heidelberg University Hospital（海德堡大学医院）；Institute for Computational Biomedicine, Heidelberg University & Heidelberg University Hospital（海德堡大学与海德堡大学医院计算生物医学研究所）；Cellzome GmbH, GlaxoSmithKline（葛兰素史克旗下Cellzome有限公司）；Department of Knowledge Technologies, Jožef Stefan Institute（约瑟夫斯蒂芬研究所知识技术系）
Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images
KAIST（韩国科学技术院）；Konyang University（建阳大学）；Chang Gung University（长庚大学）；UCL Queen Square Institute of Neurology & The Francis Crick Institute（伦敦大学学院皇后广场神经病学研究所与弗朗西斯·克里克研究所）
