Abstract : Single-cell data science aims to understand cells and their functions at individual cells and accelerate progress in the biomedical sciences via the analysis of single-cell omics data. The largest hurdle to this is the difficulty of extracting complex biological structures from millions of pieces of information across varied cell data. This mini-symposium focuses on theoretical studies of single-cell data analysis and its applications, in which biologists, applied mathematicians, and bioinformaticians working in single-cell data science worldwide will come together to discuss their research and the future development.
[03514] Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis
Format : Talk at Waseda University
Author(s) :
Yusuke Imoto (Kyoto University)
Abstract : We have developed a novel noise reduction method for single-cell RNA sequencing (scRNA-seq) data, RECODE, to resolve the curse of dimensionality (COD) in high-dimensional data analysis. RECODE can reduce technical noises in scRNA-seq data based on high-dimensional statistics theory. In this talk, we will explain biological verification, applicability, and recent progress of RECODE. Moreover, we will overview mathematical/informatical grand challenges in single-cell data science, which is the theme of this minisymposium, at the beginning.
[05223] Trajectory inference framework by entropic Gaussian mixture optimal transport
Format : Talk at Waseda University
Author(s) :
Toshiaki Yachimura (Tohoku University)
Abstract : In 1957, C.H. Waddington introduced the epigenetic landscape for cell differentiation. Recently, many attempts have been made to reconstruct this conceptual model from gene expression data. In this talk, I will introduce scEGOT, a novel trajectory inference framework of cell differentiation for time series scRNA-seq data based on Entropic Gaussian mixture optimal transport. scEGOT allows us to infer the dynamics of gene expression associated with cell differentiation. This talk is based on the WPI-ASHBi project.
[03619] Dissecting cell identity via network inference and in-silico gene perturbation
Author(s) :
Kenji Kamimoto (Washington University in St.Louis)
Abstract : Single-cell omics technology enables the acquisition of multi-dimensional data in a high-throughput manner, revealing diverse and heterogeneous cellular identities. However, understanding biological events from a gene regulatory networks (GRNs) perspective remains difficult. Here, we have developed a new method, CellOracle, for the inference and analysis of GRNs. The method can perform in silico transcription factor perturbations, simulating the consequent changes in cell identity and promoting new mechanistic insights into the regulation of cell identity.
[03878] Experimental guidance for discovering genetic networks from time series
Format : Talk at Waseda University
Author(s) :
Tomas Gedeon (Montana State University)
Breschine Cummins (Montana State University)
Steve Haase (Duke University)
Konstantin Mischaikow (Rutgers University)
Abstract : We describe an iterative network hypothesis reduction from time-series data in which dynamic expression of individual, pairs, and entire collections of genes are used to infer core network models. The result of our work is a computational pipeline that prioritizes targets for genetic perturbation to experimentally infer network structure. We apply this computational pipeline to synthetic and yeast cell-cycle data.
[03512] Geometry-aware high-dimensional vector field reconstruction using Hodge decomposition
Format : Talk at Waseda University
Author(s) :
Kazumitsu Maehara (Kyushu University)
Abstract : We propose a method based on Hodge decomposition for analyzing high-dimensional and complex molecular dynamics using single-cell omics data. Drawing inspiration from topology and differential geometry, we developed a data-driven vector field reconstruction method that smoothly captures key features of dynamics (e.g., potential, divergence, curl, and Jacobian) with reduced computational costs through appropriate connections and regularization. Our approach has the potential to contribute to biological discoveries and understanding.
[03922] Reconstructing single cell dynamics on graphs
Format : Talk at Waseda University
Author(s) :
Jianhua Xing (University of Pittsburgh)
Abstract : Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. A frontier of research is how to extract dynamical information from the snapshot data. I will first discuss our recently developed dynamo framework (Qiu et al. Cell, 2022), focusing on the underlying mathematical framework. Then I will discuss our recent efforts of reconstructing full dynamical equations using discrete calculus on graphs (Zhang et al. to be submitted).
[04897] Deep generative models to reveal cellular level dynamics and communication
Format : Talk at Waseda University
Author(s) :
Teppei Shimamura (Tokyo Medical and Dental University)
Abstract : In this talk, we present a deep generative model for investigating the dynamic changes and interactions between cells that alter various states during the onset and progression of diseases from single-cell and spatial omics data.
[03515] Functional annotation-driven unsupervised clustering for single-cell data
Format : Talk at Waseda University
Author(s) :
Keita Iida (Osaka University)
Abstract : Single-cell and spatial transcriptomics have enhanced our knowledge of molecular complexity in terms of gene expression heterogeneity in cell populations. However, conventional gene-based approaches may be insufficient in capturing such complexity as genes can interact with each other to regulate a number of biological functions. Here, we introduce ASURAT, a computational tool for simultaneous clustering and functional annotation of single-cell and spatial transcriptomes in terms of cell type, disease, biological process, and signaling pathway activity.
[03908] Modelling cell differentiation: from psuedo-time to energy landscape
Format : Online Talk on Zoom
Author(s) :
Jifan Shi (Fudan University)
Abstract : Interactions between genes determine cell development and differentiation. We first introduce pseudo-time of cells, which is also known as pluripotency. Next, we will focus on models from the perspective of energy landscape. We propose an energy landscape decomposition theory for cell differentiation with proliferation effect. Two energy landscapes collectively contribute to the establishment of non-equilibrium steady differentiation. We will also demonstrate feasible numerical methods and several interesting applications.
[03682] Integrating data and dynamics in scRNA-seq data analysis
Format : Talk at Waseda University
Author(s) :
Tiejun Li (Peking University)
Abstract : In this talk, I will review some research progress of my group on the scRNA-seq data analysis in recent years. I will mainly focus on the integration of data and dynamics approach in this area, which includes the theory and algorithms for the RNA velocity, dynamical approach for the scRNA-seq data with temporal information, and deep learning type methods. This is a series of joint works with Prof. Luonan Chen, Qing Nie, and Dr. Peijie Zhou, Jifan Shi, Yichong Wu, Qiangwei Peng, et al.
