小柯机器人

以色列耶路撒冷希伯来大学Mor Nitza、麻省理工学院和哈佛大学布罗德研究所Aviv Regev和Johanna Klughammer共同合作，近期取得重要工作进展。他们研究开发了TACCO算法，统一了单细胞和空间组学的细胞身份的注释传递和分解。相关研究成果2023年2月16日在线发表于《自然—生物技术》杂志上。

据介绍，由于技术限制（如低空间分辨率或高漏失率）和生物变化（如细胞状态的连续光谱），转移单细胞、空间和多组学数据的注释通常具有挑战性。

基于这些数据通常最好描述为细胞或分子的连续混合物这一概念，研究人员提出了一种将注释传递到细胞及其组合（TACCO）的计算框架，该框架由一个最优传输模型组成，该模型扩展了不同的包装，以注释各种数据。研究人员应用TACCO识别细胞类型和状态，在细胞和分子水平上破译空间分子组织结构，并使用合成和生物数据集解析分化轨迹。

总之，在匹配或超过单个任务专用工具精度的同时，TACCO将计算需求降低了一个数量级，并扩展到更大的数据集（例如，考虑到注释传输的运行时间为1 M个模拟漏失观测）。

附：英文原文

Title: TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics

Author: Mages, Simon, Moriel, Noa, Avraham-Davidi, Inbal, Murray, Evan, Watter, Jan, Chen, Fei, Rozenblatt-Rosen, Orit, Klughammer, Johanna, Regev, Aviv, Nitzan, Mor

Issue&Volume: 2023-02-16

Abstract: Transferring annotations of single-cell-, spatial- and multi-omics data is often challenging owing both to technical limitations, such as low spatial resolution or high dropout fraction, and to biological variations, such as continuous spectra of cell states. Based on the concept that these data are often best described as continuous mixtures of cells or molecules, we present a computational framework for the transfer of annotations to cells and their combinations (TACCO), which consists of an optimal transport model extended with different wrappers to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatiomolecular tissue structure at the cell and molecular level and resolve differentiation trajectories using synthetic and biological datasets. While matching or exceeding the accuracy of specialized tools for the individual tasks, TACCO reduces the computational requirements by up to an order of magnitude and scales to larger datasets (for example, considering the runtime of annotation transfer for 1M simulated dropout observations).

DOI: 10.1038/s41587-023-01657-3

Source: https://www.nature.com/articles/s41587-023-01657-3

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex