The structure is the message: Preserving experimental context through tensor decomposition.

Zhixin Cyrillus Tan, Aaron S Meyer
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Abstract

Recent biological studies have been revolutionized in scale and granularity by multiplex and high-throughput assays. Profiling cell responses across several experimental parameters, such as perturbations, time, and genetic contexts, leads to richer and more generalizable findings. However, these multidimensional datasets necessitate a reevaluation of the conventional methods for their representation and analysis. Traditionally, experimental parameters are merged to flatten the data into a two-dimensional matrix, sacrificing crucial experiment context reflected by the structure. As Marshall McLuhan famously stated, "the medium is the message." In this work, we propose that the experiment structure is the medium in which subsequent analysis is performed, and the optimal choice of data representation must reflect the experiment structure. We review how tensor-structured analyses and decompositions can preserve this information. We contend that tensor methods are poised to become integral to the biomedical data sciences toolkit.

结构即信息:通过张量分解保留实验背景。
近期的生物学研究在规模和粒度上都发生了革命性的变化,这得益于多重和高通量检测方法。通过对多个实验参数(如扰动、时间和遗传背景)的细胞反应进行剖析,可以获得更丰富、更有普遍意义的发现。然而,这些多维数据集需要重新评估其表示和分析的传统方法。传统的方法是合并实验参数,将数据平铺成二维矩阵,从而牺牲了结构所反映的关键实验背景。正如马歇尔-麦克卢汉的名言:"媒介即信息"。在这项工作中,我们提出实验结构是进行后续分析的媒介,数据表示的最佳选择必须反映实验结构。我们回顾了张量结构分析和分解如何保留这一信息。我们认为,张量方法有望成为生物医学数据科学工具包的组成部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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