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用于定量形态学细胞表型分析的系统数据分析流程。

Systematic data analysis pipeline for quantitative morphological cell phenotyping.

作者信息

Ghanegolmohammadi Farzan, Eslami Mohammad, Ohya Yoshikazu

机构信息

Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.

出版信息

Comput Struct Biotechnol J. 2024 Jul 14;23:2949-2962. doi: 10.1016/j.csbj.2024.07.012. eCollection 2024 Dec.

Abstract

Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.

摘要

定量形态表型分析(QMP)是一种基于图像的方法,用于在细胞和群体水平上捕捉形态特征。它的跨学科性质,从数据收集到结果分析和解释,可能会导致不确定性,尤其是在这个快速发展领域的新手当中。典型的QMP通过能够利用细微细胞形态变化的复杂方法实现高分析特异性。在这里,我们概述了一个完善QMP方法的系统工作流程。为了进行实际综述,我们描述了典型QMP的主要步骤;在每个步骤中,我们讨论了可用的方法、它们的应用、优点和缺点,以及便于实施的R函数和包。本综述不涵盖理论背景,但为感兴趣的研究人员提供了一些参考文献。它旨在拓宽未来表型组学研究的视野,并展示如何利用多年的努力以更少的投入获得更多的成果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d3cb/11298594/57c3cae97f3f/ga1.jpg

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