单细胞蛋白质数据统计分析。

Q2 Computer Science
Brooke L Fridley, Simon Vandekar, Inna Chervoneva, Julia Wrobel, Siyuan Ma
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引用次数: 0

摘要

免疫调节被认为是癌症发生和发展的一个标志,免疫细胞密度一直与癌症患者的临床预后相关。多重免疫荧光(mIF)显微镜与自动图像分析相结合,是一种新颖的、应用日益广泛的技术,可对肿瘤微环境(TME)进行评估和可视化。最近,将这项新技术应用于组织微阵列(TMA)或大型癌症研究的整个组织切片已被用来描述肿瘤微环境中不同细胞群的特征,并提高了可重复性和准确性。一般来说,mIF 数据被用来检测肿瘤和基质区免疫细胞的存在和丰度;然而,这种综合测量方法假定整个 TME 中免疫细胞的模式是一致的,而忽略了空间异质性。最近,人们利用各种统计方法对肿瘤组织间质的空间背景进行了探索。在本次 PSB 研讨会上,演讲者将介绍从 mIF 数据中评估 TIME 的一些最新统计方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistical analysis of single-cell protein data.

Immune modulation is considered a hallmark of cancer initiation and progression, with immune cell density being consistently associated with clinical outcomes of individuals with cancer. Multiplex immunofluorescence (mIF) microscopy combined with automated image analysis is a novel and increasingly used technique that allows for the assessment and visualization of the tumor microenvironment (TME). Recently, application of this new technology to tissue microarrays (TMAs) or whole tissue sections from large cancer studies has been used to characterize different cell populations in the TME with enhanced reproducibility and accuracy. Generally, mIF data has been used to examine the presence and abundance of immune cells in the tumor and stroma compartments; however, this aggregate measure assumes uniform patterns of immune cells throughout the TME and overlooks spatial heterogeneity. Recently, the spatial contexture of the TME has been explored with a variety of statistical methods. In this PSB workshop, speakers will present some of the state-of-the-art statistical methods for assessing the TIME from mIF data.

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4.50
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