CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures.
CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures.
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DOI：
10.1093/bioinformatics/bty638
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发表时间：
2019-02-01
期刊：
影响因子：
--
通讯作者：
Peirce SM
中科院分区：
文献类型：
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作者：
Corliss BA;Ray HC;Patrie JT;Mansour J;Kesting S;Park JH;Rohde G;Yates PA;Janes KA;Peirce SM
生物医学图像中结构的共定位可以帮助我们深入了解生物行为。一类共定位问题是检查与网络结构（血管、神经元、细胞骨架、细胞器）相互作用的环形结构（圆盘状，如细胞、囊泡或分子）。由于两种结构类型的密度变化，检查不同条件下的共定位事件通常会变得复杂，这会混淆传统的统计方法，因为共定位不能同时归一化为两种结构类型的密度。我们开发了一种独立于结构密度来测量共定位的技术，并将其应用于表征细胞间与血管网络的共定位。该技术可用于分析任何环形结构与任意形状的网络结构的共定位。我们提出了网络结构的循环共定位亲和力测试（CIRCOAST），这是一种新颖的统计假设测试，通过使用基于代理的蒙特卡洛建模和图像处理来探测二维 z 投影多通道图像中丰富的网络共定位，以生成每个图像特有的随机细胞放置的伪零分布。该假设检验通过确认脂肪源性干细胞 (ASC) 与内皮细胞表现出丰富的共定位而得到验证，在培养物中形成树枝状网络，然后应用于表明局部递送的 ASC 在糖尿病视网膜病变模型中与小鼠视网膜微血管具有丰富的共定位。我们证明，与因细胞或血管密度变化而混淆的通用方法相比，CIRCOAST 测试在表征细胞间共定位方面提供了卓越的功效和 I 型错误率。 CIRCOAST 源代码位于：https://github.com/uva-peirce-cottler-lab/ARCAS。 补充数据可在生物信息学在线获取。
Colocalization of structures in biomedical images can lead to insights into biological behaviors. One class of colocalization problems is examining an annular structure (disk-shaped such as a cell, vesicle or molecule) interacting with a network structure (vascular, neuronal, cytoskeletal, organellar). Examining colocalization events across conditions is often complicated by changes in density of both structure types, confounding traditional statistical approaches since colocalization cannot be normalized to the density of both structure types simultaneously. We have developed a technique to measure colocalization independent of structure density and applied it to characterizing intercellular colocation with blood vessel networks. This technique could be used to analyze colocalization of any annular structure with an arbitrarily shaped network structure. We present the circular colocalization affinity with network structures test (CIRCOAST), a novel statistical hypothesis test to probe for enriched network colocalization in 2D z-projected multichannel images by using agent-based Monte Carlo modeling and image processing to generate the pseudo-null distribution of random cell placement unique to each image. This hypothesis test was validated by confirming that adipose-derived stem cells (ASCs) exhibit enriched colocalization with endothelial cells forming arborized networks in culture and then applied to show that locally delivered ASCs have enriched colocalization with murine retinal microvasculature in a model of diabetic retinopathy. We demonstrate that the CIRCOAST test provides superior power and type I error rates in characterizing intercellular colocalization compared to generic approaches that are confounded by changes in cell or vessel density. CIRCOAST source code available at: https://github.com/uva-peirce-cottler-lab/ARCAS. Supplementary data are available at Bioinformatics online.
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影响因子：
3.7
作者：
Mendel TA;Clabough EB;Kao DS;Demidova-Rice TN;Durham JT;Zotter BC;Seaman SA;Cronk SM;Rakoczy EP;Katz AJ;Herman IM;Peirce SM;Yates PA
通讯作者：
Yates PA
影响因子：
14.8
作者：
Gartner, Zev J.;Prescher, Jennifer A.;Lavis, Luke D.
通讯作者：
Lavis, Luke D.
影响因子：
12.4
作者：
Kang SW;Lee S;Na JH;Yoon HI;Lee DE;Koo H;Cho YW;Kim SH;Jeong SY;Kwon IC;Choi K;Kim K
通讯作者：
Kim K
影响因子：
25
作者：
Longden TA;Dabertrand F;Koide M;Gonzales AL;Tykocki NR;Brayden JE;Hill-Eubanks D;Nelson MT
通讯作者：
Nelson MT
影响因子：
21.3
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