Mistic: An open-source multiplexed image t-SNE viewer.
Mistic: An open-source multiplexed image t-SNE viewer.
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DOI：
10.1016/j.patter.2022.100523
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发表时间：
2022-07-08
期刊：
影响因子：
6.5
通讯作者：
Anderson, Alexander R. A.
中科院分区：
文献类型：
--
作者：
Prabhakaran, Sandhya;Gatenbee, Chandler;Robertson-Tessi, Mark;West, Jeffrey;Beg, Amer A.;Gray, Jhanelle;Antonia, Scott;Gatenby, Robert A.;Anderson, Alexander R. A.
了解肿瘤组织的复杂生态及其细胞和微环境成分之间的时空关系正在成为转化研究的关键组成部分，特别是在免疫肿瘤学中。从患者样本中生成和分析多路复用图像对于促进这种理解至关重要。在这里，我们介绍了Mistic，一个开源的多路复用图像t-SNE查看器，它可以同时查看使用多个布局选项渲染的多个2D图像，以提供整个数据集的整体视觉预览。特别地，图像的位置可以是t-SNE或UMAP坐标。所有图像的这种分组视图允许探索性地理解给定生物标志物的特定表达模式或所有图像中的生物标志物的集合，有助于识别表达特定表型的图像，并且可以帮助选择用于后续下游分析的图像。目前，还没有免费的工具来生成这样的图像t-SNE。Mistic是一种多路复用图像t-SNE查看器Mistic可同时查看多个2D图像此分组概览允许可视化数据中的现有模式Mistic支持来自Vectra、CyCIF、t-CyCIF和CODEX的图像转化研究的一个关键组成部分是利用肿瘤组织进行诊断或预后。我们相信，这可以通过更深入地了解肿瘤组织的复杂生态及其细胞和微环境成分之间的时空关系来实现。来自患者样本的多路复用图像有助于这种理解。我们介绍了Mistic，一个开源的多路复用图像t-SNE查看器，它可以同时查看多个2D多路复用图像，以提供整个数据集的整体视觉预览。这允许探索性地理解数据中的潜在模式，例如所有图像中给定生物标志物的特定表达模式。目前，还没有免费的工具来生成这样的图像t-SNE。Mistic旨在通过提供一个易于实现的工具来填补这一空白，该工具具有简单的功能，可以一次查看多个图像。Mistic支持来自Vectra、CyCIF、t-CyCIF和CODEX的图像。组织的多重成像允许在感兴趣的组织样本上同时成像多个生物标志物，并且是临床癌症诊断和预后的关键工具。可视化和更好地理解这种复用图像的常见方法是利用降维（DR）方法，其中每个图像被抽象为缩减空间中的一个点。我们开发了Mistic，通过结合DR、图像处理和GUI编程，可以同时查看多个2D多路复用图像。
Understanding the complex ecology of a tumor tissue and the spatiotemporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immuno-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. Here, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be t-SNE or UMAP coordinates. This grouped view of all images allows an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype, and can help select images for subsequent downstream analysis. Currently, there is no freely available tool to generate such image t-SNEs. Mistic is a multiplexed image t-SNE viewer Mistic enables the simultaneous viewing of multiple 2D images This grouped overview allows visualization of existing patterns in the data Mistic supports images from Vectra, CyCIF, t-CyCIF, and CODEX A crucial component of translational research is in exploiting tumor tissue for diagnostic or prognostic purposes. We believe this can best be achieved through a deeper understanding of the complex ecology of a tumor tissue and the spatiotemporal relationships between its cellular and microenvironment components. Multiplexed images from patient samples facilitate this understanding. We present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D multiplexed images to provide an overall visual preview of the entire dataset. This allows an exploratory understanding of underlying patterns in the data such as the specific expression pattern of a given biomarker across all images. Currently, there is no free tool to generate such image t-SNEs. Mistic aims to fill this gap by providing an easy to implement tool with simple functionality to view multiple images at once. Mistic supports images from Vectra, CyCIF, t-CyCIF, and CODEX. Multiplex imaging of tissues allows the simultaneous imaging of multiple biomarkers on a tissue specimen of interest and is a critical tool for clinical cancer diagnosis and prognosis. A common way to visualize and better understand such multiplexed images is to utilize dimensionality reduction (DR) methods, where each image is abstracted as a point in the reduced space. We developed Mistic to enable the simultaneous viewing of multiple 2D multiplexed images by combining DR, image processing, and GUI programming.
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影响因子：
4.6
作者：
Bankhead P;Loughrey MB;Fernández JA;Dombrowski Y;McArt DG;Dunne PD;McQuaid S;Gray RT;Murray LJ;Coleman HG;James JA;Salto-Tellez M;Hamilton PW
通讯作者：
Hamilton PW
影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
64.5
作者：
Goltsev Y;Samusik N;Kennedy-Darling J;Bhate S;Hale M;Vazquez G;Black S;Nolan GP
通讯作者：
Nolan GP
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
48
作者：
Giesen, Charlotte;Wang, Hao A. O.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd