Prabhakaran Sandhya, Gatenbee Chandler, Robertson-Tessi Mark, West Jeffrey, Beg Amer A, Gray Jhanelle, Antonia Scott, Gatenby Robert A, Anderson Alexander R A
Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Departments of Immunology and Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.
Patterns (N Y). 2022 Jun 2;3(7):100523. doi: 10.1016/j.patter.2022.100523. eCollection 2022 Jul 8.
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.
了解肿瘤组织的复杂生态及其细胞与微环境成分之间的时空关系，正成为转化研究的关键组成部分，尤其是在免疫肿瘤学领域。从患者样本生成和分析多重图像对于促进这种理解至关重要。在这里，我们展示了Mistic，一个开源的多重图像t-SNE查看器，它能够同时查看使用多种布局选项渲染的多个二维图像，以提供整个数据集的总体视觉预览。特别是，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有助于探索性地了解给定生物标志物或生物标志物集合在所有图像中的特定表达模式，有助于识别表达特定表型的图像，并有助于选择图像进行后续的下游分析。目前，没有免费可用的工具来生成这种图像t-SNE。
