Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data

IF 2.5 4区 生物学 Q3 BIOCHEMICAL RESEARCH METHODS
Axel Andersson, Andrea Behanova, Christophe Avenel, Jonas Windhager, Filip Malmberg, Carolina Wählby
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Abstract

Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces “Points2Regions,” a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical and k -means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.

Abstract Image

Points2Regions:对基于成像的空间转录组学数据进行快速、交互式聚类。
基于成像的空间转录组学技术以属于不同 mRNA 类别的空间点的形式生成数据。分析数据的一个关键部分是识别具有相似 mRNA 类别组成的区域。这些具有生物学意义的区域可表现为不同的空间尺度。例如,细胞尺度上的 mRNA 类别组成与细胞类型相对应,而毫米尺度上的组成则与组织级结构相对应。识别这类区域的传统技术通常依赖于补充数据,如预先分割的细胞,或长时间的优化。这就限制了它们对特定尺度任务的适用性,限制了它们在探索性分析中的能力。本文介绍的 "Points2Regions "是一种用于识别具有相似 mRNA 组成的区域的计算工具。该工具的新颖之处在于通过在金字塔网格上栅格化点(代表 mRNA)来快速提取特征,并采用分层聚类和 k$ k$ 均值聚类相结合的方法进行高效聚类。这样就能在不依赖额外数据的情况下,在多个尺度上快速有效地发现区域,使其成为探索性分析的宝贵资源。在两个模拟数据集上,Points2Regions 的性能与最先进的方法相差无几,而且无需依赖分割的单元格,速度却快了好几倍。在真实世界数据集上的实验表明,Points2Regions 确定的区域与其他研究确定的区域相似,这证实了 Points2Regions 可用于提取生物相关区域。该工具以集成到 TissUUmaps 和 Napari 插件中的 Python 软件包的形式共享,提供交互式聚类和可视化功能,大大提升了用户的数据探索体验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cytometry Part A
Cytometry Part A 生物-生化研究方法
CiteScore
8.10
自引率
13.50%
发文量
183
审稿时长
4-8 weeks
期刊介绍: Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques. The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome: Biomedical Instrumentation Engineering Biophotonics Bioinformatics Cell Biology Computational Biology Data Science Immunology Parasitology Microbiology Neuroscience Cancer Stem Cells Tissue Regeneration.
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