使用SEAGAL解析空间基因关联：一个用于空间转录组学数据分析和可视化的Python软件包。

Unraveling Spatial Gene Associations with SEAGAL: a Python Package for Spatial Transcriptomics Data Analysis and Visualization.

作者信息

Wang Linhua, Liu Chaozhong, Liu Zhandong

机构信息

Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA.

Departm Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, USA.

出版信息

bioRxiv. 2023 Feb 13:2023.02.13.528331. doi: 10.1101/2023.02.13.528331.

DOI:10.1101/2023.02.13.528331

PMID:36824948

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9948987/

Abstract

SUMMARY

In the era where transcriptome profiling moves towards single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics data sets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing spatial correlations at both single-gene and gene-set levels. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations.

AVAILABILITY AND IMPLEMENTATION

The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/ . The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL .

CONTACT

linhuaw@bcm.edu.

摘要

在转录组分析朝着单细胞和空间分辨率发展的时代，传统的共表达分析缺乏充分利用此类丰富信息来揭示空间基因关联的能力。在此，我们展示了一个名为“使用L指数进行基因关联空间富集分析”（SEAGAL）的Python软件包，用于在单基因和基因集水平上检测和可视化空间基因相关性。我们的软件包将具有基因表达和对齐的空间坐标的空间转录组数据集作为输入。它允许在单基因和基因集水平上分析和可视化空间相关性。只需几行代码，输出结果就可以可视化为火山图和热图，从而为挖掘空间基因关联提供了一个简单而全面的工具。