Python(编程语言)
计算机科学
软件
文档
分割
开源
图像分辨率
人工智能
数据挖掘
情报检索
计算机图形学(图像)
计算机视觉
操作系统
作者
Krzysztof Polański,Raquel Bartolomé-Casado,Ioannis Sarropoulos,Chuan Xu,Nick England,Frode L. Jahnsen,Sarah A. Teichmann,Nadav Yayon
标识
DOI:10.1093/bioinformatics/btae546
摘要
Abstract Summary Visium HD by 10X Genomics is the first commercially available platform capable of capturing full scale transcriptomic data paired with a reference morphology image from archived FFPE blocks at sub-cellular resolution. However, aggregation of capture regions to single cells poses challenges. Bin2cell reconstructs cells from the highest resolution data (2 μm bins) by leveraging morphology image segmentation and gene expression information. It is compatible with established Python single cell and spatial transcriptomics software, and operates efficiently in a matter of minutes without requiring a GPU. We demonstrate improvements in downstream analysis when using the reconstructed cells over default 8 μm bins on mouse brain and human colorectal cancer data. Availability and Implementation Bin2cell is available at https://github.com/Teichlab/bin2cell, along with documentation and usage examples, and can be installed from pip. Probe design functionality is available at https://github.com/Teichlab/gene2probe Supplementary information Supplementary data are available online.
科研通智能强力驱动
Strongly Powered by AbleSci AI