Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data (Small Methods 5/2025)

IF 10.7 2区材料科学 Q1 CHEMISTRY, PHYSICAL

Small Methods Pub Date : 2025-05-24 DOI:10.1002/smtd.202570036

Niklas Müller-Bötticher, Sebastian Tiesmeyer, Roland Eils, Naveed Ishaque

引用次数: 0

Abstract

Segmentation-Free Analyses

Sainsc: a new tool for efficient whole organism spatial transcriptomics data analysis at the nanometre scale. Shown is a blended composite spatial map of total gene expression (top), cell types (middle) and assignment confidence (bottom) of a mouse embryo Stereo-seq dataset from Chen et al. 2022. More in article number 2401123 by Naveed Ishaque and co-workers.

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Sainsc：现场捕获数据无分割分析的计算工具（Small Methods 5/2025）

无分割分析：一种在纳米尺度上进行高效全生物空间转录组学数据分析的新工具。所示为Chen et al. 2022年的小鼠胚胎Stereo-seq数据集的总基因表达（上）、细胞类型（中）和分配置信度（下）的混合复合空间图。更多内容见Naveed Ishaque及其同事的2401123号文章。

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

Small Methods Materials Science-General Materials Science

CiteScore

17.40

自引率

1.60%

发文量

347

期刊介绍： Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.