In:
Nature Methods, Springer Science and Business Media LLC, Vol. 19, No. 2 ( 2022-02), p. 171-178
Abstract:
Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.
Type of Medium:
Online Resource
ISSN:
1548-7091
,
1548-7105
DOI:
10.1038/s41592-021-01358-2
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2022
detail.hit.zdb_id:
2163081-1
SSG:
12