PySpacell: A Python Package for Spatial Analysis of Cell Images

Autor: Auguste Genovesio, Luca Rappez, Theodore Alexandrov, Sergio Triana
Přispěvatelé: Center of Industrial Mathematics - University of Bremen, University of Bremen, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS)
Rok vydání: 2020
Předmět:
0301 basic medicine
Histology
Computer science
Input format
computer.software_genre
Mass Spectrometry
Pathology and Forensic Medicine
03 medical and health sciences
0302 clinical medicine
Image Processing
Computer-Assisted
Cluster Analysis
Spatial dependence
Cluster analysis
Spatial analysis
ComputingMilieux_MISCELLANEOUS
computer.programming_language
Microscopy
Spatial Analysis
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Cell Biology
Python (programming language)
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Toolbox
030104 developmental biology
030220 oncology & carcinogenesis
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Spatial ecology
Data mining
Analysis tools
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
computer
Zdroj: Cytometry Part A
Cytometry Part A, Wiley, 2019, ⟨10.1002/cyto.a.23955⟩
ISSN: 1552-4922
1552-4930
DOI: 10.1002/cyto.a.23955
Popis: Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single-cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single-cell level is one of the grand challenges and a key to understanding the effects of cell-cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single-cell read-out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. The proposed Python module can be used for any light microscopy images of cells as well as other types of single-cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. © 2019 International Society for Advancement of Cytometry.
Databáze: OpenAIRE
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