Giotto: a toolbox for integrative analysis and visualization of spatial expression data

Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by im...

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Vydáno v:Genome Biology Ročník 22; číslo 1; s. 78
Hlavní autoři: Dries, Ruben, Zhu, Qian, Dong, Rui, Eng, Chee-Huat Linus, Li, Huipeng, Liu, Kan, Fu, Yuntian, Zhao, Tianxiao, Sarkar, Arpan, Bao, Feng, George, Rani E., Pierson, Nico, Cai, Long, Yuan, Guo-Cheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 08.03.2021
Springer Nature B.V
BMC
Témata:
Algorithms
Animal Genetics and Genomics
Bioinformatics
Biomedical and Life Sciences
Clustering
Computational Biology - methods
Data Analysis
data collection
Datasets
Evolutionary Biology
Gene expression
Gene Expression Profiling - methods
genome
Human Genetics
Image Processing, Computer-Assisted
Immunohistochemistry - methods
In Situ Hybridization - methods
Life Sciences
Method
Microbial Genetics and Genomics
Microenvironments
Organ Specificity - genetics
Pancreas
Plant Genetics and Genomics
Proteomics
Small intestine
Software
Spatial Analysis
spatial data
Transcriptome
Transcriptomics
Visualization
ISSN:1474-760X, 1474-7596, 1474-760X
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Popis
Shrnutí:Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.
Bibliografie:ObjectType-Article-1
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-021-02286-2