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<!-- README.md is generated from README.Rmd. Please edit that file --> <!-- This line is from RStudio -->Giotto
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Default branch change!
With the release of
v3.3.0 the
default branch of Giotto has been moved from
@master to
@suite. If you want to
install the original master version use
devtools::install_github("drieslab/Giotto@master"). Visit the Giotto
Discussions page for
more information.
Website change!
We have created a new readthedocs
website to further
improve and simplify Giotto documentation and to make it easier to use
Giotto. It aggregates information from both the original Giotto package
and our extended Giotto Suite, which is our extended work-in-development
version.
- www.spatialgiotto.com links to the original master version. The old master pkgdown documentation can still be found at https://rubd.github.io/Giotto_site/
- www.giottosuite.com links to the extended suite version. The old suite pkgdown documentation can still be found at https://drieslab.github.io/Giotto_site_suite/
The Giotto package consists of two modules, Giotto Analyzer and Viewer (see www.spatialgiotto.com), which provide tools to process, analyze and visualize single-cell spatial expression data. The underlying framework is generalizable to virtually all currently available spatial datasets. We recently demonstrated the general applicability on 10 different datasets created by 9 different state-of-the-art spatial technologies, including in situ hybridization (seqFISH+, merFISH, osmFISH), sequencing (Slide-seq, Visium, STARmap) and imaging-based multiplexing/proteomics (CyCIF, MIBI, CODEX). These technologies differ in terms of resolution (single cell vs multiple cells), spatial dimension (2D vs 3D), molecular modality (protein vs RNA), and throughput (number of cells and genes).
<img src="inst/images/general_figs/overview_datasets.png" />References
- Dries, R., Zhu, Q. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biology (2021).
- Dries, R., Chen, J. et al. Advances in spatial transcriptomic data analysis. Genome Research (2021).
- Del Rossi, N., Chen, J. et al. Analyzing Spatial Transcriptomics Data Using Giotto. Current Protocols (2022).