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

By Ruben Dries, Qian Zhu, Rui Dong, C. Eng, Huipeng Li, Kan Liu, Yuntian Fu, T. Zhao, Arpan Sarkar, Feng Bao and 4 more. Genome Biology, 2021

889 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/92845400ea874dda398b08b1bb2160ec1880463f

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Citations in the corpus

Spatial components of molecular tissue biology (2022, neutral): “Benchmarks and evaluations of segmentation meth-ods, as well as integrating them in a processing pipeline 106–108 could help decision-making on nuclear versus cytoplasmic segmentation, different segmentation algorithms 109,110 and experimental design consideration on segmentation-auxiliary membrane…”
Best practices for single-cell analysis across modalities (2023, background): “Tangram 224 , GridNet 230 and eggplant 231 generate common coordinate frameworks across samples to mitigate this issue 232 .”
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, positive): “Focusing on the immune cells, we found a signi ﬁ cant presence of diverse immune subsets, namely T cells and macrophage/DC/monocytes (myeloid cells) in the IDC regions (IDC 5, 6, 7, and 8).”
Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, methodology): “To assess method robustness and efficiency we next studied the correlation between inferred CCC and the expression of known downstream genes, and compared COMMOT with three existing methods: CellChat 6 , which was designed for scRNA-seq data, and Giotto 23 and CellPhoneDB v3 25 , which were designed”
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “Many analytical tools have been developed for this task, such as SpaOTsc 21 , Giotto 71 , CellChat 72 , NicheNet 73 , ICELLNET 74 , and SingleCellSignalR 75 .”
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball patterned arrays (2021, neutral): “The distribution of speciﬁc markers ( Pcp4 and Slc17a7 ) captured by Stereo-seq exhibited the expected similarity to in situ hybridization (ISH) data taken from Allen Brain Atlas (ABA) (Lein et al., 2007) and clearer patterns than HDST (Vickovic et al., 2019) or Slide-seqV2-resolved (Stickels et al.”
An introduction to spatial transcriptomics for biomedical research (2022, methodology): “Alternatives include stereoscope, cell2location, Tangram, and destVI [93–96]. stereoscope, like RCTD, models the composition of each spot’s transcriptome as a mixture of transcripts from different cells with additional platformspecific effects.”