@Article{Zhu2018,
author={Zhu, Qian
and Shah, Sheel
and Dries, Ruben
and Cai, Long
and Yuan, Guo-Cheng},
title={Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data},
journal={Nature Biotechnology},
year={2018},
month={Dec},
day={01},
volume={36},
number={12},
pages={1183-1190},
abstract={Combining single-cell RNA-seq with sequential single-molecule FISH defines the influence of spatial location on cell identity.},
issn={1546-1696},
doi={10.1038/nbt.4260},
url={https://doi.org/10.1038/nbt.4260}
}

@Article{Eng2019,
author={Eng, Chee-Huat Linus
and Lawson, Michael
and Zhu, Qian
and Dries, Ruben
and Koulena, Noushin
and Takei, Yodai
and Yun, Jina
and Cronin, Christopher
and Karp, Christoph
and Yuan, Guo-Cheng
and Cai, Long},
title={Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+},
journal={Nature},
year={2019},
month={Apr},
day={01},
volume={568},
number={7751},
pages={235-239},
abstract={Imaging the transcriptome in situ with high accuracy has been a major challenge in single-cell biology, which is particularly hindered by the limits of optical resolution and the density of transcripts in single cells1--5. Here we demonstrate an evolution of sequential fluorescence in situ hybridization (seqFISH+). We show that seqFISH+ can image mRNAs for 10,000 genes in single cells---with high accuracy and sub-diffraction-limit resolution---in the cortex, subventricular zone and olfactory bulb of mouse brain, using a standard confocal microscope. The transcriptome-level profiling of seqFISH+ allows unbiased identification of cell classes and their spatial organization in tissues. In addition, seqFISH+ reveals subcellular mRNA localization patterns in cells and ligand--receptor pairs across neighbouring cells. This technology demonstrates the ability to generate spatial cell atlases and to perform discovery-driven studies of biological processes in situ.},
issn={1476-4687},
doi={10.1038/s41586-019-1049-y},
url={https://doi.org/10.1038/s41586-019-1049-y}
}

@article {Dries701680,
	author = {Dries, Ruben and Zhu, Qian and Dong, Rui and Eng, Chee-Huat Linus and Li, Huipeng and Liu, Kan and Fu, Yuntian and Zhao, Tianxiao and Sarkar, Arpan and Bao, Feng and George, Rani E and Pierson, Nico and Cai, Long and Yuan, Guo-Cheng},
	title = {Giotto, a toolbox for integrative analysis and visualization of spatial expression data},
	elocation-id = {701680},
	year = {2020},
	doi = {10.1101/701680},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {The rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2020/05/30/701680},
	eprint = {https://www.biorxiv.org/content/early/2020/05/30/701680.full.pdf},
	journal = {bioRxiv}
}

@ARTICLE{906424,

  author={Y. {Zhang} and M. {Brady} and S. {Smith}},

  journal={IEEE Transactions on Medical Imaging}, 

  title={Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm}, 

  year={2001},

  volume={20},

  number={1},

  pages={45-57},
}
