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Robust decomposition of cell type mixtures in spatial transcriptomics

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  • 1.

    Stickels, R. R. et al. Sensitive spatial genome wide expression profiling at cellular resolution. Nature Biotechnology (in the press).

  • 2.

    10x Genomics. 10x Genomics: Visium spatial gene expression (2020).

  • 3.

    Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 4.

    Pelkey, K. A. et al. Hippocampal GABAergic inhibitory interneurons. Physiol. Rev. 97, 1619–1747 (2017).

    CAS 
    Article 

    Google Scholar
     

  • 5.

    Cembrowski, M. S. et al. The subiculum is a patchwork of discrete subregions. elife 7, e37701 (2018).

    Article 

    Google Scholar
     

  • 6.

    Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    Article 

    Google Scholar
     

  • 7.

    Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).

    CAS 
    Article 

    Google Scholar
     

  • 8.

    Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 9.

    Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).

    Article 

    Google Scholar
     

  • 10.

    Regev, A. et al. Science forum: the Human Cell Atlas. eLife 6, e27041 (2017).

    Article 

    Google Scholar
     

  • 11.

    Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 12.

    Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 13.

    Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    CAS 
    Article 

    Google Scholar
     

  • 14.

    Townes, F. W., Hicks, S. C., Aryee, M. J. & Irizarry, R. A. Feature selection and dimension reduction for single-cell RNA-seq based on a multinomial model. Genome Biol. 20, 295 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 15.

    Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 16.

    Pliner, H. A., Shendure, J. & Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16, 983–986 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 17.

    Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    CAS 
    Article 

    Google Scholar
     

  • 18.

    Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).

  • 19.

    Tsoucas, D. et al. Accurate estimation of cell-type composition from gene expression data. Nat. Commun. 10, 2975 (2019).

    Article 

    Google Scholar
     

  • 20.

    Kozareva, V. et al. A transcriptomic atlas of the mouse cerebellum reveals regional specializations and novel cell types. Preprint at bioRxiv https://doi.org/10.1101/2020.03.04.976407 (2020).

  • 21.

    Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 22.

    Brown, A. M. et al. Molecular layer interneurons shape the spike activity of cerebellar Purkinje cells. Sci. Rep. 9, 1742 (2019).

    Article 

    Google Scholar
     

  • 23.

    Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    CAS 
    Article 

    Google Scholar
     

  • 24.

    Zhang, M. et al. Molecular, spatial and projection diversity of neurons in primary motor cortex revealed by in situ single-cell transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2020.06.04.105700 (2020).

  • 25.

    Sunkin, S. M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2012).

    Article 

    Google Scholar
     

  • 26.

    Capogna, M. Neurogliaform cells and other interneurons of stratum lacunosum-moleculare gate entorhinal–hippocampal dialogue. J. Physiol. 589, 1875–1883 (2011).

    CAS 
    Article 

    Google Scholar
     

  • 27.

    Leão, R. N. et al. OLM interneurons differentially modulate CA3 and entorhinal inputs to hippocampal CA1 neurons. Nat. Neurosci. 15, 1524–1530 (2012).

    Article 

    Google Scholar
     

  • 28.

    Gampe, K. et al. NTPDase2 and purinergic signaling control progenitor cell proliferation in neurogenic niches of the adult mouse brain. Stem Cells 33, 253–264 (2015).

    CAS 
    Article 

    Google Scholar
     

  • 29.

    Dikow, N. et al. 3p25.3 microdeletion of GABA transporters SLC6A1 and SLC6A11 results in intellectual disability, epilepsy and stereotypic behavior. Am. J. Med. Genet. A 164, 3061–3068 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 30.

    Lee, T.-S. et al. GAT1 and GAT3 expression are differently localized in the human epileptogenic hippocampus. Acta Neuropathol. 111, 351–363 (2006).

    CAS 
    Article 

    Google Scholar
     

  • 31.

    Kulkarni, A., Anderson, A. G., Merullo, D. P. & Konopka, G. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr. Opin. Biotechnol. 58, 129–136 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 32.

    Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 33.

    Sakamoto, Y., Ishiguro, M. & Kitagawa, G. Akaike Information Criterion Statistics 1st edn, Vol. 1 (Springer Netherlands, 1986).

  • 34.

    Zhou, M., Li, L., Dunson, D. & Carin, L. Lognormal and gamma mixed negative binomial regression. Proc. Int. Conf. Mach. Learn. 2012, 1343–1350 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 35.

    Swami, A. Non-Gaussian mixture models for detection and estimation in heavy-tailed noise. In Proceedings of the 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing 3802–3805 (IEEE, 2000).

  • 36.

    Turlach, B. A. & Weingessel, A. quadprog: functions to solve quadratic programming problems. R package version 1.5-5 (2013).

  • 37.

    Duchi, J. Sequential convex programming, notes for EE364b: Convex Optimization II (Stanford University, 2018).

  • 38.

    SatijaLab. Analysis, visualization, and integration of spatial datasets with Seurat. https://satijalab.org/seurat/articles/spatial_vignette.html (2020).



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