Robust decomposition of cell type mixtures in spatial transcriptomics
Stickels, R. R. et al. Sensitive spatial genome wide expression profiling at cellular resolution. Nature Biotechnology (in the press).
10x Genomics. 10x Genomics: Visium spatial gene expression (2020).
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).
Pelkey, K. A. et al. Hippocampal GABAergic inhibitory interneurons. Physiol. Rev. 97, 1619–1747 (2017).
Cembrowski, M. S. et al. The subiculum is a patchwork of discrete subregions. elife 7, e37701 (2018).
Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).
Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).
Regev, A. et al. Science forum: the Human Cell Atlas. eLife 6, e27041 (2017).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
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).
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).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
Pliner, H. A., Shendure, J. & Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16, 983–986 (2019).
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).
Bakken, T. E. et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS ONE 13, e0209648 (2018).
Tsoucas, D. et al. Accurate estimation of cell-type composition from gene expression data. Nat. Commun. 10, 2975 (2019).
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).
Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030 (2018).
Brown, A. M. et al. Molecular layer interneurons shape the spike activity of cerebellar Purkinje cells. Sci. Rep. 9, 1742 (2019).
Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).
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).
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).
Capogna, M. Neurogliaform cells and other interneurons of stratum lacunosum-moleculare gate entorhinal–hippocampal dialogue. J. Physiol. 589, 1875–1883 (2011).
Leão, R. N. et al. OLM interneurons differentially modulate CA3 and entorhinal inputs to hippocampal CA1 neurons. Nat. Neurosci. 15, 1524–1530 (2012).
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).
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).
Lee, T.-S. et al. GAT1 and GAT3 expression are differently localized in the human epileptogenic hippocampus. Acta Neuropathol. 111, 351–363 (2006).
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).
Halpern, K. B. et al. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962–970 (2018).
Sakamoto, Y., Ishiguro, M. & Kitagawa, G. Akaike Information Criterion Statistics 1st edn, Vol. 1 (Springer Netherlands, 1986).
Zhou, M., Li, L., Dunson, D. & Carin, L. Lognormal and gamma mixed negative binomial regression. Proc. Int. Conf. Mach. Learn. 2012, 1343–1350 (2012).
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).
Turlach, B. A. & Weingessel, A. quadprog: functions to solve quadratic programming problems. R package version 1.5-5 (2013).
Duchi, J. Sequential convex programming, notes for EE364b: Convex Optimization II (Stanford University, 2018).
SatijaLab. Analysis, visualization, and integration of spatial datasets with Seurat. https://satijalab.org/seurat/articles/spatial_vignette.html (2020).