Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network
Tài liệu tham khảo
Bai, 2021, Ripple walk training: a subgraph-based training framework for large and deep graph neural network
Bao, 2022, Integrative spatial analysis of cell morphologies and transcriptional states with MUSE, Nat. Biotechnol., 40, 1200, 10.1038/s41587-022-01251-z
Berghuis, 2006, Brain-derived neurotrophic factor selectively regulates dendritogenesis of parvalbumin-containing interneurons in the main olfactory bulb through the PLCγ pathway, J. Neurobiol., 66, 1437, 10.1002/neu.20319
Blondel, 2008, Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp., 2008
Carson, 2005, A digital atlas to characterize the mouse brain transcriptome, PLoS Comput. Biol., 1, e41, 10.1371/journal.pcbi.0010041
Chen, 2015, Spatially resolved, highly multiplexed RNA profiling in single cells, Science, 348, aaa6090, 10.1126/science.aaa6090
Chen, 2022, Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays, Cell, 185, 1777, 10.1016/j.cell.2022.04.003
Codeluppi, 2018, Spatial organization of the somatosensory cortex revealed by cyclic osmFISH, Nat. Methods, 15, 932, 10.1038/s41592-018-0175-z
David, 2006, Accelerator: using data parallelism to program GPUs for general-purpose uses, ACM SIGPLAN Notices, 41, 325, 10.1145/1168918.1168898
Deng, 2009, ImageNet: a large-scale hierarchical image database
Dong, 2022, Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder, Nat. Commun., 13, 1739, 10.1038/s41467-022-29439-6
Eng, 2019, Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+, Nature, 568, 235, 10.1038/s41586-019-1049-y
Femino, 1998, Visualization of single RNA transcripts in situ, Science, 280, 585, 10.1126/science.280.5363.585
Fraley, 2012
Fu, 2021, Unsupervised spatially embedded deep representation of spatial transcriptomics, bioRxiv
Gil-Sanz, 2015, Lineage tracing using Cux2-Cre and Cux2-CreERT2 mice, Neuron, 86, 1091, 10.1016/j.neuron.2015.04.019
Hafemeister, 2019, Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression, Genome Biol., 20, 296, 10.1186/s13059-019-1874-1
Hao, 2021, Integrated analysis of multimodal single-cell data, Cell, 184, 3573, 10.1016/j.cell.2021.04.048
Hartigan, 1979, Algorithm AS 136: a k-means clustering algorithm, J. R. Stat. Soc. C-Appl., 28, 100
He, 2016, Deep residual learning for image recognition
Hu, 2021, SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network, Nat. Methods, 18, 1342, 10.1038/s41592-021-01255-8
Hu, 2021, Statistical and machine learning methods for spatially resolved transcriptomics with histology, Comput. Struct. Biotechnol. J., 19, 3829, 10.1016/j.csbj.2021.06.052
Jacque, 1985, Functional maturation of the oligodendrocytes and myelin basic protein expression in the olfactory bulb of the mouse, Brain Res. Dev., 353, 277, 10.1016/0165-3806(85)90216-0
Kasukawa, 2011, Quantitative expression profile of distinct functional regions in the adult mouse brain, PLoS ONE, 6, 10.1371/journal.pone.0023228
Ke, 2013, In situ sequencing for RNA analysis in preserved tissue and cells, Nat. Methods, 10, 857, 10.1038/nmeth.2563
Kiselev, 2017, SC3: consensus clustering of single-cell RNA-seq data, Nat. Methods, 14, 483, 10.1038/nmeth.4236
Laeremans, 2013, AMIGO2 mRNA expression in hippocampal CA2 and CA3a, Brain Struct. Funct., 218, 123, 10.1007/s00429-012-0387-4
Li, 2016, Cerebral apolipoprotein-D is hypoglycosylated compared to peripheral tissues and is variably expressed in mouse and human brain regions, PLoS ONE, 11
Li, 2018, Factorizable net: an efficient subgraph-based framework for scene graph generation
Lein, 2007, Genome-wide atlas of gene expression in the adult mouse brain, Nature, 445, 168, 10.1038/nature05453
Li, 2021, CCST: cell clustering for spatial transcriptomics data with graph neural network, Nat. Comput. Sci., 2, 399, 10.1038/s43588-022-00266-5
Ling, 2011, Spatiotemporal regulation of multiple overlapping sense and novel natural antisense transcripts at the Nrgn and Camk2n1 gene loci during mouse cerebral corticogenesis, Cereb. Cortex, 21, 683, 10.1093/cercor/bhq141
Lu, 2020, Localization of area prostriata and its connections with primary visual cortex in rodent, J. Comp. Neurol., 528, 389, 10.1002/cne.24760
Ma, 2013, Cholecystokinin: an excitatory modulator of mitral/tufted cells in the mouse olfactory bulb, PLoS ONE, 8
Mamoor, 2020
Maynard, 2021, Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex, Nat. Neurosci., 24, 425, 10.1038/s41593-020-00787-0
Moncada, 2020, Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas, Nat. Biotechnol., 38, 333, 10.1038/s41587-019-0392-8
Moon, 2021, Spatial, temporal and cell-type-specific expression profiles of genes encoding heparan sulfate biosynthesis enzymes and proteoglycan core proteins, Glycobiology, 31, 1308, 10.1093/glycob/cwab054
Ortiz, 2020, Molecular atlas of the adult mouse brain, Sci. Adv., 6, 10.1126/sciadv.abb3446
Pham, 2020, stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues, bioRxiv
Raj, 2008, Imaging individual mRNA molecules using multiple singly labeled probes, Nat. Methods, 5, 877, 10.1038/nmeth.1253
Rao, 2021, Exploring tissue architecture using spatial transcriptomics, Nature, 596, 211, 10.1038/s41586-021-03634-9
Renelt, 2014, Distribution of PCP4 protein in the forebrain of adult mice, Acta Histochem., 116, 1056, 10.1016/j.acthis.2014.04.012
Rodriques, 2019, Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution, Science, 363, 1463, 10.1126/science.aaw1219
Romano, 2014, Standardized mutual information for clustering comparisons: one step further in adjustment for chance
Salehi, 2019
Shallue, 2019, Measuring the effects of data parallelism on neural network training, J. Mach. Learn. Res., 20, 1
Shan, 2022, TIST: transcriptome and histopathological image integrative analysis for spatial transcriptomics, Genom. Proteomics Bioinformatics, 20, 974, 10.1016/j.gpb.2022.11.012
Shimizu, 2009, Formation and patterning of the forebrain and olfactory system by zinc-finger genes Fezf1 and Fezf2, Dev. Growth Differ., 51, 221, 10.1111/j.1440-169X.2009.01088.x
Shlens, 2014
Sripada, 2011, Comparison of purity and entropy of k-means clustering and fuzzy c means clustering, Indian J. Comput. Sci. Eng., 2, 343
Ståhl, 2016, Visualization and analysis of gene expression in tissue sections by spatial transcriptomics, Science, 353, 78, 10.1126/science.aaf2403
Steinley, 2004, Properties of the hubert-arable adjusted rand index, Psychol. Methods, 9, 386, 10.1037/1082-989X.9.3.386
Stuart, 2019, Comprehensive integration of single-cell data, Cell, 177, 1888, 10.1016/j.cell.2019.05.031
Subhlok, 1993, Exploiting task and data parallelism on a multicomputer, ACM SIGPLAN Not., 28, 13, 10.1145/173284.155334
Szegedy, 2016, Rethinking the inception architecture for computer vision
Veličković, 2017
Vickovic, 2019, High-definition spatial transcriptomics for in situ tissue profiling, Nat. Methods, 16, 987, 10.1038/s41592-019-0548-y
Wang, 2018, Three-dimensional intact-tissue sequencing of single-cell transcriptional states, Science, 361, 10.1126/science.aat5691
Wang, 2019
Wolf, 2018, SCANPY: large-scale single-cell gene expression data analysis, Genome Biol., 19, 15, 10.1186/s13059-017-1382-0
Wu, 2021, A single-cell and spatially resolved atlas of human breast cancers, Nat. Genet., 53, 1334, 10.1038/s41588-021-00911-1
Xie, 2016, Unsupervised deep embedding for clustering analysis
Zeisel, 2018, Molecular architecture of the mouse nervous system, Cell, 174, 999, 10.1016/j.cell.2018.06.021
Zeng, 2012, Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures, Cell, 149, 483, 10.1016/j.cell.2012.02.052
Zhao, 2021, Spatial transcriptomics at subspot resolution with BayesSpace, Nat. Biotechnol., 39, 1375, 10.1038/s41587-021-00935-2
Zhou, 2022, Integrating spatial transcriptomics data across different conditions, technologies, and developmental stages, bioRxiv