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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Review Article

A Unified Probabilistic Framework for Modeling and Inferring Spatial Transcriptomic Data

Author(s): Zhiwei Huang, Songhao Luo, Zhenquan Zhang, Zihao Wang, Tianshou Zhou and Jiajun Zhang*

Volume 19, Issue 3, 2024

Published on: 07 September, 2023

Page: [222 - 234] Pages: 13

DOI: 10.2174/1574893618666230529145130

Price: $65

Abstract

Spatial transcriptomics (ST) can provide vital insights into tissue function with the spatial organization of cell types. However, most technologies have limited spatial resolution, i.e., each measured location contains a mixture of cells, which only quantify the average expression level across many cells in the location. Recently developed algorithms show the promise to overcome these challenges by integrating single-cell and spatial data. In this review, we summarize spatial transcriptomic technologies and efforts at cell-type deconvolution. Importantly, we propose a unified probabilistic framework, integrating the details of the ST data generation process and the gene expression process simultaneously for modeling and inferring spatial transcriptomic data.

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[1]
Li J, Byrne KT, Yan F, et al. Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy. Immunity 2018; 49(1): 178-193.e7.
[http://dx.doi.org/10.1016/j.immuni.2018.06.006] [PMID: 29958801]
[2]
Brücher BLDM, Jamall IS. Cell-cell communication in the tumor microenvironment, carcinogenesis, and anticancer treatment. Cell Physiol Biochem 2014; 34(2): 213-43.
[http://dx.doi.org/10.1159/000362978] [PMID: 25034869]
[3]
Berglund E, Maaskola J, Schultz N, et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat Commun 2018; 9(1): 2419.
[http://dx.doi.org/10.1038/s41467-018-04724-5] [PMID: 29925878]
[4]
Ji AL, Rubin AJ, Thrane K, et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 2020; 182(2): 497-514.e22.
[http://dx.doi.org/10.1016/j.cell.2020.05.039] [PMID: 32579974]
[5]
Peng G, Suo S, Cui G, et al. Molecular architecture of lineage allocation and tissue organization in early mouse embryo. Nature 2019; 572(7770): 528-32.
[http://dx.doi.org/10.1038/s41586-019-1469-8] [PMID: 31391582]
[6]
Peng G, Cui G, Ke J, Jing N. Using single-cell and spatial transcriptomes to understand stem cell lineage specification during early embryo development. Annu Rev Genomics Hum Genet 2020; 21(1): 163-81.
[http://dx.doi.org/10.1146/annurev-genom-120219-083220] [PMID: 32339035]
[7]
Liu C, Li R, Li Y, et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev Cell 2022; 57(10): 1284-1298.e5.
[http://dx.doi.org/10.1016/j.devcel.2022.04.009] [PMID: 35512701]
[8]
Wang M, Hu Q, Lv T, et al. High-resolution 3D spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae. Dev Cell 2022; 57(10): 1271-1283.e4.
[http://dx.doi.org/10.1016/j.devcel.2022.04.006] [PMID: 35512700]
[9]
Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015; 161(5): 1187-201.
[http://dx.doi.org/10.1016/j.cell.2015.04.044] [PMID: 26000487]
[10]
Zheng GXY, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun 2017; 8(1): 14049.
[http://dx.doi.org/10.1038/ncomms14049] [PMID: 28091601]
[11]
Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015; 161(5): 1202-14.
[http://dx.doi.org/10.1016/j.cell.2015.05.002] [PMID: 26000488]
[12]
Andrews TS, Hemberg M. Identifying cell populations with scRNASeq. Mol Aspects Med 2018; 59: 114-22.
[http://dx.doi.org/10.1016/j.mam.2017.07.002] [PMID: 28712804]
[13]
Baslan T, Hicks J. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat Rev Cancer 2017; 17(9): 557-69.
[http://dx.doi.org/10.1038/nrc.2017.58] [PMID: 28835719]
[14]
Stubbington MJT, Rozenblatt-Rosen O, Regev A, Teichmann SA. Single-cell transcriptomics to explore the immune system in health and disease. Science 2017; 358(6359): 58-63.
[http://dx.doi.org/10.1126/science.aan6828] [PMID: 28983043]
[15]
Hedlund E, Deng Q. Single-cell RNA sequencing: Technical advancements and biological applications. Mol Aspects Med 2018; 59: 36-46.
[http://dx.doi.org/10.1016/j.mam.2017.07.003] [PMID: 28754496]
[16]
Longo SK, Guo MG, Ji AL, Khavari PA. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat Rev Genet 2021; 22(10): 627-44.
[http://dx.doi.org/10.1038/s41576-021-00370-8] [PMID: 34145435]
[17]
Larsson L, Frisén J, Lundeberg J. Spatially resolved transcriptomics adds a new dimension to genomics. Nat Methods 2021; 18(1): 15-8.
[http://dx.doi.org/10.1038/s41592-020-01038-7] [PMID: 33408402]
[18]
Wu SZ, Al-Eryani G, Roden DL, et al. A single-cell and spatially resolved atlas of human breast cancers. Nat Genet 2021; 53(9): 1334-47.
[http://dx.doi.org/10.1038/s41588-021-00911-1] [PMID: 34493872]
[19]
Guilliams M, Bonnardel J, Haest B, et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 2022; 185(2): 379-396.e38.
[http://dx.doi.org/10.1016/j.cell.2021.12.018] [PMID: 35021063]
[20]
Bäckdahl J, Franzén L, Massier L, et al. Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin. Cell Metab 2021; 33(9): 1869-1882.e6.
[http://dx.doi.org/10.1016/j.cmet.2021.07.018] [PMID: 34380013]
[21]
Mantri M, Scuderi GJ, Abedini-Nassab R, et al. Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis. Nat Commun 2021; 12(1): 1771.
[http://dx.doi.org/10.1038/s41467-021-21892-z] [PMID: 33741943]
[22]
Dixon EE, Wu H, Muto Y, Wilson PC, Humphreys BD. Spatially resolved transcriptomic analysis of acute kidney injury in a female murine model. J Am Soc Nephrol 2022; 33(2): 279-89.
[http://dx.doi.org/10.1681/ASN.2021081150] [PMID: 34853151]
[23]
Andersson A, Larsson L, Stenbeck L, et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat Commun 2021; 12(1): 6012.
[http://dx.doi.org/10.1038/s41467-021-26271-2] [PMID: 34650042]
[24]
Cang Z, Nie Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat Commun 2020; 11(1): 2084.
[http://dx.doi.org/10.1038/s41467-020-15968-5] [PMID: 32350282]
[25]
Zhu J, Fan Y, Xiong Y, et al. Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics. Exp Mol Med 2022; 54(11): 2060-76.
[http://dx.doi.org/10.1038/s12276-022-00896-9] [PMID: 36434043]
[26]
Fu R, Norris GA, Willard N, et al. Spatial transcriptomic analysis delineates epithelial and mesenchymal subpopulations and transition stages in childhood ependymoma. Neuro-oncol 2022; 25(4): 786-98.
[http://dx.doi.org/10.1093/neuonc/noac219] [PMID: 36215273]
[27]
Zhu Y, Wu Z, Yan W, et al. Allosteric inhibition of SHP2 uncovers aberrant TLR7 trafficking in aggravating psoriasis. EMBO Mol Med 2022; 14(3): e14455.
[http://dx.doi.org/10.15252/emmm.202114455] [PMID: 34936223]
[28]
Fawkner-Corbett D, Antanaviciute A, Parikh K, et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 2021; 184(3): 810-826.e23.
[http://dx.doi.org/10.1016/j.cell.2020.12.016] [PMID: 33406409]
[29]
Raghubar AM, Pham DT, Tan X, et al. Spatially resolved transcriptomes of mammalian kidneys illustrate the molecular complexity and interactions of functional nephron segments. Front Med (Lausanne) 2022; 9: 873923.
[http://dx.doi.org/10.3389/fmed.2022.873923] [PMID: 35872784]
[30]
Ferreira RM, Sabo AR, Winfree S, et al. Integration of spatial and single-cell transcriptomics localizes epithelial cell–immune cross-talk in kidney injury. JCI Insight 2021; 6(12): e147703.
[http://dx.doi.org/10.1172/jci.insight.147703] [PMID: 34003797]
[31]
Marshall JL, Noel T, Wang QS, et al. High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways. iScience 2022; 25(4): 104097.
[http://dx.doi.org/10.1016/j.isci.2022.104097] [PMID: 35372810]
[32]
Espina V, Wulfkuhle JD, Calvert VS, et al. Laser-capture microdissection. Nat Protoc 2006; 1(2): 586-603.
[http://dx.doi.org/10.1038/nprot.2006.85] [PMID: 17406286]
[33]
Eng CHL, Lawson M, Zhu Q, et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 2019; 568(7751): 235-9.
[http://dx.doi.org/10.1038/s41586-019-1049-y] [PMID: 30911168]
[34]
Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 2019; 363(6434): 1463-7.
[http://dx.doi.org/10.1126/science.aaw1219] [PMID: 30923225]
[35]
Stickels RR, Murray E, Kumar P, et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat Biotechnol 2021; 39(3): 313-9.
[http://dx.doi.org/10.1038/s41587-020-0739-1] [PMID: 33288904]
[36]
Brown VM, Ossadtchi A, Khan AH, et al. Multiplex three-dimensional brain gene expression mapping in a mouse model of Parkinson’s disease. Genome Res 2002; 12(6): 868-84.
[http://dx.doi.org/10.1101/gr.229002] [PMID: 12045141]
[37]
Junker JP, Noël ES, Guryev V, et al. Genome-wide RNA Tomography in the zebrafish embryo. Cell 2014; 159(3): 662-75.
[http://dx.doi.org/10.1016/j.cell.2014.09.038] [PMID: 25417113]
[38]
Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 2015; 348(6233): aaa6090.
[http://dx.doi.org/10.1126/science.aaa6090] [PMID: 25858977]
[39]
Moffitt JR, Bambah-Mukku D, Eichhorn SW, et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 2018; 362(6416): eaau5324.
[http://dx.doi.org/10.1126/science.aau5324] [PMID: 30385464]
[40]
Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M, Cai L. Single-cell in situ RNA profiling by sequential hybridization. Nat Methods 2014; 11(4): 360-1.
[http://dx.doi.org/10.1038/nmeth.2892] [PMID: 24681720]
[41]
Codeluppi S, Borm LE, Zeisel A, et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat Methods 2018; 15(11): 932-5.
[http://dx.doi.org/10.1038/s41592-018-0175-z] [PMID: 30377364]
[42]
Lee JH, Daugharthy ER, Scheiman J, et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat Protoc 2015; 10(3): 442-58.
[http://dx.doi.org/10.1038/nprot.2014.191] [PMID: 25675209]
[43]
Ke R, Mignardi M, Pacureanu A, et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat Methods 2013; 10(9): 857-60.
[http://dx.doi.org/10.1038/nmeth.2563] [PMID: 23852452]
[44]
Wang X, Allen WE, Wright MA, et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 2018; 361(6400): eaat5691.
[http://dx.doi.org/10.1126/science.aat5691] [PMID: 29930089]
[45]
Ståhl PL, Salmén F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 2016; 353(6294): 78-82.
[http://dx.doi.org/10.1126/science.aaf2403] [PMID: 27365449]
[46]
Vickovic S, Eraslan G, Salmén F, et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat Methods 2019; 16(10): 987-90.
[http://dx.doi.org/10.1038/s41592-019-0548-y] [PMID: 31501547]
[47]
Liu Y, Yang M, Deng Y, et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 2020; 183(6): 1665-1681.e18.
[http://dx.doi.org/10.1016/j.cell.2020.10.026] [PMID: 33188776]
[48]
Su G, Qin X, Enninful A, et al. Spatial multi-omics sequencing for fixed tissue via DBiT-seq. STAR Protocols 2021; 2(2): 100532.
[http://dx.doi.org/10.1016/j.xpro.2021.100532] [PMID: 34027489]
[49]
Fu X, Sun L, Chen JY, et al. Continuous polony gels for tissue mapping with high resolution and RNA capture efficiency. BioRxiv 2021; 2021.03.
[http://dx.doi.org/10.1101/2021.03.17.435795]
[50]
Cho CS, Xi J, Si Y, et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 2021; 184(13): 3559-3572.e22.
[http://dx.doi.org/10.1016/j.cell.2021.05.010] [PMID: 34115981]
[51]
Chen A, Liao S, Cheng M, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 2022; 185(10): 1777-1792.e21.
[http://dx.doi.org/10.1016/j.cell.2022.04.003] [PMID: 35512705]
[52]
Lee Y, Bogdanoff D, Wang Y, et al. XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci Adv 2021; 7(17): eabg4755.
[http://dx.doi.org/10.1126/sciadv.abg4755] [PMID: 33883145]
[53]
Srivatsan SR, Regier MC, Barkan E, et al. Embryo-scale, single-cell spatial transcriptomics. Science 2021; 373(6550): 111-7.
[http://dx.doi.org/10.1126/science.abb9536] [PMID: 34210887]
[54]
Crosetto N, Bienko M, van Oudenaarden A. Spatially resolved transcriptomics and beyond. Nat Rev Genet 2015; 16(1): 57-66.
[http://dx.doi.org/10.1038/nrg3832] [PMID: 25446315]
[55]
Asp M, Bergenstråhle J, Lundeberg J. Spatially resolved transcriptomes—next generation tools for tissue exploration. BioEssays 2020; 42(10): 1900221.
[http://dx.doi.org/10.1002/bies.201900221] [PMID: 32363691]
[56]
Waylen LN, Nim HT, Martelotto LG, Ramialison M. From whole-mount to single-cell spatial assessment of gene expression in 3D. Commun Biol 2020; 3(1): 602.
[http://dx.doi.org/10.1038/s42003-020-01341-1] [PMID: 33097816]
[57]
Moses L, Pachter L. Museum of spatial transcriptomics. Nat Methods 2022; 19(5): 534-46.
[http://dx.doi.org/10.1038/s41592-022-01409-2] [PMID: 35273392]
[58]
Strell C, Hilscher MM, Laxman N, et al. Placing RNA in context and space-methods for spatially resolved transcriptomics. FEBS J 2019; 286(8): 1468-81.
[http://dx.doi.org/10.1111/febs.14435] [PMID: 29542254]
[59]
Liao J, Lu X, Shao X, Zhu L, Fan X. Uncovering an organ’s molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends Biotechnol 2021; 39(1): 43-58.
[http://dx.doi.org/10.1016/j.tibtech.2020.05.006] [PMID: 32505359]
[60]
Kumar V, Ramnarayanan K, Sundar R, et al. Single-cell atlas of lineage states, tumor microenvironment, and subtype-specific expression programs in gastric cancer. Cancer Discov 2022; 12(3): 670-91.
[http://dx.doi.org/10.1158/2159-8290.CD-21-0683] [PMID: 34642171]
[61]
Zhang M, Hu S, Min M, et al. Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing. Gut 2021; 70(3): 464-75.
[http://dx.doi.org/10.1136/gutjnl-2019-320368] [PMID: 32532891]
[62]
Smillie CS, Biton M, Ordovas-Montanes J, et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 2019; 178(3): 714-730.e22.
[http://dx.doi.org/10.1016/j.cell.2019.06.029] [PMID: 31348891]
[63]
Yang F, Wang W, Wang F, et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat Mach Intell 2022; 4(10): 852-66.
[http://dx.doi.org/10.1038/s42256-022-00534-z]
[64]
Zhao T, Lyu S, Lu G, et al. SC2disease: A manually curated database of single-cell transcriptome for human diseases. Nucleic Acids Res 2021; 49(D1): D1413-9.
[http://dx.doi.org/10.1093/nar/gkaa838] [PMID: 33010177]
[65]
Zhang X, Lan Y, Xu J, et al. CellMarker: A manually curated resource of cell markers in human and mouse. Nucleic Acids Res 2019; 47(D1): D721-8.
[http://dx.doi.org/10.1093/nar/gky900] [PMID: 30289549]
[66]
Hartigan JA, Wong MA. Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society series C 1979; 28(1): 100-8.
[67]
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech 2008; 2008(10): P10008.
[http://dx.doi.org/10.1088/1742-5468/2008/10/P10008]
[68]
Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: Guaranteeing well-connected communities. Sci Rep 2019; 9(1): 5233.
[http://dx.doi.org/10.1038/s41598-019-41695-z] [PMID: 30914743]
[69]
Rasmussen C. The infinite Gaussian mixture model. Adv Neural Inf Process Syst 1999; 12.
[70]
Aran D, Looney AP, Liu L, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 2019; 20(2): 163-72.
[http://dx.doi.org/10.1038/s41590-018-0276-y] [PMID: 30643263]
[71]
de Kanter JK, Lijnzaad P, Candelli T, Margaritis T, Holstege FCP. CHETAH: A selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res 2019; 47(16): e95.
[http://dx.doi.org/10.1093/nar/gkz543] [PMID: 31226206]
[72]
Andreatta M, Berenstein AJ, Carmona SJ. scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets. Bioinformatics 2022; 38(9): 2642-4.
[http://dx.doi.org/10.1093/bioinformatics/btac141] [PMID: 35258562]
[73]
Bernstein MN, Ma Z, Gleicher M, Dewey CN. CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology. iScience 2021; 24(1): 101913.
[http://dx.doi.org/10.1016/j.isci.2020.101913] [PMID: 33364592]
[74]
Cable DM, Murray E, Zou LS, et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol 2022; 40(4): 517-26.
[http://dx.doi.org/10.1038/s41587-021-00830-w] [PMID: 33603203]
[75]
Andersson A, Bergenstråhle J, Asp M, et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun Biol 2020; 3(1): 565.
[http://dx.doi.org/10.1038/s42003-020-01247-y] [PMID: 33037292]
[76]
Lopez R, Li B, Keren-Shaul H, et al. DestVI identifies continuums of cell types in spatial transcriptomics data. Nat Biotechnol 2022; 40(9): 1360-9.
[http://dx.doi.org/10.1038/s41587-022-01272-8] [PMID: 35449415]
[77]
Kleshchevnikov V, Shmatko A, Dann E, et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat Biotechnol 2022; 40(5): 661-71.
[http://dx.doi.org/10.1038/s41587-021-01139-4] [PMID: 35027729]
[78]
Miller BF, Huang F, Atta L, Sahoo A, Fan J. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat Commun 2022; 13(1): 2339.
[http://dx.doi.org/10.1038/s41467-022-30033-z] [PMID: 35487922]
[79]
Elosua-Bayes M, Nieto P, Mereu E, Gut I, Heyn H. SPOTlight: Seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res 2021; 49(9): e50.
[http://dx.doi.org/10.1093/nar/gkab043] [PMID: 33544846]
[80]
Dong R, Yuan GC. SpatialDWLS: Accurate deconvolution of spatial transcriptomic data. Genome Biol 2021; 22(1): 145.
[http://dx.doi.org/10.1186/s13059-021-02362-7] [PMID: 33971932]
[81]
Song Q, Su J. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence. Brief Bioinform 2021; 22(5): bbaa414.
[http://dx.doi.org/10.1093/bib/bbaa414] [PMID: 33480403]
[82]
Ma Y, Zhou X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat Biotechnol 2022; 40(9): 1349-59.
[http://dx.doi.org/10.1038/s41587-022-01273-7] [PMID: 35501392]
[83]
Danaher P, Kim Y, Nelson B, et al. Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nat Commun 2022; 13(1): 385.
[http://dx.doi.org/10.1038/s41467-022-28020-5] [PMID: 35046414]
[84]
Noel T, Wang QS, Greka A, Marshall JL. Principles of spatial transcriptomics analysis: a practical walk-through in kidney tissue. Front Physiol 2022; 12: 809346.
[http://dx.doi.org/10.3389/fphys.2021.809346] [PMID: 35069263]
[85]
Kleino I. Frolovaitė P, Suomi T, Elo LL. Computational solutions for spatial transcriptomics. Comput Struct Biotechnol J 2022; 20: 4870-84.
[http://dx.doi.org/10.1016/j.csbj.2022.08.043] [PMID: 36147664]
[86]
Zeng Z, Li Y, Li Y, Luo Y. Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biol 2022; 23(1): 83.
[http://dx.doi.org/10.1186/s13059-022-02653-7] [PMID: 35337374]
[87]
Charitakis N, Ramialison M, Nim HT. Comparative analysis of packages and algorithms for the analysis of spatially resolved transcriptomics data. Transcriptomics in Health and Disease 2022; pp. 165-86.
[http://dx.doi.org/10.1007/978-3-030-87821-4_7]
[88]
Li B, Zhang W, Guo C, et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat Methods 2022; 19(6): 662-70.
[http://dx.doi.org/10.1038/s41592-022-01480-9] [PMID: 35577954]
[89]
Chen J, Liu W, Luo T, et al. A comprehensive comparison on cell-type composition inference for spatial transcriptomics data. Brief Bioinform 2022; 23(4): bbac245.
[http://dx.doi.org/10.1093/bib/bbac245] [PMID: 35753702]
[90]
Yan L, Sun X. Benchmarking and integration of methods for deconvoluting spatial transcriptomic data. Bioinformatics 2023; 39(1): btac805.
[http://dx.doi.org/10.1093/bioinformatics/btac805] [PMID: 36515467]
[91]
Dries R, Chen J, del Rossi N, Khan MM, Sistig A, Yuan GC. Advances in spatial transcriptomic data analysis. Genome Res 2021; 31(10): 1706-18.
[http://dx.doi.org/10.1101/gr.275224.121] [PMID: 34599004]
[92]
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 2018; 36(5): 411-20.
[http://dx.doi.org/10.1038/nbt.4096] [PMID: 29608179]
[93]
Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell 2021; 184(13): 3573-3587.e29.
[http://dx.doi.org/10.1016/j.cell.2021.04.048] [PMID: 34062119]
[94]
Bergenstråhle J, Larsson L, Lundeberg J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 2020; 21(1): 482.
[http://dx.doi.org/10.1186/s12864-020-06832-3] [PMID: 32664861]
[95]
Palla G, Spitzer H, Klein M, et al. Squidpy: A scalable framework for spatial omics analysis. Nat Methods 2022; 19(2): 171-8.
[http://dx.doi.org/10.1038/s41592-021-01358-2] [PMID: 35102346]
[96]
Gayoso A, Lopez R, Xing G, et al. A python library for probabilistic analysis of single-cell omics data. Nat Biotechnol 2022; 40(2): 163-6.
[http://dx.doi.org/10.1038/s41587-021-01206-w] [PMID: 35132262]
[97]
Biancalani T, Scalia G, Buffoni L, et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat Methods 2021; 18(11): 1352-62.
[http://dx.doi.org/10.1038/s41592-021-01264-7] [PMID: 34711971]
[98]
Long Y, Ang KS, Li M, et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat Commun 2023; 14(1): 1155.
[http://dx.doi.org/10.1038/s41467-023-36796-3] [PMID: 36859400]
[99]
Sarkar A, Stephens M. Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis. Nat Genet 2021; 53(6): 770-7.
[http://dx.doi.org/10.1038/s41588-021-00873-4] [PMID: 34031584]
[100]
Chen M , Luo S, , Cao M, , et al. Exact distributions for stochastic gene expression models with arbitrary promoter architecture and translational bursting. Phys Rev E 2022; 105(1-1): 014405.
[http://dx.doi.org/10.1103/PhysRevE.105.014405]
[101]
Luo S, Zhang Z, Wang Z, et al. Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model. bioRxiv 2022.
[http://dx.doi.org/10.1101/2022.07.17.500373]
[102]
Zhou T, Zhang J. Analytical results for a multistate gene model. SIAM J Appl Math 2012; 72(3): 789-818.
[http://dx.doi.org/10.1137/110852887]
[103]
Zhang Z, Liang J, Wang Z, Zhang J, Zhou T. Modeling stochastic gene expression: From Markov to non-Markov models. Math Biosci Eng 2020; 17(5): 5304-25.
[http://dx.doi.org/10.3934/mbe.2020287] [PMID: 33120554]
[104]
Peccoud J, Ycart B. Markovian modeling of gene-product synthesis. Theor Popul Biol 1995; 48(2): 222-34.
[http://dx.doi.org/10.1006/tpbi.1995.1027]

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