215 related articles for article (PubMed ID: 37081484)
1. Size matters: the impact of nucleus size on results from spatial transcriptomics.
Mohammadi E; Chojnowska K; Bieńkowski M; Kostecka A; Koczkowska M; Żmijewski MA; Jąkalski M; Ingelsson M; Filipowicz N; Olszewski P; Davies H; Wierzbicka JM; Hyman BT; Dumanski JP; Piotrowski A; Mieczkowski J
J Transl Med; 2023 Apr; 21(1):270. PubMed ID: 37081484
[TBL] [Abstract][Full Text] [Related]
2. Comparative Methods for Demystifying Spatial Transcriptomics.
Sammeth M; Mudra S; Bialdiga S; Hartmannsberger B; Kramer S; Rittner H
Methods Mol Biol; 2024; 2802():515-546. PubMed ID: 38819570
[TBL] [Abstract][Full Text] [Related]
3. Spatial Transcriptomics in Kidney Tissue.
Raghubar AM; Crawford J; Jones K; Lam PY; Andersen SB; Matigian NA; Ng MSY; Healy H; Kassianos AJ; Mallett AJ
Methods Mol Biol; 2023; 2664():233-282. PubMed ID: 37423994
[TBL] [Abstract][Full Text] [Related]
4. SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information.
Li H; Li H; Zhou J; Gao X
Bioinformatics; 2022 Oct; 38(21):4878-4884. PubMed ID: 36063455
[TBL] [Abstract][Full Text] [Related]
5. Tissue RNA Integrity in Visium Spatial Protocol (Fresh Frozen Samples).
Antico F; Gai M; Arigoni M
Methods Mol Biol; 2023; 2584():191-203. PubMed ID: 36495450
[TBL] [Abstract][Full Text] [Related]
6. A comprehensive comparison on cell-type composition inference for spatial transcriptomics data.
Chen J; Liu W; Luo T; Yu Z; Jiang M; Wen J; Gupta GP; Giusti P; Zhu H; Yang Y; Li Y
Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35753702
[TBL] [Abstract][Full Text] [Related]
7. A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex.
Huuki-Myers LA; Spangler A; Eagles NJ; Montgomery KD; Kwon SH; Guo B; Grant-Peters M; Divecha HR; Tippani M; Sriworarat C; Nguyen AB; Ravichandran P; Tran MN; Seyedian A; ; Hyde TM; Kleinman JE; Battle A; Page SC; Ryten M; Hicks SC; Martinowich K; Collado-Torres L; Maynard KR;
Science; 2024 May; 384(6698):eadh1938. PubMed ID: 38781370
[TBL] [Abstract][Full Text] [Related]
8. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST.
Long Y; Ang KS; Li M; Chong KLK; Sethi R; Zhong C; Xu H; Ong Z; Sachaphibulkij K; Chen A; Zeng L; Fu H; Wu M; Lim LHK; Liu L; Chen J
Nat Commun; 2023 Mar; 14(1):1155. PubMed ID: 36859400
[TBL] [Abstract][Full Text] [Related]
9. Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level transcriptome to nuclear morphology.
Zhu S; Kubota N; Wang S; Wang T; Xiao G; Hoshida Y
bioRxiv; 2023 Dec; ():. PubMed ID: 38187541
[TBL] [Abstract][Full Text] [Related]
10. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution.
Li B; Zhang W; Guo C; Xu H; Li L; Fang M; Hu Y; Zhang X; Yao X; Tang M; Liu K; Zhao X; Lin J; Cheng L; Chen F; Xue T; Qu K
Nat Methods; 2022 Jun; 19(6):662-670. PubMed ID: 35577954
[TBL] [Abstract][Full Text] [Related]
11. Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno.
Shi X; Yang Y; Ma X; Zhou Y; Guo Z; Wang C; Liu J
Nucleic Acids Res; 2023 Dec; 51(22):e115. PubMed ID: 37941153
[TBL] [Abstract][Full Text] [Related]
12. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data.
Miller BF; Huang F; Atta L; Sahoo A; Fan J
Nat Commun; 2022 Apr; 13(1):2339. PubMed ID: 35487922
[TBL] [Abstract][Full Text] [Related]
13. A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.
Srinivasan S; Leshchyk A; Johnson NT; Korkin D
RNA; 2020 Oct; 26(10):1303-1319. PubMed ID: 32532794
[TBL] [Abstract][Full Text] [Related]
14. SPACEL: deep learning-based characterization of spatial transcriptome architectures.
Xu H; Wang S; Fang M; Luo S; Chen C; Wan S; Wang R; Tang M; Xue T; Li B; Lin J; Qu K
Nat Commun; 2023 Nov; 14(1):7603. PubMed ID: 37990022
[TBL] [Abstract][Full Text] [Related]
15. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence.
Song Q; Su J
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33480403
[TBL] [Abstract][Full Text] [Related]
16. STGIC: A graph and image convolution-based method for spatial transcriptomic clustering.
Zhang C; Gao J; Chen HY; Kong L; Cao G; Guo X; Liu W; Ren B; Wei DQ
PLoS Comput Biol; 2024 Feb; 20(2):e1011935. PubMed ID: 38416785
[TBL] [Abstract][Full Text] [Related]
17. Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA.
Hu J; Coleman K; Zhang D; Lee EB; Kadara H; Wang L; Li M
Cell Syst; 2023 May; 14(5):404-417.e4. PubMed ID: 37164011
[TBL] [Abstract][Full Text] [Related]
18. BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices.
Tao Y; Sun X; Wang F
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38385877
[TBL] [Abstract][Full Text] [Related]
19. Alignment and integration of spatial transcriptomics data.
Zeira R; Land M; Strzalkowski A; Raphael BJ
Nat Methods; 2022 May; 19(5):567-575. PubMed ID: 35577957
[TBL] [Abstract][Full Text] [Related]
20. STEEL enables high-resolution delineation of spatiotemporal transcriptomic data.
Chen Y; Zhou S; Li M; Zhao F; Qi J
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36857617
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]