These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
130 related articles for article (PubMed ID: 39387817)
1. SpatialDeX is a Reference-Free Method for Cell Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors. Liu X; Tang G; Chen Y; Li Y; Li H; Wang X Cancer Res; 2024 Oct; ():. PubMed ID: 39387817 [TBL] [Abstract][Full Text] [Related]
2. Computational solutions for spatial transcriptomics. Kleino I; Frolovaitė P; Suomi T; Elo LL Comput Struct Biotechnol J; 2022; 20():4870-4884. PubMed ID: 36147664 [TBL] [Abstract][Full Text] [Related]
3. 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]
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. 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]
6. stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics. Li K; Li J; Tao Y; Wang F Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38628114 [TBL] [Abstract][Full Text] [Related]
7. SpatialCTD: A Large-Scale Tumor Microenvironment Spatial Transcriptomic Dataset to Evaluate Cell Type Deconvolution for Immuno-Oncology. Ding J; Li L; Lu Q; Venegas J; Wang Y; Wu L; Jin W; Wen H; Liu R; Tang W; Dai X; Li Z; Zuo W; Chang Y; Lei YL; Shang L; Danaher P; Xie Y; Tang J J Comput Biol; 2024 Sep; 31(9):871-885. PubMed ID: 39117342 [TBL] [Abstract][Full Text] [Related]
8. STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks. Li Y; Luo Y Genome Biol; 2024 Aug; 25(1):206. PubMed ID: 39103939 [TBL] [Abstract][Full Text] [Related]
9. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Liu Y; Li N; Qi J; Xu G; Zhao J; Wang N; Huang X; Jiang W; Wei H; Justet A; Adams TS; Homer R; Amei A; Rosas IO; Kaminski N; Wang Z; Yan X Genome Biol; 2024 Oct; 25(1):271. PubMed ID: 39402626 [TBL] [Abstract][Full Text] [Related]
10. RETROFIT: Reference-free deconvolution of cell-type mixtures in spatial transcriptomics. Singh R; He X; Park AK; Hardison RC; Zhu X; Li Q bioRxiv; 2023 Jun; ():. PubMed ID: 37333291 [TBL] [Abstract][Full Text] [Related]
11. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Wan X; Xiao J; Tam SST; Cai M; Sugimura R; Wang Y; Wan X; Lin Z; Wu AR; Yang C Nat Commun; 2023 Nov; 14(1):7848. PubMed ID: 38030617 [TBL] [Abstract][Full Text] [Related]
12. Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks. Li Y; Luo Y bioRxiv; 2023 Jun; ():. PubMed ID: 37333198 [TBL] [Abstract][Full Text] [Related]
13. A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data. Liu Y; Li N; Qi J; Xu G; Zhao J; Wang N; Huang X; Jiang W; Justet A; Adams TS; Homer R; Amei A; Rosas IO; Kaminski N; Wang Z; Yan X bioRxiv; 2023 Aug; ():. PubMed ID: 37662370 [TBL] [Abstract][Full Text] [Related]
14. Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics. Wang L; Hu Y; Gao L Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38426323 [TBL] [Abstract][Full Text] [Related]
15. Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics. Sang-Aram C; Browaeys R; Seurinck R; Saeys Y Elife; 2024 May; 12():. PubMed ID: 38787371 [TBL] [Abstract][Full Text] [Related]
16. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Elosua-Bayes M; Nieto P; Mereu E; Gut I; Heyn H Nucleic Acids Res; 2021 May; 49(9):e50. PubMed ID: 33544846 [TBL] [Abstract][Full Text] [Related]
17. Spatially informed cell-type deconvolution for spatial transcriptomics. Ma Y; Zhou X Nat Biotechnol; 2022 Sep; 40(9):1349-1359. PubMed ID: 35501392 [TBL] [Abstract][Full Text] [Related]
18. A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data. Ospina O; Soupir A; Fridley BL Methods Mol Biol; 2023; 2629():115-140. PubMed ID: 36929076 [TBL] [Abstract][Full Text] [Related]
19. 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]
20. SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics. Swain AK; Pandit V; Sharma J; Yadav P Commun Biol; 2024 May; 7(1):639. PubMed ID: 38796505 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]