285 related articles for article (PubMed ID: 36906603)
1. Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve.
Charytonowicz D; Brody R; Sebra R
Nat Commun; 2023 Mar; 14(1):1350. PubMed ID: 36906603
[TBL] [Abstract][Full Text] [Related]
2. MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data.
Fan J; Lyu Y; Zhang Q; Wang X; Li M; Xiao R
Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36208175
[TBL] [Abstract][Full Text] [Related]
3. scDeconv: an R package to deconvolve bulk DNA methylation data with scRNA-seq data and paired bulk RNA-DNA methylation data.
Liu Y
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35453146
[TBL] [Abstract][Full Text] [Related]
4. HArmonized single-cell RNA-seq Cell type Assisted Deconvolution (HASCAD).
Chiu YJ; Ni CE; Huang YH
BMC Med Genomics; 2023 Oct; 16(Suppl 2):272. PubMed ID: 37907883
[TBL] [Abstract][Full Text] [Related]
5. NNICE: a deep quantile neural network algorithm for expression deconvolution.
Jin YW; Hu P; Liu Q
Sci Rep; 2024 Jun; 14(1):14040. PubMed ID: 38890415
[TBL] [Abstract][Full Text] [Related]
6. Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces.
Ding J; Regev A
Nat Commun; 2021 May; 12(1):2554. PubMed ID: 33953202
[TBL] [Abstract][Full Text] [Related]
7. stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics.
Li C; Chan TF; Yang C; Lin Z
Bioinformatics; 2023 Oct; 39(10):. PubMed ID: 37862237
[TBL] [Abstract][Full Text] [Related]
8. Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia.
Wang R; Zheng X; Wang J; Wan S; Song F; Wong MH; Leung KS; Cheng L
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136933
[TBL] [Abstract][Full Text] [Related]
9. Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.
Tiong KL; Luzhbin D; Yeang CH
BMC Bioinformatics; 2024 Jun; 25(1):209. PubMed ID: 38867193
[TBL] [Abstract][Full Text] [Related]
10. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.
Maden SK; Kwon SH; Huuki-Myers LA; Collado-Torres L; Hicks SC; Maynard KR
Genome Biol; 2023 Dec; 24(1):288. PubMed ID: 38098055
[TBL] [Abstract][Full Text] [Related]
11. Estimation of immune cell content in tumor using single-cell RNA-seq reference data.
Yu X; Chen YA; Conejo-Garcia JR; Chung CH; Wang X
BMC Cancer; 2019 Jul; 19(1):715. PubMed ID: 31324168
[TBL] [Abstract][Full Text] [Related]
12. A single-cell and spatial RNA-seq database for Alzheimer's disease (ssREAD).
Wang C; Acosta D; McNutt M; Bian J; Ma A; Fu H; Ma Q
Nat Commun; 2024 Jun; 15(1):4710. PubMed ID: 38844475
[TBL] [Abstract][Full Text] [Related]
13. Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis.
Chen Y; Wang Y; Chen Y; Cheng Y; Wei Y; Li Y; Wang J; Wei Y; Chan TF; Li Y
Nat Commun; 2022 Nov; 13(1):6735. PubMed ID: 36347853
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion.
Wang W; Zhou X; Wang J; Yao J; Wen H; Wang Y; Sun M; Zhang C; Tao W; Zou J; Ni T
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37529921
[TBL] [Abstract][Full Text] [Related]
16. Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes.
Cobos FA; Panah MJN; Epps J; Long X; Man TK; Chiu HS; Chomsky E; Kiner E; Krueger MJ; di Bernardo D; Voloch L; Molenaar J; van Hooff SR; Westermann F; Jansky S; Redell ML; Mestdagh P; Sumazin P
Genome Biol; 2023 Aug; 24(1):177. PubMed ID: 37528411
[TBL] [Abstract][Full Text] [Related]
17. Reference-free inferring of transcriptomic events in cancer cells on single-cell data.
Eralp B; Sefer E
BMC Cancer; 2024 May; 24(1):607. PubMed ID: 38769480
[TBL] [Abstract][Full Text] [Related]
18. Single-Cell Capture, RNA-seq, and Transcriptome Analysis from the Neural Retina.
Dharmat R; Kim S; Li Y; Chen R
Methods Mol Biol; 2020; 2092():159-186. PubMed ID: 31786788
[TBL] [Abstract][Full Text] [Related]
19. Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data.
Gan Y; Chen Y; Xu G; Guo W; Zou G
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37313714
[TBL] [Abstract][Full Text] [Related]
20. MLSpatial: A machine-learning method to reconstruct the spatial distribution of cells from scRNA-seq by extracting spatial features.
Zhu M; Li C; Lv K; Guo H; Hou R; Tian G; Yang J
Comput Biol Med; 2023 Jun; 159():106873. PubMed ID: 37105115
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
