579 related articles for article (PubMed ID: 33159064)
1. Benchmarking of cell type deconvolution pipelines for transcriptomics data.
Avila Cobos F; Alquicira-Hernandez J; Powell JE; Mestdagh P; De Preter K
Nat Commun; 2020 Nov; 11(1):5650. PubMed ID: 33159064
[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. 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]
4. Normalization of Single-Cell RNA-Seq Data.
Risso D
Methods Mol Biol; 2021; 2284():303-329. PubMed ID: 33835450
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Omnibus and robust deconvolution scheme for bulk RNA sequencing data integrating multiple single-cell reference sets and prior biological knowledge.
Chen C; Leung YY; Ionita M; Wang LS; Li M
Bioinformatics; 2022 Sep; 38(19):4530-4536. PubMed ID: 35980155
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. Single cell transcriptomics in human osteoarthritis synovium and in silico deconvoluted bulk RNA sequencing.
Huang ZY; Luo ZY; Cai YR; Chou CH; Yao ML; Pei FX; Kraus VB; Zhou ZK
Osteoarthritis Cartilage; 2022 Mar; 30(3):475-480. PubMed ID: 34971754
[TBL] [Abstract][Full Text] [Related]
11. Data Analysis in Single-Cell Transcriptome Sequencing.
Gao S
Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition.
Dietrich A; Sturm G; Merotto L; Marini F; Finotello F; List M
Bioinformatics; 2022 Sep; 38(Suppl_2):ii141-ii147. PubMed ID: 36124800
[TBL] [Abstract][Full Text] [Related]
14. New generative methods for single-cell transcriptome data in bulk RNA sequence deconvolution.
Nishikawa T; Lee M; Amau M
Sci Rep; 2024 Feb; 14(1):4156. PubMed ID: 38378978
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.
Slovin S; Carissimo A; Panariello F; Grimaldi A; Bouché V; Gambardella G; Cacchiarelli D
Methods Mol Biol; 2021; 2284():343-365. PubMed ID: 33835452
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. Computational deconvolution of transcriptomics data from mixed cell populations.
Avila Cobos F; Vandesompele J; Mestdagh P; De Preter K
Bioinformatics; 2018 Jun; 34(11):1969-1979. PubMed ID: 29351586
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
