580 related articles for article (PubMed ID: 33159064)
21. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.
Jew B; Alvarez M; Rahmani E; Miao Z; Ko A; Garske KM; Sul JH; Pietiläinen KH; Pajukanta P; Halperin E
Nat Commun; 2020 Apr; 11(1):1971. PubMed ID: 32332754
[TBL] [Abstract][Full Text] [Related]
22. CDSeqR: fast complete deconvolution for gene expression data from bulk tissues.
Kang K; Huang C; Li Y; Umbach DM; Li L
BMC Bioinformatics; 2021 May; 22(1):262. PubMed ID: 34030626
[TBL] [Abstract][Full Text] [Related]
23. Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching.
Guo X; Huang Z; Ju F; Zhao C; Yu L
Adv Sci (Weinh); 2024 Feb; 11(7):e2306329. PubMed ID: 38072669
[TBL] [Abstract][Full Text] [Related]
24. A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy.
Deng W; Li B; Wang J; Jiang W; Yan X; Li N; Vukmirovic M; Kaminski N; Wang J; Zhao H
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36631398
[TBL] [Abstract][Full Text] [Related]
25. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments.
Tian L; Dong X; Freytag S; Lê Cao KA; Su S; JalalAbadi A; Amann-Zalcenstein D; Weber TS; Seidi A; Jabbari JS; Naik SH; Ritchie ME
Nat Methods; 2019 Jun; 16(6):479-487. PubMed ID: 31133762
[TBL] [Abstract][Full Text] [Related]
26. Dataset including whole blood gene expression profiles and matched leukocyte counts with utility for benchmarking cellular deconvolution pipelines.
O'Connell GC
BMC Genom Data; 2024 May; 25(1):45. PubMed ID: 38714942
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. Bulk brain tissue cell-type deconvolution with bias correction for single-nuclei RNA sequencing data using DeTREM.
O'Neill NK; Stein TD; Hu J; Rehman H; Campbell JD; Yajima M; Zhang X; Farrer LA
BMC Bioinformatics; 2023 Sep; 24(1):349. PubMed ID: 37726653
[TBL] [Abstract][Full Text] [Related]
29. Tissue-specific deconvolution of immune cell composition by integrating bulk and single-cell transcriptomes.
Chen Z; Ji C; Shen Q; Liu W; Qin FX; Wu A
Bioinformatics; 2020 Feb; 36(3):819-827. PubMed ID: 31504185
[TBL] [Abstract][Full Text] [Related]
30. Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples.
Nadel BB; Oliva M; Shou BL; Mitchell K; Ma F; Montoya DJ; Mouton A; Kim-Hellmuth S; Stranger BE; Pellegrini M; Mangul S
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34346485
[TBL] [Abstract][Full Text] [Related]
31. An accessible, interactive GenePattern Notebook for analysis and exploration of single-cell transcriptomic data.
Mah CK; Wenzel AT; Juarez EF; Tabor T; Reich MM; Mesirov JP
F1000Res; 2018; 7():1306. PubMed ID: 31316748
[TBL] [Abstract][Full Text] [Related]
32. Semi-CAM: A semi-supervised deconvolution method for bulk transcriptomic data with partial marker gene information.
Dong L; Kollipara A; Darville T; Zou F; Zheng X
Sci Rep; 2020 Mar; 10(1):5434. PubMed ID: 32214192
[TBL] [Abstract][Full Text] [Related]
33. A Zipf-plot based normalization method for high-throughput RNA-seq data.
Wang B
PLoS One; 2020; 15(4):e0230594. PubMed ID: 32271772
[TBL] [Abstract][Full Text] [Related]
34. SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencing.
Nelson ME; Riva SG; Cvejic A
BMC Bioinformatics; 2022 Aug; 23(1):328. PubMed ID: 35941549
[TBL] [Abstract][Full Text] [Related]
35. Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.
Nguyen H; Nguyen H; Tran D; Draghici S; Nguyen T
Nucleic Acids Res; 2024 May; 52(9):4761-4783. PubMed ID: 38619038
[TBL] [Abstract][Full Text] [Related]
36. Deconvolution analysis of cell-type expression from bulk tissues by integrating with single-cell expression reference.
Luo Y; Fan R
Genet Epidemiol; 2022 Dec; 46(8):615-628. PubMed ID: 35788983
[TBL] [Abstract][Full Text] [Related]
37. 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]
38. Random forest based similarity learning for single cell RNA sequencing data.
Pouyan MB; Kostka D
Bioinformatics; 2018 Jul; 34(13):i79-i88. PubMed ID: 29950006
[TBL] [Abstract][Full Text] [Related]
39. Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing.
Kim B; Lee E; Kim JK
Methods Mol Biol; 2019; 1935():25-43. PubMed ID: 30758818
[TBL] [Abstract][Full Text] [Related]
40. spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data.
Bae S; Choi H; Lee DS
Genome Med; 2023 Mar; 15(1):19. PubMed ID: 36932388
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]