170 related articles for article (PubMed ID: 34293324)
1. AutoGeneS: Automatic gene selection using multi-objective optimization for RNA-seq deconvolution.
Aliee H; Theis FJ
Cell Syst; 2021 Jul; 12(7):706-715.e4. PubMed ID: 34293324
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer.
Lei H; Guo XA; Tao Y; Ding K; Fu X; Oesterreich S; Lee AV; Schwartz R
Bioinformatics; 2022 Jun; 38(Suppl 1):i386-i394. PubMed ID: 35758822
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution.
Cai M; Yue M; Chen T; Liu J; Forno E; Lu X; Billiar T; Celedón J; McKennan C; Chen W; Wang J
Bioinformatics; 2022 May; 38(11):3004-3010. PubMed ID: 35438146
[TBL] [Abstract][Full Text] [Related]
10. Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.
Huuki-Myers LA; Montgomery KD; Kwon SH; Cinquemani S; Eagles NJ; Gonzalez-Padilla D; Maden SK; Kleinman JE; Hyde TM; Hicks SC; Maynard KR; Collado-Torres L
bioRxiv; 2024 Apr; ():. PubMed ID: 38405805
[TBL] [Abstract][Full Text] [Related]
11. De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution.
Liao J; Qian J; Fang Y; Chen Z; Zhuang X; Zhang N; Shao X; Hu Y; Yang P; Cheng J; Hu Y; Yu L; Yang H; Zhang J; Lu X; Shao L; Wu D; Gao Y; Chen H; Fan X
Nat Commun; 2022 Oct; 13(1):6498. PubMed ID: 36310179
[TBL] [Abstract][Full Text] [Related]
12. A2Sign: Agnostic Algorithms for Signatures-a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution.
Boldina G; Fogel P; Rocher C; Bettembourg C; Luta G; Augé F
Bioinformatics; 2022 Jan; 38(4):1015-1021. PubMed ID: 34788798
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.
Kang K; Meng Q; Shats I; Umbach DM; Li M; Li Y; Li X; Li L
PLoS Comput Biol; 2019 Dec; 15(12):e1007510. PubMed ID: 31790389
[TBL] [Abstract][Full Text] [Related]
15. sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data.
Lall S; Ghosh A; Ray S; Bandyopadhyay S
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35037023
[TBL] [Abstract][Full Text] [Related]
16. A novel method for predicting cell abundance based on single-cell RNA-seq data.
Peng J; Han L; Shang X
BMC Bioinformatics; 2021 Aug; 22(Suppl 9):281. PubMed ID: 34433409
[TBL] [Abstract][Full Text] [Related]
17. Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters.
Domanskyi S; Szedlak A; Hawkins NT; Wang J; Paternostro G; Piermarocchi C
BMC Bioinformatics; 2019 Jul; 20(1):369. PubMed ID: 31262249
[TBL] [Abstract][Full Text] [Related]
18. Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology.
Sturm G; Finotello F; Petitprez F; Zhang JD; Baumbach J; Fridman WH; List M; Aneichyk T
Bioinformatics; 2019 Jul; 35(14):i436-i445. PubMed ID: 31510660
[TBL] [Abstract][Full Text] [Related]
19. scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.
Andreatta M; Berenstein AJ; Carmona SJ
Bioinformatics; 2022 Apr; 38(9):2642-2644. PubMed ID: 35258562
[TBL] [Abstract][Full Text] [Related]
20. A mixture model for expression deconvolution from RNA-seq in heterogeneous tissues.
Li Y; Xie X
BMC Bioinformatics; 2013; 14 Suppl 5(Suppl 5):S11. PubMed ID: 23735186
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
