219 related articles for article (PubMed ID: 32804935)
1. Deconvolving the contributions of cell-type heterogeneity on cortical gene expression.
Patrick E; Taga M; Ergun A; Ng B; Casazza W; Cimpean M; Yung C; Schneider JA; Bennett DA; Gaiteri C; De Jager PL; Bradshaw EM; Mostafavi S
PLoS Comput Biol; 2020 Aug; 16(8):e1008120. PubMed ID: 32804935
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.
Dai R; Chu T; Zhang M; Wang X; Jourdon A; Wu F; Mariani J; Vaccarino FM; Lee D; Fullard JF; Hoffman GE; Roussos P; Wang Y; Wang X; Pinto D; Wang SH; Zhang C; ; Chen C; Liu C
Sci Adv; 2024 May; 10(21):eadh2588. PubMed ID: 38781336
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Cellular deconvolution of GTEx tissues powers discovery of disease and cell-type associated regulatory variants.
Donovan MKR; D'Antonio-Chronowska A; D'Antonio M; Frazer KA
Nat Commun; 2020 Feb; 11(1):955. PubMed ID: 32075962
[TBL] [Abstract][Full Text] [Related]
6. Comprehensive evaluation of deconvolution methods for human brain gene expression.
Sutton GJ; Poppe D; Simmons RK; Walsh K; Nawaz U; Lister R; Gagnon-Bartsch JA; Voineagu I
Nat Commun; 2022 Mar; 13(1):1358. PubMed ID: 35292647
[TBL] [Abstract][Full Text] [Related]
7. Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data.
Wang J; Roeder K; Devlin B
Genome Res; 2021 Oct; 31(10):1807-1818. PubMed ID: 33837133
[TBL] [Abstract][Full Text] [Related]
8. Not every estimate counts - evaluation of cell composition estimation approaches in brain bulk tissue data.
Toker L; Nido GS; Tzoulis C
Genome Med; 2023 Jun; 15(1):41. PubMed ID: 37287013
[TBL] [Abstract][Full Text] [Related]
9. Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.
Mandric I; Schwarz T; Majumdar A; Hou K; Briscoe L; Perez R; Subramaniam M; Hafemeister C; Satija R; Ye CJ; Pasaniuc B; Halperin E
Nat Commun; 2020 Oct; 11(1):5504. PubMed ID: 33127880
[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. Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.
Fan J; Wang X; Xiao R; Li M
PLoS Genet; 2021 Mar; 17(3):e1009080. PubMed ID: 33661921
[TBL] [Abstract][Full Text] [Related]
12. Accurate estimation of cell-type composition from gene expression data.
Tsoucas D; Dong R; Chen H; Zhu Q; Guo G; Yuan GC
Nat Commun; 2019 Jul; 10(1):2975. PubMed ID: 31278265
[TBL] [Abstract][Full Text] [Related]
13. Cell-Type Heterogeneity in Adipose Tissue Is Associated with Complex Traits and Reveals Disease-Relevant Cell-Specific eQTLs.
Glastonbury CA; Couto Alves A; El-Sayed Moustafa JS; Small KS
Am J Hum Genet; 2019 Jun; 104(6):1013-1024. PubMed ID: 31130283
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. Multimodal joint deconvolution and integrative signature selection in proteomics.
Pan Y; Wang X; Sun J; Liu C; Peng J; Li Q
Commun Biol; 2024 Apr; 7(1):493. PubMed ID: 38658803
[TBL] [Abstract][Full Text] [Related]
17. Single-Cell Deconvolution of Head and Neck Squamous Cell Carcinoma.
Qi Z; Liu Y; Mints M; Mullins R; Sample R; Law T; Barrett T; Mazul AL; Jackson RS; Kang SY; Pipkorn P; Parikh AS; Tirosh I; Dougherty J; Puram SV
Cancers (Basel); 2021 Mar; 13(6):. PubMed ID: 33799782
[TBL] [Abstract][Full Text] [Related]
18. Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors.
Hippen AA; Omran DK; Weber LM; Jung E; Drapkin R; Doherty JA; Hicks SC; Greene CS
Genome Biol; 2023 Oct; 24(1):239. PubMed ID: 37864274
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
