186 related articles for article (PubMed ID: 34346485)
1. 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]
2. Improved cell composition deconvolution method of bulk gene expression profiles to quantify subsets of immune cells.
Chiu YJ; Hsieh YH; Huang YH
BMC Med Genomics; 2019 Dec; 12(Suppl 8):169. PubMed ID: 31856824
[TBL] [Abstract][Full Text] [Related]
3. Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data.
Gervin K; Salas LA; Bakulski KM; van Zelm MC; Koestler DC; Wiencke JK; Duijts L; Moll HA; Kelsey KT; Kobor MS; Lyle R; Christensen BC; Felix JF; Jones MJ
Clin Epigenetics; 2019 Aug; 11(1):125. PubMed ID: 31455416
[TBL] [Abstract][Full Text] [Related]
4. The Gene Expression Deconvolution Interactive Tool (GEDIT): accurate cell type quantification from gene expression data.
Nadel BB; Lopez D; Montoya DJ; Ma F; Waddel H; Khan MM; Mangul S; Pellegrini M
Gigascience; 2021 Feb; 10(2):. PubMed ID: 33590863
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Comparative Analysis of Cell Mixtures Deconvolution and Gene Signatures Generated for Blood, Immune and Cancer Cells.
Alonso-Moreda N; Berral-González A; De La Rosa E; González-Velasco O; Sánchez-Santos JM; De Las Rivas J
Int J Mol Sci; 2023 Jun; 24(13):. PubMed ID: 37445946
[TBL] [Abstract][Full Text] [Related]
8. imply: improving cell-type deconvolution accuracy using personalized reference profiles.
Meng G; Pan Y; Tang W; Zhang L; Cui Y; Schumacher FR; Wang M; Wang R; He S; Krischer J; Li Q; Feng H
Genome Med; 2024 Apr; 16(1):65. PubMed ID: 38685057
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing.
Bhattacharya A; Hamilton AM; Troester MA; Love MI
Nucleic Acids Res; 2021 May; 49(8):e48. PubMed ID: 33524140
[TBL] [Abstract][Full Text] [Related]
11. EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.
Racle J; Gfeller D
Methods Mol Biol; 2020; 2120():233-248. PubMed ID: 32124324
[TBL] [Abstract][Full Text] [Related]
12. Deconvolution of heterogeneous tumor samples using partial reference signals.
Qin Y; Zhang W; Sun X; Nan S; Wei N; Wu HJ; Zheng X
PLoS Comput Biol; 2020 Nov; 16(11):e1008452. PubMed ID: 33253170
[TBL] [Abstract][Full Text] [Related]
13. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.
Decamps C; Arnaud A; Petitprez F; Ayadi M; Baurès A; Armenoult L; ; Escalera S; Guyon I; Nicolle R; Tomasini R; de Reyniès A; Cros J; Blum Y; Richard M
BMC Bioinformatics; 2021 Oct; 22(1):473. PubMed ID: 34600479
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples.
Dimitrakopoulou K; Wik E; Akslen LA; Jonassen I
BMC Bioinformatics; 2018 Nov; 19(1):408. PubMed ID: 30404611
[TBL] [Abstract][Full Text] [Related]
16. Complete deconvolution of cellular mixtures based on linearity of transcriptional signatures.
Zaitsev K; Bambouskova M; Swain A; Artyomov MN
Nat Commun; 2019 May; 10(1):2209. PubMed ID: 31101809
[TBL] [Abstract][Full Text] [Related]
17. Cell-type deconvolution from DNA methylation: a review of recent applications.
Titus AJ; Gallimore RM; Salas LA; Christensen BC
Hum Mol Genet; 2017 Oct; 26(R2):R216-R224. PubMed ID: 28977446
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Computational deconvolution of DNA methylation data from mixed DNA samples.
Ferro Dos Santos MR; Giuili E; De Koker A; Everaert C; De Preter K
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38762790
[TBL] [Abstract][Full Text] [Related]
20. Semi-supervised Nonnegative Matrix Factorization for gene expression deconvolution: a case study.
Gaujoux R; Seoighe C
Infect Genet Evol; 2012 Jul; 12(5):913-21. PubMed ID: 21930246
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
