260 related articles for article (PubMed ID: 35665803)
1. Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types.
Tiong KL; Lin YW; Yeang CH
Biol Open; 2022 Jun; 11(6):. PubMed ID: 35665803
[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. In vitro downregulated hypoxia transcriptome is associated with poor prognosis in breast cancer.
Abu-Jamous B; Buffa FM; Harris AL; Nandi AK
Mol Cancer; 2017 Jun; 16(1):105. PubMed ID: 28619028
[TBL] [Abstract][Full Text] [Related]
4. Transcriptome analysis of heterogeneity in mouse model of metastatic breast cancer.
Ionkina AA; Balderrama-Gutierrez G; Ibanez KJ; Phan SHD; Cortez AN; Mortazavi A; Prescher JA
Breast Cancer Res; 2021 Sep; 23(1):93. PubMed ID: 34579762
[TBL] [Abstract][Full Text] [Related]
5. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.
Sun Z; Wang T; Deng K; Wang XF; Lafyatis R; Ding Y; Hu M; Chen W
Bioinformatics; 2018 Jan; 34(1):139-146. PubMed ID: 29036318
[TBL] [Abstract][Full Text] [Related]
6. Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in human breast cancers.
Yu TJ; Ma D; Liu YY; Xiao Y; Gong Y; Jiang YZ; Shao ZM; Hu X; Di GH
Mol Ther; 2021 Jul; 29(7):2350-2365. PubMed ID: 33677091
[TBL] [Abstract][Full Text] [Related]
7. scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis.
Nale V; Chiodi A; Di Nanni N; Cifola I; Moscatelli M; Cocola C; Gnocchi M; Piscitelli E; Sula A; Zucchi I; Reinbold R; Milanesi L; Mezzelani A; Pelucchi P; Mosca E
BMC Bioinformatics; 2023 Nov; 24(1):445. PubMed ID: 38012590
[TBL] [Abstract][Full Text] [Related]
8. Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes.
Khan A; Katanic D; Thakar J
BMC Bioinformatics; 2017 Jun; 18(1):295. PubMed ID: 28587632
[TBL] [Abstract][Full Text] [Related]
9. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.
Geddes TA; Kim T; Nan L; Burchfield JG; Yang JYH; Tao D; Yang P
BMC Bioinformatics; 2019 Dec; 20(Suppl 19):660. PubMed ID: 31870278
[TBL] [Abstract][Full Text] [Related]
10. Conservation of immune gene signatures in solid tumors and prognostic implications.
Chifman J; Pullikuth A; Chou JW; Bedognetti D; Miller LD
BMC Cancer; 2016 Nov; 16(1):911. PubMed ID: 27871313
[TBL] [Abstract][Full Text] [Related]
11. scClustViz - Single-cell RNAseq cluster assessment and visualization.
Innes BT; Bader GD
F1000Res; 2018; 7():. PubMed ID: 31016009
[TBL] [Abstract][Full Text] [Related]
12. Expression and methylation patterns partition luminal-A breast tumors into distinct prognostic subgroups.
Netanely D; Avraham A; Ben-Baruch A; Evron E; Shamir R
Breast Cancer Res; 2016 Jul; 18(1):74. PubMed ID: 27386846
[TBL] [Abstract][Full Text] [Related]
13. Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.
Gabasova E; Reid J; Wernisch L
PLoS Comput Biol; 2017 Oct; 13(10):e1005781. PubMed ID: 29036190
[TBL] [Abstract][Full Text] [Related]
14. Selecting single cell clustering parameter values using subsampling-based robustness metrics.
Patterson-Cross RB; Levine AJ; Menon V
BMC Bioinformatics; 2021 Feb; 22(1):39. PubMed ID: 33522897
[TBL] [Abstract][Full Text] [Related]
15. FGMD: A novel approach for functional gene module detection in cancer.
Jin D; Lee H
PLoS One; 2017; 12(12):e0188900. PubMed ID: 29244808
[TBL] [Abstract][Full Text] [Related]
16. Discovery of a transcriptomic core of genes shared in 8 primary retinoblastoma with a novel detection score analysis.
Alvarez-Suarez DE; Tovar H; Hernández-Lemus E; Orjuela M; Sadowinski-Pine S; Cabrera-Muñoz L; Camacho J; Favari L; Hernández-Angeles A; Ponce-Castañeda MV
J Cancer Res Clin Oncol; 2020 Aug; 146(8):2029-2040. PubMed ID: 32474753
[TBL] [Abstract][Full Text] [Related]
17. Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data.
Yang Y; Sun H; Zhang Y; Zhang T; Gong J; Wei Y; Duan YG; Shu M; Yang Y; Wu D; Yu D
Cell Rep; 2021 Jul; 36(4):109442. PubMed ID: 34320340
[TBL] [Abstract][Full Text] [Related]
18. Transcriptomic pan-cancer analysis using rank-based Bayesian inference.
Vitelli V; Fleischer T; Ankill J; Arjas E; Frigessi A; Kristensen VN; Zucknick M
Mol Oncol; 2023 Apr; 17(4):548-563. PubMed ID: 36562628
[TBL] [Abstract][Full Text] [Related]
19. Accurate Single-Cell Clustering through Ensemble Similarity Learning.
Jeong H; Shin S; Yeom HG
Genes (Basel); 2021 Oct; 12(11):. PubMed ID: 34828276
[TBL] [Abstract][Full Text] [Related]
20. A robust method to isolate
Gupta V; Lazzaro BP
Fly (Austin); 2022 Dec; 16(1):62-67. PubMed ID: 34612794
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
