Complex heatmaps reveal patterns and correlations in multidimensional genomic data

Bioinformatics

Volumn 32, Issue 18, 2016, Pages 2847-2849

  Gu, Zuguang ^a   Eils, Roland ^a,b   Schlesner, Matthias ^a  

^a GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^b UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GRAPHICS; GENE EXPRESSION; GENOMICS; HUMAN; METABOLISM; SOFTWARE;

COMPUTER GRAPHICS; GENE EXPRESSION; GENOMICS; HUMANS; METABOLIC NETWORKS AND PATHWAYS; SOFTWARE;

EID: 84992187637     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btw313     Document Type: Article

References (6)

1. Buettner, F. et al. (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol., 33, 155-160.
2. Collisson, E.A. et al. (2014) Comprehensive molecular profiling of lung adenocarcinoma. Nature, 511, 543-550.
3. Eisen, M.B. et al. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci, 95, 14863-14868.
4. Galili, T. (2015) dendextend: an R package for visualizing, adjusting, and comparing trees of hierarchical clustering. Bioinformatics, 31, 3718-3720.
5. Gao, J. et al. (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal., 6, pl1.
6. Sturm, D. et al. (2012) Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell, 22, 425-437.