Visualization of omics data for systems biology

By Nils Gehlenborg, S. O’Donoghue, Nitin S. Baliga, Alexander Goesmann, Matthew A. Hibbs, Hiroaki Kitano, Oliver Kohlbacher, H. Neuweger, Reinhard Schneider, Dan Tenenbaum and 1 more. Nature Methods, 2010

640 citations recorded by the source corpus.

High-throughput studies of biological systems are rapidly accumulating a wealth of 'omics'-scale data. Visualization is a key aspect of both the analysis and understanding of these data, and users now have many visualization methods and tools to choose from. The challenge is to create clear, meaningful and integrated visualizations that give biological insight, without being overwhelmed by the intrinsic complexity of the data. In this review, we discuss how visualization tools are being used to help interpret protein interaction, gene expression and metabolic profile data, and we highlight emerging new directions.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f34cc17937e7623f8e09ef4e3805ce017223d2f7

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Citations in the corpus

Spatially resolved, highly multiplexed RNA profiling in single cells (2015, positive): “To better understand the correlations for all gene pairs, we adopted a hierarchical clustering approach, commonly used in the analysis of both bulk and single-cell expression data (29, 30), to organize these genes on the basis of their correlation coefficients (Fig.”
Ecological metabolomics: overview of current developments and future challenges (2011, methodology): “Recent rapid improvements in analytical methods and in the ability of computer hardware and software to interpret and visualize large data sets (Gehlenborg et al. 2010) have multiplied the possibilities of rapidly identifying and simultaneously quantifying an increasing number of compounds (e.g.,…”
Visualization of image data from cells to organisms (2010, neutral): “The visualization methods suited for this are also commonly used in systems biology: heat maps and projections in two-dimensional maps 57 .”
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle? (2012, background): “[5] and integratively analyse omics-scale data are cru-”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “…al., 2004b yFiles http://www.yworks.com/en/index.html Becker & Rojas, 2001 yWays http://www.yworks.com/en/products_yfiles_extensionpackages_ep2.htm Summaries of Suderman and Hallett (2007), Pavlopoulos et al. (2008), Gehlenborg et al. (2010) and Fung et al. (2012) compare some of the options above.”
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FunRich: An open access standalone functional enrichment and interaction network analysis tool (2015, background): “While the use of genomics, transcriptomics, proteomics and metabolomics has advanced our biological understanding, integrative analysis of this heterogeneous data still remains a daunting task [2,3].”
A survey of two-dimensional graph layout techniques for information visualisation (2013, background): “Graphs that have this kind of structure are actually quite common.(35,43,84,85) The most popular way of visualising these underlying data is to use retinal variables such as the colour (and colour gradients), shape and size of the drawings of the nodes; additionally, glyphs can also be used.”
Hive plots - rational approach to visualizing networks (2012, background): “network visualizations more approachable and informative [20, 29].”