Abstract
Recent publications have shown that the majority of studies cannot be adequately reproduced. The underlying causes seem to be diverse. Usage of the wrong statistical tools can lead to the reporting of dubious correlations as significant results. Missing information from lab protocols or other metadata can make verification impossible. Especially with the advent of Big Data in the life sciences and the hereby-involved measurement of thousands of multi-omics samples, researchers depend more than ever on adequate metadata annotation. In recent years, the scientific community has created multiple experimental design standards, which try to define the minimum information necessary to make experiments reproducible. Tools help with creation or analysis of this abundance of metadata, but are often still based on spreadsheet formats and lack intuitive visualizations. We present an interactive graph visualization tailored to experiments using a factorial experimental design. Our solution summarizes sample sources and extracted samples based on similarity of independent variables, enabling a quick grasp of the scientific question at the core of the experiment even for large studies. We support the ISA-Tab standard, enabling visualization of diverse omics experiments. As part of our platform for data-driven biomedical research, our implementation offers additional features to detect the status of data generation and more.
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References
Bostock, M., Ogievetsky, V., Heer, J.: D\(^3\) data-driven documents. IEEE Trans. Vis. Comput. Graph. 17(12), 2301–2309 (2011)
Brazma, A.: Minimum information about a microarray experiment (MIAME)-successes, failures, challenges. Sci. World J. 9, 420–423 (2009)
Brazma, A., et al.: Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat. Genet. 29(4), 365–371 (2001)
Collins, F.S., Tabak, L.A.: NIH plans to enhance reproducibility. Nature 505(7485), 612 (2014)
Fisher, R.: Introduction to “the arrangement of field experiments”. J. Min. Agric. Gr. Br. 33, 503–513 (1926)
Fisher, R.A.: The Design of Experiments. Oliver and Boyd, Edinburgh, London (1937)
Friedrich, A., Kenar, E., Kohlbacher, O., Nahnsen, S.: Intuitive web-based experimental design for high-throughput biomedical data. BioMed Res. Int. 2015 (2015). Article ID 958302, 8 p.
Gonzalez-Beltran, A., Maguire, E., Georgiou, P., Sansone, S.A., Rocca-Serra, P.: Bio-GraphIIn: a graph-based, integrative and semantically-enabled repository for life science experimental data. EMBnet. J. 19(B), 46 (2013)
González-Beltrán, A., Maguire, E., Rocca-Serra, P., Sansone, S.A.: The open source ISA software suite and its international user community: knowledge management of experimental data. EMBnet. J. 18(B), 35 (2012)
González-Beltrán, A., Maguire, E., Sansone, S.A., Rocca-Serra, P.: linkedISA: semantic representation of ISA-Tab experimental metadata. BMC Bioinform. 15(14), S4 (2014)
Haug, K., et al.: Metabolights-an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41(D1), D781–D786 (2012)
Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., Hirakawa, M.: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38(Suppl. 1), D355–D360 (2009)
Lamichhane, S., et al.: Dynamics of plasma lipidome in progression to islet autoimmunity and type 1 diabetes: type 1 diabetes prediction and prevention study (DIPP). bioRxiv, p. 294033 (2018)
Mohr, C., et al.: qPortal: a platform for data-driven biomedical research. PloS One 13(1), e0191603 (2018)
Pettitt, C.: dagre - graph layout for JavaScript (2014). https://github.com/dagrejs/dagre
Rayner, T.F., et al.: A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB. BMC Bioinform. 7(1), 489 (2006)
Reips, U.D., Neuhaus, C.: WEXTOR: a web-based tool for generating and visualizing experimental designs and procedures. Behav. Res. Methods, Instrum., Comput. 34(2), 234–240 (2002)
Sansone, S.A., et al.: The first RSBI (ISA-TAB) workshop: “can a simple format work for complex studies?”. OMICS J. Integr. Biol. 12(2), 143–149 (2008)
Sansone, S.A., et al.: Toward interoperable bioscience data. Nat. Genet. 44(2), 121 (2012)
Spellman, P.T., et al.: Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol. 3(9), research0046-1 (2002)
Taylor, C.F., et al.: The minimum information about a proteomics experiment (MIAPE). Nat. Biotechnol. 25(8), 887–893 (2007)
Tyanova, S., Mann, M., Cox, J.: MaxQuant for in-depth analysis of large SILAC datasets. In: Warscheid, B. (ed.) Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC): Methods and Protocols. MMB, vol. 1188, pp. 351–364. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-1142-4_24
Vasilevsky, N.A., et al.: On the reproducibility of science: unique identification of research resources in the biomedical literature. PeerJ 1, e148 (2013)
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Friedrich, A., de la Garza, L., Kohlbacher, O., Nahnsen, S. (2019). Interactive Visualization for Large-Scale Multi-factorial Research Designs. In: Auer, S., Vidal, ME. (eds) Data Integration in the Life Sciences. DILS 2018. Lecture Notes in Computer Science(), vol 11371. Springer, Cham. https://doi.org/10.1007/978-3-030-06016-9_7
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DOI: https://doi.org/10.1007/978-3-030-06016-9_7
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