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@article{1372526, author = {Sedlář, Karel and Vídeňská, Petra and Skutkova, Helena and Rychlik, Ivan and Provaznik, Ivo}, article_location = {AUCKLAND}, article_number = {April}, doi = {http://dx.doi.org/10.4137/EBO.S38546}, keywords = {metagenomics; OTU table; 16S rRNA; bipartite graph; visualization analysis; graph modularity}, language = {eng}, issn = {1176-9343}, journal = {EVOLUTIONARY BIOINFORMATICS}, title = {Bipartite Graphs for Visualization Analysis of Microbiome Data}, url = {http://insights.sagepub.com/bipartite-graphs-for-visualization-analysis-of-microbiome-data-article-a5661}, volume = {12}, year = {2016} }
TY - JOUR ID - 1372526 AU - Sedlář, Karel - Vídeňská, Petra - Skutkova, Helena - Rychlik, Ivan - Provaznik, Ivo PY - 2016 TI - Bipartite Graphs for Visualization Analysis of Microbiome Data JF - EVOLUTIONARY BIOINFORMATICS VL - 12 IS - April SP - 17-23 EP - 17-23 PB - LIBERTAS ACAD SN - 11769343 KW - metagenomics KW - OTU table KW - 16S rRNA KW - bipartite graph KW - visualization analysis KW - graph modularity UR - http://insights.sagepub.com/bipartite-graphs-for-visualization-analysis-of-microbiome-data-article-a5661 L2 - http://insights.sagepub.com/bipartite-graphs-for-visualization-analysis-of-microbiome-data-article-a5661 N2 - Visualization analysis plays an important role in metagenomics research. Proper and clear visualization can help researchers get their first insights into data and by selecting different features, also revealing and highlighting hidden relationships and drawing conclusions. To prevent the resulting presentations from becoming chaotic, visualization techniques have to properly tackle the high dimensionality of microbiome data. Although a number of different methods based on dimensionality reduction, correlations, Venn diagrams, and network representations have already been published, there is still room for further improvement, especially in the techniques that allow visual comparison of several environments or developmental stages in one environment. In this article, we represent microbiome data by bipartite graphs, where one partition stands for taxa and the other stands for samples. We demonstrated that community detection is independent of taxonomical level. Moreover, focusing on higher taxonomical levels and the appropriate merging of samples greatly helps improving graph organization and makes our presentations clearer than other graph and network visualizations. Capturing labels in the vertices also brings the possibility of clearly comparing two or more microbial communities by showing their common and unique parts. ER -
SEDLÁŘ, Karel, Petra VÍDEŇSKÁ, Helena SKUTKOVA, Ivan RYCHLIK a Ivo PROVAZNIK. Bipartite Graphs for Visualization Analysis of Microbiome Data. \textit{EVOLUTIONARY BIOINFORMATICS}. AUCKLAND: LIBERTAS ACAD, 2016, roč.~12, April, s.~17-23. ISSN~1176-9343. Dostupné z: https://dx.doi.org/10.4137/EBO.S38546.
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