Bipartite Graphs for Visualization Analysis of Microbiome Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027162%3A_____%2F16%3AN0000130" target="_blank" >RIV/00027162:_____/16:N0000130 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14310/16:00093546 RIV/00216305:26220/16:PU119240
Result on the web
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888752/" target="_blank" >https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888752/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4137/EBO.S38546" target="_blank" >10.4137/EBO.S38546</a>
Alternative languages
Result language
angličtina
Original language name
Bipartite Graphs for Visualization Analysis of Microbiome Data
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
GJ - Diseases and animal vermin, veterinary medicine
OECD FORD branch
—
Result continuities
Project
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Evolutionary Bioinformatics
ISSN
1176-9343
e-ISSN
—
Volume of the periodical
2016
Issue of the periodical within the volume
12 (Suppl 1)
Country of publishing house
NZ - NEW ZEALAND
Number of pages
7
Pages from-to
17-23
UT code for WoS article
000382989300003
EID of the result in the Scopus database
—