VennPlex-A Novel Venn Diagram Program for Comparing and Visualizing Datasets with Differentially Regulated Datapoints

被引:87
作者
Cai, Huan [1 ]
Chen, Hongyu [2 ]
Yi, Tie [1 ]
Daimon, Caitlin M. [1 ]
Boyle, John P. [3 ]
Peers, Chris [3 ]
Maudsley, Stuart [2 ]
Martin, Bronwen [1 ]
机构
[1] NIA, Metab Unit, NIH, Baltimore, MD 21224 USA
[2] NIA, Receptor Pharmacol Unit, NIH, Baltimore, MD 21224 USA
[3] Univ Leeds, Leeds Inst Genet Hlth & Therapeut, Inst Cardiovasc Res, Leeds, W Yorkshire, England
基金
美国国家卫生研究院;
关键词
CEREBRAL-ISCHEMIA; EULER DIAGRAMS; HYPOXIA; EXPRESSION; ASTROCYTES;
D O I
10.1371/journal.pone.0053388
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
070301 [无机化学]; 070403 [天体物理学]; 070507 [自然资源与国土空间规划学]; 090105 [作物生产系统与生态工程];
摘要
With the development of increasingly large and complex genomic and proteomic data sets, an enhancement in the complexity of available Venn diagram analytical programs is becoming increasingly important. Current freely available Venn diagram programs often fail to represent extra complexity among datasets, such as regulation pattern differences between different groups. Here we describe the development of VennPlex, a program that illustrates the often diverse numerical interactions among multiple, high-complexity datasets, using up to four data sets. VennPlex includes versatile output features, where grouped data points in specific regions can be easily exported into a spreadsheet. This program is able to facilitate the analysis of two to four gene sets and their corresponding expression values in a user-friendly manner. To demonstrate its unique experimental utility we applied VennPlex to a complex paradigm, i.e. a comparison of the effect of multiple oxygen tension environments (1-20% ambient oxygen) upon gene transcription of primary rat astrocytes. VennPlex accurately dissects complex data sets reliably into easily identifiable groups for straightforward analysis and data output. This program, which is an improvement over currently available Venn diagram programs, is able to rapidly extract important datasets that represent the variety of expression patterns available within the data sets, showing potential applications in fields like genomics, proteomics, and bioinformatics.
引用
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页数:6
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