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bigPint: A Bioconductor visualization package that makes big data pint-sized
- Source :
- PLoS Computational Biology, Vol 16, Iss 6, p e1007912 (2020), PLoS Computational Biology
- Publication Year :
- 2020
- Publisher :
- Public Library of Science (PLoS), 2020.
-
Abstract
- Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.<br />Author summary Biological disciplines face the challenge of increasingly large and complex data. One necessary approach toward eliciting information is data visualization. Newer visualization tools incorporate interactive capabilities that allow scientists to extract information more efficiently than static counterparts. In this paper, we introduce technology that allows multiple independent layers of interactive visualization written in open-source code. This technology can be repurposed across various biological problems. Here, we apply this technology to RNA-sequencing data, a popular next-generation sequencing approach that provides snapshots of RNA quantity in biological samples at given moments in time. It can be used to investigate cellular differences between health and disease, cellular changes in response to external stimuli, and additional biological inquiries. RNA-sequencing data is large, noisy, and biased. It requires sophisticated normalization. The most popular open-source RNA-sequencing data analysis software focuses on models, with little emphasis on integrating effective visualization tools. This is despite sound evidence that RNA-sequencing data is most effectively explored using graphical and numerical approaches in a complementary fashion. The software we introduce can make it easier for researchers to use models and visuals in an integrated fashion during RNA-sequencing data analysis.
- Subjects :
- Big Data
0301 basic medicine
Science and Technology Workforce
Source code
Computer science
Big data
Datasets as Topic
Gene Expression
Molecular biology assays and analysis techniques
Careers in Research
Bioconductor
Mathematical and Statistical Techniques
0302 clinical medicine
Computer graphics (images)
Cluster Analysis
Graphics
Biology (General)
Pseudocode
media_common
Nucleic acid analysis
Ecology
Software Engineering
Genomics
Research Assessment
RNA analysis
Reproducibility
Professions
Computational Theory and Mathematics
Modeling and Simulation
Engineering and Technology
Research Article
Computer and Information Sciences
Science Policy
QH301-705.5
media_common.quotation_subject
Research and Analysis Methods
03 medical and health sciences
Cellular and Molecular Neuroscience
Data visualization
Interactivity
Computer Graphics
Genetics
Hierarchical Clustering
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Sequence Analysis, RNA
Software Tools
business.industry
Data Visualization
Computational Biology
Biology and Life Sciences
Genome Analysis
Visualization
Molecular biology techniques
030104 developmental biology
People and Places
Scientists
Population Groupings
business
Software
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 16
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....6e5d2ac1b88d144493873081b33ab4a7