OmicCircos: A Simple-to-Use R Package for the Circular Visualization of Multidimensional Omics Data
Ying Hu1, Chunhua Yan1, Chih-Hao Hsu1
1Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD 20850, USA.
Cancer Informatics
|February 15, 2014
Summary
OmicCircos is an R package for visualizing genomic variations like mutations and CNVs. It offers advanced features for detailed analysis and high-quality circular plots in bioinformatics.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Genomic variations require advanced visualization tools.
- Existing tools may lack comprehensive features for diverse data types.
Purpose of the Study:
- To introduce OmicCircos, an R package for visualizing genomic variations.
- To provide a flexible platform for integrating and analyzing diverse genomic data.
Main Methods:
- OmicCircos utilizes R/Bioconductor environment for statistical and graphic functions.
- It supports various visualization formats including scatterplots, line plots, heatmaps, and text labels.
- Features include independent track drawing, zoom functions, and link-polygons.
Main Results:
- OmicCircos generates high-quality circular plots for genomic variations.
- It visualizes mutation patterns, copy number variations (CNVs), expression, and methylation patterns.
- The package facilitates representation of relationships among genomic features.
Conclusions:
- OmicCircos is a versatile R package for comprehensive genomic variation visualization.
- Its unique features enable detailed analysis and integration of multi-omic data.
- The software is open-source and available via Bioconductor.
Related Concept Videos
01:22Interpreting R Charts
504
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
504
01:11Overview of Minitab
1.1K
Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to...
1.1K
01:11Introduction to R
4.4K
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
4.4K
01:25Statistical Methods for Analyzing Epidemiological Data
1.3K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.3K
01:20Biostatistics: Overview
1.2K
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
1.2K
01:12Statistical Software for Data Analysis and Clinical Trials
1.7K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.7K