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i2d: an R package for simulating data from images and the implications in biomedical research
Xiaoyu Liang1,2, Ying Hu3, Chunhua Yan3
1Department of Psychiatry, Yale School of Medicine, New Haven, CT 06511, USA.
Bioinformatics (Oxford, England)
|November 27, 2020
Summary
The R package i2d simulates data from images, enabling quantitative analysis in biological research. It also offers novel graph clustering methods for dissecting gene networks.
Area of Science:
- Biomedical research
- Biological research
- Computational biology
Background:
- Image analysis is crucial for biomedical and biological research but is limited by human visual capacity and challenges in quantitative data extraction.
- Computational methods are essential for overcoming these limitations and fully exploiting image data.
Purpose of the Study:
- To introduce a novel R package, i2d, for simulating data from images using digital convolution.
- To enable the extraction and analysis of complex quantitative information from images in biomedical and biological research.
Main Methods:
- The i2d R package utilizes digital convolution to transform image data into simulated datasets.
- The package incorporates three novel and efficient graph clustering algorithms designed for analyzing simulated data.
Main Results:
- The i2d package facilitates the transformation of images into analyzable datasets, enhancing quantitative insights.
- The integrated graph clustering methods effectively dissect complex gene networks into functionally similar sub-clusters.
Conclusions:
- The i2d R package provides a valuable tool for advancing quantitative image analysis in life sciences.
- The package's capabilities support the exploration of gene networks and biological functions through simulated data analysis.
