期刊
ARTIFICIAL INTELLIGENCE REVIEW
卷 56, 期 7, 页码 7263-7278出版社
SPRINGER
DOI: 10.1007/s10462-022-10357-4
关键词
CNN; Single-cell RNA-seq; Omics image; Fast Fourier Transform
This study proposes a method to transform high-dimensional molecular measurements into two-dimensional images, improving the representation and classification of biological samples using automated image recognition methods.
Different omics profiles, depending on the underlying technology, encompass measurements of several hundred to several thousand molecules in a biological sample or a cell. This study develops upon the concept of omics imagification as a process of transforming a vector representing these numerical measurements into an image with a one-to-one relationship with the corresponding sample. The proposed imagification process transforms a high-dimensional vector of molecular measurements into a two-dimensional RGB image to enable holistic molecular representation of a biological sample and to improve the classification of different biological phenotypes using automated image recognition methods in computer vision. A transformed image represents 2D coordinates of molecules in a neighbour-embedded space representing molecular abundance and gene intensity. The proposed method was applied to a single-cell RNA sequencing (scRNA-seq) data to imagify gene expression profiles of individual cells. Our results show that a simple convolutional neural network trained on single-cell transcriptomics images accurately classifies diverse cell types outperforming the best-performing scRNA-seq classifiers such as support vector machine and random forest.
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