Omics Imagification: Transforming High-throughput Molecular Representation of a Cell into an Image Article Swipe

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Derong Liu , Seid Miad Zandavi , Yuk Ying Chung , Ali Anaissi , Fatemeh Vafaee ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.21203/rs.3.rs-1919175/v1

Different omics profiles, depending on the underlying technology, encompass measurements of several hundred to several thousands of 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 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 these single-cell transcriptomics images accurately classifies diverse cell types outperforming the best-performing scRNA-seq classifiers such as Support Vector Machine and Random Forrest.

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Artificial Intelligence

Computer Science

Convolutional Neural Network

Support Vector Machine

Computational Biology

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Artificial intelligence Pattern recognition (psychology) Computer science Convolutional neural network Representation (politics) Support vector machine Image (mathematics) Sample (material) Computational biology Process (computing) Expression (computer science) Biology Chemistry Political science Politics Chromatography Operating system Law Programming language

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Type: preprint
Language: en
Landing Page: https://doi.org/10.21203/rs.3.rs-1919175/v1
PDF: https://www.researchsquare.com/article/rs-1919175/latest.pdf
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DOI: https://doi.org/10.21203/rs.3.rs-1919175/v1

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Title: Omics Imagification: Transforming High-throughput Molecular Representation of a Cell into an Image

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-08-03

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Authors: Derong Liu, Seid Miad Zandavi, Yuk Ying Chung, Ali Anaissi, Fatemeh Vafaee

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Landing page: https://doi.org/10.21203/rs.3.rs-1919175/v1

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Concepts: Artificial intelligence, Pattern recognition (psychology), Computer science, Convolutional neural network, Representation (politics), Support vector machine, Image (mathematics), Sample (material), Computational biology, Process (computing), Expression (computer science), Biology, Chemistry, Political science, Politics, Chromatography, Operating system, Law, Programming language

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