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Fotomics: fourier transform-based omics imagification for deep learning-based cell-identity mapping using single-cell omics profiles

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Abstract

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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Data and code availability

All the datasets used in this study are public and accessible from Gene Expression Omnibus (GEO).The entire code base, including the python implementation of the proposed method and compared techniques, are available at https://github.com/VafaeeLab/Fotomics-Imagification

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Author information

Author notes

  1. Seid Miad Zandavi and Derong Liu have contributed equally to this work.

Authors and Affiliations

  1. School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney, Australia

    Seid Miad Zandavi & Fatemeh Vafaee

  2. School of Computer Science, The University of Sydney, Sydney, Australia

    Derong Liu, Vera Chung & Ali Anaissi

  3. Programs in Metabolism and Medical Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Seid Miad Zandavi

  4. Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA, USA

    Seid Miad Zandavi

  5. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Seid Miad Zandavi

  6. OmniOmics.Ai Pty Ltd, Sydney, NSW, Australia

    Seid Miad Zandavi & Fatemeh Vafaee

  7. UNSW Data Science Hub, University of New South Wales (UNSW), Sydney, Australia

    Fatemeh Vafaee

Authors
  1. Seid Miad Zandavi
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  2. Derong Liu
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  3. Vera Chung
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  4. Ali Anaissi
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  5. Fatemeh Vafaee
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Contributions

FV and SMZ conceived and led the study and guided the method development. SMZ and DL developed the Fotomics method and the corresponding python package. SMZ, DL and FV conducted the analyses and produced the results. FV and SMZ wrote the manuscript. FV produced images. VC and AA provided input on method evaluation. All authors reviewed the manuscript and approved it.

Corresponding author

Correspondence to Fatemeh Vafaee.

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Zandavi, S.M., Liu, D., Chung, V. et al. Fotomics: fourier transform-based omics imagification for deep learning-based cell-identity mapping using single-cell omics profiles. Artif Intell Rev 56, 7263–7278 (2023). https://doi.org/10.1007/s10462-022-10357-4

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  • DOI: https://doi.org/10.1007/s10462-022-10357-4

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