OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

Nucleic Acids Res. 2024 Nov 27;52(21):e99. doi: 10.1093/nar/gkae915.

Abstract

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.98 ± 0.02 for lung cancer subtype differentiation and 0.83 ± 0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

MeSH terms

  • Algorithms
  • Area Under Curve
  • Breast Neoplasms* / genetics
  • Breast Neoplasms* / metabolism
  • Breast Neoplasms* / pathology
  • Cell Line, Tumor
  • Deep Learning
  • Female
  • Genomics / methods
  • Humans
  • Lung Neoplasms* / genetics
  • Lung Neoplasms* / metabolism
  • Lung Neoplasms* / pathology
  • Multiomics
  • Neural Networks, Computer