Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a \beta-Variational Autoencoder
<|reference_start|>Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a \beta-Variational Autoencoder: Biomedical imaging and RNA sequencing with single-cell resolution improves our understanding of white blood cell diseases like leukemia. By combining morphological and transcriptomic data, we can gain insights into cellular functions and trajectoriess involved in blood cell differentiation. However, existing methodologies struggle with integrating morphological and transcriptomic data, leaving a significant research gap in comprehensively understanding the dynamics of cell differentiation. Here, we introduce an unsupervised method that explores and reconstructs these two modalities and uncovers the relationship between different subtypes of white blood cells from human peripheral blood smears in terms of morphology and their corresponding transcriptome. Our method is based on a beta-variational autoencoder ({\ss}-VAE) with a customized loss function, incorporating a R-CNN architecture to distinguish single-cell from background and to minimize any interference from artifacts. This implementation of {\ss}-VAE shows good reconstruction capability along with continuous latent embeddings, while maintaining clear differentiation between single-cell classes. Our novel approach is especially helpful to uncover the correlation of two latent features in complex biological processes such as formation of granules in the cell (granulopoiesis) with gene expression patterns. It thus provides a unique tool to improve the understanding of white blood cell maturation for biomedicine and diagnostics.<|reference_end|>
@article{mert2024multimodal,
    title={Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid
  Leukemia Patients using a \beta-Variational Autoencoder},
    author={Gizem Mert, Ario Sadafi, Raheleh Salehi, Nassir Navab, Carsten Marr},
    journal={arXiv preprint arXiv:2408.06720},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06720},
    primaryClass={cs.CV cs.LG q-bio.QM}
}