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Title: Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity.
Austin Authors: Halawani, Raid;Buchert, Michael ;Chen, Yi-Ping Phoebe
Affiliation: Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia.
School of Cancer Medicine, La Trobe University, Melbourne, Victoria, Australia.
Olivia Newton-John Cancer Research Institute
Issue Date: 18-Jul-2023
Date: 2023
Publication information: Computers in Biology and Medicine 2023-07-18; 164
Abstract: Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcription profiles and mutation spectra even when originating from a single progenitor cell. Single-cell and spatial transcriptomics sequencing have recently emerged as key technologies for unravelling tumour heterogeneity. Single-cell sequencing promotes individual cell-type identification through transcriptome-wide gene expression measurements of each cell. Spatial transcriptomics facilitates identification of cell-cell interactions and the structural organization of heterogeneous cells within a tumour tissue through associating spatial RNA abundance of cells at distinct spots in the tissue section. However, extracting features and analyzing single-cell and spatial transcriptomics data poses challenges. Single-cell transcriptome data is extremely noisy and its sparse nature and dropouts can lead to misinterpretation of gene expression and the misclassification of cell types. Deep learning predictive power can overcome data challenges, provide high-resolution analysis and enhance precision oncology applications that involve early cancer prognosis, diagnosis, patient survival estimation and anti-cancer therapy planning. In this paper, we provide a background to and review of the recent progress of deep learning frameworks to investigate tumour heterogeneity using both single-cell and spatial transcriptomics data types.
DOI: 10.1016/j.compbiomed.2023.107274
Journal: Computers in Biology and Medicine
Start page: 107274
PubMed URL: 37506451
ISSN: 1879-0534
Type: Journal Article
Subjects: Cancer single-Cell RNA-seq
Cancer spatial transcriptomic
Deep learning
Molecular subtypes
Multi-modal learning
Precision oncology
tumour microenvironment
Appears in Collections:Journal articles

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