Joint and individual analysis of breast cancer histologic images and genomic covariates

DOI10.1214/20-AOAS1433zbMath1498.62197arXiv1912.00434WikidataQ112684242 ScholiaQ112684242MaRDI QIDQ2078283

Iain Carmichael, Melissa A. Troester, Linnea Olsson, Heather D. Couture, Marc Niethammer, Charles M. Perou, Benjamin C. Calhoun, Katherine A. Hoadley, Joseph Geradts, James Stephen Marron, Jan Hannig

Publication date: 28 February 2022

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.00434

zbMATH Keywords

image analysisinterpretabilitydimensionality reductiongene expressiondeep learningbreast cancer histopathologymultiview data

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Artificial neural networks and deep learning (68T07)

Related Items

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Uses Software

• SciPy
• NumPy
• Matplotlib
• Scikit
• JIVE
• scikit-image
• pandas
• Seaborn
• PyTorch
• ImageNet
• GitHub
• Grad-CAM
• Pathomic Fusion
• pyjive
• Microscope

Cites Work

• Unnamed Item
• Unnamed Item
• Angle-based joint and individual variation explained
• Multivariate analysis and Jacobi ensembles: largest eigenvalue, Tracy-Widom limits and rates of convergence
• Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
• A flag representation for finite collections of subspaces of mixed dimensions
• Modeling Motor Learning Using Heteroscedastic Functional Principal Components Analysis
• Structural learning and integrative decomposition of multi‐view data
• Canonical analysis of several sets of variables

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