Statistical methods at the forefront of biomedical advances [electronic resource] / Yolanda Larriba.

Intro
Preface
Contents
1 Multivariate Disease Mapping Models to Uncover Hidden Relationships Between Different Cancer Sites
1.1 Introduction
1.2 M-Models for Multivariate Spatio-Temporal Areal Data
1.2.1 Model Implementation and Identifiability Constraints
1.3 Joint Analysis of Lung, Colorectal, Stomach, and LOCP Cancer Mortality Data in Spanish Provinces
1.3.1 Descriptive Analysis
1.3.2 Model Fitting Using INLA
1.4 Discussion
References
2 Machine Learning Applied to Omics Data
2.1 Introduction
2.2 Data Types
2.2.1 Genomics
2.2.2 Immunomics

2.3 Challenges in the Omics Data Analysis
2.4 Machine Learning Techniques
2.4.1 Random Forests
2.4.2 Multinomial Logistic Regression
2.4.3 Association Rules
2.5 Application
2.5.1 Study Subjects
2.5.2 Material and Methods
2.5.3 Results
2.5.3.1 Random Forest and LASSO Multinomial Logistic Regression
2.5.3.2 Association Rules
2.6 Conclusions and Future Work
Appendix
References
3 Multimodality Tests for Gene-Based Identification of Oncological Patients
3.1 Introduction
3.2 Analysing the Number of Groups

3.3 Application to the Gene Expression of Breast Cancer Patients
3.4 Conclusions and Future Work
Author Contribution Statement
Appendix
Genes Presenting a Multimodal Pattern
References
4 Hippocampus Shape Analysis via Skeletal Models and Kernel Smoothing
4.1 Introduction
4.2 Methodology
4.2.1 Kernel Smoothing on the Polysphere
4.2.1.1 Density Estimation
4.2.1.2 Gradient and Hessian Density Estimation
4.2.1.3 Polysphere-on-Scalar Regression Estimation
4.2.2 Density Ridges
4.2.2.1 Population Euclidean Case
4.2.2.2 Sample Polyspherical Case

4.2.2.3 Bandwidth Selection
4.2.2.4 Euler Iteration
4.2.2.5 Indexing Ridges
4.3 Results
4.3.1 An Illustrative Numerical Example
4.3.2 Main Mode of Variation of Hippocampus Shapes
4.4 Discussion
Proofs
References
5 Application of Quantile Regression Models for Biomedical Data
5.1 Introduction
5.2 The New Testing Procedure
5.2.1 Bootstrap Approximation
5.2.2 Computational Aspects
5.3 Simulation Study
5.4 Real Data Application
5.5 Conclusions
Appendix
References
6 Advances in Cytometry Gating Based on Statistical Distances and Dissimilarities

6.1 Introduction
6.2 Dissimilarities and Distances
6.2.1 Wasserstein Distance
6.2.2 Maximum Mean Discrepancy
6.2.3 Kullback-Leibler Divergence
6.2.4 Hellinger Distance
6.2.5 Friedman-Rafsky Statistic
6.3 Applications to the Gating Workflow
6.3.1 Grouping Cytometric Datasets
6.3.1.1 Ungated Cytometry Datasets
6.3.1.2 Gated Cytometry Datasets
6.3.2 Template Production
6.3.2.1 Ungated Cytometry Datasets
6.3.2.2 Gated Cytometry Datasets
6.3.3 Interpolation Between Cytometry Datasets
6.3.3.1 Gate Transportation
6.3.3.2 Reduction of Batch Effects