Our book is available in printed and electronic format at this link. You can use the code LLJM20 for a 20% discount on Routlege.com
Case studies vignettes are available here (excluding the methodological aspects).
The R code for each case study can be downloaded here.
Lê Cao K-A. and Welham Z. Multivariate Data Integration Using R: Methods and Applications with the mixOmics package. CRC Chapman & Hall.
‘…an excellent book for computational biologists, bioinformaticians, statisticians, data scientists, and graduate students who work with high-throughput omics data. [And ]covers most fundamental concepts of multi-omics data integration, while focusing on their implementations in the mixOmics R package.’ [Y Cui, Michigan State Univ, Biometrics, Sept 22]. The handbook complements the suite of cutting-edge tutorials and case studies on our website.
Table of Contents
I Modern biology and multivariate analysis
1. Multi-omics and biological systems
2. The cycle of analysis
3. Key multivariate concepts and dimension reduction in mixOmics
4. Choose the right method for the right question in mixOmics
II mixOmics under the hood
5. Projection to Latent Structures
6. Visualisation for data integration
7. Performance assessment in multivariate analyses
III mixOmics in action
8. mixOmics: get started
9. Principal Component Analysis (PCA)
10. 10 Projection to Latent Structure (PLS)
11. Canonical Correlation Analysis (CCA)
12. PLS – Discriminant Analysis (PLS-DA)
13. N − data integration
14. P − data integration