Fusing heterogeneous data sets
arXiv:1908.09653
Abstract
In systems biology, it is common to measure biochemical entities at different levels of the same biological system. One of the central problems for the data fusion of such data sets is the heterogeneity of the data. This thesis discusses two types of heterogeneity. The first one is the type of data, such as metabolomics, proteomics and RNAseq data in genomics. These different omics data reflect the properties of the studied biological system from different perspectives. The second one is the type of scale, which indicates the measurements obtained at different scales, such as binary, ordinal, interval and ratio-scaled variables. In this thesis, we developed several statistical methods capable to fuse data sets of these two types of heterogeneity. The advantages of the proposed methods in comparison with other approaches are assessed using comprehensive simulations as well as the analysis of real biological data sets.
arXiv commentPhD thesis, 173 pages, 60 figures
References in corpus (18)
- Least Angle Regression
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Discussion of "Least angle regression" by Efron et al
- Rejoinder to "Least angle regression" by Efron et al
- Practical Bayesian Optimization of Machine Learning Algorithms
- Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
- A Tutorial on Bayesian Optimization
- Cross-Validation for Unsupervised Learning
- Logistic principal component analysis via non-convex singular value thresholding
- XPCA: Extending PCA for a Combination of Discrete and Continuous Variables
- Multinomial Multiple Correspondence Analysis
- Generalized simultaneous component analysis of binary and quantitative data