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1.11 Abbreviations
ОглавлениеIn this book we will use a lot of abbreviations. Below follows a table with abbreviations used including the chapter(s) in which they appear. A small character ‘s’ in front of an abbreviation means ‘sparse’, e.g., sMB-PLS is the method sparse MB-PLS. For many methods mentioned below there are sparse versions; such as sPCA, sPLS, sSCA, sGCA, sMB-PLS and sMB-RDA. These are not mentioned explicitly in the table.
Table 1.2 Abbreviations of the different methods
| Abbreviation | Full Description | Chapter |
|---|---|---|
| ACMTF | Advanced coupled matrix tensor factorisation | 5 |
| ASCA | ANOVA-simultaneous component analysis | 6 |
| BIBFA | Bayesian inter-battery factor analysis | 9 |
| DIABLO | Data integration analysis biomarker latent component omics | 9 |
| DI-PLS | Domain-invariant PLS | 10 |
| DISCO | Distinct and common components | 5 |
| ED-CMTF | Exponential dispersion CMTF | 9 |
| ESCA | Exponential family Simultaneous Component Analysis | 5 |
| GAS | Generalised association study | 4,9 |
| GAC | Generalised association coefficient | 4 |
| GCA | Generalised canonical analysis | 2,5,7 |
| GCD | General coefficient of determination | 4 |
| GCTF | Generalised coupled tensor factorisation | 9 |
| GFA | Group factor analysis | 9 |
| GPA | Generalised Procrustes analysis | 9 |
| GSCA | Generalised simultaneous component analysis | 5 |
| GSVD | Generalised singular value decomposition | 9 |
| IBFA | Inter-battery factor analysis | 9 |
| IDIOMIX | INDORT for mixed variables | 9 |
| INDORT | Individual differences scaling with orthogonal constraints | 9 |
| JIVE | Joint and individual variation explained | 5 |
| LiMM-PCA | Linear mixed model PCA | 6 |
| L-PLS | PLS regression for L-shaped data sets | 8 |
| MB-PLS | Multiblock partial least squares | 7 |
| MB-RDA | Multiblock redundancy analysis | 10 |
| MBMWCovR | Multiblock multiway covariates regression | 10 |
| MCR | Multivariate curve resolution | 5,8 |
| MFA | Multiple factor analysis | 5 |
| MOFA | Multi-omics factor analysis | 9 |
| OS | Optimal-scaling | 2,5 |
| PCA | Principal component analysis | 2,5,8 |
| PCovR | Principal covariates regression | 2 |
| PCR | Principal component regression | 2 |
| PESCA | Penalised ESCA | 9 |
| PE-ASCA | Penalised ASCA | 6 |
| PLS | Partial least squares | 2 |
| PO-PLS | Parallel and orthogonalised PLS regression | 7 |
| RDA | Redundancy analysis | 7 |
| RGCCA | Regularized generalized canonical correlation analysis | 5 |
| RM | Representation matrix approach | 9 |
| ROSA | Response oriented sequential alternation | 7 |
| SCA | Simultaneous component analysis | 2,5 |
| SLIDE | Structural learning and integrative decomposition | 9 |
| SMI | Similarity of matrices index | 4 |
| SO-PLS | Sequential and orthogonalised PLS regression | 7,10 |
