Tool's name | Scientific question | Method and tool's characteristics | Data’s characteristics | ||||
---|---|---|---|---|---|---|---|
Supervised Unsupervised | Method families | Summary | Updated | Omics | Hypothesis | ||
BCC (Bayesian Consensus Clustering) | I) Description of samples' interactions | Unsupervised | Statistics | Computes a samples' clustering for each omics dataset by using a probabilistic model, then merges clusters to get a consensus cluster across omics datasets | No | Multi-omics (quantitative) | Normal distribution Different omics on the same set of samples |
iCluster (iClusterPlus / iClusterBayes) | I) Description of samples' interactions | Unsupervised | Statistics / Dimension reduction | Starts with a latent variables regression across datasets by using a probabilistic model, then uses these joint latent variables for samples' clustering | Yes | Multi-omics (quantitative and qualitative) | Linearity assumption Normal noise distribution Different omics on the same set of samples |
JIVE (Joint and Individual Variation Explained) | I) Description of samples/variables' interactions | Unsupervised | Dimension reduction | Decomposes each dataset in three terms: a joint effect (across datasets), an individual effect (specific to the dataset) and a noise effect | No | Multi-omics (quantitative) | Linearity assumption |
LRAcluster (Low-Rank Approximation Cluster) | I) Description of samples' interactions | Unsupervised | Statistics / Dimension reduction | Probabilistically computes a common low-dimensional subspace across omics, then uses the K-means algorithm to cluster samples on this subspace | Yes | Multi-omics (quantitative and qualitative) | Linearity assumption Different omics on the same set of samples |
MCIA (Multiple co-inertia analysis) (MCOA) | I) Description of samples/variables' interactions | Unsupervised | Dimension reduction | Projects each dataset on a subspace, then maximizes co-inertia between subspaces to get major information shared by datasets | Yes | Multi-omics (quantitative) | Linearity assumption Different omics on the same set of samples |
mixKernel | I) Description of samples/variables' interactions II) Variables selection III) Phenotype prediction | Supervised Unsupervised | Dimension reduction | Transforms datasets with kernels, then applies usual dimension reduction methods | Yes | Multi-omics (quantitative and qualitative) | Datasets with the same rows or columns |
mixOmics (with PCA, PLS, rCCA, Diablo…) | I) Description of samples/variables' interactions II) Variables selection III) Phenotype prediction | Supervised Unsupervised | Dimension reduction | Contains many matrix factorization methods for multivariate analysis and functions for data visualization. The main analysis method for one single dataset is the PCA. For two datasets or more, the main methods are the PLS and rCCA, and their extentions for discriminant analysis, variable selection ('sparse') and multi-blocks analysis | Yes | Multi-omics (quantitative and qualitative) | Linearity assumption Datasets with the same rows or columns |
moCluster (from MOGSA) | I) Description of samples' interactions | Unsupervised | Statistics / Dimension reduction | Computes latent variables by using a PCA's extension, then clusters them and finally select the best subtype model | Yes | Multi-omics (quantitative) | Linearity assumption Different omics on the same set of samples |
MOFA (Multi-Omics Factor Analysis)(MOFA2) | I) Description of samples' interactions III) Phenotype prediction | Unsupervised | Statistics / Dimension reduction | Factorizes datasets with a Bayesian approach to get a small number of latent factors usable for different purposes | Yes | Multi-omics (quantitative and qualitative) | Linearity assumpion |
NEMO (NEighborhood based Multi-Omics clustering) | I) Description of samples' interactions | Unsupervised | Similarity-based | Creates one similarity matrix by dataset, then merges them and finally clusters the merged matrix by Spectral clustering | No | Multi-omics (quantitative) | Euclidean distance metric |
PINS (Perturbation clustering for data INtegration and disease Subtyping)(PINSPlus) | I) Description of samples' interactions | Unsupervised | Similarity-based /Network | Does several clustering to identify how often samples are clustered together. Clusterings are made on different datasets, with data perturbed by adding gaussian noise, and different clustering methods are used | Yes | Multi-omics (quantitative) | Different omics on the same set of samples |
RGCCA (Regularized Generalized Canonical Correlation Analysis)(sGCCA) | I) Description of samples/variables' interactions II) Variables selection III) Phenotype prediction | Supervised Unsupervised | Dimension reduction | Computes latent variables for each dataset by maximizing correlations within and/or between datasets | Yes | Multi-omics (quantitative and qualitative) | Linearity assumption Different omics on the same set of samples |
SNF (Similarity Network Fusion) | I) Description of samples' interactions | Unsupervised | Similarity-based /Network | Creates a similarity matrix then an associated network for each dataset, then iteratively fuses the networks to keep only strong correlations between samples across omics | No | Multi-omics (quantitative and qualitative) | Different omics on the same set of samples Euclidean distance metric |