https://github.com/bioFAM/MOFA

Multi-Omics Factor Analysis
https://github.com/bioFAM/MOFA

Last synced: 23 days ago
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Multi-Omics Factor Analysis

• Host: GitHub
• URL: https://github.com/bioFAM/MOFA
• Owner: bioFAM
• License: lgpl-3.0
• Created: 2016-10-28T12:36:48.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-09-30T15:48:39.000Z (almost 4 years ago)
• Last Synced: 2024-05-17T15:06:25.012Z (about 2 months ago)
• Language: R
• Homepage:
• Size: 116 MB
• Stars: 227
• Watchers: 29
• Forks: 55
• Open Issues: 11

Lists

• awesome-multi-omics - MOFA - Argelaguet - multi-omics factor analysis - [paper 1](https://doi.org/10.15252/msb.20178124), [paper 2](https://www.biorxiv.org/content/10.1101/837104v1), [application](https://doi.org/10.1101/519207) (Software packages and methods / Multi-omics correlation or factor analysis)
• awesome-single-cell - MOFA - [python, R] - Multi‐Omics Factor Analysis, a framework for unsupervised integration of multi‐omics data sets. MOFA is a method for disentangling the different sources of heterogeneity in bulk and single-cell multi-omics data sets. It identifies the latent factors that drive unique and shared variability in the different assays. The factors can be used for visualisation, pseudotime reconstruction, imputation, among other functionalities. [Paper](http://msb.embopress.org/content/14/6/e8124) (Software packages / Multi-assay data integration)
• awesome-single-cell - MOFA - [python, R] - Multi‐Omics Factor Analysis, a framework for unsupervised integration of multi‐omics data sets. MOFA is a method for disentangling the different sources of heterogeneity in bulk and single-cell multi-omics data sets. It identifies the latent factors that drive unique and shared variability in the different assays. The factors can be used for visualisation, pseudotime reconstruction, imputation, among other functionalities. [Paper](http://msb.embopress.org/content/14/6/e8124) (Software packages / Multi-assay data integration)
• awesome_single_cell - MOFA - [python, R] - Multi‐Omics Factor Analysis, a framework for unsupervised integration of multi‐omics data sets. MOFA is a method for disentangling the different sources of heterogeneity in bulk and single-cell multi-omics data sets. It identifies the latent factors that drive unique and shared variability in the different assays. The factors can be used for visualisation, pseudotime reconstruction, imputation, among other functionalities. [Paper](http://msb.embopress.org/content/14/6/e8124) (Software packages / Multi-assay data integration)