Recent plan to start writing a multi-omics (including metagenomic / 16S / transcriptomics / proteomics / metabolomics) correlation analysis of the R package, to avoid duplication of-create the wheel, just in online research under the already existing R package before starting tool part are listed below:
1. mixOmics
Should be in the field of multiple sets of the most famous a R package, a dedicated team, we made more than ten years, and references also relatively high.
官网：http://mixomics.org/
文章：mixOmics: An R package for ‘omics feature selection and multiple data integration
Github：https://github.com/mixOmicsTeam/mixOmics
Bookdown：https://mixomicsteam.github.io/Bookdown/
Features:
- Omics wide data, gene / transcript / protein / metabolism are involved (my role model);
- Unique multi-variable dimensionality reduction analysis and visualization methods (I did not learn statistics, so we do not do too much statistical methods, with emphasis on visualization, including a variety of ways to show the associated angle).
Contents:
Statistical Methods : PCA / IPCA / CCA / PLS / PLS-DA / MixMC / MINT / DIABLO
Visualization : 2D and 3D scatter / correlation network / cluster / correlation graphs / arrows in FIG / DIABLO FIG ring / load FIG.
In addition, this package also comes with a lot of demo data, specifically to see the official documents.
2. tRanslatome
In 2014 he published, but fortunately has been maintained.
Article: tRanslatome: AN R / Translational Control the Bioconductor Package Penalty for to portray
the Bioconductor: https://bioconductor.org/packages/release/bioc/html/tRanslatome.html
of the blog: http://www.mybiosoftware.com/tag/translatome
GitHub: https://github.com/tomateba/tRanslatome (five years ago the source code)
Features:
- Genomics: mainly for the expression of genes associated with, including transcriptome, and proteome translation group;
- Statistical Methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR
- 可视化：scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots
3. OmicsARules
Recently out of a R package, we created a new association method.
文章：OmicsARules: a R package for integration of multi-omics datasets via association rules mining
Github：https://github.com/BioinformaticsSTU/OmicsARules
Features:
- Mainly for genomic and transcriptome data, including gene mutation sites and non-coding an RNA;
- Measurement Method for Lamda3 invention created association rules, visualization is not the point.
4. iCluster / iClusterPlus
iCluster algorithm (United latent variable models) developed a decade ago, multi-omics data clustering for cancer. Last year when they developed a new iClusterPlus package, made some upgrades.
文章1：Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis
Github：https://github.com/cran/iCluster
Bioconductor：https://bioconductor.org/packages/release/bioc/html/iClusterPlus.html
文章2：A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data
This model algorithm based tools, I can only feel powerless and frustrated, will use the good.
5. integrOmics
More than a decade of tools, although at that time made a Bioinformatics, not followed by maintenance updates, scrap it.
文章：integrOmics: an R package to unravel relationships between two omics datasets
Github：https://github.com/cran/integrOmics
6. moCluster
This is an algorithm, and the like icluster, integrate the main data clustering, for cell type or disease and the like molecules. Scenario is relatively simple.
7. MCIA
This package but also said omicade4: the Analysis of Multiple Inertia CO-OMICS Datasets , namely multi-co-inertia analysis. Multivariate data analysis methods, similar to PCA show it, more limited.
Bioconductor：http://bioconductor.org/packages/release/bioc/html/omicade4.html
Github：https://github.com/aedin/omicade4
8. Other
There are many other R package do omics data integration, or, or association based on a new algorithm only for the aspects of gene mutations and gene expression relations, is relatively unpopular, such as:
- CNAmet
- PLRS
- NuChart
- MOO
- Mergeeomics (This package is mainly to do GWAS / TWAS / EWAS / eQTL etc., a bit mean)
In addition to the above usual R package, the plurality of sets of learning more tools and methods which refer to a review: https://jme.bioscientifica.com/view/journals/jme/62/1/JME-18-0055.xml