Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-multi-omics
List of software packages for multi-omics analysis
https://github.com/mikelove/awesome-multi-omics
- paper 1 - 6115.1406)
- paper
- PMA - Witten - Sparse Multi CCA - [paper 1](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2697346/), [paper 2](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2861323/)
- paper
- gesca - Hwang - RGSCA regularized generalized structured component analysis - [paper](https://doi.org/10.1007/s11336-009-9119-y)
- paper
- RGCCA - Tenenhaus - Regularized Generalized CCA and Sparse Generalized CCA - [paper 1](https://www.ncbi.nlm.nih.gov/pubmed/28536930), [paper 2](https://www.ncbi.nlm.nih.gov/pubmed/24550197)
- paper
- scca - Lee - Sparse Canonical Covariance Analysis for High-throughput Data - [paper](https://doi.org/10.2202/1544-6115.1638)
- STATIS/DiSTATIS - Abdi - structuring three-way statistical tables - [paper](https://doi.org/10.1002/wics.198)
- paper
- paper
- paper
- RIMBANET - Zhu - Reconstructing Integrative Molecular Bayesian Networks - [paper](https://doi.org/10.1371/journal.pbio.1001301)
- FactoMineR - Abdi - MFA: multiple factor analysis - [paper](https://doi.org/10.1002/wics.1246)
- JIVE - Lock - joint & individual variance explained - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3671601/)
- pandaR - Schlauch - Passing Attributes between Networks for Data Assimilation - [paper](https://doi.org/10.1093/bioinformatics/btx139)
- omicade4 - Meng - MCIA: multiple co-interia analysis - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053266/)
- STATegRa - Planell - DISCO, JIVE, & O2PLS - [paper](https://doi.org/10.3389/fgene.2021.620453)
- paper
- GFAsparse - Khan - group factor analysis sparse [paper 1](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147909/), [paper 2](https://doi.org/10.1093/bioinformatics/btw207)
- paper 1 - AOS1519), [paper 3](https://arxiv.org/abs/1311.6186)
- CCAGFA - Klami - Bayesian Canonical Correlation Analysis and Group Factor Analysis - [paper 1](https://doi.org/10.1109/TNNLS.2014.2376974), [paper 2](http://www.jmlr.org/papers/v18/16-509.html)
- CMF - Klami - collective matrix factorization - [paper](https://arxiv.org/abs/1312.5921)
- moGSA - Meng - multi-omics gene set analysis - [paper](https://doi.org/10.1101/046904)
- iNMF - Yang - integrative NMF - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/26377073/)
- BASS - Zhao - Bayesian group factor analysis - [paper](https://arxiv.org/abs/1411.2698)
- missMDA - Voillet - multiple imputation for multiple factor analysis (MI-MFA) - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048483/)
- PLSCA - Beaton - Partial Least Square Correspondence Analysis - [paper](https://doi.org/10.1037/met0000053)
- mixOmics - Rohart - various methods - [paper1](https://doi.org/10.1371/journal.pcbi.1005752), [paper2](https://doi.org/10.1093/bioinformatics/bty1054)
- mixedCCA - Yoon - sparse CCA for data of mixed types - [paper](https://arxiv.org/abs/1807.05274)
- SLIDE - Gaynanova - Structural Learning and Integrative Decomposition of Multi-View Data - [paper](https://arxiv.org/abs/1707.06573)
- fCCAC - Madrigal - functional canonical correlation analysis to evaluate covariance - [paper](https://doi.org/10.1093/bioinformatics/btw724)
- TSKCCA - Yoshida - Sparse kernel canonical correlation analysis - [paper](https://doi.org/10.1186/s12859-017-1543-x)
- paper
- AJIVE - Feng - angle-based JIVE - [paper](https://arxiv.org/abs/1704.02060)
- 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)
- PCA+CCA - Brown - [paper](https://doi.org/10.1371/journal.pgen.1007841)
- JACA - Zhang - Joint Association and Classification Analysis - [paper](https://arxiv.org/abs/1811.08511)
- paper
- pCIA - Min - penalized COI - [paper](https://www.ncbi.nlm.nih.gov/pubmed/30165424)
- paper
- paper
- OmicsPLS - Bouhaddani - O2PLS implemented in R, with an alternative cross-validation scheme - [paper](https://doi.org/10.1186/s12859-018-2371-3)
- SCCA-BC - Pimentel - Biclustering by sparse canonical correlation analysis - [paper](https://doi.org/10.1007/s40484-017-0127-0)
- mixKernel - Mariette - kernel method for unsupervised multi-omics integration - [paper 1](http://dx.doi.org/10.1093/bioinformatics/btx682), [paper 2](http://dx.doi.org/10.1093/nargab/lqac014)
- WON-PARAFAC - Kim - weighted orthogonal nonnegative parallel factor analysis - [paper](https://doi.org/10.1038/s41467-019-13027-2)
- BIDIFAC - Park - bidimensional integrative factorization - [paper 1](https://doi.org/10.1111/biom.13141), [paper 2](https://arxiv.org/abs/2002.02601)
- SmCCNet - Shi - sparse multiple canonical correlation network analysis - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6931269/)
- msPLS - Csala - multiset sparse partial least squares path modeling - [paper](https://doi.org/10.1186/s12859-019-3286-3)
- paper
- D-CCA - Shu - Decomposition-based Canonical Correlation Analysis - [paper](https://doi.org/10.1080/01621459.2018.1543599)
- COMBI - Hawinkel - Compositional Omics Model-Based Integration - [paper](https://doi.org/10.1093/nargab/lqaa050)
- DPCCA - Gundersen - Deep Probabilistic CCA - [paper](http://proceedings.mlr.press/v115/gundersen20a.html)
- MEFISTO - Velten - spatial or temporal relationships - [preprint](https://doi.org/10.1101/2020.11.03.366674)
- MultiPower - Tarazona - Sample size in multi-omic experiments - [paper](https://doi.org/10.1038/s41467-020-16937-8)
- mixedCCA - Yoon - sparse CCA for data of mixed types - [paper](https://arxiv.org/abs/1807.05274)
- smCIA/ssmCIA - Min - Sparse (structured sparse) multiple co-Inertia analysis - [paper](https://doi.org/10.1186/s12859-020-3455-4)
- MuVI - Qoku - Integrate noisy feature sets - [paper](https://arxiv.org/abs/2204.06242)
- paper
- ade4 - Dray - Implementing the Duality Diagram for Ecologists - [paper](http://dx.doi.org/10.18637/jss.v022.i04)
- paper
- paper - 128X(199609)10:5/6%3C463::AID-CEM445%3E3.0.CO;2-L)
- paper
- paper
- THEME - Verron - THEmatic Model Exploration - [paper](https://doi.org/10.1002/cem.2759)
- paper 1
- iCluster - Shen - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2800366/)
- PSDF - Yuan - Data fusion by Bayesian nonparametric Dirichlet modeling - [website](https://sites.google.com/site/patientspecificdatafusion/), [publication](https://doi.org/10.1371/journal.pcbi.1002227)
- MDI - Kirk - [paper1](https://academic.oup.com/bioinformatics/article/28/24/3290/244641), [paper2](https://www.degruyter.com/document/doi/10.1515/sagmb-2015-0055/html)
- iClusterPlus - Mo - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3600490/)
- BCC - Lock - Bayesian consensus clustering - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3789539/)
- iBAG - Wang - Integrative Bayesian Analysis of Genomics - [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3546799/)
- SNF - Wang - [paper](https://www.ncbi.nlm.nih.gov/pubmed/24464287)
- paper
- clusternomics - Gabasova - [paper](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005781)
- publication
- IBOOST - Wong - [paper](https://doi.org/10.1186/s13059-019-1640-4)
- Spectrum - John - [paper](https://doi.org/10.1093/bioinformatics/btz704)
- NEMO - Rappoport - Similarity-based Clustering - [paper](https://doi.org/10.1093/bioinformatics/btz058)
- INF - Chierici and Bussola - [paper](https://doi.org/10.1101/2020.04.01.020685)
- ClustOmics - Brière - Consensus clustering - [paper](https://doi.org/10.1186/s12859-021-04279-1)
- MOGONET - Tongxin Wang - Multi-Omics Graph cOnvolutional NETworks - [paper](https://pubmed.ncbi.nlm.nih.gov/34103512/)
- maui - Ronen - Stacked VAE + clustering predictive of survival - [paper](https://doi.org/10.26508/lsa.201900517)
- IntegrativeVAEs - Simidjievski - Variational autoencoders + classification - [paper](https://doi.org/10.3389/fgene.2019.01205)
- OmiVAE - Xiaoyu Zhang - Integrated Multi-omics Analysis Using Variational Autoencoders - [paper](https://arxiv.org/abs/1908.06278)
- DeepProg - Poirion - DL and ML ensemble + survival prediction - [paper](https://doi.org/10.1186/s13073-021-00930-x)
- SHAE - Wissel - Supervised Hierarchical Autoencoder + survival prediction - [preprint](https://doi.org/10.1101/2021.09.16.460589)
- MolTi-DREAM - Didier - identifying communities from multiplex networks, and annotated the obtained clusters [article](https://dx.doi.org/10.12688%2Ff1000research.15486.2)
- NetICS - Christos Dimitrakopoulos - Network-based integration of multi-omics data for prioritizing cancer genes - [paper](https://pubmed.ncbi.nlm.nih.gov/29547932/)
- RWR-MH - Valdeolivas - Random walk with restart on multiplex and heterogeneous biological networks [article](https://doi.org/10.1093/bioinformatics/bty637)
- MOGAMUN - Novoa-del-toro - A multi-objective genetic algorithm to find active modules in multiplex biological networks [preprint](https://www.biorxiv.org/content/10.1101/2020.05.25.114215v1)
- RWRF - Wen - Random Walk with Restart for multi-dimensional data Fusion [paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04029-3)
- cardelino - - gene expression states to clones (SNVs from scRNA-seq + bulk exome data) -
- clonealign - Campbell - gene expression states to clones (scRNA-seq + scDNA-seq (CNV)) - [paper](https://doi.org/10.1101/344309)
- CiteFuse - Kim - CITE-seq data analysis [paper](https://doi.org/10.1093/bioinformatics/btaa282)
- CoSpar - Wang - infer dynamics by integrating state and lineage information - [paper](https://www.biorxiv.org/content/10.1101/2021.05.06.443026v1)
- MSFA - De Vito - multi-study factor analysis: same features, different samples - [paper](https://arxiv.org/abs/1611.06350)
- InterSIM - Chalise - methylation, gene expression and protein expression - [paper](https://dx.doi.org/10.1016%2Fj.cmpb.2016.02.011)
- MOSim - Martinez-Mira - RNA-seq, ATAC-seq (DNase-seq), ChIP-seq, small RNA-seq and Methyl-seq. - [preprint](http://dx.doi.org/10.1101/421834)
- OmicsSIMLA - Chung - DNA, CNV, WGBS, RNAseq, Protein expression - [paper](https://doi.org/10.1093/gigascience/giz045)
- Multivariate data analysis: The French way
- A practical data processing workflow for multi-OMICS projects
- Measuring multivariate association and beyond
- Multi-omic data integration enables discovery of hidden biological regularities
- Dimension reduction techniques for the integrative analysis of multi-omics data
- A review on machine learning principles for multi-view biological data integration
- More Is Better: Recent Progress in Multi-Omics Data Integration Methods
- Multi-omics approaches to disease
- Statistical data integration: Challenges and opportunities
- Multi-omic and multi-view clustering algorithms: review and cancer benchmark
- Current multiblock methods: Competition or complementarity? A comparative study in a unified framework
- Integrative omics for health and disease
- Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data
- Integrated omics: tools, advances and future approaches
- Evaluation of integrative clustering methods for the analysis of multi-omics data
- Consistency and overfitting of multi-omics methods on experimental data - [code](https://github.com/mccabes292/movie)
- Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration
- Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection
- Multitable methods for microbiome data integration - [code](https://github.com/krisrs1128/multitable_review)
- Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis
- Large-scale benchmark study of survival prediction methods using multi-omics data - [code](https://github.com/HerrMo/multi-omics_benchmark_study)
- Multiview learning for understanding functional multiomics
- Metabolomics and multi-omics integration: a survey of computational methods and resources
- Benchmarking joint multi-omics dimensionality reduction approaches for cancer study
- Multi-omics Data Integration, Interpretation, and Its Application
- State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing - [code](https://github.com/krassowski/multi-omics-state-of-the-field)
- Data-Driven Modeling of Pregnancy-Related Complications
- Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis
- Machine learning for multi-omics data integration in cancer
- A multivariate analysis approach to the integration of proteomic and gene expression data
- The duality diagram in data analysis: Examples of modern applications - [R notebook](http://lbbe-shiny.univ-lyon1.fr/Reproducible_Research/06-AAS.Thioulouse.2011/)
- Integrative omics analysis. A study based on Plasmodium falciparum mRNA and protein data
- A community effort to assess and improve drug sensitivity prediction algorithms
- Inferring gene–gene interactions and functional modules using sparse canonical correlation analysis
- TCGA2STAT: simple TCGA data access for integrated statistical analysis in R - [R notebook](http://www.liuzlab.org/TCGA2STAT/)
- Integrating single-cell transcriptomic data across different conditions, technologies, and species.
- Reference trait analysis reveals correlations between gene expression and quantitative traits in disjoint samples - [R notebook](https://daskelly.github.io/reference_traits/reference_trait_analysis_walkthrough.html)
- Comprehensive integration of single cell data
- Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology
- Identifying subpathway signatures for individualized anticancer drug response by integrating multi-omics data
- Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy - omics in pregnancy using stacked generalization
- MultiAssayExperiment - Ramos - Software for the integration of multi-omics experiments in Bioconductor - [paper](https://doi.org/10.1158/0008-5472.CAN-17-0344).
- muon - Bredikhin - [Multimodal omics analysis framework](https://doi.org/10.1101/2021.06.01.445670)
- MultiBaC - Ugidos - [MultiBaC: A strategy to remove batch effects between different omic data types](https://doi.org/10.1177/0962280220907365) and [R package publication](https://doi.org/10.1093/bioinformatics/btac132)
- Mathematical Frameworks for Integrative Analysis of Emerging Biological Data Types - [Hackathon details](https://github.com/BIRSBiointegration/Hackathon) - June 14-19, 2020 in Banff, Canada
Keywords
bioinformatics
4
multi-omics
3
single-cell
3
cancer-genomics
2
data-integration
2
scrna-seq
2
high-dimensional-data
1
data-fusion
1
unsupervised-learning
1
parafac
1
matlab
1
genomics
1
principal-component-analysis
1
pls
1
pca
1
partial-least-squares-regression
1
omics
1
latent-variable-models
1
biostatistics
1
bioc-devel
1
metric-multidimensional-scaling
1
distatis
1
3-way-mds
1
scverse
1
scatac-seq
1
scanpy
1
muon
1
multimodal-omics-analysis
1
multimodal-data
1
mudata
1
cite-seq
1
anndata
1
r
1
gene-expression
1
somatic-mutations
1
gibbs-sampling
1
clonal-clustering
1
latent-factor-model
1
deep-learning
1
autoencoder
1
multiview
1
multiblock-structures
1
integrative-analysis
1