Gemelli is a tool box for running Robust Aitchison PCA (RPCA), Joint Robust Aitchison PCA (Joint-RPCA), TEMPoral TEnsor Decomposition (TEMPTED), and Compositional Tensor Factorization (CTF) on sparse compositional omics datasets.
RPCA can be used on cross-sectional datasets where each subject is sampled only once. CTF can be used on repeated-measure data where each subject is sampled multiple times (e.g. longitudinal sampling). TEMPTED is specifically designed for longitundal (time series) repeated measure studies, especially when samples are irregularly sampled across subjects. Joint-RPCA allows for the exploration of multiple omics datasets with shared samples at once. All these methods are unsupervised and aim to describe sample/subject variation and the biological features that separate them.
The preprocessing transform for both RPCA and CTF is the robust centered log-ratio transform (rlcr) which accounts for sparse data (i.e. many missing/zero values). Details on the rclr can be found here and a interactive introduction into the transformation can be found here. In short, the rclr log transforms the observed (nonzero) values before centering. RPCA and CTF then perform a matrix or tensor factorization on only the observed values after rclr transformation, similar to Aitchison PCA performed on dense data. If the data also has an associated phylogeny it can be incorporated through the phylogenetic rclr, details can be found here.
To install the most up to date version of gemelli, run the following command
# pip (only supported for QIIME2 >= 2018.8)
pip install gemelli
Note: that gemelli is not compatible with python 2, and is compatible with Python 3.4 or later.
Gemelli can be run standalone or through QIIME2 and as a python API or CLI.
If you have a cross-sectional study design with only one sample per subject then RPCA is the appropriate method to use in gemelli. For examples of using RPCA we provide tutorials below exploring the microbiome between body sites.
Joint-RPCA allows for the exploration of those feature that seperate jointly across sample groupings and the potential interactions of those features.
If you have a repeated measures study design with multiple samples per subject over time or space then CTF is the appropriate method to use in gemelli. For optimal results CTF requires samples for each subject in each time or space measurement. If that is not the case and your study has irregular time sampling, then TEMPTED should be used. TEMPTED also allows for the projection of new data into an existing factorization which is necessary for machine learning. For examples, explore the tutorials below.
- CTF QIIME2 CLI
- CTF QIIME2 API
- Phylogenetic CTF QIIME2 CLI
- Phylogenetic CTF QIIME2 API
- TEMPTED QIIME2 CLI
- TEMPTED QIIME2 API
For an introduction to these QC methods see the tutorial here. Examples are also provided in the RPCA tutorials here (RPCA QIIME2 CLI) & here (RPCA Python API & CLI). Users are encrouaged to report the QC/CV results for thier data.
If you found this tool useful please cite the method(s) you used:
Martino, C. and Shenhav, L. et al. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat. Biotechnol. (2020) doi:10.1038/s41587-020-0660-7
@article {Martino2020,
author = {Martino, Cameron and Shenhav, Liat and Marotz, Clarisse A and Armstrong, George and McDonald, Daniel and V{\'a}zquez-Baeza, Yoshiki and Morton, James T and Jiang, Lingjing and Dominguez-Bello, Maria Gloria and Swafford, Austin D and Halperin, Eran and Knight, Rob},
title = {Context-aware dimensionality reduction deconvolutes gut microbial community dynamics},
year = {2020},
journal = {Nature biotechnology},
}
Martino, C. et al. A Novel Sparse Compositional Technique Reveals Microbial Perturbations. mSystems 4, (2019)
@article {Martino2019,
author = {Martino, Cameron and Morton, James T. and Marotz, Clarisse A. and Thompson, Luke R. and Tripathi, Anupriya and Knight, Rob and Zengler, Karsten},
editor = {Neufeld, Josh D.},
title = {A Novel Sparse Compositional Technique Reveals Microbial Perturbations},
volume = {4},
number = {1},
elocation-id = {e00016-19},
year = {2019},
doi = {10.1128/mSystems.00016-19},
publisher = {American Society for Microbiology Journals},
URL = {https://msystems.asm.org/content/4/1/e00016-19},
eprint = {https://msystems.asm.org/content/4/1/e00016-19.full.pdf},
journal = {mSystems}
}
Martino, C. et al. A Novel Sparse Compositional Technique Reveals Microbial Perturbations. mSystems 4, (2019)
@ARTICLE{Martino2022,
author = {Martino, Cameron and McDonald, Daniel and Cantrell, Kalen and
Dilmore, Amanda Hazel and Vázquez-Baeza, Yoshiki and Shenhav,
Liat and Shaffer, Justin P and Rahman, Gibraan and Armstrong,
George and Allaband, Celeste and Song, Se Jin and Knight, Rob},
title = {Compositionally Aware Phylogenetic {Beta-Diversity} Measures
Better Resolve Microbiomes Associated with Phenotype},
volume = {7},
number = {3},
elocation-id = {e0005022},
year = {2022},
doi = {10.1128/msystems.00050-22},
publisher = {American Society for Microbiology Journals},
URL = {http://dx.doi.org/10.1128/msystems.00050-22},
journal = {mSystems},
}
Shi, p. et al. Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies. bioRxiv, (2023)
@ARTICLE{Shi2023,
author = {Shi, Pixu and Martino, Cameron and Han, Rungang and Janssen,
Stefan and Buck, Gregory and Serrano, Myrna and Owzar, Kouros and
Knight, Rob and Shenhav, Liat and Zhang, Anru R},
title = {{Time-Informed} Dimensionality Reduction for Longitudinal
Microbiome Studies},
year = {2023},
doi = {10.1101/2023.07.26.550749},
URL = {https://www.biorxiv.org/content/10.1101/2023.07.26.550749v1},
journal = {bioRxiv},
}
- The compositional data wiki
- The code for OptSpace was translated to python from a MATLAB package maintained by Sewoong Oh (UIUC).
- TenAls translated from Sewoong Oh
- Transforms and PCoA : Scikit-bio
- Data For Examples : Qiita